Three Archetypes Are Emerging Inside Marketing

Archetype 1: The GTM Engineer (Marketing AI and Automation Lead, Mktg Engineer)

This is a developer/builder. Often technical in background, they frequently come out of web development, marketing technology, or revenue operations. This person actually builds the agents, automations, and integrations that the subject matter experts and strategy folks design. But they also know marketing flows and systems inside out.

Toast recently posted a role titled “GTM Engineer, Marketing Operations AI Innovation” that captures this archetype well. The mandate: be embedded directly with marketing teams, building AI-powered agents, automations, and workflows that permanently transform how marketers execute. Specific responsibilities include designing and deploying custom AI agents, integrating AI APIs and MCP servers, building evaluation frameworks to measure agent quality, and coaching marketers. Required experience includes growth marketing plus hands-on work with agent frameworks and marketing tools.

→ See the full Toast GTM Engineer Job Description (Or here if it’s taken down)

Stripe looks like it’s building an entire GTM Engineer Army:

→ See the full job description here.

This role is growing quickly: LinkedIn listed over 3,000 open GTM Engineer positions in January 2026, up from around 1,400 six months ago. They are in hot demand and often require sizeable salaries.

Archetype 2: The Marketing AI Center of Excellence Lead (Head of Marketing AI, Senior Director of AI Projects)

This role is less about execution and more about enablement at scale within marketing. It leads the internal function that sets standards, runs pilots, tracks what’s working, and spreads best practices across a large marketing organization. It’s an internal consulting arm for AI adoption, sitting inside marketing.

These are appearing primarily in enterprise marketing teams, companies with 20+ person functions where you need someone to coordinate so you don’t have 15 different teams running 15 different experiments with no shared learning.

Who you’re looking for: A senior program or project leader with real marketing depth. Someone who has run complex cross-functional initiatives, is a natural educator and communicator, and has enough technical literacy to evaluate tools without necessarily being a builder themselves. Often, this is a growth role for existing people on the team who are good at navigating the organization, plus innovative and hungry.

Some enterprises have AI enablement roles inside individual teams. Sprout Social’s CMO, Scott Morris, recently promoted a long-time web developer into a “Senior AI and Creative Technology Strategist” role, reporting to Creative Operations. Their mandate is to evaluate and implement AI tools specifically for designers, animators, writers, and developers, and coach each discipline through adoption.

What makes this role distinct is the people challenge as much as the technical one. The JD explicitly calls out the need to anticipate emotional responses to change, including fear of replacement, and to navigate creative team dynamics without direct management authority. That’s a very different skill set than the cross-functional CoE that’s running pilots and tracking metrics across all of marketing. You could see this role emerging within in demand gen or product marketing as well.

→ See the full Sprout Social Senior AI & Creative Technology Strategist job description

Archetype 3: The VP of Marketing Excellence and Transformation (Also called: VP of Marketing AI, Head of Marketing Operations and AI)

This is the senior marketing leadership role that anchors the whole AI marketing function. CrowdStrike recently made this hire, titling it “Vice President, Marketing Excellence and Transformation,” a direct report to the CMO. A few things that stand out from their job description:

• Responsible for defining the AI use case roadmap across marketing

• Owns governance, legal/compliance coordination, and vendor evaluation

• Leads cross-functional working groups with sales, IT, and legal

• Serves as executive sponsor for AI transformation within marketing

• Builds and manages a small internal team of specialists

That last point is key: at enterprise scale, this person might have the GTM Engineer and the CoE Lead reporting into them. They’re not doing the building or the enablement themselves. They’re owning the transformation mandate and creating the conditions for both to happen. (At large-scale companies, the GTM Engineer could also sit within centralized AI Engineering as well)

One other striking line from the CrowdStrike JD: “Prior CMO experience is a plus.” That tells you how senior and strategic this role really is.

Who you’re looking for: A senior marketing leader comfortable with ambiguity, with a strong process and systems brain, who can operate across organizational boundaries. They don’t need to write code. They need to know what’s possible and drive transformation across a complex organization.

→ See the full CrowdStrike Vice President, Marketing Excellence & Transformation job description

Marketing’s Best Friend - The AI Enablement Engineer

The next role might not report to the CMO. But it will be among marketing’s to collaborators.

The AI Enablement Engineer

The AI Enablement Engineer is a deeply technical function, typically living in engineering or IT, whose job is to build the agent infrastructure for the whole company. Think MCP server integrations connecting AI agents to internal tools, context engineering across systems and knowledge bases, and special-purpose agents built for specific functions like data analysis or legal review. The common thread is ungating multipliers for others, making AI genuinely work for people who wouldn’t otherwise get value.

Critically, this person also owns the guardrails. As agents get access to more systems, more data, and have more ability to take action, someone has to define what they can touch, what they can output, and who can see what. The AI Enablement Engineer works closely with security, legal, and IT to establish data access rights for agents, ensure sensitive data isn’t exposed through outputs, and set the boundaries that allow the rest of the company to build and experiment with confidence. This is increasingly one of the most important parts of the role. Agents without proper access controls aren’t just a trust risk, but could be an existential business risk if something goes really wrong.

For marketing teams, this person is the difference between running lightweight automations and having access to a real technical foundation built on guardrails you can trust.

You might be wondering how this differs from the GTM Engineer. In theory, the GTM Engineer is marketing-native and focused on GTM use cases, while the AI Enablement Engineer is an engineering function serving the whole company at a deeper infrastructure level. In practice, at smaller companies these roles often collapse into one person, someone technically minded who handles whatever needs handling, whether that’s a marketing automation or a company-wide agent framework. The distinction becomes most meaningful at enterprise scale, where marketing is often a customer of a separate engineering function.

→ Maxime Beauchemin (creator of Apache Airflow and Apache Superset) wrote the definitive piece on this role last week. Worth a read!

What This Looks Like at Different Company Sizes

At a large enterprise (30+ person marketing team): You’re building dedicated functions. A VP of Marketing Excellence and Transformation anchoring the function, one or two GTM engineers, and a CoE Lead driving internal enablement. The coordination overhead is real and someone needs to own it. Samsara’s CMO, Meagen Eisenberg is one of the better examples I’ve seen of this done well. She’s built a substantial in-house GTM engineering team (10+) within marketing, doing incredibly sophisticated work. She recently joined the Future of Marketing Podcast here.

At a mid-market company (10 to 20 person marketing team): You probably can’t justify fully dedicated AI roles yet. Identify your existing ops leader and give them an explicit AI mandate. Find your one or two most technically inclined people and give them room to build. Run pilots with structure so the learning is shared.

At a lean, high-growth startup (3 to 8 person marketing team): The playbook here is less about roles and more about hiring profile. Prioritize generalists who are already using AI tools fluently. A PMM or demand gen marketer who can build their own automations and generate assets at quality is doing the work of two specialists right now.

The Through-Line

Across all of these patterns, a few things stay consistent.

You need navigators: people who can move across organizational boundaries, align with IT and legal, and translate between what marketing needs and what’s technically possible. Program management skills married to real marketing depth.

You need builders: people who can actually construct the workflows, agents, and integrations, even if that’s primarily no-code.

And you need subject matter experts who are AI-fluent: your best demand gen leaders, your best PMMs, your best content strategists. If they’re using AI well, they’re going to be dramatically more productive. If they’re not, they’ll be increasingly outpaced by people who are.

The org chart isn’t being torn up. It’s being reconfigured. The CMOs who understand the new configuration will have a meaningful advantage.

→ Have you seen job descriptions for new AI roles in marketing? I’m building a library and would love yours. Drop it in the comments or send it my way.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

If your team is doing real AI work, running agents, scaling content operations, personalizing at volume, you are consuming tokens. And unlike most costs in your budget, tokens don’t come with a fixed cost. They scale with usage, and usage is scaling faster than most first-year plans assumed if your team is pushing the envelope.

A few weeks ago, Nvidia CEO Jensen Huang predicted that engineers will soon negotiate “how many tokens come with my job” when they negotiate their salary. In part, that’s because Nvidia engineers are expected to use at least half of their salary in tokens! While Jensen’s lens is engineering, the underlying point applies to marketing too. Token consumption is becoming a proxy for how seriously a team is using AI, and right now, almost nobody has enough history to forecast confidently. Or how overall marketing tech budgets will be affected.

The amazing of Benchmarkit and I are running a Marketing Benchmark Survey to see how AI has changed marketing budgets in 2027. Have you decreased headcount in proportion to tech spend, anticipating this AI and token issue? By how much?

👉 Take the 2027 Marketing Budget Benchmark Survey — under 6 minutes, and we need 50 more respondents to hit statistical significance.

The Benchmarks That Have Held for Years

We’ve been tracking B2B marketing budget ratios for years, and they’ve been remarkably stable over time averaging out to:

• ~45% headcount

• ~45% programs

• ~10% technology

In Ray’s reports we break it down by company size, funding and growth rate since those nuances matter, but the consistency has been remarkable.

This year, all three lines are in motion as revenue / headcount is expected to grow radically, as AI replaces apps, but adds costs, and as media prices soar, AEO/GEO demands new budget, and organic traffic tanks.

What the Early Data Is Showing

The first signals from the survey, combined with my ongoing CMO conversations, are pointing in a few directions.

Technology budgets are up. Early data shows tech allocations up 2 to 3 points year over year, driven by AI tooling, data infrastructure, and orchestration. But it’s not a clean line. SaaS rationalization is happening at the same time, as AI replaces point solutions and teams consolidate their stacks. Whether your net tech budget is higher than planned depends a lot on how aggressively you’ve been cutting legacy tools alongside adding new ones.

Headcount is a tale of two companies. At traditional SaaS companies, headcount is roughly flat while expectations are rising. The same team is being asked to support more products, more segments, more pipeline, with AI utilization increasingly part of the performance conversation. But at AI-native companies, headcount is growing fast in absolute terms. The interesting thing is that the ratio is still inconsistent, because revenue is growing even faster than hiring, and organic traffic often outpaces paid at these companies. Either way you slice it, the headcount percentage of the total marketing budget is under pressure, just for very different reasons depending on the kind of company you're running.

Programs are holding, with continued efficiency pressure. CFOs want to see AI driving down cost per outcome, not just improving output quality.

The Cost Nobody Modeled Last Fall

Here’s the question I keep hearing: how do we actually budget for tokens?

There are two versions of this problem, and most teams are dealing with both at once.

The first is internal consumption. If your team is running agents, scaling content operations, or building personalization into customer journeys, you’re burning tokens. Possibly more than you anticipated.

The second problem is new AI-native software you want to buy. As you adopt AI-native software, you’re increasingly encountering platform fees plus token-based consumption charges. It sounds straightforward until you try to forecast it. Even the vendors selling you these tools often can’t tell you what average usage looks like, let alone what small, medium, and large usage looks like. And while throttling your own users or your own team is an option, it’s not a great one if your’e trying to drive AI productivity.

Why This Moment Is Worth Capturing

We’re at an interesting point: far enough into 2026 to have real signals, early enough that the data can still shape how people think about the rest of the year.

This year’s budget survey isn’t just about confirming the consistent benchmarks of the past. This is the first year that real spend on tokens, AI tooling costs, and SaaS rationalization all hit the budget simultaneously, and where headcount spend is really being offset by tech investments. How does it all roll up?

If your experience is relevant, and it IS, your data point matters.

👉 Take the survey here — under 6 minutes, and the full results go to everyone who participates.

Huge thanks to Ray Rike and the BenchmarkIt team for running this, and to Jon Miller for collaborating.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

A fantastic senior lifecycle marketer I know, Darci Terlizzi, is looking for her next role. I’ve been referring her to a number of people with the wholehearted endorsement, “I would hire her again in a second,” and then raving about the specifics: her can-do attitude, how nothing ever falls through the cracks, her proactivity, and the quality of her work.

She once made light of my own work ethic - gifting me a fridge magnet that read “Perfection is our goal, excellence will be tolerated.”

But she didn’t reflect anger or irritation at my drive and constant requests for improvements. She found a way to make us both smile and laugh. Plus, she was a YES machine, “Sure, and what if we…” And most delightfully, she always delivered. These three things reinvigorated my own work style, and shaped how I hire. Some things can be taught, some are a matter of will and state of mind.

Lead with Yes

There’s nothing more frustrating than employees who are “No machines.” We know there is too much to do in too little time with too few resources - we work under those same constraints. It’s a breath of fresh air when you encounter an employee who leads with yes, adds to or explores the idea, and then proposes actionable next steps. Darci was a pro at saying yes… then summarizing things I’d asked her to do, recommending how we should scope and sequence the work. She’d deliver on the most important things and then she’d quietly complete the less important in her time gaps. It was lovely!

Proactively Improve Processes & Deliverables

Many employees will do their own job very well, but stop at the boundaries. Darci would delightfully bring ideas about how to improve processes and deliverables, leading with what SHE’D MAKE BETTER instead of complaints about what was wrong. If Darci needed budget or support - I’d help her find it, because her recommendations were always well-thought through. Darci would clearly outline the structure and vision to bring others along. Darci wasn’t certified in Six Sigma or Kaizen, but she might as well have been. We were a constant improvement engine.

Make it Pleasant

Darci and I worked together on high-stakes advertising for Larry Ellison at Oracle. Sometimes his demands were unpleasant, sometimes revisions were endless. Sometimes, fantastic ideas were trashed subjectively. Sometimes other employees were really unpleasant and difficult. But Darci kept it pleasant. She came with a smile, she came with energy, she came with ideas. She had made a choice on how she wanted to live, and it wasn’t resentful, disgruntled, or irritated - no matter what life gave her. She reminded me over and over who I wanted to be as a person, no matter the situation.

Get it Done

Of course, the essence of why I’d hire Darci again is that she delivered, over and over, on increasingly difficult challenges. She’d be open about needing to learn new skills or when we needed to juggle deadlines. But if Darci said she’d get it done, she’d always get it done. If I needed changes or revisions, she turned them around quickly. I knew I could count on her outstanding work.

It’s not just the work, it’s the relationships

As an A-student, I was fixated on delivering good work for good grades.

When I started working, I sometimes mistakenly focused on the work product more than how I made people feel. I came to realize, in part by observing my best employees, that it wasn’t just the work product that led to success. The positive attitude, the energizing energy, the humor in hard times, the collaborative relationships, the personal connections… they were often just as critical as the work product itself. I’m grateful for people like Darci and others who have made me a better employee, a better manager, and a better person.

She’s available!

*If you’re looking for a Senior Lifecycle Manager or a Senior Marketing Growth Manager, reach out to Darci Terlizzi up on LinkedIn - you won’t regret it. Highlights sheet below:

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

• An urgent sales request comes in for the biggest deal of the quarter…

• The CEO has a brilliant campaign idea...

• A competitor launches something new that must be addressed…

The content roadmap can shift (reasonably) all the time. But in the long term, it pays to have a strategic roadmap to prioritize which content will have the biggest impact and to be sure you have full coverage instead of a random assortment of content.

Last week, I wrote about how market maturity should shape your content strategy. But strategy alone isn’t enough. You need a system.

I begged Shannon Pragger, a marketer and advisor I admire, to share the planning templates she’s used to help me and many others create a high-impact content roadmap. She said yes! You can download the full set of content templates here, and she can help you if you want to work together.

Pre-Work: Start With Buyer Questions, Not Content Ideas

Let’s assume you’ve already identified the key personas in your buying committee. Before mapping content, you need to map the questions the buyers are trying to answer at each stage of their journey. Not generic questions. Here’s an excerpt from a content strategy of a wealth management firm - notice the specificity of questions to the stage and product:

When you map persona × stage × question first, content planning becomes much sharper.

Today, AI can dramatically accelerate, or do this step for you. I often use prompts to pressure-test persona assumptions, asking AI to simulate how a CFO, technical buyer, or end user might evaluate our product at each stage. It’s not perfect, but it quickly surfaces blind spots and question gaps we might miss on our own.

Here’s the template:

Step 2: Audit What You Already Have

Most marketing teams have vast troves of content, but little understanding of how their coverage is across personas and stages of the buying funnel. A simple audit helps wrangle the chaos and start to systematically assess how effective the whole corpus of content is. A simple audit should capture:

• Persona

• Funnel stage

• Topic / pillar

• Content type

• Performance (traffic, engagement, pipeline influence)

• Gaps

The goal isn’t just inventory.

It’s clarity:

• Where are we overweight?

• Where are we thin?

• What’s performing?

• What’s noise?

AI makes audits faster, too. You can upload your content corpus and ask AI to classify assets by persona, funnel stage, and topic cluster. It won’t be flawless, but it can give you an 80% first pass in minutes, something that used to take weeks.

Step 3: Map Content by Persona and Stage

Now we move from audit to architecture. Using the persona question work and the audit, map:

• Persona

• Buying stage

• Content type

• Asset title or idea

This is where you identify:

• Missing awareness content for technical buyers

• Weak consideration assets for champions

• Thin decision-stage proof

You’re building a matrix that shows not just what you have, but what’s needed.

Once gaps are visible, AI can help generate structured outlines aligned to specific persona questions. Instead of starting from a blank page, you’re starting from a mapped need, and asking AI to build within that constraint.

Step 4: Turn Strategy Into a Managed Plan

Once priorities are clear, translate them into a working editorial calendar.

This creates sequencing and accountability, especially important when aligning to market maturity shifts.

You can download the full set of content templates here.

Modern Content Types (A Quick Trigger List)

As you build your plan, it’s helpful to sanity-check your formats.

Are you only producing blogs and whitepapers?

Or are you leveraging:

• Interactive demos

• Benchmark reports

• Short-form video

• Champion enablement decks

• ROI one-pagers

• Community-driven content

Shannon shared the following reference list she uses trigger new ideas. This overlaps with last week’s post, but it’s super useful when you’re actively planning.

Match the Journey to the Market

Too many teams build lots of assets, ship campaigns, check the boxes…

But they don’t have visibility into their coverage across buying committee personas and sales stages, in this market, at this moment. With so much historic and new content being crated all the time, it becomes hard to see the forest through the trees.

There are more AI tools than ever promising to optimize content, from intent modeling to personalization to community automation. Many of them are powerful. But AI amplifies whatever structure sits beneath it. Without a clear map of personas, stages, and market maturity, it simply helps you produce more, not better.

Do you have templates or prompts to share?

Do you have any marketing templates you’re willing to share? My original inspiration for this blog was “Show me your templates.” I was sick of hearing about marketing strategy without operational examples of how it worked in practice. I’ve tried to bring that to life through this blog and collaborations. If you have a template that would help others, please comment below or hit me up on LinkedIn.

Shannon Prager is the CMO Matic, helping redefine how AI and healthcare come together, giving medical providers time back and helping care teams work smarter. She also advises high-growth startups and provides marketing and content strategy support. You can find her on LinkedIn or at sprager@leaditmarketing.com

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

What content will actually perform?

Shannon Prager, a former colleague of mine, has led several major content strategy engagements with me, auditing what exists, identifying what works, and building prioritization frameworks that accelerate pipeline impact. Below, she shares the strategic lens behind that work. In a follow-up post, we’ll share specific planning templates.

Devising a Content Strategy in an AI-First World

Buyers are skeptical. AI has flooded the market with interchangeable messaging. Decisions are shaped quietly, across channels, long before a sales conversation begins.

Yet many teams still plan content around a predictable, linear funnel, regardless of market dynamics. That’s a problem.

Content strategy doesn’t just shift by stage. It shifts by market context.

Are you:

• Building a new category?

• Challenging an entrenched incumbent?

• Competing in a crowded, established space?

Each scenario changes what buyers need to see, and when.

This framework helps align content to the right stage, at the right time, based on the type of market you’re in, so your strategy drives pipeline, not just fills a calendar.

Why The Same Funnel Doesn’t Work Everywhere

We often talk about content in terms of top, middle, and bottom of funnel. But that oversimplifies reality.

What actually changes isn’t just the funnel. It’s the buyer’s belief state.

When I say New Concept, New Paradigm, or Established Market, I’m not describing how innovative your product is. I’m describing how much the buyer’s thinking has to move.

• New Concept: The buyer isn’t yet convinced this solution is necessary.

• New Paradigm: The buyer must rethink how they approach the problem.

• Established Market: The buyer believes the problem and solution type are clear.

In AI, you can see all three at once:

• New Concept: AI agents completely replacing roles (SDR agents, finance agents) are a New Concept. Buyers need education and proof that this is safe and necessary. (Some of these are moving quickly to new paradigm)

• New Paradigm: AI-native platforms redefining CRM, analytics, or coding workflows represent a New Paradigm. Buyers must rethink how the function should operate.

• Established Market: Traditional SaaS vendors adding AI features operate in an Established Market. Buyers want comparisons, validation, and proof of ROI.

Each market type changes what content moves a deal forward.

• A buyer evaluating AI agents first needs proof that this is safe and necessary, not a feature comparison grid.

• A buyer considering an AI-native platform needs help rethinking how the function should operate, not just more case studies.

• A buyer in an established SaaS category wants case studies, benchmarks, proof, and differentiation.

If your content doesn’t match the buyer’s mindset in that context, it will miss, no matter how polished it is.

Modern Content Types

How you express this strategy matters, but format follows intent. Whether you use LinkedIn carousels, benchmark reports, interactive demos, or short-form video, the question isn’t “what’s trending?”

It’s: Does this format support the buyer’s belief shift in this market?

The chart below outlines how different content types map to stage and context. Use it to pressure-test your plan, not to expand your production list.

Match the Journey to the Market

Too many teams create strong content in a vacuum and wonder why it doesn’t convert. Content doesn’t fail because teams aren’t producing enough of it. It fails when it’s planned without context.

Before you build the next asset, ask: What does our buyer need to believe right now, given the market we’re competing in?

When content aligns to:

• the buyer’s mindset

• the stage of their decision

• and the maturity of the market

it stops being background noise and starts moving deals.

The teams that win won’t create the most content. They’ll plan with intent, say fewer things more clearly, and meet buyers where decisions are actually made.

If momentum feels slow, the answer usually isn’t “produce more.” It’s “plan smarter.”

Shannon Prager is the CMO Matic, helping redefine how AI and healthcare come together, giving medical providers time back and helping care teams work smarter. She also advises high-growth startups and provides marketing and content strategy support. You can find her on LinkedIn or at sprager@leaditmarketing.com

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

What?!

If you haven’t lived through a business unit structure before, this moment can feel disorienting. If you have lived through it, you may have a little PTSD.

In this article, I break down three predictable problems CMOs run into, how to reduce chaos, and how to keep perspective (and a little optimism). Many of my clients face this challenge, and I’ve been through it at five companies personally!

Why Companies Move to Business Units

Companies move to business units when they want focus, accountability, and increased growth in a new product, industry, or focus area. This can be their first multi-product, a big company focusing strategy, or a new acquisition they don’t want to break apart. Creating a BU creates a team that really cares about something that might get lost in a bigger company. But the tension between centralization and decentralization is real.

The General Manager Arrives

When companies move to business units, they typically hire or appoint a general manager to lead. This is often a very senior executive, sometimes at a President or near C-suite level, who “philosophically” owns profit and loss.

I say philosophically because, in B2B tech companies, the GM often owns a sub-set of full P&L and is forced to influence across the business still.

GMs usually own product management, engineering, and sales engineering, plus an allocated budget for marketing. Sometimes they get direct ownership of sales. Sometimes they get direct ownership of product marketing, sometimes just dotted-line ownership. PMM ownership brings positioning, market research, messaging, and content closer to the business unit’s specific goals and growth targets. This is where things start to get complicated for the CMO.

Problem #1: Marketing Loses Its “Brain”

When marketing loses product marketing to business units, there’s a big problem. Product marketing is often the strategy center of marketing. It’s where positioning, market intelligence, and deep customer understanding live. It informs campaigns, demand generation, public relations, analyst strategy, and sales enablement. It has to be in lock-step for things to perform well.

The intent is good. Closer alignment to BU goals. Faster execution, but the unintended consequence is fragmentation. Instead of one shared source of truth about the market and the company’s differentiation, you end up with multiple local optimizations.

Problem #2: Business Units Drift From the Corporate Story

Once product marketing lives inside the business units, another issue almost always emerges. Business unit teams naturally optimize for what helps their business win, sometimes at the expense of what makes sense for the company as a whole.

At this point, many CMOs end up hiring or inventing a new function, often called Corporate Product Marketing, Portfolio PMM, or Marketing Intelligence. This role exists to pull together overall strategy, positioning, market intelligence, and campaign foundations, even after the original product marketing team has been fully distributed into the business units.

It’s a necessary correction, but it’s also expensive and politically delicate. You’re rebuilding something you just dismantled.

Problem #3: Shared Resources Become a Political Minefield

The third challenge is resource allocation. Many marketing teams remain centralized while being expected to support multiple business units. Design, email marketing, paid demand generation, campaign operations. They don’t belong to any one BU, but every BU depends on them.

This creates complexity for the CMO, who has to decide allocation at a macro level, and daily friction for individual contributors trying to prioritize their work.

At Atlassian, this was most painful in the design organization and in email marketing. We initially used a round-robin intake model. On paper, it maximized shared capacity. In reality, every business unit felt underserved.

What ultimately worked was allocating individual people to business units based on a combination of current revenue scale and future growth potential. Each business unit had a fixed team and had to prioritize within that constraint.

In email, we agreed on a specific allocation of emails: the large business unit received 50% , the medium, 25%, the smallest, 10%, and corporate retained 15%. We lost some shared-capacity benefits, but the model reduced conflict dramatically.

The Core Tension (and How to Manage It)

The hardest part of a business unit reorg isn’t the org chart. It’s alignment. General managers are under intense pressure to grow their business. That often means prioritizing what’s best for the business unit over what’s best for the company. While this tension can be frustrating for CMOs, it’s also the entire point. BUs exist to create focus where focus would otherwise be diluted in a diversified portfolio.

The mistake most companies make is letting that tension play out informally. When allocation and prioritization get negotiated on the fly, in every meeting and at every level, politics multiply and frustration spikes.

Here’s what I’ve learned to do instead:

1. Force clarity before chaos

Agree on allocation frameworks as early as possible. People, design capacity, email volume, campaign dollars. Agree on the model instead of renegotiating priorities request by request. Clear frameworks don’t eliminate conflict, but shift debates from personal to structural.

2. Protect the “brain” of marketing

If product marketing moves into the business units, someone must be explicitly responsible for corporate or portfolio product marketing. Someone needs to own overall positioning, market intelligence, and how the pieces fit together into a coherent company story.

3. Accept the tension as designed

Business units will sometimes optimize for themselves at the expense of the whole. That’s not a failure of leadership or collaboration. It’s a structural tradeoff. The CMO’s role is not to eliminate the tension, but to manage it consciously and keep it from turning into chaos or resentment.

4. Zoom out and keep perspective

The first time I got caught in business unit politics, I got incredibly worked up. I couldn’t see that this was a stage of the business. These problems can feel (personal) and permanent when you’re inside them. At Atlassian, Oracle, and across many companies I’ve advised, there’s a constant ebb and flow between centralized efficiency and decentralized focus. Just as you get comfortable with one model, the company often swings back the other way. Hold on and take it all in stride.

P.S. Sales Faces Similar (Sometimes Worse) Challenges

Sales organizations face many of the same challenges in a business unit structure. Sometimes sales teams live fully inside the business units. Sometimes business units influence and enable a centralized sales team. Both models have real benefits and real drawbacks.

With BU-owned sales, profit, and loss ownership becomes more real, but customers can get a fragmented experience if multiple reps reach out independently. In overlay models, prioritization becomes the challenge. Core sales teams tend to focus on the fastest, easiest paths to commission.

I’ve seen companies acquire another business, keep it as an independent business unit, and make its sales team an overlay, only to struggle because the core sales team never truly prioritized the product. The BU couldn’t break through its own company to reach the customer.

That’s a longer conversation for another day, but it’s worth knowing marketing is not the only one struggling through the strategy.

Hang in there! BUs can drive growth too

Business units can cause a lot of complexity (and sometimes pain) but they can also drive real focus, learning, and GROWTH, which every CMO desires most.

The CMO can’t eliminate the tension caused by the BU design, it’s stucturual. But they can try to manage through it with clarity, perspective, and optimism : )

Any other advice you’d share from your hard-earned experience?

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

AI is fundamentally changing what’s possible: pulling signals from calls, emails, documents, chats, and CRM data, and analyzing momentum across all of it, to understand what actually moves deals forward.

For years, marketers did the best they could with incomplete data and rigid, incomplete measurement frameworks. First-, last-touch and multi-touch attribution weren’t wrong so much as necessary shortcuts, compressed versions of reality in a world where most meaningful signals and full data simply weren’t accessible.

That’s what’s changed. AI is set to massively improve attribution by reconstructing the full story of a deal and letting patterns of influence emerge across people, time, and activity - instead of relying on proxy weighting.

I’ve been watching Mada Seghete and Alex Bauer, co-founders of Upside closely over the past year. What first caught my attention wasn’t just the breadth of signals they were pulling together, but how they visualized them, creating the automated version of the board slides I used to build by hand, with many touches, many buyers, over long periods of time, finally telling a complete story.

After my Brand vs. Demand is a Measurement Crisis post showed that many marketers want to invest more in brand but feel constrained by attribution limitations, I asked Mada and Alex if they would share how they’re approaching marketing attribution in an AI-first world. Below she covers the following with screenshots that will have you drooling : )

• Why Traditional Models Fail

• What’s Actually Changed with AI
  1. Unstructured context is now capturable
  2. Analysis that was too expensive is now feasible
  3. We moved from data scarcity to data abundance

• A Different Approach: Questions Instead of Models

• The Prerequisite: Data Quality

• What This Looks Like in Practice

• How This Applies at Different Stages of Growth

I. The Attribution Problem Everyone Knows

Marketers hate attribution because it has been fundamentally inaccurate. It drives endless debates and turf wars over credit. It over-credits what’s easy to measure: late-stage clicks, form fills, “sourced” fields, and under-credits what’s hard to track: brand awareness, events, partner influence, the dark funnel, internal forwarding chains.

The problem isnt’t effort it is access. Marketers can only measure a fraction of the signals shaping deals, so attribution models are often necessary shortcuts rather than sources of truth.

Plus the data they DO have is bad: Adverity’s 2025 research found nearly half (45%) of the data marketers use to make decisions is incomplete, inaccurate, or out-of-date. And 43% of CMOs believe less than half of their marketing data can be trusted.

II. Why Traditional Models Fail

Deals are too complex for any single model.

My experience: I spent a full day analyzing 10,000 touchpoints for one customer’s $3M deal. The activation moment? A BD partner convinced an executive to take a look at the tool. The exec forwarded a note to a manager, who forwarded it to the client team. This was in addition to hundreds of marketing and sales interactions that built awareness.

The insight: No rule-based system would catch this. The internal team didn’t even know it happened. And no human would invest this time for every deal.

Even sophisticated multi-touch approaches struggle because:

• Tracking is incomplete and noisy - identity gaps, cross-device behavior, missing interactions

Image: Example of the type of persona data found in different systems

• Correlation is often mistaken for causation

• Journeys get oversimplified -everyone wants to know “but what is the one thing that really brought this deal?”

• External factors distort observed paths - pricing, competition, timing

• True incrementality still requires experimentation

So leaders settle for “good enough” models they don’t trust (because they need something for Finance), while actually running on instinct.

III. What’s Actually Changed with AI

Three fundamental shifts:

1. Unstructured context is now capturable

Conversation intelligence extracts signals from calls, emails, and meetings. The “why” behind a demo request often lives in buyer language, not UTM parameters.

Image: Example of how an LLM can extract signals from unstructured data.

2. Analysis that was too expensive is now feasible

Questions like “what sequence of touches typically precedes a closed-won enterprise deal?” have always been theoretically answerable. The problem was the work required: manually reconstructing deal timelines, normalizing across systems to recover signals buried inside unstructured data, and accounting for duplicate or missing touchpoints.

For most teams, the insight wasn’t worth the effort. But thanks to AI, that math has changed.

3. We moved from data scarcity to data abundance

The context exists across calls, emails, CRM, marketing automation, web behavior, and chat. The hard part is stitching it into a coherent, trustworthy account story.

The key reframe:

Static rules like “credit goes to sales unless there was a marketing event within 7 days” aren’t stupid, they have been necessary compromises. When the cost of answering more nuanced questions exceeded their value, compression was rational.

But that’s no longer the case.

IV. A Different Approach: Questions Instead of Models

The AI paradigm shift: AI has enabled teams to stop forcing deals into attribution models and start answering specific questions about each deal. Then aggregate those answers into insights and recommendations.

Attribution has always been greyscale—forcing nuanced reality into a few buckets. What’s now possible is answering “what worked” questions in full color, deal by deal, then aggregating patterns.

The questions you can now answer:

What actually activated this deal? Example: “The inflection point was a partner referral to the CFO, followed by an internal forward chain we wouldn’t have seen in CRM.”

Image: Example how AI can make connections between structured and unstructured data to understand the deal story.

What role did each channel play at each stage? Example: “Paid search drove initial awareness, but the webinar attendance correlated with deals moving from Stage 2 to Stage 3.”

One of our customers found that a third of their revenue in 2025 came from 3rd party referrals that did not show up in any other attribution models.

Image: Example of results from Upside deep research agents looking for specific channel influence on deals .

What sequences lead to new opportunities? Example: “Deals that start with content engagement + SDR outreach convert at 2x the rate of cold outbound alone.”

Image: Example of GTM motions detected by Upside AI Deep Research agents analyzing a set of deals.

Image: Example of GTM channel progression detected by Upside AI Deep Research agents analyzing a set of deals.

Who are the real buying personas, and which ones do AEs consistently miss? Example: “Technical evaluators appear in 80% of closed-won deals but are added as contact roles in only 30%.”

Image: Example of persona presence detected in buying groups (beyond contact roles in Salesforce) by Upside and impact on win rates.

V. The Prerequisite: Data Quality

The critical caveat: AI is only as good as its data.

Wrong campaign dates, missing statuses, incomplete records. Without careful guidance, mess like this causes AI to reach wrong conclusions just as it would a human. Garbage in, garbage out.

In my experience, the prerequisite for AI-powered deal analysis is a data layer that:

• Resolves identities across systems

• Reconstructs timelines from fragmented touchpoints

• De-duplicates and heals messy GTM data

Image: Example of the type of persona data found in different systems needed to be merged into one identity.

That’s what my team has been building for the past two years. But the principle applies regardless of tooling: if your underlying data is scattered and incomplete, you’ll just get bad answers more quickly. Even AI can’t perform miracles.

AI also needs a way to be checked AND check itself. While AI is getting better, it still makes mistakes and needs a way to be debugged. Any analytics and data tool using AI needs a debug console—a way that a human can check the results.

Image: Visual representation of a deal with filters and citations for AI insights.

VI. What This Looks Like in Practice

Custom, smart insights for every org: Traditional rules-based models often get it wrong. But you can ask AI to understand what type of influence a campaign actually had on an opportunity, and whether it was the first meaningful interaction with the buying group. AI does this much better than rules-based systems because it won’t get bogged down by edge cases, like someone visiting the website during an event before the attendee list was uploaded, which would make direct traffic look like the first touch.

Image: Events influence on deals sorted by type of touch in the deal cycle defined by customer.

Faster reporting + hidden signals: Another reduced reporting time from 2-3 hours to minutes and identified a 5% pipeline increase from in-person hosted events. This signal was invisible in their previous attribution model.

Measuring things that were impossible: AEO influence on pipelines is incredibly hard to measure because people don’t click on links. But in many cases prospects mention how they heard about you on calls or in forms. By combining citation traffic, call mentions, and email threads, Assembled tracked actual AEO influence on the pipeline.

Image: AEO analysis for Assembled team.

VII. How This Applies at Different Stages of Growth

This approach requires enough deal volume to see patterns.

If you’re running a large enough business (meaningful spend across multiple channels, significant deal volume, consistent GTM investment) then asking “what actually happened in our deals?” is one of the most important questions you can answer.

But if you’re earlier-stage, the reality is different. Volumes are lower. Noise is higher. You might do forensic deal analysis and see patterns that don’t generalize yet. That doesn’t mean the approach doesn’t matter. It means how you use it has to evolve with you.

For smaller teams, the goal isn’t statistical significance—it’s building directional confidence. You’re looking for patterns: Do deals that involve events close faster? Do certain personas correlate with larger deals? Even without perfect data, that framing matters.

The mistake smaller companies make isn’t that they can’t do this perfectly. It’s that they don’t do it at all. Or they force everything into last-touch attribution and conclude that nothing except bottom-funnel works.

VIII. Conclusion: A New Question

The wrong question has always been: “Which attribution model should we use?”

The better question is: “Do we actually know what happened in our deals?”

Attribution doesn’t have to be the starting point anymore. When you begin with reconstructing deal reality—who was involved, what signals mattered, how influence accumulated over time—the “what worked” answers emerge naturally from there.

And once you can defend those answers to Finance with real evidence, rather than simplified models or gut instinct, the budget conversations change entirely.

Thank you Mada and Alex for the insights. For more details on Upside, visit upside.tech (This is not a sponsored blog post, just an area of interest for me and my clients)

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

Right now, we’re collecting data for the Marketing Budget Benchmark Survey, and I’m sharing some early signals from the data and my anecdotal insights while we await the statistically significant truth.

👉 Take the Marketing Budget Benchmark Survey here (<6 minutes, promise)

My 2026 Budget Predictions

As mentioned, the full data isn’t in yet, but based on dozens of conversations with my clients, CMO friends, and my CMO AI clubs, I’m seeing several patterns emerging.

📉 1. Marketing Budgets Are Down as a % of Revenue

For many B2B companies, marketing spend as a percentage of revenue is down several points year over year.

The familiar mandate, “do more with less,” has finally become operational:

• AI is expected to deliver real productivity gains

• CFOs are prizing efficient growth

• Growth expectations remain high, even as budget ratios compress

The budget didn’t disappear; the tolerance for inefficiency did.

2. Marketing Tech Budgets Are Up Meaningfully

AI is no longer a pilot line item. As companies move from experimentation → production, tech spend is climbing fast:

• AI tools

• Data infrastructure

• Inference costs

• Orchestration layers

• Internal GPTs and automation stacks

Early survey data shows tech budgets up 2–3% year over year.

That’s a huge shift when you consider that:

• 10% has historically been a “normal” martech allocation

• A 2–3% swing represents a major rebalancing, not a rounding error

AI isn’t free. And it’s no longer optional.

3. Headcount Isn’t Being Cut, It’s Being Frozen

For most established companies, the tech budget increase isn’t coming from layoffs. Instead:

• CMOs are slowing hiring

• Teams are holding steady while expectations rise

In other words:

Headcount budgets are being reallocated, not slashed.

Teams are expected to:

• Produce more pipeline

• Support more products

• Serve more segments …with roughly the same number of humans.

2026 is the year CMOs are expected to prove AI’s revenue contribution, not just its productivity story. No pressure.

A Tale of Two Cities: AI-Native vs. SaaS

One of the most interesting splits I’m seeing:

🚀 AI-Native Companies

• Often in hypergrowth

• Heavy investment in brand, experience, and hiring

• Surprisingly less obsession with AI efficiency internally

• Hiring like crazy as they build companies from scratch and add new skills

Why?

Many AI-native companies already benefit from:

• New budgets for AI products from buyers

• Massive inbound demand

• Viral word-of-mouth

Their existential challenge isn’t efficiency, it’s standing out in noisy, fast-moving markets. They may have smaller marketing teams - because their revenue is growing even faster than hiring.

Traditional SaaS (Adding AI)

This group has it harder. They’re:

• Protecting an existing revenue base

• Replatforming products with AI

• Re-educating the market

• Doing all of it with tighter budgets

These teams are leaning hard into AI for efficiency, because they have to. They’re trying to fund the future without starving the present, a difficult balancing act. Their customers’ budgets for new SaaS investments might be frozen, as they await the impact of vibe coding and agent-building transformation. Every % of revenue growth is hard-earned.

Why This Survey Matters (We Need You)

Budget trade-offs have real implications for a team’s success. And anecdotes only get us so far. If you want:

• Real benchmarks

• Stage-specific comparisons

• Insight into how your budget stacks up

• Clarity on where peers are investing (and cutting)

…we need volume.

👉 Please take the Marketing Budget Benchmark Survey now

It takes under 6 minutes, and the output will synthesize hundreds of 2026 marketing budgets across stages, segments, and motions, giving you real leverage in planning, board conversations, and prioritization.

With Gratitude

Huge thanks to Ray Rike and the BenchmarkIt team for orchestrating this effort, and to Jon Miller for collaborating.

I’ve long admired BenchmarkIt’s approach to market research. If you’re looking to add credible data into marketing, they’re worth a serious look.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

Most marketing leaders want to invest more in brand, but can’t defend it with data.

After the response to last week’s post, “Brand vs. Demand Isn’t a Budget Fight. It’s a Measurement Crisis,” I asked Pranav Piyush, CEO of Paramark, to weigh in. Measuring impact is his company’s focus, and it’s the missing link for unlocking brand investments. Thanks, Pranav, for writing this:

Why “brand is hard to measure” is wrong

Most teams say brand is hard to measure. That’s not quite true. Brand is hard to measure with traditional methods like touch- or click-attribution. First-touch, last-touch, and multi-touch all suffer from the same problem.

Brand investments, TV, online video, earned social, creators, sponsorships, don’t usually push people straight into your funnel. Very few buyers click an ad, fill out a form, and convert on the spot. Instead, brand creates mental availability. People notice you. Remember you. Trust you. And when they’re ready to act, they don’t click, they search. They ask peers. They type your name into Google or ChatGPT.

So, by definition, the touch-based-attribution game is rigged against brand.

Brand almost always loses, not because it didn’t matter, but because brand rarely shows up as a click, a form fill, or a last touch.

You need a measurement framework that puts all investments on an equal footing.

There’s a different question we can ask: “What would have happened if this investment didn’t exist?” That’s incrementality. And this is where brand starts to look measurable.

Practically, this means using econometrics and experimentation to understand the causal impact of brand and demand investments on pipeline, revenue, and efficiency. This approach has been common in consumer marketing for years. It’s now becoming much more viable in B2B.

How to do this for your own brand

Over the last couple of years, my team and I have worked with top B2B SaaS brands on exactly this topic. After analyzing billions in marketing investments, here’s a foolproof method to measure the impact of your brand.

Step 1: Align on the measure of success

Before you build any experiment or model, define what success looks like. Brand investments don’t directly translate into revenue in the short-term. You have to translate them into leading signals that predict future revenue. In B2B when consideration cycles are longer, you don’t have months to wait to understand or demonstrate the impact of your marketing efforts.

At a high level, there are two levels of measurement you should consider:

• Campaign-level impact: Did this specific campaign move top-of-funnel metrics?

• Holistic business impact: Did your brand channels improve pipeline growth?

While it’s tempting to only measure top-of-funnel with metrics like awareness and consideration collected through surveys, we recommend behavioral metrics like search query volume or ICP traffic to your site. This removes all types of bias and inaccuracy present in survey-based metrics.

It’s also tempting to only focus on revenue metrics, but that’s often a mistake. If your consideration cycles are long, as they usually are in B2B SaaS, it might be weeks or months before your marketing investments show up in your revenue metrics.

Step 2: Form a testable hypothesis

The core idea here is incrementality: you are not trying to see whether brand “gets credit” in last-touch attribution. You’re trying to see what changes when brand investment is present versus when it’s absent.

Example hypothesis:

“If we increase brand spend this quarter, we will see an improvement in traffic and sales-qualified-opportunities (aka pipeline) compared to a period or region without brand support.”

This sets up a falsifiable, causal statement that you can design a measurement around. The key is having a credible counterfactual. What did demand look like with sustained brand investment, and what did it look like without?

Here’s an example of what this looks like:

Step 3: Set up an incrementality test

Now, you’re ready to put this hypothesis to a test with an incrementality test. There are a few ways to structure this:

• Geo-tests: Run your brand campaigns in some regions but not others.

• Control vs test groups: Treat some audiences with brand campaigns and others without, holding all else equal. This only works for owned channels where you can neatly split your audience into test and control groups.

• Time-based tests: Compare performance before, during, and after sustained brand investment. (Sometimes has an issue of other outside factors)

These approaches help you get at causal impact, not just correlation.

One of the biggest mistakes teams make is expecting instant results from brand investment. A proper test requires weeks or months of setup, depending on your exact hypothesis and volumes. Below is an example of a statistically valid test design for a top B2B brand we work with.-

In addition to platforms like Paramark, there are various open source methodologies (MarketMatching, CausalImpact, GeoLift) that allow you to design statistically valid incrementality tests on your own.

Step 4: Combine incrementality with holistic modeling

If your data volume and tooling allow, you should also layer in Marketing Mix Modeling (MMM) to estimate how different channels contribute to outcomes over the long run. MMM and incrementality tests answer related but distinct questions:

Incrementality tests help you see what happens when you change brand spend at any given point of time.

MMM helps you see the impact of those brand channels over time based on your historical data. In the example below, you can see how YouTube, OOH, Podcasts, and events are on an equal footing with search, email, and other direct response channels.

Together, they bring rigor to your measurement strategy. Once you have results, whether from geo-tests or modeling, the final step is narrative:

Don’t just show the numbers. Tell the story:

• “When brand was present, pipeline grew by X%.”

• “In test regions with brand campaigns, CAC decreased while pipeline grew faster.”

• “Direct traffic and branded search share increased, supporting higher mental availability.”

This is how brand goes from nice to have to defensible.

One final note. AI doesn’t change anything fundamental about this approach, but it changes how much more affordable it is to do this in practice. Incrementality used to require heavy data science and long cycles, which put it out of reach for many B2B teams. AI and ML lower that barrier by automating data ingestion, model updates, and pattern detection, making it possible to see directional impact much faster.

Tools like Paramark use AI to continuously analyze marketing and business data, helping teams understand how brand investment changes demand efficiency, pipeline, and cost curves — turning incrementality from a theoretical ideal into something operators can actually use in planning.

What’s a possible impact?

Uplift from brand investments is, of course, dependent on the amount of spend relative to the target audience and duration, but a brand analysis by BCG found that companies that invested in brand and sustained that investment saw: +2% increase in revenue,+4% boost in shareholder return, and +6% increase in funnel conversion:

When Paramark runs tests with customers, they are often looking for (and see) a lift of more than 7-9% against a control group in opportunity volume.

When this starts to make sense (and what to do if you’re smaller)

It’s worth pausing here to recognize that this approach doesn’t magically work for every company, at every stage. Incrementality requires scale. Typically, companies spending north of a few million dollars/year are good candidates for incrementality measurement.

If you’re running a large enough business (meaningful spend across multiple channels, significant demand volume, consistent brand investment), then asking “what would have happened without brand?” is not only reasonable, it’s one of the most important questions you can ask. A modest improvement in conversion rates or cost per opportunity can materially change growth economics. That’s where incrementality measurement really earns its keep.

But if you’re earlier-stage, the reality may look different.

Volumes are lower. Noise is higher. You might run a brand campaign and see… nothing obvious happen. Not because it didn’t work, but because the signal gets lost in week-to-week variability. In those moments, trying to force the same level of rigor can feel frustrating, or worse, misleading.

That doesn’t mean brand doesn’t matter yet. It means how you measure it has to evolve with you.

For smaller teams, the goal isn’t to prove causality with precision. It’s to build directional confidence. You’re looking for patterns and even earlier leading indicators.

You notice that inbound conversations sound different after a brand push (Gong used to track mentions via Gong recordings). Outbound reply rates tick up (use the time-based experimentation framework). People seem to already “know who you are” when sales engages them. Branded search slowly becomes a bigger share of traffic. None of these signals are definitive on their own, but together, they tell a story about momentum.

The mistake smaller companies make isn’t that they can’t measure brand perfectly. It’s that they don’t measure it at all, or they try to force it into last-touch attribution and conclude it doesn’t work.

The better move is to start with the right mental model early: brand is measured at the system level, not the channel level. Even if your tools and data aren’t sophisticated yet, that framing matters. Looking at aggregate search query volume or how overall traffic is trending may be a sufficient first step.

Because when scale eventually arrives, when spend and demand volume increases and efficiency starts to matter as much as growth, you’re not suddenly changing how you think about brand. You’re just upgrading the rigor of how you measure it. Incrementality isn’t a switch you flip one day. It’s a curve you grow into.

And the teams that navigate this well are the ones that treat brand measurement as something that matures alongside the company, not something you postpone until the arguments get uncomfortable.

The question we should be asking

The most important shift in “winning” the brand investment challenge is narrative.

• The wrong question is: “What’s the impact of brand?”

• The better one is: “Are we measuring brand the right way?”

Because when brand gets cut, demand doesn’t fail immediately. It just gets more expensive. Slower. Harder. Less predictable. By the time teams feel the impact, the decision has already been made, and the cause is rarely obvious.

The data shows leaders already know brand matters. The opportunity now is to close the gap between belief and behavior. Not by turning brand into performance marketing, but by measuring it in ways that reflect how it actually creates value.

Because this was never a budget fight. It was a measurement crisis.

And once you see that, the path forward becomes a lot clearer.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

In Chat GPT

🔥 What temperature means

• Lower temperature (0.0 – 0.3)

  • Deterministic, factual, precise, repetitive

  • Good for: math, legal docs, summarizing, extracting data, code you need to run

• Medium temperature (0.4 – 0.7)

  • Balanced, still grounded, but with light variation

  • Good default

• High temperature (0.8 – 1.5+)

  • Creative, random, imaginative

  • More humor, metaphor, risk-taking

  • Good for: brainstorming, stories, product names, taglines, “surprising answers”

🎛 How to use it in prompts

You can explicitly tell the model:

• “Answer with temperature 0 — be literal and strictly factual.”

• “Use a temperature around .5 — thoughtful, but grounded.”

• “Respond at temperature 1.2 — bold ideas, no safe answers.”

• “Give me three versions at low, medium, and high temperature for comparison.”

Even though you’re not setting the API parameter directly in ChatGPT, the model understands this instruction and adjusts style.

📌 Practical prompt patterns

Try these phrases depending on your goal:

🧠 When you need accuracy

“Temperature 0 — list the key steps for depreciating equipment according to GAAP.”

✍️ When drafting content

“Temperature .6 — rewrite this email to a CFO with clarity and authority.”

💡 When brainstorming big, weird ideas

“Temperature 1.3 — Generate 10 insane marketing stunts we could pull off for Cannes.”

🎨 When you want options

“Give me 3 variations:

• temp 0 = literal

• temp .7 = creative

• temp 1.2 = unexpected”

🧪 Pro tip

• If the model feels too boring → ask for higher temp

• If it’s hallucinating or hand-wavy → lower temp

In Claude:

In Cluade it’s a bit different:

Temperature range: 0.0 to 1.0 (defaults to 1.0)

When to adjust it:

• Lower values (0.0-0.3): For analytical tasks, factual questions, multiple choice, or when you need consistent, focused outputs

• Higher values (0.7-1.0): For creative writing, brainstorming, or when you want more diverse and varied responses

In Gemini

Gemini describes Temperature as a dial between predictability and imagination.

You can’t adjust Temperature in the standard consumer Gemini web app (gemini.google.com). Why? In Pro Search, Gemini often performs multiple searches and internal "thinking" steps. Google manages the temperature dynamically during this process, for example, it might use a Temperature of 0.0 while extracting data from a webpage, but a 1.0 while writing the final summary.”

To manually force temperature in Gemini, you have to use Google AI Studio or the Gemini API.

🛠️ How to Adjust It

If you want to play with this setting, you should use Google AI Studio:

1. Open a Chat: Start a new prompt in the center pane.

2. Locate the Sidebar: On the right-hand side, look for the Run Settings or Model Settings panel.

3. Adjust the Slider: Move the Temperature slider.

   • Slide Left (toward 0): For “just the facts” and rigid structure.

   • Slide Right (toward 2): For “wild and creative” ideas.

⚠️ Important Considerations for Gemini 3

If you are using the latest Gemini 3 models (Pro or Flash), there is a significant shift in how you should use this tool:

• Stick to 1.0: Google officially recommends keeping Temperature at 1.0 for Gemini 3. Because these models use advanced “Thinking” and reasoning paths, forcing a low temperature (like 0) can actually cause the model to get stuck in loops or degrade its reasoning performance.

• Use “Thinking” Levels Instead: Instead of lowering temperature for accuracy, try adjusting the Thinking Level (low, medium, or high) in the API settings. High thinking provides better accuracy without the risks of manual temperature tuning.

Pro Tip: If Gemini is being too “wordy” or “robotic,” lowering the temperature to 0.7 is often a safer “sweet spot” than dropping it all the way to 0.

Did I just get too geeky for carilu.com? It just seemed like such a fun unlock, I couldn’t help sharing… or using it immediately myself:

GrowthX

I’m dedicating this post to GrowthX, the AI-native content agency I’ve been lucky enough to work with up close. I’ve followed the founder’s work for years while teaching myself SEO and AEO, and recently chose GrowthX as the content partner for Lovable. Their team blends AI generation with human craft, and the results are real: meaningful traffic lifts, LLM referrals, and pipeline.

I’m now collaborating with them more directly and learning a ton about how AI + content can scale together. If you’re trying to show up in search and AEO, publish more without sacrificing quality, or explore AI-native content models, GrowthX is worth a serious look.

Carilu Dietrich is a CEO and CMO advisor. Current and past clients include Lovable, Weights & Biases, Sprout Social, Bill, 1Password, Miro and others. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

Huge thanks to Ray Rike and his BenchMarkit team, and my collaborators Jon Miller and Bill Macitis. Together, we surveyed 168 B2B marketing leaders for the 2026 B2B Brand vs. Demand Marketing Benchmark Report.

The results highlight a paradox many of us feel acutely. Highlights of the data are below, and you can see a full interactive site and benchmark yourself vs peers here

Highlights:

• The current reality: demand dominates

• The desired future: brand matters more than budgets show

• The real blocker: measurement

• AI-native companies are leaning into brand

The current reality: demand dominates

Today, the median marketing budget allocation is ~70% demand generation and ~25–30% brand.

Pipeline pressure is intense, and when budgets tighten, demand gets protected. 55% of leaders say they would fight hardest to preserve demand spend if cuts were required. Only 11% would protect brand.

This isn’t because leaders don’t believe in brand.

The desired future: brand matters more than budgets show

When asked about the ideal mix, CMOs told a different story: 50% demand / 40% brand.

Even more telling, 73% believe brand investment makes demand generation more efficient, and 63% say brand directly fuels demand.

Belief is not the problem.

Measurement is.

The real blocker: measurement

Only 28% of companies can directly tie brand investment to pipeline.

As a result, brand ROI is often measured using proxies like website traffic or branded search, useful signals, but not enough to defend brand in a CFO or boardroom discussion. Plus new changes in LLM search behaviors are affecting website traffic in ways unrelated to brand spend - undermining its measurement effectiveness.

This creates a vicious cycle:

Brand is under-measured → brand is under-defended → brand gets cut first → demand efficiency suffers → pressure increases.

What AI-native companies are revealing

One pattern I’m seeing alongside the data is worth clarifying.

Many AI-native companies appear more brand-forward, but it’s not because they’ve discovered a new playbook or abandoned demand. In most cases, they’re operating in markets with extraordinary inbound demand. Buyers are already searching, experimenting, and self-educating.

That changes the job of marketing.

Instead of manufacturing demand or aggressively capturing it, these teams are focused on shaping perception, earning trust, and standing out. The heavy investment shows up in brand investments: rich product design, clear narrative, founder-led social, video, YouTube, influencer collaborations, and high-visibility experiences.

Our data actually suggests this isn’t an “earlier stage” phenomenon. Early-stage founders have always invested heavily in brand. What’s different here is percentage of budget and intensity of focus at AI-native companies, enabled by strong market pull.

Our data isn’t broken out by AI-Native and non AI-Native, but shows clear brand priority in early revenue stages:

*see more breakdowns by company size at the full interactive benchmark report here.

The takeaway

Most marketing leaders believe in brand.

They just don’t yet have measurement systems to prove it’s directly contributing to revenue. Longer B2B sales cycles, with many people on the buying committee and many touches over many months, make measurement an ongoing challenge. Massive changes in website traffic and behavior are making the most-common brand measurement unstable.

Until brand is connected more effectively to demand efficiency, pipeline velocity, and revenue durability, it will continue to lose budget battles, even as leaders know it’s essential. Perhaps something I’ll explore in an upcoming newsletter : )

Want more data? Marketing Budget Survey Live

Ray Rike and team are launching a 2026 B2B Marketing Budget and Productivity Survey. We need your help! Please consider taking a 6-minute survey here. We’ll explore how AI is changing budgets, if headcount %s are going down and what trade-offs your peers had to make this budget season. If you take the survey, we’ll share back the insights!

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

I’ve been tracking 1Mind, a formidable player in the AI-led sales space based on personal endorsements I’ve heard from CMOs at HubSpot, Samsara, New Relic, Seismic, and others.

1Mind offers “AI Superhumans” that have been stacking up some amazing results:

→ HubSpot saw a 25% revenue lift from their 1Mind Superhuman
→ Nutanix saw 2x conversion vs. their previous qualifying chatbot
→ Seismic created $1m in pipe in just a few weeks after going live

Building on those results and momentum, 1Mind announced a big fundraise a few weeks ago to double down on expansion. 1Mind’s Series A, led by Battery Ventures, brings total funding to a whopping $40M and features support from a wide number of investors, operators, and current customers.

If you haven’t seen 1Mind yet, it’s worth a look. Details below.

I really admire Amanda as a kickass female founder, trailblazer, and community builder. She’s building something really high impact, again. But don’t just take my word for it.

Below:
1. What 1Mind Actually Does (And Why AI-Led Sales Is a Whole New Motion)
2. The Customer Momentum Is Real (And Wild)
3. Where it works best
4. What it looks like
5. What 1Mind Buyers Are Worried About
What Does this Mean for the Future?

1. What 1Mind Actually Does (And Why AI-Led Sales Is a Whole New Motion)

1Mind provides life-like AI agents with faces, voices, and brilliant GTM brains. While other AI agents can just qualify inbound, the 1Mind Superhumans work across the sales lifecycle from inbound to retention: qualifying prospects like an SDR, pitching like an AE, getting deep into technical topics like a Sales Engineer, and knowing all the details like Customer Support. All that 24x7. 1Mind is poised to completely re-imagine the old SDR → AE → SE → CSM handoff chain, and give customers a better experience while they’re at it.

1Mind’s product is trained on deep company content - messaging, pitch decks, call recordings, RFPs, sales methodologies, technical product details, and more. Beyond qualifying, pitching, and answering questions, she can join Zoom meetings, take turns speaking, create dynamic content like relevant slides or customer stories, scope and price, build ROI models on the fly, answer RFPs, run demos, and even go all the way to close for PLG/SMB deals. It’s crazy.

She can even have a sense of humor (if programmed to : )

What she can’t do (yet): build human relationships or navigate complex org politics.

2. The Customer Momentum Is Real (And Wild)

Already in production at more than 45 companies, 1Mind has seen some really impressive results: At HubSpot, the 1Mind Superhuman “Fiona” now handles work that would take 83 SDRs and 19 Sales Engineers - getting 88% more buyer engagement and 78% more free trials and, as mentioned above, delivered a 25% increase in revenue. She covers the PLG / small business segment whose margins wouldn’t have allowed for sales coverage before.

A large social network used a 1Mind Superhuman to double ACV, shorten the sales cycle by 22 days, and go all the way to close for their SMB/PLG business.

3. Where it works best

The AI Super-human so far is having the most momentum anywhere there’s a high volume of inbound - across both PLG and SLG. For PLG companies, it offers instant, always-on sales coverage that’s more interactive than webpages, emails, and chatbots. She also offers a cost structure for selling in PLG that just wouldn’t be feasible with costly reps. For SLG companies, it offers broader assistance, technical depth, and coverage, most often in sales engineer capacity.

While it often competes with agents that only qualify and book meetings, it frequently wins based on its ability to help companies sell their products beyond that limited scope.

Outbound is still early. You can embed a superhuman into outbound emails or LinkedIn flows, but full-cycle outbound isn’t here yet. That’s where real humans still shine.

4. What it looks like

Companies can customize their Superhumans for different brand expressions as well as different knowledge. Here are just a few:

5. What 1Mind Buyers Are Worried About

Amanda sees a few different objectives popping up, the most frequent:

“Will buyers actually talk to it?”

Short answer: yes. Buyers are increasingly comfortable having real conversations with AI. Some for surprisingly long times - Kahalow shared that one CIO had an hour-long conversation late into the evening with 1Mind’s SuperHuman Mindy (when a salesperson never would have been available). The average duration is about 7 minutes.

“Will this slow our buyers down?”

Reality: buyer response rates are much higher than expected. Plus, Amanda shared, even if only 10% engage, that’s still 10x a chatbot’s performance.

“How does it integrate?”

1Mind integrates across CRMs and marketing platforms, including Salesforce, HubSpot, Zoom, Teams, Slack, and others. The Superhumans are able to pull what they need into conversations and, more reliably than most salespeople, put details from conversations back to CRM objects, message the team in Slack, and log every conversation for humans to inspect.

What Does this Mean for the Future?

Kahalow doesn’t shy away from the fact that 1Mind will replace many human sellers. She’s already seeing that AI-led sales will soon be the default for high-inbound GTM, that SDR orgs will shrink but become more specialized, and human sellers will focus on relationships, org navigation, and complex multi-threaded deals. Outside of that, “AI will handle the rest - end to end.”

Final Thoughts

A lot of AI projects have been focused on replacing existing structures and frameworks - like an AI SDR replacing a human SDR. But several CMOs have told me that their AI SDR trips up when (inevitably) contacted by a customer who needs to be routed elsewhere - only to have to start over from scratch with the support bot.

An AI Superhuman that can go beyond qualifying across a prospect’s entire journey doesn’t fit as neatly into a single department’s existing flow (or budget) - but provides a superior customer experience. The most impactful AI projects might be those facilitating new ways of working, bridging different departments and processes. 1Mind is betting on it.

If you’re a GTM leader who needs to do more with less (always) and meet escalating buyer expectations with speed and quality, you might want to take a look at 1Mind.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

Millions of users. 100,000 new sites created every day. Regular social virality.

But when success compounds that quickly, the success itself becomes part of the advantage, and buzz fuels its own growth.

That’s what makes Lovable fascinating, and frustrating, to study. (And why I’ve hesitated to write this blog.)

You can’t simply copy their tactics because their momentum and market moment is already the multiplier. But we can learn from how they built the foundation and what modern marketing techniques they use to keep the snowball rolling.

Since I advised Lovable, some details still need to stay under wraps. But here’s what I can share about what’s behind the growth — and what others can actually replicate.

Lessons from Lovable’s Growth

Lovable unlocked the potential of every person to create beautiful and fully-functioning websites and apps themselves, using only natural language.

It’s not just an AI tool; it’s the bridge between ideas and execution for the 99% of people on the planet who can’t code. Individuals use it to create personal websites and tools. Entrepreneurs use it to create applications, websites, and businesses without needing to rely on a developer. And businesses use it to create their own SaaS replacement apps, internal apps, prototype app ideas, and standalone websites. How did they drive growth?

1. Ride a Macro Wave Bigger Than You

Lovable hit at exactly the right moment, when every person and every company was trying to figure out how to use AI to improve their work. Instead of fighting for attention, they expertly surfed the wave with a great product, best-in-class social, and deep community investments. Market pull beats grinding for demand creation every time. It’s why we’re seeing many AI companies with XX growth instead of % growth SaaS companies. Voracious appetite and AI innovation budgets are driving outsized AI hyper growth.

2. Deliver Euphoria and Utility

But what Lovable did do, was create a product that “just works” and feels magical.

Users type a few sentences — and within moments, they have a beautiful, functional website or app. That instant creation, especially for people who’ve never been able to build before, drives what users describe as “creation euphoria.”

It’s delight-driven growth. A non-developer builds the app they always imagined; a product manager turns a product requirement doc into a live prototype; an entrepreneur brings an idea to life in hours instead of months. That first “wow” moment fuels a viral loop of sharing, pride, and curiosity.

A recent campaign played on the “so easy to use, even a kid can make apps”

3. Move at the Speed of AI

The AI rush is moving forward at lightspeed, competitor pressure is high, and Lovable is shipping new features constantly and quickly. They are deeply connected to every major LLM, and have tested and integrated new language models even before they launch. They rolled out agent capabilities, a seamless back-end cloud, and Shopify Integration, and continue to deliver new innovations every few weeks, not months. The engineering and growth teams work in tight sprints, are constantly scanning the AI market for what they can learn from others, and use AI dev tools themselves to speed delivery. They are working harder and moving way faster than traditional SaaS companies - but it’s also easier for them to do so being smaller, and having freedom from a historic install base and legacy products to support.

Check out this early-days post by Anton on the maniacle focus on speed in product development and in the product itself. Continues to this day. A more recent marketing case-in-point, this entire OpenAI GPT-5 launch and video was created in just 24 hours! (I’m hearing that almost every founder is asking marketing teams to turn launches around in days, not weeks.)

4. Treat Social as a Core Growth Engine

Lovable’s early visibility didn’t come from paid campaigns. It came from storytelling and social dominance. They built early momentum on X and YouTube, then expanded on LinkedIn, Instagram, and TikTok, mixing first-party storytelling, customer showcases, and influencer collaborations. Posts are a combination of real use cases (and ROI) created on Lovable, hype for community events, thoughts on AI trends, highlights of new team members, and some controversial takes. (An early mega-viral post suggested that Lovable buy Figma, when Lovable was still tiny.)

The CEO, Anton Osika, is a social force of nature, with big ideas and a dedicated, mind-melded social media partner. This person’s eye for what will go viral, his boldness, testing mentality, and care for every word have driven great momentum and visibility for Anton and for Lovable’s brand social.

Hiring Elena Verna, who brought her own 170,000 followers, propelled growth even further and gave the company great reach across her writing, speaking, and teaching.

And, Felix Haas in design, is a significant influencer in his own right, sharing design best practices for creating the most “Lovable” sites with more than 65K followers.

Lovable employees comment, like, and reshare posts quickly to spark algorithmic momentum - and for big announcements, engage groups of colleagues, friends, and family to share the love. It’s a highly intentional, coordinated social motion that compounds every announcement.

5. Build a Brand That Feels Warm and Human

In a market crowded with powerful, dark-mode, sterile AI brands, Lovable made warmth its differentiator. From the name to the rainbow palette to the tagline “Build Something Lovable,” everything about the brand is approachable and inclusive. The friendliness extends into the product experience itself. Nad Chishtie, Felix Haas and the team do amazing work across the stack.

6. Invest in Community Early

Community isn’t a channel at Lovable, it’s a central part of the growth engine — creating learning, belonging, feedback loops, and free marketing all at once.

Lovable invested heavily in community spaces like Discord and Reddit from the early days, even hiring some of its most active, engaged, and skilled community members.

They’ve also had massive success hosting and sponsoring hackathons around the world, from student events to tech-conference attendees, to high-profile sessions with will.i.am and MC Hammer. Their 6-Week “Shipped” Build-a-Thon and recent “She Builds” event for women let users go deep, learning, building, and sharing together — turning early adopters into power users and evangelists.

7. Feature Customer Stories & Success

Almost any successful marketing department is constantly featuring customer stories. Who better to sell the product than happy customers with real proof. So leading with customers is not an innovative tactic, per se, but is one of the central aspects of Lovable’s social, YouTube, hackathons, community, press, speaking and template libraries. Customers, customers, customers.

Success Snowballs

The part that’s so hard to replicate is how significantly visibility creates visibility. Lovable’s incredible growth brought in high-profile investors and even celebrities to amplify the brand. The high-profile press around “Fastest company ever to $100M” brought in requests for more interviews and to speak at all the major conferences.

That network effect is hard to recreate — but it’s powered by the same fundamentals: a remarkable product, a Lovable brand, and a word-of-mouth engine underpinned by social and community investments.

What Can Others Replicate

1) Start with a massive market - it’s easier to ride a wave than try to create demand
2) Design for delight, not just utility - The earlier in the experience, the better. Consumer products have an advantage here over more complicated B2B products.
3) AI has given business a new speed - And speed compounds
4) Treat social as a core growth engine - Make it someone’s full-time job to get it right.
5) Build a brand that feels human - In the world of dark-mode sameness, there’s room for warmth and differentiation.
6) Invest in Community Early - Driving learning, connection, and active engagement is a significant growth multiplier (and recruiting playground).
7) Feature Customer Stories & Success - Nothing builds trust or sells better.

Press, investors, and customers all follow momentum. Your job is to create the early spark that makes others want to join in.

Easier said than done!

But that’s the work.

Hypergrowth stories look effortless from the outside. But inside, they’re a mix of chaos, big bets, ruthless execution, and a team sprinting at full speed long before their “overnight” success.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

Working with thousands of events each year, the team at cred has seen firsthand how AI is influencing the stories that get picked for the big stages. Organizers are flooded with AI-washed submissions, raising the bar for what it takes to stand out. So differentiation, credibility, and a clear point of view are essential. For speakers looking to speak at top conferences, consider:

• Controversial: Have an unexpected take? Events love when sessions break away from the norm—the most ambitious, unconventional ideas often win.

• Highlighting unseen innovation: Show how your work applies AI in ways the world hasn’t seen before. (Easy, right?!)

• Focusing on practical insights: Demonstrate you’ll deliver actionable takeaways, not fluff.

• Sharing newsworthy updates: Make your session more compelling by offering to announce any launches or fundraising news on stage.

• Creating engaging conversations: Consider co-presenters to deliver dynamic discussions. Especially and always big-name customers!

Hot new AI events

While almost every traditional conference has incorporated AI, several new AI-focused conferences have exploded onto the scene. Cred helps clients decide where to attend, sponsor, and speak, reviewing speaker rosters, program content, and onsite opportunities. Below are the top conferences they think shouldn’t be missed:

• Best New Event: HumanX, April 6-9, 2026, San Francisco, 6,500 attendees

• Best for Networking: Ai4, August 4-6, 2026, Las Vegas, NV, 12k attendees

• Best for Early-Stage Cos: GenAI Week, July 2026, Santa Clara, 30k attendees

• Best for Developers: NVIDIA GTC, March 16-19, San Jose, CA, 25k attendees

• Best for Media Coverage: Fortune Brainstorm AI SF, Dec 8-9, 300 top-tier

• Honorary Mentions: Cerebral Valley, AI Summit NY, The AIConference, MIT AI, and TedAI.

Read below for the details on each.

Best New Event: HumanX

AI conferences have exploded, but none made a bigger first splash than HumanX. Created by the team behind Money20/20, HLTH, and Shoptalk, its 2025 debut drew the who’s who of AI and earned rave reviews for high-caliber networking. The momentum continues in 2026 (April 6–9, San Francisco) with an even stronger lineup of enterprise leaders, VCs, and innovators shaping the industry’s future. Expect 6,500+ attendees (75% VP-level or higher) and 300+ media, with programming focused on how AI is transforming commerce, culture, and society. If you want to be in the room with some of the brightest minds in AI, this is it.

Best for Networking: Ai4

Launched in 2018 as an “AI in Finance” event, Ai4 has evolved into a global cross-industry gathering for innovators and executives. Whether you’re advancing your career or closing your next deal, it’s the place to connect. The 2026 conference (August 4–6, Las Vegas) features dozens of co-located summits—from Startups & VCs to AI Policy and Diversity in AI—plus nonstop meetups that foster high-value introductions. Expect 12,000+ attendees from 85+ countries exploring the latest in AI agents, generative AI, and the AI best practices shaping the future.

Best for Early-Stage Companies: GenAI Week

New to the game and looking for where to even begin? This flagship event (July 2026, Santa Clara) brings together early-stage founders, investors, and builders for five days of pitching, learning, and connection. Idea- and seed-stage teams can pitch directly to top-tier VCs while exploring live demos, exhibits, and peer-led sessions designed to spark momentum. With 30,000+ attendees, 400 speakers, 300 exhibitors, and 1,000 investors, GenAI Week blends startup energy with real deal flow—an unmatched launchpad for new AI ventures. The week also offers a high-impact stage to experience breakthrough demos (like Tesla’s RoboTaxi debut, Alef’s flying electric car, and Unitree’s humanoid robots) and engage in cutting-edge talks and workshops

Best for Developers: NVIDIA GTC

It’s one thing to have big ideas, but it takes crazy talented developers to turn those ideas into reality. Dubbed the “Super Bowl of AI” by The New York Times, NVIDIA GTC is where developers suit up to play on the industry’s biggest field. With both visionary keynotes, deep-dive technical sessions, and hands-on training, it’s a great opportunity to roll your sleeves up and learn where the industry is really going next. In 2025, it drew 25,000 in-person attendees, 300,000 virtual participants, and 400+ exhibitors, setting a high bar for 2026. Hosted March 16–19 in San Jose, GTC remains the premier event for developers, researchers, business leaders, and creators shaping the frontier of accelerated computing.

Best for Media Visibility: Fortune Brainstorm AI SF

Can’t quite book an interview with the press? Try going to the source. At Fortune Brainstorm AI SF, you don’t just hear reporters on stage—you sit across from them. Interactive sessions and editor-led networking make it one of the rare events offering direct access to Fortune journalists and other top-tier media. The 2025 conference (December 8–9, San Francisco) will convene 300 technologists, Fortune 500 executives, investors, and policymakers to tackle AI’s most pressing questions in this pivotal moment. If shaping the story matters as much as hearing it, this is your event.

Honorary Mentions

There are countless AI events on the horizon, so to help you round out your calendar, we’ve also highlighted five additional gatherings drawing some of the industry’s most influential speakers and thought leaders.

Cerebral Valley: November 12 | San Francisco, CA

Organized by the popular Newcomer outlet, Cerebral Valley connects an application-only audience of venture-backed AI founders, leading venture firms, technology executives, and members of the media. It’s an intimate gathering, making a great environment for networking.

AI Summit NY: December 10-11 | New York, NY

The AI Summit New York 2025 will bring together over 5,000 attendees, including enterprise leaders, technologists, and innovators, to explore the practical applications of artificial intelligence. Held at the Javits Center, the summit features multiple content stages, immersive workshops, live demos, and a startup & investor village.

The AI Conference: September 2026 | San Francisco, CA

The AI Conference draws engineers, researchers, executives, and founders to explore the cutting edge of AI, covering everything from foundation models, AGI, and infrastructure to ethics, regulation, and applied use cases. It stands out for its combination of deep technical content and its ability to bring together top experts and emerging innovators in one space, fostering collaboration between research, industry, and investment.

TED AI: October | San Francisco, CA

TEDAI brings together 1000+ global AI leaders, innovators, and creators for TED Talks, panels, and immersive experiences exploring the transformative impact of AI. The event also features a 48-hour hackathon, fostering collaboration and bold ideas at the forefront of AI innovation.

MIT AI Conference: October | Mountain View, CA

The MIT AI Conference brings together 500+ industry leaders, researchers, and innovators to explore how AI is shaping the future of work, knowledge, and technology. Benefit from interactive formats—TIM Talks, startup lightning pitches, and collaborative workshops—that foster a hands-on, community-driven experience.

What other new conferences have you prioritized?

Huge thanks to Caitlin Bartley from Cred for sharing her insider perspective. You can learn more about her team’s event management and speaker placement work at www.credpr.com.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

That’s exactly the challenge Kevin Marasco, Chief Growth Officer at Tebra, faced. Tebra’s all-in-one platform for independent medical practices covers a wide set of products, creating competitive challenges from hundreds of platform and niche competitors. So Kevin and his team used AI to build an agent orchestration that lets sales reps get instantly updated kill points, battle cards, and email sequences right from within Slack, where they spend most of their day.

Kevin is one of the most hands-on AI innovators I’ve met in GTM — when we spoke together on a panel at the 10X CEO conference in New York last month, his Competitive Battlecard Orchestration was a clear audience favorite.

Kevin and team have daisy-chained 11 agents together to create effective, informed sales sequences. He’s found that agents are more effective with specific tasks vs broad tasks, and thus created an 11-agent flow. He daisy-chained them together to create an automated end-to-end process. The agents include:

1. Research the competitor

2. Create an overview

3. Create a messaging hierarchy

4. Write an overview of the competitor’s business

5. Write as an overview of the competitor’s positioning and messaging

6. Create a feature table for the competitor

7. Summarize the competitors’ strengths and weaknesses

8. Create a competitive battlecard

9. Identify and review quotes to support the battlecard’s claims

10. Draft a sales FAQ to sell against the competitor

11. Draft a sales email sequence to sell against the competition

It takes about 5-10 minutes for the orchestration to run. Resources are updated to a repository each time it’s run, and salespeople can chat to them directly through Slack for strategic and tactical advice on the fly. If a battlecard doesn’t exist, the rep can chat directly to the agent from Slack to call for one to be created. Crazy productivity improvement over what would have taken product marketers days or a week.

His toolset? DoubleO and Slack. DoubleO is an AI automation platform built for non-developers — founded by former leaders from Meta, Rippling, and PlayStation. Unlike many legacy workflow tools, DoubleO was built AI-first, making it easier to chain multiple agents together seamlessly.

Sneak peek of his workflow automation - with hallucination checks, security checks, and a timesaving calculator all built-in.

Here’s a screenshot of some specific competitor trap-setting questions generated by the flow fed right into Slack

It can also create demo scripts:

And email sequences

Watch his whole workflow in action in his quick video here:

Inside his Prompts

Want a few more goodies? Here’s his baseline prompt (which you can adapt for your own GTM workflows):

“You behave like an expert Marketer, with a thorough understanding of principles, methodologies, frameworks, operations, and best practices related to your role (including growth marketing, product marketing, brand marketing, communications, demand generation). You have in-depth knowledge of how to practically apply each skill, process, and framework to achieve a desired outcome. 

When writing customer-facing copy or content, you write concrete, concise, pithy, persuasive copy that focuses on customer value, benefits, and outcomes. When writing blog content, you adhere to SEO best practices and focus on he search intent of the user. You always format your output to align with the format needed by the platform you are publishing to. You always make sure your copy is relevant to the user’s company by ensuring it factually represents the products sold by the user’s company and aligns with the user’s company’s brand guidelines.”

And the specific battlecard prompt itself

“Review any relevant context about your company and then to review the information gathered about your competitor in previous steps, and create a detailed battlecard to help your company’s sales team sell against your competitor. Include only the most salient and important information. Include the firmographic data points, Products (a list/brief summary of the products they sell), Pricing (the specific pricing/packaging for their products, Strengths (what their product is particularly and uniquely good at relative to your company’s product), Weaknesses (what their product is particularly and uniquely bad at relative to your company’s product), How we win (unique elements your company should highlight to convey their product as clearly better vs. the competitor), Watch-outs (areas the competitor is better than your company and should be avoided in sales conversations), and Positioning comparison (e.g. “they say vs. we say”).  At the end of the battlecard, write a 1-sentence, clean summary that very clearly highlights the TL;DR for how to sell against that competitor and position against them.  Be detailed and specific in your response, focusing on concrete differences and expanding on concepts where needed.”

Pretty cool to see inside someone else’s machine! Do you have an AI workflow for competitive intel you’d be willing to share? Weigh in on this LinkedIn thread here where people are currently sharing more great examples, tools and tips.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

I first saw the announcement from VP of Marketing Leslie Lee, who then pointed me to a follow-up from Alan Roll, Deel’s Head of Brand and Creative. Alan shared a detailed breakdown of how the video came together on LinkedIn. This wasn’t a quick gimmick from amateurs — it was a sophisticated creative team using AI at its highest level. It’s another reminder that the most impactful uses of AI aren’t shortcuts, but experts pushing the tools to their limits.

Watch the video and read the behind-the-scenes below:

But first - quick ask- I’m doing a survey on the most common use marketing & sales AI use cases to help leaders understand where peers are focusing. Please take the survey and I’ll report back here.\

Now on to the video:

You can read Alan’s whole LinkedIn post here, or I’ve extracted the text below:

How 6 creatives used 6 AI tools to create 1 launch video in 6 Days

There was a lot of buzz when we launched our Deel AI Workforce video last week, and a few questions about how we did it.

TLDR:
The process we creatives have been using for decades is still the same process... kinda. With AI in the loop, some steps are exponentially faster than before, and some can happen at the same time. But with this speed comes a level of unpredictability.

A combination of tools (Whisk, Flow, Visual Electric, Eleven Labs, Chat GPT, Gemini) gave us the best way to mirror our creative process. Don’t try to do it all in one tool.

AI feature access varies due to local AI laws and restrictions. What my team in the US and LATAM could do was different from those in the UK. For global teams, it may require folks to get on a video call and tag-team different parts of the script.

Here’s the play-by-play of how it came to be:

Step 1: Develop concepts & scripts
This was done the old-fashioned way, and we gave ourselves a one-day brainstorm limit. We generated a few clips with Veo 3 to validate the concept.

We developed a VO script and shooting script with scene breakdowns, including camera directions and character actions.

Step 2: Art Direction, Location Scouting, & Casting - where we combined creatives and AI, and the magic really happened
We input a combination of mood boards and prompts into Google Whisk. This replicated the Art Direction process, defining our “locations” and “props.”

We did the same with our “cast” and their “wardrobe.” We also defined the mood with prompt directions for lighting and camera. The outputs of this phase were both images and “aggregated” prompts that could be copied and pasted into Flow.

We developed the avatars that appeared over our cast’s heads using simple sketches crafted in Figma that were then brought into Visual Electric.

The avatar characters forced us to go back to our “casting” process to make some adjustments. A great example of what would have cost a fortune in reshooting was simply managed in a few hours and with additional Google credits.

Step 3: Production & Post at the same time
We generated an AI voiceover leveraging Eleven Labs and dropped it into our edit timeline in Adobe Premiere.

The shooting script guided our shot generation. Using the images and prompts generated in Whisk, we turned to Google Flow using Veo 3, where we added camera direction and character performance to the prompts. This was a process of trial and error, some of which were quite comical. We evolved the edit shot by shot, generating new clips as we went. (Pro tip: having an expert DP and an editor on the team really helped us nail this part of the process.)

We went back to basics for final finishing. Editing was still manual, as was compositing, and the final audio mix.

It takes a village! Huge thanks to Malena Romano, Jean-Baptiste Di Marco, Dylan Groat, William Simmons, Dimitri W., and Grace Stuart

Impressive! Kudos to the team!

Have your visual teams been creating video using AI? Would love to hear more.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

At the 10X CEO Conference two weeks back, Prukalpa ⚡ Sankar, founder and co-CEO of Atlan, laid out a detailed roadmap and toolset of their journey to becoming AI-Native that was impressive.

After substantial focus and push, this company of 400 has reimagined roles and jobs to be done, built more than 152 agents that made 4,000 runs in just 5 weeks. They have massively sped up processes and unlocked new responsiveness that wasn’t possible before, especially in customer support. The company is AI-ifying across every role and toolset. I share her tech stack, process, and takeaways below.

For her:

'Being "AI First" means AI is considered as the primary solution. But being "AI Native" means AI is fundamental—integrated into the fabric of our thinking, operations, and processes. It means reimagining workflows from first principles in a world powered by AI, rather than simply layering AI onto existing processes.”

Four-Part AI-Native Roadmap

Atlan took a four-part journey on its sprint to AI-Native: Phase 1: AI Taskforce, Phase 2: Shift the culture, Phase 3: Embed into hiring, Phase 4: Rethink the org chart.

Phase 1: AI taskforce

Prukalpa’s first step, one many teams have taken, was to assemble an elite team of people who had already shown passion and skill in AI. They were tasked with taking the most impactful company use-cases live, determining company-wide tooling, and driving enablement.

Phase 2: Shift the culture

She then launched an AI Native mission to define overall strategy across three long-term pillars:

Next came an AI productivity challenge to the company - a time-bound contest where team members could win prizes and be celebrated for identifying where they could leapfrog their productivity with AI. More than 300 people contributed, and AI started entering workflows across the business.

Phase 3: Embed into hiring

Next, AI became part of the hiring process. Atlan introduced hiring veto power for candidates who didn’t have enough AI curiosity or AI skill. In the interviews, Atlan made it explicit that candidates were expected to use AI extensively, even and especially in interview project rounds. They even asked candidates to open up AI and prompt in front of the interviewer to see how they interact and think using AI. Their guidance to the team on interviewing for AI skills:

Phase 4: Rethinking the org chart

Then, Atlan went on to rethink the org chart - starting specifically with the CX team, which had high costs, and where Atlan thought their unique processes and flows wouldn’t be met well by external tools.

They started mapping “a day in the life,” employee by employee, and evaluating the ‘jobs to be done’ to see which could be most impacted by AI:

In the new world org, one (human) customer success manager would have several (agent) employees, for instance, a call intelligence lead (to prep and follow up), or a health intelligence lead (for reading product analytics, feeding intelligence back for the decision-making).

They found that the more specific they could make the agents, the better the agents did, ultimately becoming better than humans at doing the sub-task. This resulted in a single customer service agent having up to nine agents doing specific aspects of their job for them:

The results for a company of just 400 were impressive. They now have more than 152 agents in their AI registry, and have had more than 4,000 agent runs in just 5 weeks. Many processes were reduced from several hours to less than one, and experienced some capabilities that just weren’t even feasible before (mid-call briefings on attendees by an agent, for instance) are now joyfully in flow.

Why Atlan Moved From Prompts to Agents

Atlan’s early initiatives used prompts with Notebook LLMs but evolved to agents for even more impact. While prompts are simple to set up and great for static tasks (summaries, rewrites, Q&A), they don’t capture memory or continuity across steps, and can’t autonomously decide what to do next.

The agents can reason, plan, and take actions across different tools/data sources (such as calendars, CRM, and product data), and iterate to achieve their goals. This was useful for Atlan’s complex tasks like monitoring account health,
drafting personalized success plans and triggering updates.

Though the agents are more complex to design and govern, the results have been more than worth it for Atlan.

What was their tech stack?

Key Learnings from Atlan’s AI-Native Journey

1. Pick the right problems – Rethinking core, high-impact workflows from first principles led to the biggest wins.

2. Specialize your agents – Narrow, task-specific agents consistently outperform general ones.

3. Context is king – Data quality and tight feedback loops make or break effectiveness. Especially in customer-facing forums, the data must be right.

4. Build cross-functional pods – Pair a platform owner, an engineer, and a workflow expert to move fast and effectively.

5. Make it fun – Contests, playful names, and community kept adoption high and sticky. You need the team’s participation and feedback for success. After introducing herself in Slack like a (human) employee, Atlan’s Health Intelligence agent, Hermioni, was celebrated with a party and a cake:

The irony?

While Prakulpa’s team is an impressive rocket of growth and AI deployment, she’s measured about her AI optimism: “I think all of the companies that survive will have extensive AI agents - I don’t know that it will be a competitive advantage. But at a high-growth startup, it’s hard to hire fast enough - so if we can do more with less, we can do more.”

Have you started rethinking workflows for an AI-Native world yet? I’d love to hear your stories.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

Jump to the best parts:

• (03:50) → From Keywords to Prompts – long prompts vs short searches, how to collect and track prompts, baselining visibility

• 12:25 → Affiliate Influence & Industry Nuance – how affiliates take an outsized role in visibility, B2B vs B2C contrasts

• 15:22 → Content That LLMs Love – comparison pages, alternatives, listicles, off-property publishing strategies

• 31:00 → Advertising & The Future of AI Search – will LLMs allow ads?, Google’s AI mode, perplexity experiments

Highlights of our conversation and more resources below:

• Behind the scenes - Which sites rank high in LLMs?

• Wait… what do we call it?

• Prompts are much different than keywords

• Measuring visibility (Before you try to move it)

• Automated tracking in action (Sceenshots!)

• Speed matters more than ever (New pages can index in 48 hours!)

• The content that moves results

• What results look like (so far)

• Ads inside LLMs?

• A 14-Day AX Playbook (steal this)

• Profound’s Answer Engine Optimization Guide

• My takeaways for CMOs

Behind the Scenes - Which Sites Rank High in LLMs?

Many of us have seen Nick’s research from earlier this year, sharing how sites previously lower priority for marketers are now driving many top responses:

• Wikipedia & Reddit show up constantly. You likely weren’t optimizing for either—now you need a plan. (Wikipedia requires real press/third-party notability; most early-stage companies can’t force it.)

• Q&A format and comparisons outperform more traditional thought leadership - this is why Reddit and Forbes, which index content in this way, both show up high as sources.

• Paywalls hurt indexing; traditional media that’s strong for comms visibility and authority doesn’t help in LLMs if it's behind sign-in.

• Affiliate/listicle ecosystems dominate certain categories (e.g., Wirecutter and mattress reviews). If your category is affiliate-heavy and you’re not in those lists, you can be invisible in AI answers.

While I found this data fascinating, it only underscored my desire to use the platform that customizes the data to my own audiences and terms.

Wait.. what do we call it?

Nick’s team prefers “Agent Experience” over Answer Engine Optimization or Generative Engine Optimization (GEO). Why? Because it’s not just the evolution of SEO - your new primary “visitor” isn’t a human—it’s an AI agent crawling, reading, and synthesizing your content into answers. Design, CTAs, and pretty pixels matter less to bots than structure, clarity, and citations. Your job shifts from “make a great page for people” to “make a great answer for agents.”

Prompts are much different than keywords

Old world: optimize for keywords and clicks

New world: optimize for prompts and citations

Prompts are longer and richer than searches. The average prompt is 20+ words; the average Google query is 3–5. That extra context changes everything: you’re not trying to rank for “best CRM”—you’re trying to be cited in “I’m a 50-person B2B SaaS with a PLG motion and a small SDR team—what CRM should I use and why?”

How to build your prompt set (Step Zero): Before you can measure, you need to create a focus on the right prompts. You can set up a process in two ways:

1. Add “How did you hear about us?” to forms (if viable, make it open text, accept “ChatGPT/LLM”). Follow up and ask: “What prompt did you use?”

2. Run an internal “pretend you’re a prospect” exercise with your team. Create a short Google form and ask everyone to pretend like they’re one of your prospects. Ask them to write a prompt looking for a solution like yours.

Pull out common words or themes. Those become your tracked prompts.

Measuring visibility (Before you try to move it)

You can’t improve what you can’t see. While Profound automates this process, Nick suggested that those just getting started could also create a simple Eexcel tracker capturing Prompt (verbatim), Platform (ChatGPT/Perplexity/Claude/Gemini), Top answers (who’s mentioned), Links cited (domains + pages), Is our brand mentioned? (Y/N), and “Visibility Score” (% of prompts where you appear).

Then it’s time to decide your posture:

• Offense: You show up in ~20% or less → build net-new content (listicles, Q&A, comparisons).

• Defense: You show up in ~80–90% → protect/refresh the specific pages getting cited.

Also: make sure LLM crawlers aren’t blocked. Profound finds Cloudflare and other filters quietly stopping bot access (and your citations) without you realizing it. Crawlers don’t like JavaScript and love plain old text.

Automated tracking in action

In case you’re interested in seeing Profound’s dashboard, here are a few screenshots (I’m not sponsored by them, just ended up on a deep dive based on the interest of my clients). Among the core features of Profound is the visibility score and ranking, customizable over different periods, topics, tags, and platforms:

The platform lets you look through trending prompts.

While ChatGPT is providing the bulk of the traffic to most brands right now, strong AEO programs will optimize across a number of different engines, each potentially with its own nuances.

Speed matters more than ever (New pages can index in 48 hours!)

The biggest misconception Nick hears regularly: “LLMs only use old training data.” Not anymore. ChatGPT (and friends) often live-search for bottom-of-funnel queries and can pick up new pages within ~48 hours. That means velocity is your secret weapon—ship, ship, ship. You might see immediate results.

The content that moves results

From Nick’s experience, if you publish nothing else, publish these:

1. “Best [Category] tools” listicles

2. Head-to-head comparisons (e.g., You vs. Competitor A vs. Competitor B)

3. Vendor alternatives (e.g., “Top SendGrid alternatives”)—Nick’s gold standard is Postmark’s honest alternatives page.

4. Structured Q&A addressing purchase criteria (pricing, support, integrations, security, who it’s for)

He suggests brands be brave about hosting competitor names on their sites. Prospects already know them—and the agent is going to assemble a comparison anyway. If you don’t supply your POV, it will pull (or hallucinate) someone else’s.

One of Profound’s own secrets to success is really high-quality content on external sites from partners as well - especially for competitor comparisons.

• On-site: fastest to publish; you control the message.

• Off-site: often more authoritative to LLMs and infinitely more scalable. Build a partner network and syndicate high-quality, balanced pieces at scale (with editorial approval), especially listicles and comparisons.

What results look like (so far)

• Profound says ~15% of their leads cite ChatGPT as first touch (self-reported). Vercel’s CEO shared ~10%. of their traffic is coming from LLMs.

• Branded search lifts as buyers start in ChatGPT, then Google your name; a third-party study pegged this at ~10% lift in B2C / ~15–17% lift in B2B.

• Case studies Nick can share publicly:

  • Ramp: ~7× AI visibility in a key category

  • Airbyte: 3× brand visibility in a week

  • Statsig: jumped from ~22% → ~50% visibility within a week

Early but telling pattern: LLM-referred prospects arrive more educated and convert at higher rates. AI Content and AEO Agency GrowthX reports their customers are seeing 3-5X higher conversion rates on LLM-sourced traffic. Even if raw traffic looks modest, it can create more qualified pipeline.

Ads inside LLMs?

Short version: yes, they’re coming (they already exist in a limited form). Google’s AI mode is monetized; Perplexity experimented; OpenAI will feel pressure to add formats over time, though Sam Altman seems hesitant. Don’t bet your 2025 on LLM ads, but watch the space closely.

A 14-Day AX Playbook (steal this)

Day 1–2: Instrumentation

• Add “How did you hear about us?” (open text).

• Stand up your AX Visibility Log (sheet or tool).

• Confirm LLM crawlers aren’t being blocked.

Day 3–5: Prompt discovery

• Email recent leads who cited LLMs → ask for their exact prompts.

• Run the internal prompt drive (Google Form). Cluster themes.

Day 6–8: Foundation content

• Publish 1–2 “Best [Category]” listicles.

• Publish 2–3 comparisons (you vs. top rivals).

• Convert FAQs into scannable Q&A pages (one question per page, schema if you have it).

Day 9–12: Off-site syndication

• Identify relevant partner/affiliate/editorial sites.

• Pitch balanced listicles and an alternative piece; fund freelance quality if needed.

Day 13–14: Daily cadence + refresh

• Log visibility across top prompts daily.

• Update pages cited by LLMs (fresh data, clearer tables, pricing, who it’s for).

• Expand to adjacent prompts weekly.

→ Profound’s Answer Engine Optimization Guide

Want even more? Profound is just about to publish this detailed “Answer Engine Optimization Guide” — download it here first : )

Profound Answer Engine Optimization Guide 2025

25.3MB ∙ PDF file

Download

Download

My takeaways for CMOs

• Agent Experience is not a side quest. It’s coming for us quickly.

• Velocity > perfection. LLMs can cite new content in ~48 hours. Ship fast.

• Comparisons win. If you don’t write them, someone else will—and the agent will trust them.

• Off-site scale matters. Syndication networks work.

• Measure reality. Can’t improve what you can’t track

Are you seeing traction with your AEO and GEO efforts? Would love to hear your real-world stories.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

This is the frontier we’re walking into, an agentic world where AI isn’t just a tool, but the distributor, operator, and sometimes even the buyer. And it has massive implications for how we design organizations, orchestrate GTM motions, and retire the “old playbooks” that many of us have been running. How do we restructure our websites? How do we get humans-in-the-loop at the right moment?

That’s why I’m thrilled to join the Agentic Distribution Summit hosted by Hard Skill Exchange this week. It’s one of the most forward-looking (and free!) gatherings I’ve seen on how AI will reshape go-to-market — not in theory, but in practice.

The Lineup Is Stacked

The conference brings together operators across the C-suite who are already pushing on the edges of this new model:

• Olivia Nottebohm, COO of Box, on redesigning the org for agentic advantage

• Jeanne DeWitt Grosser, COO of Vercel, and Robin Daniels, CBO of Zensai, on building an AI-native business

• Wade Foster, CEO of Zapier, on agent-led GTM

• Mark Roberge, Co-Founder Stage 2 Capital on the science of agentic scaling

• James Roth, CRO of ZoomInfo, on AI for predictive pipeline

• Amanda Kahlow, CMO of 1Mind on navigating the messy middle of GTM

• Kady Srinivasan, CMO of You.com, on APIs, accuracy, and the agentic GTM stack

• Latané Conant, CMO of 6Sense, on designing AI workflows that sell themselves

• Plus sessions including

  • Jaleh Rezaei, CEO & Co-Founder, Mutiny

  • Ashley Steppian, the Head of Marketing & Growth at Hex

  • Everett Berry, Head of GTM Engineering, Clay

  • Mario Moscatiello, Head of Growth at Airbyte

• And dozens more incredible leaders

My Session: Agentic Distribution for Growth

On September 16th at 1pm PT, I’ll be moderating a killer panel with:

• Omer Gotlieb – CEO, Salespeak.ai

• Lisa Sharapata🌟– VP AI & GTM Strategy, Metadata

• Tooba Durraze, Ph.D. – CEO, Amoeba AI

• Marcel Santilli – CEO, GrowthX AI

• Kevin Marasco – Chief Growth Officer, Tebra

We’ll dig into some of the big, practical shifts that are already starting to show up:

⚡ Agents as the new interface: becoming the front door to your buyer journey

⚡ Agents as operators: running GTM campaigns, monitoring pipeline, even creating content

⚡ Agents as distributors: autonomously pushing offers and products

⚡ Governance and trust: what happens when machines transact with each other?

⚡ The future of GTM teams: what roles emerge, and which ones evolve away?

We’re already seeing hints of this in eCommerce, where agents are browsing, comparing, even placing items in carts. In B2B, it’s earlier days — but the signs are clear: AI is researching vendors, shortlisting options, and even drafting RFP responses. The question isn’t if this comes to B2B, but how fast.

Why This Matters

For leaders in marketing, sales, and product, this isn’t just about adding another tool to the stack. It’s about preparing for a structural shift in how growth happens. Distribution used to mean ads, websites, and outbound calls. Distribution tomorrow might mean your “bid agent” negotiating against a buyer’s agent inside an LLM.

That’s a whole different playbook.

Register to watch live and get the replays

I’d love for you to join us this week. You can check out the full agenda and register here: Agentic Distribution Summit

This is a wild ride. I’m thankful to Julia Nimchinski for pushing the envelope on her events and bringing me into the discussion. The future is arriving so quickly, it’s scary.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.

The Timeless Tension: Brand vs. Demand

Every CMO knows the push and pull: how much budget to dedicate to brand—building long-term awareness, affinity, and trust—versus how much to invest in demand generation—driving the immediate pipeline CEOs and CFOs care about.

For the past several years, with interest rates climbing and investors demanding efficiency, the pendulum swung heavily toward demand generation. CMOs were under pressure to prove ROI and cut costs. Pipeline became the north star metric, while brand investments were often cut or deprioritized as “nice to have.”

But something has shifted.

AI Brings Brand Back to the Forefront

Many of the top AI-Native companies are investing in brand, some even more than demand generation. Companies like OpenAI, Anthropic, Cursor, and Lovable have had relatively small marketing departments in their earlier stages of growth, focusing more on brand, communications, and community experience than traditional demand-generation and paid activities. Why?

• Differentiation: AI has made software faster, cheaper, and easier to build. In crowded categories, the product isn’t enough of a moat. A distinct, memorable brand—mission, vision, personality, voice, visual identity—is a strong differentiator. Anthopic’s core brand value of acting for the global good underpins their value and message through everything they do. Lovable’s brand stands out against Replit’s for its warmth and approachability for the everyday people it sells to.

• Experience: In a crowded market, customers are not just buying the technology, but buying the experience of interacting with the company. Brand shapes that experience.

• B2B and B2C Crossover: Many AI products are bringing B2C experiences into B2B settings, or asking individuals to bring their favorite AI to work. B2C has long had an easier time matching brand investments to revenue because of shorter sales cycles and has long sold on emotion in addition to logic.

• Revenue impact: The irony is that brand, often seen as fluffy, is more directly impacting revenue. Companies with a strong identity stand out, attract more organic attention, and convert faster.

A Case in Point: Lovable

Take Lovable, the AI-native platform that lets anyone build apps through vibe-coding, where I’ve been advising lately. Its brand—both the word and the feeling—is working overtime for growth.

• The look and feel of the product is designed to be beautiful and fun, not sterile enterprise software.

• The voice is human, approachable, and yes, lovable. That makes people want to use it, share it, and evangelize it. It speaks to the 99% of people who don’t code and invites them into a world of technology that has previously seemed unapproachable to non-technical folks.

• The growth has been built on product, experience, and social media love, with paid advertising and demand generation taking an almost microscopic role.

• The momentum the brand creates in the market is palpable. Customers, influencers, and even journalists are drawn to the story not just because of the technology and growth, but because of the identity behind it. (Mad props to Nad Chishtie and Felix Haas for building such an energizing look and feel)

That’s the multiplier effect of brand: it doesn’t just complement demand; it accelerates it.

The CMO’s New Balancing Act

So where does that leave CMOs?

• Demand will always matter. The board still wants revenue efficiency and pipeline visibility.

• But brand is back. In the age of AI, companies that rely only on demand tactics risk blending into the noise.

The challenge for today’s leaders is not choosing one or the other—it’s orchestrating both. The right balance is dynamic, shifting with market cycles, company stage, and competitive pressure.

The 10 Pillars of Brand in an AI-First World

So what does "brand" actually mean in practice? It's more than just a logo or tagline. Here are the ten elements that separate memorable brands from forgettable ones in an AI-saturated market—in the order you'd actually build and roll them out:

1. Brand Identity & Recognition
Your foundational identity elements—company name, URL, logo, color palette, typography, and product UI. This starts with the basics: is your name memorable and brandable? Can people spell it, say it? (We almost renamed Atlassian because of these issues) Then it extends to how you present yourself visually. In a crowded AI world where products can feel similar, these core identity elements create instant recognition and help you stand out from the sea of sameness or laughable names.

2. Brand Values & Purpose
The principles that guide your company and how you communicate them. This foundation informs everything else you build. Customers and employees want to connect with brands that stand for something, especially as AI raises new ethical questions that require clear stances.

3. Brand Story & Narrative
The overarching story about who you are, why you exist, and what you're building toward. Built on your values, this story becomes the throughline for all communications. Storytelling fuels both press coverage and word-of-mouth. AI can help distribute stories, but the narrative itself must be authentic and compelling.

4. Voice & Tone
The personality is expressed through language, copy, and customer communications. Your story and values inform how you sound. AI-generated content risks sounding generic, so a strong, human, recognizable voice cuts through the noise and makes people actually want to read what you write.

5. Customer Experience
Every touchpoint, from website onboarding to support chats to product interactions. Experience is increasingly inseparable from brand. Frictionless, delightful experiences reinforce identity and drive the loyalty that keeps customers coming back. This can include even smooth off-boarding in cancellations, or empathetic error messages.

6. Emotional Content
Your brand's ability to trigger emotional connection through content, especially video and campaigns. Video is the currency of attention, and AI makes it cheaper to create—but harder to make memorable. Brands must infuse emotion and originality, and if it’s authentic to their brand, humor.

7. Community & Advocacy
How much your brand is loved, shared, and evangelized by customers, developers, or fans. Start building this early—community-led growth scales brand reach faster than paid channels, especially in ecosystems powered by AI where word-of-mouth moves at internet speed.

8. Advertising & Awareness
Paid campaigns, media buys, and amplification strategies. Once you have strong foundational elements, paid media can amplify them effectively. In an AI-first search world where organic reach is getting harder, this becomes one of the few controllable levers for visibility and mindshare.

9. Thought Leadership & Influence
Many AI-first companies are investing very deeply here - in informative podcasts, press, how-to videos, data reports, and insights - materials that help educate and inform on the new world order. This has always been critical in marketing, but the voraciousness everyone has to learn (and the crazy noise of everything AI) makes excellent thought leadership even more valuable at the moment. In AI-driven industries, being seen as a category shaper builds credibility that goes beyond product features and creates competitive moats - just take a look at Carta’s amazing data reports and resulting momentum.

10. Social Impact & Reputation
In the current AI gold rush, this isn’t factoring in quite as heavily here outside of a few companies that are more centrally dedicated to the cause (Anthropic), though Google did release data on how much energy an AI prompt uses earlier this week. I expect we’ll see more in this category as these AI-native companies grow—ESG commitments, philanthropy, and impact initiatives. Social trust is fragile, and social impact can reinforce the positive vision of a company. Plus, companies with authentic impact have more goodwill and resilience when crises hit.

Share Your Perspective

I’d love to know how you are thinking about brand versus demand right now. Are you shifting spend toward one or the other? What metrics do you use to prove a brand’s value?

Please take two minutes to fill out the survey here

I’ll share the results in an upcoming post so we can all benchmark where our peers are leaning in 2025.

Carilu Dietrich is a former CMO, most notably the head of marketing who took Atlassian public. She currently advises CEOs and CMOs of high-growth tech companies. Carilu helps leaders operationalize the chaos of scale, see around corners, and improve marketing and company performance.