Liza Adams

Beyond AI Search: What Happens When AI Takes Buying Actions on Your Website

Liza Adams · October 15, 2026 ·

What happens when AI shops your website? A realistic buyer scenario on a real website

Taking the next buying step was easier than getting all the information the buyer needed.

That’s what I found when I gave ChatGPT Work a realistic B2B buying situation and asked it to evaluate fit, work through pricing, explore trial and sales options, and go as far as it could before a person needed to take over.

The AI found pricing, trial, and sales paths and could take meaningful steps through the website. But a key question about migration scope and timing was still unanswered when the site asked for contact information.

Agent readiness is part of this, but my focus here is the conversion path: whether an AI acting for a buyer can get the information it needs, make sensible choices, take the next step, and know when a person should take over.

Quick Take

AI search has taught us to look at how AI sees, describes, and recommends our brands. As AI gets better at acting for buyers, we need to look farther into the buying process too.

A few things I took away from this experiment:

Getting the information was the weakest part of the experience. Understanding scored 2.9 out of 5, while choosing the next step and taking it both scored 4.7. Knowing when a person needed to take over scored 4.9.
Pricing, trial, and sales paths were relatively easy to reach, while important questions about migration and services remained unresolved before contact capture.
Asking for contact information is not inherently a problem. The site can still answer what it knows, explain what needs human input, and tell the buyer why a person needs to step in.
The AI respected the approval boundaries and stopped before providing identity or company information, creating an account, submitting a form, booking a meeting, or making a commitment.
This was one controlled experiment, not a benchmark. I want to see whether the same patterns hold across other websites, buyer situations, and AI systems.

Beyond AI search: how AI helps earlier in the journey and later in the buying process

Watch or Listen Instead

To support different ways of processing information and different time constraints, this newsletter is also available as a 4-min AI video explainer and 18-min AI podcast with two AI hosts. Both were created with Gemini Notebook and reviewed by me for accuracy and responsible AI use.

From AI Search to AI Action

Much of my work on changing buyer behavior has focused on AI search: Are you visible in the answer? How does AI describe you? Does it recommend you for the right situations?

As AI starts taking actions on behalf of buyers, we need to consider another experience alongside the one we design for human visitors. Min Wang, a longtime digital marketing leader and now Co-founder and CEO of Betoken, Inc. calls it Agent Experience (AX).

We’ve spent years improving website experiences for people through clear navigation, helpful content, and easy next steps. AI agents need many of the same things, but they may interact with websites differently. Can they find pricing tucked inside interactive elements, compare product options, and take the next step? And can they recognize when a person needs to step in? These are questions worth testing alongside traditional UX.

I’ve explored this in my AI search keynote, AI search article, and earlier newsletters including Your Buyer Changed: Four AI-Driven Shifts Breaking the GTM Playbook. I’ve also written about preparing for AI that acts, not just answers.

This experiment went farther down the buying path. The vendor was already in consideration, and I wanted to see what happened when AI tried to help a buyer take action on the website. I wanted to know whether the AI could use the website well enough to help the buyer make progress toward conversion.

What I Asked AI to Do

I used a specific buyer situation: a CMO at a mid-sized B2B software company evaluating a replacement marketing platform for a 50-person team. The company wanted to keep its current CRM and was working on a fairly short timeline.

I used ChatGPT Work to run the experiment. This is an agentic workflow, meaning the AI can carry out a multi-step task more autonomously and use tools, such as a browser, along the way. In this case, it used its cloud browser to navigate the vendor’s website, follow links, and interact with pages. It then analyzed the experience, suggested ways the site could better support an AI acting for a buyer, and built an interactive dashboard summarizing the results.

I didn’t prescribe a route. Across three repeated runs, the AI chose its own path. I also tested four specific areas:

Determine the likely product plan and cost.
Explore the free trial.
Explore the sales or demo path.
Ask one factual buying question through chat.

Before testing, I set clear approval boundaries. The AI could research and interact with the website, but it could not provide personal or company information, create an account, start a trial, submit a form, book a meeting, or make a purchase or commitment without human approval.

Those boundaries gave each run a clear stopping point.

The Biggest Gap Was Understanding

I looked at the experience across four checks: could the AI understand what the buyer needed, choose an appropriate next step, take that step on the website, and know when a person needed to take over?

Understanding scored 2.9 out of 5. Choosing and acting both scored 4.7, and knowing when a person should take over scored 4.9.

The scores reflect the experience created by the AI and website together, not separate grades for either one. For each finding, I also looked at whether it primarily reflected the website, the AI’s behavior, or the interaction between the two.

The AI could identify the likely plan, baseline pricing, core capabilities, CRM compatibility, and onboarding requirement. All three open-ended runs ended on a sales-assisted path, but they got there differently. One moved to a demo quickly, another focused first on plan and price, and the third spent more time on fit and integrations.

The weaker part was getting all the information the buyer needed before moving to the next step.

What the AI could and could not learn: enough to move forward, not enough to validate the timeline

Not Every Unanswered Question Was a Website Problem

Several questions were still unresolved, but for different reasons:

The buyer hadn’t provided enough information. Final cost depended on the number of paid seats.
The website hadn’t answered the question yet. Migration scope and timing, along with some service details, were still unclear before contact capture.
The test stopped by design. We did not see what happened after the point where the site required account creation or contact information.

That distinction is important. It kept me from treating every unanswered question as a website problem.

Knowing When to Involve a Person

I initially thought of these as “human handoffs,” but that implies we watched an actual salesperson take over. We didn’t. What we observed were points where the AI recognized that a person needed to step in.

The AI stopped before providing identity or company details, creating an account, submitting a form, booking a meeting, or making a commitment. That was the right behavior.

When should a person take over? What AI can help with and where a person should step in

The bigger question for me is how much useful buying information should be available before that point.

Gartner’s 2026 B2B buyer research points to a mix of self-service and human support. Seventy percent preferred a completely digital, self-service buying experience, while 69% said they wanted a sales rep to validate AI-generated insights.

We authorized the AI to ask one factual question about migration timing and onboarding. The website accepted the question, but did not answer it before asking for an email address. I would not call that a failure. There are good reasons to bring a person into a complex B2B purchase. But the site could still give the buyer something useful first.

For example, a response could look something like this:

“Whether a 4 to 6 week migration is realistic depends on your current setup, what needs to move or be rebuilt, and the support you need. Our standard onboarding covers [specific scope]. A specialist would need to assess [specific unknowns] before confirming timing and any extra services. If you’d like us to review your situation, share your email and we can continue from there.”

Illustrative example, not the vendor’s actual response.

The website does not have to answer everything before asking for contact information. But it can make baseline information easier to find, explain what varies by buyer, and clarify what requires human assessment. That guidance should be available on relevant pages, not only through chat. It gives both AI and human buyers a better starting point before sales steps in.

Andy Crestodina, co-founder and CMO of Orbit Media Studios, recently studied what happens when AI helps shortlist a vendor and then sends the buyer to the website. Across 97 B2B lead-generation sites, visitors referred by AI were more likely to become leads than visitors from other organic sources.

My experiment looks at the next step. What happens when the AI stays involved after the recommendation and starts taking buying actions on the buyer’s behalf?

Andy sees that progression coming too:

Andy Crestodina, co-founder and CMO of Orbit Media

“When AI recommends a brand, the visitor is much more likely to become a lead. That’s because buyers trust these recommendations. So it’s really a short jump from the buyer filling out the form, to letting AI do it for them. It’s going to happen. It may already be happening.”

Test AI Across the Buying Journey

This website was relatively easy for the AI to move through. It could reach pricing, trial, sales, and chat paths without repeatedly getting blocked or confused. The bigger weakness was getting the information the buyer needed.

There’s some early evidence that the website itself can make a difference. In one controlled study of 300 browser-agent runs, an agent-ready version of the same prototype website had much higher task success and required fewer steps than the conventional version. It’s one early study on a prototype, but it reinforces why I think this is worth testing.

That study looked at agent task success. My experiment adds a specific buyer situation and asks whether the AI can help that buyer make progress toward conversion.

On other sites, I’d watch for:

The AI can’t find what it needs. Navigation may be unclear, important information may be buried inside interactive elements, or key pages may be hard to reach.
The AI can’t understand what to do. Labels may be vague, options may be hard to compare, or the next step may not be clear.
The AI can’t take the next step. Pop-ups, login walls, forms that ask for information too early, or controls the AI cannot use may stop it.
The AI can’t recover when something goes wrong. Errors, dead ends, or unclear alternatives may leave it unsure how to continue.

I’d use AI itself to test the buying journey and watch where it slows down, gets stuck, misses information, or chooses poorly. Some stopping points are appropriate places for a person to step in. The goal is to remove the unnecessary ones that keep the AI from helping the buyer.

The Context Should Follow the Buyer Into Sales

The experiment stopped before an actual sales conversation, so we did not test what happened next. But it raised another question: if AI has already worked through pricing, checked fit, gathered requirements, and identified open questions, how much of that context should follow the buyer into sales?

I think the answer is quite a bit. Making the buyer start over wastes work for both the buyer and the seller.

Min Wang has led digital marketing at Smartsheet, Brocade, Cisco, NetApp, and Dell, and is now building in AI. She sees the same connection between the website experience and what happens when sales steps in:

Min Wang, Co-founder and CEO of Betoken, Inc.

“We’ve spent years designing digital experiences around what a human buyer needs to know and do next. As AI acts on the buyer’s behalf, we must look at that same journey through an Agent Experience (AX) lens: Can an AI agent quickly get what it needs to evaluate us, and where is the optimal human handover?

When Sales steps in, both buyer and seller should inherit total context from that AI interaction, freeing human teams to focus on what actually closes enterprise deals: high-empathy alignment, multi-stakeholder consensus, customized ROI modeling, and complex negotiations.”

What We’re Still Figuring Out

This was one controlled experiment with one AI system, one website, one buyer situation, and a limited number of runs. Agent behavior can vary by model, instructions, website state, and permissions. This was not a benchmark or conversion study.

I still want to know how consistent these results are across AI systems and websites, which buying questions should be answered before a buyer shares contact information, and where a person should step in. I also want to know whether making buying information clearer for AI improves the experience for people too. I suspect there’s a lot of overlap, but we didn’t test that here.

My hypothesis is that a better agent-assisted buying experience will improve conversion as more buyers delegate research and early buying tasks to agents. If the AI can’t get the information it needs, choose the right path, or take the next step, the buying journey can break down before a person gets involved. We didn’t measure conversion here, so I want to test whether improving that experience helps more agent-assisted journeys reach the intended conversion point.

One Experiment I’d Run

Give an AI one realistic buyer situation from your business. Define what it can do and where a person needs to take over, then let it choose how to proceed.

You can start with a prompt like this, using an AI tool that can navigate and interact with websites.

Sample prompt

“Act as a B2B buyer evaluating [product category] for [company situation]. Visit [vendor website] and determine whether the product meets our needs, what it would likely cost, and what options are available to move forward.

Use only information you can verify during this website visit. If information is unavailable, unclear, or depends on details I haven’t provided, say so rather than guessing.

You may navigate pages, follow links, and explore pricing, trial, demo, and sales options. Do not provide personal or company information, create an account, start a trial, submit a form, book a meeting, or make a purchase or commitment without my explicit approval.

Choose your own path through the website. At the end, summarize what you learned, what remains unanswered, where you encountered difficulties, and when human involvement was needed.”

Watch four things:

Can it get the information the buyer needs?
Does it choose an appropriate next step?
Can it take that step on your website?
Does it know when a person needs to take over?

Then look at what is still unresolved and why. Did the AI need more information from the buyer? Was the information unavailable on the website? Or did the test stop by design?

We’ve spent a lot of time learning how AI sees, describes, and recommends our brands. As AI starts taking action for buyers, I think we need to test more of the buying journey too.

The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey.

For applied learning: Explore our applied AI workshops, offering both strategic sessions to shift mindsets through practical AI uses and hands-on sessions to build human+AI workflows. You’ll leave with either a clear plan or working solutions. Office hours provide follow-on support to keep momentum going.

For ongoing advisory: I work with leaders as a strategic advisor through their AI transformation, providing guidance, frameworks, and a sounding board as they build. If that’s what you need, let’s talk.

For team transformation: I partner with GTM leaders to move from scattered AI adoption to an operating model where human and AI teammates work together across workflows.

For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many conferences and corporate events.

If any of this resonated, I’d love to hear from you. Reply to this email, find me on LinkedIn, or come say hi at an event.

How Box Turned 67 AI Opportunities Into a Clearer Plan for What to Build Next

Liza Adams · October 1, 2026 ·

Sixty-seven AI opportunities is a good problem to have. But once the list gets that long, you need a way to decide what deserves attention first.

Box is the secure content layer supplying context to any AI agent, serving more than 120,000 paying customers. Its marketing organization already had real AI momentum. Teams were experimenting, and marketing had a growing set of agents.

Our discovery work with the marketing organization identified 67 possible AI use cases. Before we could prioritize them, we first had to understand how the work actually happened across people, processes, and systems.

From there, we needed to make three decisions for each one: What should happen to the work? Is it worth pursuing now? And how much should AI own?

Those questions became the heart of the blueprint we built with Box.

Quick Take

The blueprint turned 67 possibilities into a sequenced plan the team could act on:

More than 30 conversations across Marketing, SDR, Operations, and IT were translated into documented workflows, 8 core insights, 15 jobs to be done, and 67 possible AI use cases.
The Sorting Machine process helped us decide what the work needed before choosing the technology.
Impact, effort, and risk helped us decide what to do now and what dependencies to address first.
The Trust Ladder helped us decide how much of each workflow AI should handle and where human judgment should stay involved.
The resulting blueprint sequenced opportunities by maturity and dependencies. Box is now turning it into internal timelines, business cases, and project plans.
Reimagine the work first, then the roles, then the org. Let the work tell you what needs to change before you redraw the org chart.

From discovery to decisions: 30+ conversations, mapped workflows, 8 insights, 15 jobs to be done, 67 use cases, 3 lenses

Watch or Listen Instead

To support different learning styles and time constraints, this newsletter will also be available as a 4-min cinematic AI video explainer and a 20-min AI podcast with two AI hosts. Both were made with Google’s Gemini Notebook and personally reviewed for accuracy and responsible AI use.

Map the Work as a System

My business partner, Megan Cabrera, led the day-to-day discovery and blueprint work with Box. She ran stakeholder interviews, mapped workflows, developed the AI blueprint, and drafted the recommendations. I provided strategic direction, developed the frameworks, guided prioritization, and reviewed the work as it evolved.

Megan spoke with more than 30 people across Marketing, SDR, Operations, and IT to understand how the work supporting marketing actually happened. How did a campaign move from idea to execution? Where were the handoffs? Which systems held the information? Who owned each step? Where did manual coordination slow things down?

The interviews were only the first step. Megan translated what we heard into documented workflows so we could see the work as a system. Marketing is creative and cross-functional, and much of that work isn’t always documented end to end. Mapping the workflows made the handoffs, dependencies, ownership, and opportunities for change visible.

From there, Megan identified 8 core insights and 15 jobs to be done, each tied to an outcome the team needed to achieve, then broke them into 67 specific AI use cases. For example, planning and launching a campaign might include AI use cases for drafting the brief, identifying audiences, or analyzing past performance.

That order was deliberate. The workflow gives you the context to decide whether the work should move faster, change, stop, or become something new before you decide where AI belongs.

For Tricia Gellman, CMO of Box, that systems view was a big part of the value:

Tricia Gellman, CMO of Box

“One of the most important parts of this work was translating what we heard in the interviews into actual workflows so we could assess the system. Once you can see how the work really happens, you can decide what to accelerate, what to change, and where AI can add value.”

That systems view gave us something concrete to take into the next step: evaluating the 67 opportunities.

Three Questions Helped Us Sort 67 Opportunities

Once the workflows were mapped and the 67 opportunities identified, we used three lenses, each answering a different question.

1. What should happen to the work?

The first was the Sorting Machine, which asks what a workflow needs before deciding how AI should be used. Work can take one of five paths:

Speed it up when the work is useful but takes too long or too much manual effort.
Sharpen it when the outcome is right but the process can work better.
Rethink it when the outcome is still useful but the process is not effective or not working.
Build something new when AI makes work practical that was previously too hard, slow, or expensive with an all-human team.
Stop doing it when the work no longer creates enough value to justify the time.

The point is to decide what the work needs before deciding what to build. I introduced the Sorting Machine and Trust Ladder in Think Before You Build: A Practical System for Human+AI Teams. That newsletter goes deeper on both frameworks. The Box engagement gave us a chance to apply them across a much broader set of work.

The Sorting Machine: speed it up, sharpen it, rethink it, build something new, or stop doing it

I’m especially interested in the concept of “Build something new” because AI removes constraints that influenced how we designed work in the first place. That can mean reading every sales call, continually refreshing competitive intelligence, tailoring insights to each account, or running scenarios before a decision. Work like this was often not done because it was too slow, difficult, or expensive with people alone.

I went deeper on those opportunities in 5 Reimagined Workflows: Work We Skipped That AI Now Does.

2. Is It Worth Doing Now?

The Sorting Machine tells you what kind of change the work needs, but not whether to pursue it now. So we evaluated each opportunity across impact, effort, and risk:

Impact – potential business benefit, including breadth, efficiency, revenue, and quality.
Effort – readiness of the tools, data, and process; the systems that need to connect; and the functions that need to be involved.
Risk – sensitivity of the information and output, exposure if something goes wrong, and how much autonomy AI would have.

Megan used AI to work through the volume while making the final call:

Megan Cabrera, GTM Strategy and Operations Advisor, Revenue Engine Labs

“With 67 use cases, AI helped us get through the volume, but I never let the scoring stand on its own. I asked it to justify every rating, checked those ratings against what we heard in discovery, and changed them when they didn’t hold up. AI made the analysis faster. The context still came from the people doing the work.”

A strong AI idea can still move later in the sequence. The data may need work, ownership may need to become clearer, or the process may need to change before AI is added.

The visual below shows a representative example of an impact, effort, and risk matrix. It is not Box’s actual matrix, use cases, or scores.

Sample impact vs. effort and risk matrix for AI opportunities

That helped us sequence 67 possibilities into a roadmap instead of treating them as 67 separate projects.

3. How Much of the Work Should AI Own?

The third lens was the Trust Ladder, which helps determine how much autonomy AI should have in a workflow. That depends on the work, the risk of a bad output, the quality of the inputs and instructions, and how easily a person can catch an error.

There are four levels:

A person leads and AI assists. The person makes the decisions while AI helps with suggestions, drafts, or analysis.
AI creates the first output and a person reviews it. Nothing gets used until a person has checked it.
AI runs the workflow and a person spot-checks it. The work is proven enough that every output no longer needs review, but a person still owns quality.
AI operates autonomously. This is reserved for well-proven, low-risk work where routine human review is no longer needed.

The Box blueprint combined the Sorting Machine, impact, effort, and risk, and the Trust Ladder into one way to evaluate each opportunity.

The same AI platform can operate at different levels depending on the job. It might help a marketer think through positioning at Level 1, create drafts for review at Level 2, and handle a proven, low-risk workflow with much more independence elsewhere.

The Trust Ladder treats autonomy as a decision about the work rather than a feature of the tool.

Three questions: what should happen to the work, is it worth doing now, and how much should AI own

The Roadmap Came After Those Decisions

Once we worked through the opportunities, the harder dependencies became clearer:

Data needed to be better connected.
Ownership needed to be clearer.
Some workflows needed to change.
Teams depended on shared systems and knowledge.

Those dependencies drove the roadmap, from foundational projects to data and workflow improvements to individual agents, orchestration across agents, and new ways of working.

An agent inherits the environment you give it. Inconsistent information produces inconsistent output. Unclear ownership stays unclear. And if the workflow itself needs to change, automating it can make the wrong process faster. Some of the most useful AI work happens before anyone builds the AI.

The Human Work Changes Too

Changing a workflow can also change who does the work, who owns decisions, where expertise is needed, and which handoffs disappear. People may need new skills. Leaders may need to redraw ownership. Teams may need shared definitions and outcomes. And someone still has to decide where human judgment adds the most value and where AI can take on more responsibility.

That’s why I believe we should reimagine the work first, then the roles, then the org. I wouldn’t start by redrawing the org chart. The work tells you which roles need to change and what the organization needs to support.

Box’s roadmap included enablement, training, governance, and evolving roles alongside the technology and workflow work.

For me, this is People First. AI Forward. in practice: every AI decision gets a people lens while we keep pushing toward work that wasn’t practical before.

I wrote more about that in AI Is Making the Human Side of Work Harder to Avoid. As AI removes some of the technical constraints around work, people still have to work through trust, ownership, incentives, roles, and decision rights.

Now Box Is Putting the Blueprint Into Action

The final blueprint gave Box a sequenced set of use cases and the dependencies behind them. Box is now building internal timelines, business cases, and project plans around those choices.

The team has a clearer way to decide what to pursue, what needs to happen first, and how much AI should own.

If Your AI Opportunity List Is Getting Long

A growing list of AI ideas can be a good sign. The harder part is deciding what deserves attention now.

Before you turn that list into a roadmap, ask:

What should happen to the work?
Is it worth doing now?
How much should AI own?

Those three questions helped us turn 67 possibilities into a plan Box could act on.

The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey.

For team transformation: I partner with GTM leaders to move from scattered AI adoption to an operating model where human and AI teammates work together across workflows.

For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many conferences and corporate events.

If any of this resonated, I’d love to hear from you. Reply to this email, find me on LinkedIn, or come say hi at an event.

AI Is Making the Human Side of Work Harder to Avoid

Liza Adams · September 17, 2026 ·

Basketball players in Marketing, Sales and CS jerseys celebrating individual goals while the team loses

Marketing hits its lead goal. Sales hits its revenue number. Customer Success hits its renewal target. And the customer can still have a lousy experience.

You wouldn’t call a basketball team successful because every player hit an individual stat while the team lost. A point guard can lead in assists, a center in rebounds, and a forward in points, and they can still lose to a less talented team that plays better together. Players still have individual roles and stats, but the team is also judged on the outcome. We understand that in sports. At work, we often reward each function for hitting its own number even when the customer experience suffers.

Nobody has to be doing a bad job. Each team can be doing exactly what it was asked to do. The customer moves across Marketing, Sales, and Customer Success, while our goals, systems, processes, and incentives often stop at the functional boundary.

For years, some of those gaps were hard to fix. Customer information sat in different systems, context got lost in handoffs, and connecting what one function knew to the next took more time and coordination than teams could sustain. AI can remove more of those constraints.

As it does, the human questions get harder to avoid. Will we trust what we learn? Will we act when the evidence challenges something we believe? Will Marketing, Sales, and Customer Success change how they work when a better customer outcome requires different choices?

Quick Take

AI can solve more of the mechanics behind disconnected GTM work. What I’m seeing is how quickly that exposes the harder human work.

AI can carry customer and market context across functions. People still have to decide where functional accountability ends and shared accountability begins.
Better information only helps when people trust it enough to act, especially when it challenges an existing belief or decision.
Reimagining a workflow can change roles, ownership, decision rights, and how teams work together.
I’ve seen AI improve a Marketing-to-Sales handoff enough to move SDR open rates from 15% to 40%. The workflow improved, but the teams still had to make the organizational decisions around it.
Companies start from different places. History, hierarchy, culture, regulation, resources, and what they need to protect affect how quickly the human side can change.

Watch or Listen Instead

To support different learning styles and time constraints, this newsletter is also available as a 5-min cinematic AI video explainer and a 18-min AI podcast with two AI hosts. Both were made with Gemini Notebook and personally reviewed for accuracy and responsible AI use.

AI Can Solve More of the Handoff Now

I’ve seen this with a global cybersecurity company I work with. One workflow gave SDRs consistent company, industry, role, and messaging context instead of requiring them to recreate it for every prospect.

How AI improved the SDR handoff: marketing context, AI workflow, tailored outreach, open rates from 15% to 40%

Reps saved one to three hours a day, and open rates increased from 15% to 40%.

AI solved an important part of the handoff. The workflow could carry context, but Marketing and Sales still had to decide what they should optimize for together, which customer signals to trust, and what to change when new evidence challenged how they were working.

Those are the kinds of human questions I keep seeing as more of the mechanics get easier.

Where the Human Work Shows Up

In my work with GTM teams, I’ve observed three key human challenges. They aren’t new, and I don’t think they’re the only ones. But as AI makes more of the mechanics easier, they’re becoming harder to work around.

1. Better Information Doesn’t Create Shared Outcomes

AI can make customer context much easier to share. That still doesn’t align what each team is accountable for.

Marketing can see what happens after a lead becomes an opportunity. Sales can know more about what brought the buyer in. Customer Success can start with more of the history. But if Marketing is measured on leads, Sales on the close, and Customer Success on renewal, each team can make a rational decision for its own number that works against the customer outcome.

The measures don’t need to be identical, and I don’t think the answer is to abandon functional goals. Sales still needs a quota. Marketing and Customer Success need measures tied to their own responsibilities. The harder question is where to add shared accountability so each function also has a stake in the customer outcome.

That could mean a shared goal, a common scorecard, or in some cases incentives tied partly to outcomes such as getting the right customers, time to value, retention, expansion, or overall customer satisfaction.

2. More Insight Requires More Trust

AI can synthesize far more customer and market information than any one person can process. The challenge comes when the evidence contradicts something we’ve already invested in.

Customer conversations may challenge positioning we’ve spent a year building.
Win/loss analysis may suggest the problem isn’t sales execution.
Support tickets may show that something we considered minor is affecting customers far more than we realized.

Trust doesn’t mean accepting AI output at face value. We still need to check the evidence, add context, apply judgment, and decide whether it’s strong enough to act on.

It also requires trust between people. Someone needs to be able to bring uncomfortable evidence into a cross-functional conversation without it becoming an attack on someone’s work.

Curiosity and intellectual humility become more valuable as AI gives us more ways to test what we think we know. That only helps if we’re willing to be wrong and change our minds when the evidence supports it.

3. Reimagining Work Means Letting Go of Some Old Work

Changing a workflow gets harder when it changes who does the work, who owns the decision, or where someone’s expertise is needed.

People have spent years building expertise, careers, and influence around existing roles and processes. When AI makes a different way of working possible, we may be asking them to reconsider work they’re good at, responsibilities they own, or where they believe they add value.

That uncertainty deserves to be taken seriously. But if work that once required several people, multiple handoffs, and approval steps can now happen differently, we should be willing to rethink the process.

Stacy Simpson, CMO of athenahealth, is working through this now. Her team mapped the full campaign process and identified where individual agents could take on parts of the work. They’re now at the point where the process itself can change.

Stacy Simpson, CMO of athenahealth

“We’re in the middle of the story. We know the outcome we want, but the process isn’t predetermined. We started by identifying where individual agents could help. Now we’re asking how the process itself should change, how agents hand work to each other, and where humans need to step in.”

Changing the process also changes what we need from the people doing the work. Siddharth Taparia, Global CMO of JLL is thinking about that from the role and talent side.

Siddharth Taparia, Global CMO of JLL

“AI fundamentally changes what we ask people to do and the skills they need. For example, a campaign manager will spend less time building campaigns and more time orchestrating and evaluating AI outputs. That means we have to rethink how we develop talent. The skills that matter most are shifting. Strategic thinking, cultural awareness, curiosity, and adaptability will become critical.”

We may not know exactly what every role becomes yet. That makes it more important to involve people in redesigning the work and help them see where their judgment, experience, and relationships can add more value.

I went deeper on five examples in 5 Reimagined Workflows: Work We Skipped That AI Now Does.

Different Starting Points

Organizations are starting from very different places. An established company may have customers, revenue, specialized roles, approval layers, regulatory requirements, and systems that already work. Changing any of those carries real risk.

A smaller company may have fewer people, less budget, and less technical support, but also less to unwind. When adding people isn’t an option, there can be more reason to rethink how the work gets done.

I think what a company has to protect may be a bigger factor than size. The more that already works, the more carefully people have to decide what should change and what should stay.

I covered this topic in more detail in The Boldest AI Moves are Coming From the Smallest Budgets.

Where Competitive Advantage May Come From

AI capabilities will keep advancing, and access to similar models and tools will get easier. The difference I’m watching is what companies do when AI gives them better information or makes a different way of working possible.

Same AI, different outcome: two companies respond differently to the same customer insight

Over time, a company willing to change how the work gets done learns more than how to use AI. Its people learn where AI works well, where human judgment is needed, and how to change when new evidence gives them a reason to.

That’s where I think some of the advantage comes from. Technology can be bought. Trust, judgment, shared accountability, and the ability to change how people work together take longer to build.

Michael DiPietro, an operating advisor at JMI Equity who works across its portfolio companies, keeps the test grounded in business results:

Michael DiPietro, Operating Advisor at JMI Equity

“We encourage our portfolio companies to focus their AI initiatives on high-impact workflows and change. These should not only drive better business outcomes but ideally create a better customer experience. As one of our portfolio company leaders said, ‘It’s not AI transformation, it’s business transformation.’ We want them to think of AI as a means, not an end.”

I also don’t think this stays a competitive advantage forever. Over time, a more connected, context-aware experience may simply become something customers expect.

Customers don’t care how our org chart works. They expect us to know what they’ve already told us, carry that context across the journey, and respond as one company. As AI makes more of that possible, some of the gaps we’ve learned to live with become harder to justify.

I wrote more about the CMO’s role in carrying that customer context across the business in CEOs Are Looking for a Different Kind of CMO. Are You It?

Think big, start small, move fast. Try something, learn, adjust, and be willing to zig and zag.

A Few Questions I’d Ask

These are three questions I’d bring into a GTM leadership conversation:

Shared outcomes – Where should Marketing, Sales, and Customer Success keep distinct goals, and where should they share accountability for the customer outcome?
Trust – What happens when customer or market evidence challenges something an influential person or function believes?
Willingness to change the work – If we designed this work from scratch today, would we give the same people and functions the same responsibilities and decision rights?

What I’m Still Figuring Out

What I’m still trying to understand is what helps people move from agreeing that work should change to being willing to change their own work.

Hands-on experience seems to help. I see belief change when people use AI on their own problems, see what it can and can’t do, and have a hand in redesigning the workflow.

I’m also watching what happens when the next role isn’t clear yet. People may be more willing to let go of familiar work when they can see where their judgment, experience, and relationships will add more value. But we won’t always have that answer before the work starts changing.

We’re asking people to help redesign work that may also change their own roles. I think they need a real hand in shaping what comes next.

The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey.

For team transformation: I partner with GTM leaders to move from scattered AI adoption to an operating model where human and AI teammates work together across workflows.

For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many conferences and corporate events.

If any of this resonated, I’d love to hear from you. Reply to this email, find me on LinkedIn, or come say hi at an event.

CEOs Are Looking for a Different Kind of CMO. Are You It?

Liza Adams · September 3, 2026 ·

One of the CEOs I spoke with wanted a CMO who could walk into a board meeting and make a credible case for where the market was heading: what customers were telling them, what was changing around those customers, what it could mean for the business, and what the company should consider doing next.

A couple of CEOs asked me recently what to look for in a CMO now, given what AI is making possible.

I spent years leading marketing. My work since then with CMOs and GTM teams, founders, leadership teams, and PE and VC partners has given me a view of what AI is changing in the role and what companies increasingly expect.

Three areas consistently come up: knowing the customer and market at a depth that wasn’t practical before, leading brand and trust across the company, and carrying customer context across the GTM journey.

Quick Take

What connects these three areas for me is an outside-in view of the business, starting with the customer and market rather than the marketing function.

Know the customer and market at a depth that wasn’t practical before – AI can connect signals teams couldn’t reasonably track at the same scale or timing.
Lead brand and trust across the company – AI brings customer, employee, leadership, community, and company signals into the same view.
Carry customer context across the GTM journey – AI can connect context across Marketing, Sales, and CS and help close the gaps customers experience.
Bring people through the change – The CMO still has to build trust, influence across functions, and help teams change how they work.
Connect all of this to business and financial outcomes – A broader view of the customer, market, company, and customer journey gives the CMO more to bring to business decisions.

These are observations from current work and conversations, rather than a best- practice model.

Watch Or Listen Instead

This newsletter is also available in two additional formats for different learning styles. The 6-min cinematic AI video explainer is a broad overview of the argument. The 18-min AI podcast goes deeper. Both were made with Gemini Notebook and personally reviewed for accuracy and responsible AI use.

What Experience Gives Us

Experience builds judgment. You’ve seen enough situations to recognize patterns, ask better questions, and identify problems earlier. That experience is useful, but it can also make us rely too heavily on what worked before.

CMOs have always been expected to understand customers, earn trust, know the business, and work across the leadership team. AI is making more of that practical at a scale and cadence that were hard before: working across far more customer and market information, helping teams rethink workflows, roles, handoffs, and the customer journey.

Katy Keim has lived the CMO-to-CEO path more than once. She was CMO at Lithium Technologies and EVP of Marketing at ServiceSource before becoming CEO of LQ Digital and most recently LeanData.

Katy Keim, CEO of LeanData

“I’ve been in the CMO seat, and I know how easy it is to default to what worked before. The leaders who stand out aren’t running a playbook. They’re curious. They’re learning from their peers, picking up new signals from the market, and connecting it all back to the customer. It’s about pattern recognition from their experience, not stale tactics. This market demands a new growth mindset from marketing leaders.”

In my work, three areas show how AI can make this broader CMO role more practical:

1. Know the Customer and Market at a Depth that Wasn’t Practical Before

AI lets us bring together more customer and market information, more often, then connect signals that would otherwise stay separate.

A few examples I’m seeing:

Bring more customer signals together – Usage, support tickets, call notes, engagement, renewal history, and other internal data can be analyzed alongside public information such as funding, acquisitions, leadership changes, or industry news. Declining engagement and usage mean something different when they happen alongside a leadership change. An acquisition may signal an expansion opportunity.
Keep market and competitive intelligence current – AI can watch competitor positioning, pricing, reviews, category conversations, analyst commentary, and the language buyers use. Teams can move from periodic competitive updates to a much more current view. I’ve found this to be one of the clearest examples of work an all-human team couldn’t keep up with at the same cadence.
Pressure-test decisions with simulated buyers – Before a pricing change, repositioning, or campaign, teams can build AI advisors trained on what they know about buyer priorities, challenges, roles, objections, communication styles, and sources they trust. They use them to challenge the thinking before it lands in market.

Human judgment stays central. AI handles collection, synthesis, analysis, and scale. People decide what applies, where the AI may be wrong, and what the business should do.

That gives the CMO a richer view of customers and the market to bring into product, GTM, strategy, and board conversations. I went deeper on several of these workflows in 5 Reimagined Workflows: Work We Skipped That AI Now Does.

2. Lead Brand and Trust Across the Company

Brand is what people experience and say about a company.

Marketing contributes to that experience. So do Product, Sales, Customer Success, HR, leadership, partners, and employees.

AI makes those connections easier for people outside the company to see. Ask an AI to search about a company and it can pull from the website, customer reviews, analyst coverage, community conversations, press, employee experiences, and other public information. Buyers increasingly get a full view assembled from many sources, not just the story marketing publishes.

That gives the CMO a broader leadership opportunity: help the company understand what those experiences add up to, where they reinforce the brand promise, and where they don’t.

Anna Griffin has led brands across complex enterprise environments including CA Technologies, Smartsheet, Intercom, and now Commvault. Her experience spans the parts of the company that create the brand and the people outside it who ultimately decide what to believe.

Chief Market Officer of Commvault

“Brand has never been what you say about yourself. It’s what every customer, employee, partner, and prospect experiences and says about you. That’s always been true. What AI has done is make it visible all at once, at scale, in ways that are very hard to ignore. You either build it intentionally across the whole company, or the market and AI build it for you.”

A few ways I’m seeing this show up in the work with AI:

Look at the company from the outside in – Search as a buyer, customer, candidate, or partner. Look at the situations they’re in, the problems they’re trying to solve, what they hear about you, and where the story feels unclear or inconsistent.
Be useful and honest about where you fit – Create content around customer situations, questions, and outcomes rather than leading with features and capabilities. Be clear about who you’re ideal for, where you’re not, and what customers actually achieved. That gives people and AI clearer evidence about when to recommend you.
Find gaps marketing can’t close alone – If the brand promises “easy to work with” but onboarding is painful, that requires work with the team responsible for onboarding. If the company talks about transparency while candidates experience something different, changing the careers page only fixes one part of the problem.

AI search is part of this too: make owned properties clear, create useful content, structure information so machines can understand it, and monitor how AI represents the company. I went deeper on this outside-in view in When AI Judges Your Brand Before Humans Do.

But AI is also picking up experiences marketing doesn’t control. I saw a post recently where a job candidate asked for the salary range before an interview. The CEO responded that if money was their main driver, they weren’t the right fit. I wondered how that same CEO would respond if a buyer asked for a price range before agreeing to a sales call.

Marketing has been trying to explain what brand is for years. AI search may have just shown it for us.

Anna also uses a phrase I like: brand is rented, not owned. You keep earning it through the experiences people have with you and the evidence those experiences leave behind.

3. Carry Customer Context Across the GTM Journey

Customers experience one company, even though we organize ourselves into Marketing, Sales, and Customer Success. They experience the marketing, sales conversation, handoff, onboarding, product, support, renewal, and everything between.

AI is particularly useful here because it can carry context from one part of that journey to another.

There’s also evidence that AI can help people work across those boundaries. A Harvard-led field study with P&G professionals found that AI helped commercial and R&D specialists produce more balanced solutions that drew on both areas of expertise. Wharton professor and AI researcher Ethan Mollick described the result this way: “The distinction between specialists virtually disappeared in AI-aided conditions.”

The harder part is getting people to trust new inputs, change workflows, and reconsider practices that have worked for years. A sales leader may trust instinct built over decades. A CS leader may trust the data their team already uses. The CMO has to bring people along through real work, shared wins, and clear human judgment.

Carry context across the customer journey – AI can look across calls, CRM notes, onboarding feedback, support tickets, usage, and renewal data so the customer’s story doesn’t keep restarting as they move between teams.
Find where the experience breaks – Something showing up as a CS problem may have started with expectations set during the sales process. An adoption problem may trace back to positioning or onboarding. AI makes those connections easier to see.
Work toward shared customer outcomes – When I ask Marketing, Sales, and CS what success looks like for the same customer, the answers come back as functional metrics: qualified leads, sales-cycle length, churn. Then we ask what result the customer needs from all three, such as time to first value, adoption, satisfaction, retention, or expansion. The conversation changes.

I explored what this could look like organizationally in No Daylight: How AI is Reorganizing GTM Teams Around Customers, Not Departments.

Changing shared goals, workflows, and long-standing ways of working still requires trust between leaders and compassion for the people doing the work. Some people move quickly with AI. Others need more time, support, practice, or evidence.

AI can help connect the work. Leaders still have to bring people along.

What This Adds Up To

CMOs are also being held more accountable for the results of the business. In the Spring 2025 CMO Survey, 63% reported increased pressure from the CFO and 61% from the CEO to prove marketing’s value. IBM’s 2025 CMO Study found that 64% of CMOs globally are accountable for profitability and 53% for revenue growth.

That requires connecting what customers experience and what GTM does to the economics of the business.

I often start with time to value, customer satisfaction, and net revenue retention because they begin with outcomes the customer experiences. From there, we use the company’s data to connect those outcomes to adoption, retention, expansion, referrals, payback, efficiency, and growth. That’s a more useful board conversation than trying to force every dollar of revenue back to a marketing touch.

Kate Bullis, Global Marketing Practice Leader at ZRG, runs 20 to 25 executive marketing searches a year and sees firsthand how the hiring conversation is changing.

Kate Bullis, Global Marketing Practice Leader at ZRG Partners

“The most successful GTM leaders aren’t just functional experts; they’re business leaders who think like GMs. CEOs are expecting GTM to show up this way. Great GTM leaders optimize entire customer and market experiences, not just their department, because they understand customers don’t care about your org chart. The importance of this is not new, but AI has made it increasingly important.”

Much of this broader role depends on influence. Product, Sales, CS, HR, Finance, and other leaders bring expertise and accountability of their own. The CMO earns influence by bringing something useful to the table: customer intelligence that changes a decision, a clearer view of the market, a gap in the customer experience, or evidence that connects the work to the business.

The company has a role too. Some leadership teams want the CMO operating this broadly; others have a different mandate. Company stage, business model, goals, and leadership relationships will affect what makes sense.

Some CMOs will want to stay focused on leading the marketing function, and some companies will continue to want that. A more functional role brings clearer boundaries and deeper focus within marketing, but potentially less influence over the customer experience, market decisions, and business outcomes beyond the function.

The broader role comes with tradeoffs too: more shared ownership, more influence without direct authority, and more accountability for outcomes the CMO may not fully control. If CEOs want that kind of business leader, the mandate, measures, decision rights, and relationships around the role need to support it.

Both sides have to want the broader version. It takes two to tango.

A Few Questions Worth Asking

If I were advising those CEOs today, these are some of the questions I’d suggest they ask:

What’s a playbook you believed in that you no longer use? What evidence changed your mind?
Tell me something AI helped you see about customers or the market that changed a business decision.
Describe a cross-functional change that required people to work differently. How did you bring them along?
Walk me through a time AI helped you connect a customer or GTM issue to a financial outcome. What did you do with that insight?

I’d pay as much attention to the follow-ups as the first answer. Can they get into the actual work, explain what AI helped them see or do, where it fell short, what they changed, and what they learned?

Nobody has all the right answers yet. I’d look for intellectual humility, curiosity, ability to bring people through change cross-functionally, and evidence that they’re learning from real work. That can be hard at this level because we’ve spent our careers being rewarded for what we know and what has worked before. AI keeps asking us to question both.

I’m still learning with the leaders and teams working through this. What I see so far is an opportunity for a broader business role, with deeper accountability for customers, the market, the company experience, and business outcomes.

For CMOs, the question is whether that’s the role you want to grow into, and whether you understand what comes with it.

The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey.

For team transformation: I partner with GTM leaders to move from scattered AI adoption to an operating model where human and AI teammates work together across workflows.

For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many conferences and corporate events.

If any of this resonated, I’d love to hear from you. Reply to this email, find me on LinkedIn, or come say hi at an event.

Your Data Problem Is Smaller Than You Think. Your AI Payoff Is Bigger.

Liza Adams · August 20, 2026 ·

A data readiness project comes with a cost and a timeline. What it unblocks is often unclear, so it gets funded on faith or it doesn’t get funded at all.

That’s where I started with a marketing team. I could have spent the session showing them how to fill out their channel performance decks faster, and automating that would have saved real hours. We went a different direction.

I asked what it would take for the same workflow to tell them which blog posts compete for the same readers, which persona they’re under-serving, and what to test next. Then a second AI agent would read across all three channels (blog, gated assets, and webinar) and challenge the plan before anyone spent budget on it.

Everyone wanted that version. So I asked one question: how do you tag your content today, by topic, by persona, or by funnel stage?

There was very little tagging at all. The closest thing was a product code sitting inside the blog page address, like the “exp” in /blog/exp/cut-expense-report- cycle-time/. AI had nothing it could filter on, group by, or read as a category.

That team isn’t unusual. In Dun & Bradstreet’s latest AI Momentum Survey of 10,000 businesses across 32 countries, only 6 percent said their enterprise data is fully ready to support AI at scale. In the same survey, more than three quarters reported some measurable return from AI anyway.

That question uncovered our first data gaps. Working out what the better version needed uncovered the rest. We built it so we could test it and see the outputs. Based on the outputs, we refined, and that’s how we confirmed data was the issue.

Quick Take

The usual advice is to get your data in order first, then use AI. But there’s another way.

Ask AI for the more valuable version of the job, and it will tell you what your data is missing and how each piece would be used.

Here’s what happened when we tried it with one marketing team.

Key takeaways:

Build first with dummy data. Set a higher goal, reimagine the workflow, and find out what AI can and can’t do before you invest in getting the real data.
Filling out a report faster works fine on messy data. The problem is when you ask for analysis by persona, product, or funnel stage.
You can find some gaps before you build anything. Ask your team how the data behind that workflow gets categorized today. If you get different answers, you’ve found one already.
Sometimes the answer is that the data was never collected and never will be. Better to learn that in one working session than after months of data work.
Be clear about which demos are running on dummy data. Otherwise the disappointment gets pinned on AI.
Fix one gap first, the one that blocked what you wanted most. A small win gives you something working to expand from.

There’s a prompt at the end you can run on your own workflow.

The four steps we took, in order.

Four steps: decide the output, ask what it takes, build on dummy data, close one gap

Decide the more valuable output you want, ask AI what that output requires, build it on dummy data where the real data isn’t there, then start by closing the one gap that blocked what you wanted most.

Watch Or Listen Instead

To cater to different learning styles and time constraints, this newsletter is also available as a 9-min AI video explainer and an 11-min AI podcast with two AI hosts. Both were made with Gemini Notebook and personally reviewed for accuracy and responsible AI use.

Speed Hides the Problem

A workflow that pulls three channel performance exports, formats them, and populates a template saves hours whether or not anything is tagged well.

The data problem shows up when you want analysis instead of a finished report: results by persona, recommendations by funnel stage, or a coach that challenges your plan. All of that asks AI to sort and compare by category. If nothing in the file shows those categories, you get observations back. This blog did well, that one didn’t. It’s accurate but not as helpful.

That’s the difference between faster, better, and different, and each level asks more of your data. (That framing is reframe four in The 8 Reframes That Change How GTM Teams See AI.)

What each level of AI work (faster, better, different) asks of your data — What each level asks of your data.

Here is the same channel report with and without those data gaps.

Sample blog channel report with no tags, goals or definitions

With no tags, no goals, and no agreed definitions, you get a list of your most- read posts and no judgment on whether any number is good, because there’s nothing to judge it against.

The same channel report after closing the data gaps

Once we closed the data gaps, the same workflow could tell you the expense- management cluster aimed at finance buyers is your fastest-growing topic, and the one page in it that matches reader intent keeps people reading for 38 seconds. Your highest-traffic page loses them in one second and is tagged to the wrong product. Rewrite that page and publish more in that cluster.

The first version stops at one bad page. The second tells you what to write next, for which buyer, and where the gaps are.

Both run on synthetic data, including the tags that made the second one possible.

Build It With Dummy Data

We didn’t stop to fix anything first, because we didn’t yet know what needed fixing. The team generated dummy files that kept the same structure as their real exports, with fake names and numbers, so we weren’t processing sensitive data in the experiment.

Check your company’s responsible AI policies before you start. If the data you’d need is customer-identifying or regulated, find out what your policy allows you to collect and where you’re allowed to process it.

If your policy allows it, you’ll eventually build with real data. If it doesn’t, dummy files let you keep going, and you’ll know from the start that closing the gap may mean redacting fields, replacing identifiers with codes, or asking for aggregated numbers only.

Then we built on the dummy files.

A channel analyst agent reads a single export and produces a completed KPI report with insights and recommendations.
A coach agent reads across three channels and pushes back on the plan.

Channel analyst and team coach agent workflows with human review gates

The dummy fields standing in for data we didn’t have became the requirements list.

The usual path vs. building first with dummy data

Eight Things We Were Missing

We decided the output we wanted, then asked AI what it would take to produce it. Recommendations by category meant tags had to exist for topic, persona, and funnel stage. A trend meant a date field, which two of the three exports didn’t have. Judging whether a number was good meant a goal for the period, a benchmark to compare against, a baseline from your own history, and a target. Knowing what a page actually accomplished meant conversion tracking. And the funnel stage definitions had to be written down where the AI could use them.

Each answer we wanted turned into a requirement, eight in all, and the dummy files filled those in.

They picked the tags first, on one channel, because that was the gap blocking the recommendations they wanted.

Eight things we were missing and what we couldn't do without them

Tell Them What They’re Seeing

There’s a version of this that goes poorly. You show a team an impressive output built on dummy data, everyone gets excited to try it on their real data, it doesn’t come close, and they decide AI is overhyped.

So I pre-empted this at the start of the session. The AI is doing what it can with what it has.

It stops people from blaming the tool for a weak answer, and it turns the excitement into a list of data requirements for consideration.

Fix the Gap that Blocked What You Wanted

Close one gap, not the full list. Pick whatever blocked the output you most wanted. If it’s recommendations by persona, tag one channel. If leadership asks for trends every quarter, add a date field to that one export.

Getting all of it in place can run six months, stall the rest of the process, and be out of date by the time it’s done. Closing one gap gets you started and leaves you with something working you can expand from.

Dun & Bradstreet’s chief strategy officer made the same point. A pilot can run on the data you have. Scaling across the systems people depend on is where AI- ready data becomes the constraint. We can run the pilot with one workflow on one channel, then address the data gaps needed to scale.

Ask your analytics team what data you have and you get a list of everything. Ask whether persona can become a field on the blog export and you get a yes or a no. Go to whoever owns the data with the second question.

Julie Zadow, Growth Marketing Advisor at The Riverside Company, sees this from the investor side, across a portfolio of marketing leaders.

Julie Zadow, Growth Marketing Advisor at The Riverside Company

“I work across a portfolio of CMOs, and I’ve seen the same data challenge play out repeatedly: someone walks into budget planning and says the data needs work, but nobody knows exactly what they’re approving. If a CMO walks in with four specific fields and the workflow each one unblocks, that’s a completely different conversation.”

When you ask that question, answers come back in one of four ways.

The field already exists and nobody was using it.
Adding it is a small piece of work rather than a project.
It isn’t there yet, so you start collecting it and the workflow includes it later.
It will never exist, so you change what the workflow produces and build around what you have.

Any of those is better than funding a data readiness project and finding out at the end. Until people see the output they want, they keep using the same data they always have.

Justin Parnell, my business partner and a forward deployed engineer who specializes in AI automation and agents, built a territory planner for a client whose data warehouse wasn’t ready. The agent had to rank accounts and map each rep’s stops for the day.

Justin Parnell, Forward Deployed Engineer and Founder of Justin GPT

“Instead of waiting, we built on synthetic data matching the structure the warehouse would eventually have. The first maps the agent drew put pins in the middle of lakes, and chasing that told us precisely what the real data had to look like, down to storing zip codes as text so a zip like 02134 doesn’t lose its leading zero and land you in the wrong state.

The data team never had to tell us they hadn’t collected it. We found it first, and came to them with a tested checklist instead of a vague ask.”

Some answers depend on someone else’s timeline. If the field lives in a system another team or a vendor owns, getting it means asking them and waiting. Find that out before you commit to a date. (On sorting which workflows deserve the effort, see Think Before You Build.)

Think big, start small, move fast.

Run This on Your Own Workflow

Open your AI tool of choice and give it this prompt:

I want to build an AI workflow that does this job: [describe it in plain language, including the analysis and recommendations you want, not just the report].

Here are the inputs I can give it today: [list your exports and documents, with column headers if you have them].

Tell me what fields have to exist in those inputs and what has to change about how they’re structured. Then tell me what other sources I’d need and what definitions we’d have to agree on first. After that, what you could produce today versus what I’d have to add, and which one to fix first, and why.

Then flag anything on that list we can’t get retroactively, so I know what to stop chasing.

Pick one workflow you wish AI could run for you. Write down every field, definition, and target it would need, then pull the export you’d feed it and compare. That list is your starting point, and it’s shorter than fixing your whole data foundation. Close a few of those gaps and the same workflow starts telling you what to do rather than what happened.

The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey.

For team transformation: I partner with GTM leaders to move from scattered AI adoption to an operating model where human and AI teammates work together across workflows.

For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many conferences and corporate events.

If any of this resonated, I’d love to hear from you. Reply to this email, find me on LinkedIn, or come say hi at an event.