AI agents

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.

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.

5 Reimagined Workflows: Work We Skipped That AI Agents Now Do

Liza Adams · July 9, 2026 ·

5 Reimagined Workflows: campaign synthesis, competitive intel, sales enablement, AI search, customer signals

There’s a whole category of work your team’s not doing. Reading every sales call. Refreshing battlecards every week. Tailoring the message to each deal. You skipped it because it was slow, manual, or expensive to do by hand.

Some of the most important work also needed you to look outward and keep looking, at the whole market, at each account’s world, at what buyers say across hundreds of calls. Your team looks at what it can see, which is its own data. Not from a lack of effort, but because one team can only do so much. AI can look outward, and do it continuously.

That made sense before AI. For a lot of this work, the reason you skipped it is gone. The question worth asking about any task you skipped: is it still hard, or just historically hard?

Quick Take

The GTM workflows I see being reimagined aren’t revolutionary. They’re practical, teams are running them today, and they’re changing how the work gets done. They’re the work we always wanted to do, but it was more than an all-human team could handle.

What I share here comes from working with teams just a few months into agentic workflows becoming more real and accessible for GTM teams. These teams lean AI-forward, so take it as early signal, not a perspective on the whole market. And the point is never AI. It’s the work AI makes possible.

Key takeaways:

AI now makes more work doable. But we have to fix the habit of not doing this work.
The best inputs are already in your systems: your calls, usage data, support tickets, win/loss notes.
Human teams look at what they can see, which is their own data. But AI can look outward too, at the market, at each customer’s world, at industries adjacent to yours, and bring back ideas with the reasoning.
Five agentic workflow examples: build one plan from insights across all signals, watch the market and inform strategy, tailor the proof and practice to each deal, see how you show up when buyers ask AI, and understand how your customers are changing inside and out.
AI handles what it does well: collection, synthesis, analysis, and execution at scale. Humans keep judgment and the final call.

Watch Or Listen Instead

To cater to different learning styles and time constraints, this newsletter is also available as a 6-min cinematic AI video explainer and a 15-min AI podcast with two AI hosts. Both were made with Google’s NotebookLM and personally reviewed for accuracy and responsible AI use.

5 Most Reimagined GTM Workflows

Each one runs as an agentic workflow. It moves through the steps on its own, pauses where your judgment is needed, uses other AI and tools to handle parts of the work, and delivers finished files you can use. You guide it, make the key decisions, and review the output.

1. Build one plan from every signal (campaign synthesis)

Today, with an all-human team:

Teams coordinate in meetings and alignment calls, but something still gets missed.
When the plan changes, it’s hard to reflect those changes wherever needed.
The plan leans on one or two sources and misses what cuts across all of them, including what’s shifting in the market and in your buyers’ world.

Campaign synthesis workflow: what you feed AI, the human+AI workflow, and what you get

With human+AI:

The plan is built from every source at once, internal signals and the market, and stays current when inputs change.
Positioning applies across the buying committee because it reflects all of it
The buyer gets one consistent story across every touchpoint, which builds trust earlier in the cycle.

The value is the synthesis, not just the drafts. A change made in the plan stage is one fix, not multiple ones, that gets applied throughout quickly.

Christine Matu, VP of Product Marketing at Blue J, shared her thoughts after a team hands-on AI workshop.

Christine Matu, VP of Product Marketing at Blue J

“We used to pull personas, messaging, customer call transcripts and win/loss interviews from various sources and stitch them together every campaign. We want to ensure we’re always build from what customers actually say instead of the loudest opinion in the room, and that was challenging to do manually. Now it’s within reach. I’m looking forward to campaigns that resonate and reflect what our customers care about most, in real time.”

Blue J hands-on AI workshop: messaging assessment output from Claude Cowork — Blue J Hands-On AI Workshop – Messaging Assessment Output from Claude Cowork

2. Watch the market and turn it into strategy (competitive intel)

Today, with an all-human team:

Someone owns competitive intel, but watching every competitor’s site, pricing, reviews, and your own win/loss data is more than one person can keep up with, so it happens in bursts.
Battlecards get built once and then go out of date. By the time a competitor’s new claim reaches your reps, a few deals are already lost to it.

Competitive intel workflow: what you feed AI, the human+AI workflow, and what you get

With human+AI:

AI watches continuously across the market, adjacent categories, and your own calls.
Reps get current battlecards with both sides, offense and defense, tied to the objections buyers are raising now.
Leaders see the change early. When a competitor repositions or a new objection spreads across calls, you can adjust messaging and packaging before it impacts pipeline.

The most insightful input is your own call recordings and customer reviews. What a buyer says about a competitor is more honest than what the competitor says about itself.

3. Tailor the proof and practice to each deal (sales enablement)

Today, with an all-human team:

Enablement builds one deck and one pitch for everyone, because tailoring the proof to each deal takes more time than any team has. So reps get generic material and adapt it on their own.
The buyer’s own costs and savings sit in a spreadsheet a rep may or may not build well under deal pressure.
Reps practice on live prospects. The hard objection gets worked out in a real deal and they learn too late, typically in the loss review.

Sales enablement workflow: what you feed AI, the human+AI workflow, and what you get

With human+AI:

Reps get proof built for the deal they’re working, an ROI case using the buyer’s own numbers, and messaging informed by what buyers in your category actually struggle with, not just your own talking points.
The tools are self-serve and always current, so every rep gets the same quality of prep, whether it’s the day before a big call or ten minutes before.
Reps practice the hard conversation before the call, against a simulated buyer who pushes back, so the first real objection isn’t the first time they’ve answered it.

The simulator isn’t scripted. Each run is a live conversation, so the rep faces a different buyer and/or situation every time.

4. See how you show up when buyers ask AI (AI search)

Today, with old habits:

We optimized for keywords and rankings. That still helps AI find us, but ranking for a term and being the one AI recommends are not the same thing.
We led with features and capabilities. AI can’t tell from a feature list who we serve best or which problems we’re the right choice for, so it fills the gap by guessing, and you may not like the guess.
We treated our own website as the source of truth. AI also reads review sites, communities, and third parties, and leans on what others say about us, not only what we say about ourselves.

AI search workflow: what you feed AI, the human+AI workflow, and what you get

With human+AI:

You see how AI describes and recommends you across the major engines, whether you show up, how you’re framed, whether you’re on the short list, and which sources were cited.
You find the gaps that lose deals like a wrong description, a missing proof point, a competitor recommended over you, before more buyers form the same impression.
The fix is clarity. When you say plainly who you serve best and where you’re not a fit, AI routes the right buyers to you and steers the wrong-fit ones elsewhere. You win more of the deals you should win, and spend less time on the ones you can’t serve well.

One run is a snapshot but it’s not the full picture. Reliable signal takes many runs over time, and for some teams an AEO/GEO tool built for scale might be the better solution. I go deeper in When AI Judges Your Brand Before Humans Do.

5. Understand how customers are changing, inside and out (customer signals)

Today, with an all-human team:

The signs that an account is growing or at risk are split across your own systems, usage, tickets, engagement, call notes, and the customer’s own world, funding, acquisitions, leadership changes. No one watches across all of it.
CS has too many accounts to watch closely, so the quiet ones get noticed only near renewal or after a problem shows up.
The signal that matters most is often external and easy to miss. A customer being acquired is a churn risk. A customer acquiring is potentially an expansion opportunity. You usually find out too late to act on it.

Customer signals workflow: what you feed AI, the human+AI workflow, and what you get

With human+AI:

The workflow reads your internal signals and watches each account’s public external world, then scores the account for expansion or risk with the reasons behind it.
Your team sees which accounts need attention and why, early enough to act. That includes changes in the customer’s business you’d never see in your own data.
The customer gets the right support at the right moment, a check-in when they’re stuck, a conversation when their business is changing, an offer that fits where they actually are.

The insight that matters most is often outside your data. A customer being acquired can tell you more about churn risk than any usage trend.

This is also where silos cost you: the signal one team sees that another needs. AI reading across all of it is how fewer things fall through the handoffs. More on that in No Daylight.

The Outside-In Advantage

All five workflows share the outside-in advantage. The campaign reads the market alongside your own numbers. Competitive intel watches adjacent categories, where substitutes and new entrants tend to appear first. AI search shows you how the market already sees you. Customer signals look at public events in your customer’s world that usage data alone would miss. Even enablement gets better when it pulls in what buyers across the market struggle with.

A human still judges whether an outside idea applies. Some are useful, some aren’t. What changes is you have options you didn’t have before.

What’s Common Across Workflows

One thing was true of every workflow: A human makes the final call and is accountable.

The best workflows use human gates. You approve the plan before it goes forward. You review the output before it goes out. Quality checks run at key points.

At each gate, we instruct AI to show its work, not just hand over an answer: options with tradeoffs, confidence levels, risks flagged, what’s likely to land and what isn’t. That’s critical thinking built into the workflow. More on that in AI Is Only as Sharp as Your Questions.

Some workflows earn more autonomy over time. Once one proves itself, you can check the output instead of every step. Keep the gates where a mistake is costly or hard to fix. Let it run where the work is low-stakes and easy to undo.

It’s Only as Good as What You Feed It

These workflows are only as good as what you feed them and how you guide them. They need clean data from a single source of truth, clear instructions and context, the right access and permissions, human oversight on the judgment calls, and governance for safety, compliance, and cost. Someone has to own each one. And the outside-in research uses public signals only, news, filings, and announcements, not private surveillance.

But those are not reasons to wait. You don’t need all of it perfect to start. Pick one workflow, get the data and guardrails right for that one, and learn. A simple way to start: upload company-approved documents and data rather than connecting systems directly to AI.

Think big, start small, move fast.

Faster is the Floor

If your AI strategy is focused on efficiency alone, you’re building a case for fewer people. If it’s focused on reimagining work to grow the business, you’re building a case for why humans are essential.

Growing companies keep adding humans. Stagnant ones start asking why they need so many.

The difference is whether you use AI only to do old work faster, or also to do new work that wasn’t possible before.

Laura Heisman, Chief Marketing Officer of Dynatrace, got hands on keyboard with her leadership team instead of just directing the work.

Laura Heisman, CMO of Dynatrace

“This isn’t about using AI to write a better email. It’s about rethinking how we actually do things. The leaders who win this chapter are the ones who translate AI into better ways of working for their customers and teams.”

Dynatrace marketing leadership team — Dynatrace Marketing Leadership Team

There’s a timing piece to this. Today, reimagined work is an advantage. Over time, it becomes what customers expect, and the advantage fades. So the question is when you do this work, and who decides. The teams that move now get ahead of it. Wait, and you’re matching a standard someone else set.

You can build these in whatever AI tools your team already uses. They all run agentic workflows now.

If you want the system for deciding what to hand to AI and how much, I offer some guidance in Think Before You Build.

Digital twins help here too, used as thinking partners more than task-doers. I’ve gone deep on those lately. A Cox Automotive team and a Morningstar team each built their own.

Where to Start

Pick one task your team stopped doing because it was too hard, too slow, or too expensive to do at scale. Ask whether it’s still hard, or just historically hard. Most of the time now, it’s the second one. That’s your first workflow.

Reimagining from a blank page is hard. It gets easier once you’ve seen a few examples, because you stop asking “what could AI do” and start asking “what did we stop doing that’s worth another look.”

What’s the one task your team stopped doing that might be worth another look?

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. See real examples: a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily 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.

Stop Telling People to Reimagine Work With AI. Show Them.

Liza Adams · May 14, 2026 ·

Your market intelligence is probably broken. Automating it as-is would just be a faster path to failure.

AR tracks the analyst reports. PMM runs customer interviews and listens to Gong calls. CI does competitive research. Brand watches customer reviews. Each team actions what they see. Rarely does anyone pull it all together, consistently, to find what cuts across every source. So you get fast action on individual signals and little action on the signals that only appear when you look at everything at once. Those that should drive your messaging, your positioning, and your buying committee strategy get missed.

You don’t fix that by adding more meetings or better hand-offs. You redesign the workflow.

Productivity-only AI makes the case for fewer people. Reimagined work makes the case for more of what people uniquely bring. This newsletter shows you what reimagined work looks like.

Why Reimagining Is Hard

Writing a newsletter that says “reimagine your work” is easy. Doing the reimagining is much harder. Three conditions need to be in place. I call them Belief, Reframe, and Space.

Belief is about the tool. What you can build is limited by what you believe AI is capable of. If your team’s experience with AI is mostly faster drafts and summarized reports, that’s the ceiling they’ll build to. They can’t picture a workflow that reads every Gong call, customer review, win/loss note, and analyst report from the quarter and tells them which three buyer objections are gaining steam and what messaging is working against them. They’ve never seen one. (I went deeper on this in Your Team Will Only Build What They Believe AI Can Do.)
Reframe is about you. Humans think linearly and incrementally. Given the problem of unreliable taxis, the natural conclusion was “we need more taxis.” Uber and Lyft didn’t solve for more taxis. They reframed the problem as trust plus access to vehicles and built a different system: an app, drivers using their own cars, and a layer that made strangers comfortable getting in. The leap from “more of what we have” to “something different” takes work.
Space is about your team. You need room to innovate, and most teams don’t have it. Your people are already busy delivering on what’s on their plate. Carving out room to rethink the work itself feels like a luxury you can’t afford, even when you can’t afford not to.

Three conditions for reimagining work with AI: belief, reframe and space

This newsletter is here to give you a head start on all three.

Quick Take

This piece walks through a real workflow I built in Claude Cowork and demoed live at Content Jam in Chicago a few weeks ago. It shows what becomes possible when market intelligence stops sitting in silos and starts producing tailored content strategy for every member of a buying committee. It also covers what it takes to make the outputs genuinely good.

Reimagining a workflow means doing work that was not possible before. Faster reports and better drafts are good starting points, and they are not redesigned work.
AI can do things at scale that a human team cannot, no matter how well- coordinated. That is where redesign starts.
The quality of what comes out depends on the quality of thinking you put in. There are no shortcuts on that part.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles and time constraints, this newsletter is available as a 5-min AI cinematic video and a 19-min AI podcast with two AI hosts. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

The Shift That Made This Possible

AI just crossed a capability threshold that changes what workflows can do. My friend Lisa Sharapata, Founder and CEO of 4Profit GTM and former CMO of Metadata, captured the change in a fun, visual way.

The old analogies (building the plane while flying it, changing a tire on a moving car) describe upgrades to something we already understood. Lisa reframed it with her post and the video below. It goes beyond fast. It’s different.

Lisa Sharapata, Founder & CEO of 4Profit GTM and former CMO of Metadata.io

“We’re not just building in motion anymore. The motion itself is transforming mid-flight. We’re dealing with a motorcycle on the highway that transforms into a plane and takes off while moving. It’s fundamentally different. That’s what agentic GTM actually feels like. I helped drive this at Metadata, and I’m guiding leaders as they work through the same right now.”

Still from Lisa Sharapata's AI transformation analogy video — AI Transformation Analogy Video by Lisa Sharapata

Sam Altman describes five levels of AI: Chatbot, Reasoner, Agent, Innovator, and Organization in the 60-sec video below. We just crossed from Reasoner into Agent late last year/early this year. Innovator and Organization come next.

What changed in practice: AI moved from giving answers to delivering work. That is the agentic shift, and it is what makes a new category of workflow possible. (More details here, AI Moved Fast: What Changed and What to Do Next.)

What AI Can Do That Your Team Can’t

Most of this work today gets done in batches by people doing their best with limited hours: a dozen Gong calls reviewed, a sample of reviews skimmed, one pricing scenario at a time. Rethinking starts with the capabilities that change what’s possible when you stop working in batches. Look for these in your own work.

When you build a workflow around capabilities like these, the output is work a human team alone couldn’t produce at this quality, consistency, and cost.

One thing to check before you start: is the workflow itself sound? Automating a broken process gives you a faster path to failure. Innovating starts with workflows worth redesigning, not workflows that need fixing first.

The Workflow in Action

I built a Market Signals-to-Content Strategy workflow in Claude Cowork and demoed it live at Content Jam in Chicago. The workflow turns market intelligence into a persona-tailored content strategy. For the demo, I ran a product launch through it.

Note: the company and data in this demo are fictional. The workflow, the build, and the outputs are real.

Reimagined human+AI workflow: market signals to content strategy

The inputs were all market data:

The launch brief captured how the product fits the current market.
The beta survey reflected the voice of customer.
The case study showed where the market validated the product.
The ROI data quantified where the product creates value for the buyer.
The product specs translated market needs into capability.

The workflow doesn’t care what kind of market intelligence you load into it. The same workflow runs on analyst reports, Gong calls, competitive signals, or any combination you have.

AI reads everything and builds a content plan. You review and approve the plan, or redirect and challenge it, before a single asset gets produced. Then AI generates four outputs in parallel, each tailored to a different member of the buying committee, each quality-checked before it reaches you.

Imagine doing this with an all-human team in less than 10 mins. Not possible without AI.

Agentic workflow running in Claude Cowork — Agentic Workflow Executed in Claude Cowork

Here are the outputs of the workflow.

Data Manager: Technical Brief. Built for the person evaluating whether this fits the architecture.

Sample technical brief produced by the workflow

IT Manager: Integration Guide. Built for the person who has to set it up without breaking everything else.

Sample integration guide produced by the workflow

Finance Manager: ROI Calculator. Built for the person who has to justify the investment. Inputs are adjustable. The math updates as you change them.

Sample ROI calculator produced by the workflow

You are accountable for the outcome. You are in control at every gate. And even with the built-in quality check, you still review the outputs and make the final call.

Seeing a workflow run end to end is what shifts what teams believe is possible. That’s the first condition in motion.

If you’re interested, here are the links to each of the HTML outputs. Download them and run them on a browser:

Also, here’s a 6-min cinematic video (built with NotebookLM) explaining this workflow.

Showing What’s Possible

Last week, I ran a strategic hands-on AI workshop in Boston for the global marketing team at Klaviyo. About 200 marketers in cross-functional groups spent the day reimagining real workflows from their day-to-day work. By the end of the day, every group built something, and the show-and-tell was truly inspiring. Just as the team learned from me, I learned from them as well.

Klaviyo marketing team AI workshop in Boston, May 2026 — Klaviyo Marketing Team AI Workshop to Reimagine Workflows, Boston, May 2026

The workshop worked because Jamie Domenici, CMO of Klaviyo, and her leadership team created Space. They blocked off the time, brought the right people into the room, put hands on keyboards, challenged the team, and gave them permission to try.

Jamie Domenici, Chief Marketing Officer at Klaviyo

Jamie put it plainly:

“We’re past, ‘How do we do this faster?’ The work now is rethinking how marketing grows the business: how to scale what works, how to use AI as a sparring partner, how to build secure infrastructure, and when to build versus buy. We carved out the time and put everyone in a room with hands on keyboards.

In just a few short hours, the teams built new workflows spanning creative, analytics, product launch, and integrated campaigns. That’s what it looks like when people can put the thinking into practice.”

You don’t get this kind of output by handing people the tools and asking them to innovate on their own time. You get it by giving them space, examples, and the chance to learn from each other in the same room. The conditions you create for your team count for more than the tools.

The Outputs Are Only as Good as Your Thinking

A word of caution before you walk away inspired.

Anthropic’s AI Fluency Index, published in February 2026, found that when AI produces a polished artifact, users are measurably less likely to question its reasoning. The more finished the output looks, the less people verify it.

The workflow handles execution. What it cannot do is supply good judgment, honest inputs, or the willingness to push back on what it gives you. Accept the first answer and you get average work at scale. Garbage in, garbage out is not a cliché. It is the most predictable failure mode in AI-assisted work.

The work pays off when you treat AI as a sparring partner, not an answer machine. Neuroscientist Vivienne Ming ran an experiment with real-world prediction teams and found that the 5 to 10 percent who outperformed everyone, including professional prediction market experts, were the ones who pushed back, demanded evidence, and challenged their own assumptions. The two qualities that separated them were perspective-taking and intellectual humility, or being ok with being wrong.

Keri Brooke, CMO of Enverus, saw this firsthand in a working session where we explored what reimagined workflows could look like for her team:

Keri Brooke, Chief Marketing Officer at Enverus

“We’ve been pushing hard on automation and our team has built a lot. And while we’ve always worked well together, AI is pushing us to think and operate as one team in ways we hadn’t before.

Seeing real examples of reimagined workflows, not just faster automation, made it real and inspired the team. Now the question isn’t how we speed up what we’re already doing. It’s what we do with the time AI gives back.”

That last line is the Reframe in action. The question shifts from “how do we go faster” to “what do we do with the time AI gives back.” That’s a different conversation entirely.

Two prior newsletters go deeper on this. AI Made Me More Human covers five qualities AI develops in you when you treat it as a thinking partner. How to Think Critically with AI, No Matter What You’re Working On gives you a practical framework for getting past the first answer.

This Will Keep Evolving

Reimagining is a practice. As AI advances, the ceiling of what’s possible keeps rising. Keep innovating and you keep growing.

I’m still working through two things myself.

The first is where to put the human gates. Too few and quality drifts. Too many and the workflow stops being agentic. The right balance varies by team, by workflow, and by how much risk you can absorb if something slips through.

The second is when to rebuild. The capability is moving fast enough that a workflow you build today might be solvable with a single prompt in six months. Knowing when to invest versus when to wait is genuinely hard, and I don’t have a clean answer yet.

Where to Start

Productivity-only AI makes the case for fewer people. Reimagined work makes the case for more of what people uniquely bring.

Pick one workflow where your team is working harder than the output warrants. Map what AI can do in that workflow that your team can’t do at scale. Build a small version and test it.

You don’t need Belief, Reframe, and Space perfectly aligned to start. Pick one and the others tend to follow.

Think big, start small, move fast.

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 (use cases and roadmaps) or hands-on building (create AI teammates during the workshop). You’ll leave with either a clear plan or working solutions. Office hours provide follow-on support to keep momentum going.

For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide 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 Moved Fast: What Changed and What to Do Next

Liza Adams · March 5, 2026 ·

Scale AI: democratized building, centralized enablement

You’ve seen the LinkedIn posts. Someone built an app in 20 minutes. Someone else automated a full content workflow over the weekend. What those posts don’t show: the enterprise security policies, the legal reviews, the IT constraints, the fact that half your team doesn’t have access to the same tools. AI capabilities jumped in the last eight weeks. Your team’s security policies, legal reviews, and IT constraints stayed right where they were.

Quick Take

AI platforms shipped more capability in the last eight weeks than in the previous six months. Autonomous agents, desktop AI assistants, enterprise workflow studios, all went live in a short timeframe. If you feel like you missed a month and everyone else already caught up, you didn’t and they haven’t.

The progression I’ve been writing about still holds: Tool to Teammate to Workflow. But what’s possible inside each stage changed fast enough that your mental model from December needs an update.

Key takeaways:

AI teammates now have agency. They plan, execute, and check in at decision points. The shift from “I ask, it answers” to “I delegate, it delivers” is real, and most teams haven’t caught up to it yet.
Self-contained workflows stay inside your AI platform. Anyone can build them today. System-connected workflows touch your CRM, HRIS, or marketing automation. Those need IT and Legal. That determines whether you can start now or need to loop in others first.
The gap between “what’s possible” and “what’s possible for your team” depends on constraints like security policies, legal reviews, tool access, and organizational complexity.
Not everyone needs to be orchestrating human+AI workflows now. Knowing where you are is more important than racing to the next stage.
There’s an interactive AI Evolution Framework guide at the end that helps you find your starting point, the right platforms, and whether you need IT involved.

AI Video Explainer and AI Podcast Versions of This Newsletter

To support different learning styles, this newsletter is available as an 6-min AI video explainer (see below) and a 19-min AI podcast with two AI hosts. If you haven’t seen these AIs in action, they’re worth a view. The tech is advancing in amazing ways. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.

Here’s What Actually Happened

Eight weeks that changed AI: platform launches from Anthropic, OpenAI, Google and others

That’s eight weeks. For context, Custom GPTs launched two years ago and most teams are still figuring those out.

If the people you work with seem confused, stressed, or frozen, this is why. The capabilities moved but the constraints didn’t. IT policies, security reviews, legal approvals, tool access, organizational complexity, these are all right where you left them. And nobody is mapping the difference between “what’s technically possible” and “what’s actually possible for your team, in your org, with your constraints.”

The Stages Haven’t Changed. What’s Inside Them Has.

If you’ve been reading this newsletter, you know the progression: Tool to Teammate to Workflow.

Stage 1 got better. When AI gives you a ‘good enough’ answer, you stop pushing. You stay at Stage 1 longer than you need to.

Stage 2 is where the real change happened. AI teammates now have agency. They plan, break work into steps, execute, and check in with you at decision points. The shift from “I ask, it answers” to “I delegate, it delivers” is real. They work inside your computer, navigating files, apps, and browsers to get the job done while you focus on something else.

Anthropic’s head of product described Cowork as enabling AI to “deliver polished, near-final work.” OpenAI’s CEO said you can go “from a clean sheet of paper to sophisticated software in a few hours.”

But the biggest change is at Stage 3. Workflows split into two distinct modes, and the line between them changes who needs to be involved, what you can build on your own, and where governance actually matters.

The interactive AI Evolution Framework guide maps each stage in detail, including platforms and who needs to be involved.

The AI progression: from tools to teammates to workflows

Self-Contained vs System-Connected Workflows

The most important distinction in this whole framework is one you won’t find in any AI product announcement. It’s the line between self-contained and system- connected workflows.

1) Self-contained workflows keep everything inside your conversation or uploaded files within your AI platform. You chain multiple AI teammates together. You feed the output of one into the next. You produce a complete sales toolkit, a campaign brief with supporting assets, a market analysis with recommendations, all from a single starting prompt.

No external system connections. No sensitive data flowing through APIs. You can build these today, by yourself, without permission from anyone.

2) System-connected workflows reach into your CRM, your HRIS, your marketing automation platform. They read and/or write sensitive data. They need connectors, security reviews, and often legal sign-off.

You’ll hear the term MCP (Model Context Protocol) more and more. It’s how AI connects to business systems, and it already has over 100 million monthly downloads. Anthropic and Microsoft both launched enterprise connector ecosystems. This infrastructure is moving fast.

These workflows need an enablement team, working with IT and legal, to build the connections. You bring the knowledge and expertise of the jobs to be done. They provide the infrastructure.

This helps answer the common question: “Can I build this myself, or do I need to loop in others first?”

For self-contained work, start. For system-connected work, partner with your enablement team.

Who Makes This Work at Scale?

When I say “enablement team,” I don’t mean a new department. I mean a function. It might live in marketing ops, rev ops, IT, or a small cross-functional group. Someone who understands how work flows across teams, knows how to build automations and integrations, and can work with IT and Legal on security. People with this combination are hard to find right now.

They’re responsible for:

Training people to build well from the start
Supporting them when they get stuck
Prioritizing which system connections to build now and which to hold
Setting up governance so the organization isn’t flying blind
Watching the costs, because usage-based pricing on automation tools, API calls, and connectors adds up fast once workflows go live

That last one catches people off guard. Most AI subscriptions are flat-rate but most workflow infrastructure is not. Few teams are budgeting for this yet. The training, support, governance, and cost management are what keep it from breaking.

Here’s what it looks like when you find these people. Some may be in your org already.

A cybersecurity company's human+AI marketing org with AI teammates — Human+ AI Transformation Case Study with Builders and Enablers

At a global cybersecurity company I work with, Marketing Ops stepped into this role. Everyone across marketing built their own AI teammates freely. Over 200 were created during experimentation. 57 are now in daily use. But when those teammates needed to connect to CRM, marketing automation, or internal repositories, Marketing Ops handled the integration and governance. They worked with Legal and IT to make sure the access was responsible and secure. Full case study here.

The model is democratized building, centralized enablement. Everyone builds. A smaller team makes it safe and scalable. This works because the people closest to the work know what to build. A product marketer knows what a positioning teammate should do. A field marketer knows what an event planning teammate needs. You don’t want to bottleneck that. But when those teammates need to connect to business systems, you need someone who knows how to do that without creating risk.

Dominic Abou-Jaoude is the Director of Marketing Data & Analytics at Sophos, sits on his company’s AI Committee, and built an internal triage system that helps people figure out when to bring in his team.

Dominic Abou-Jaoude, Director of Marketing Data & Analytics at Sophos

“The way I know when to step in is pretty straightforward. If someone is building something with their own files and their own thinking, they should just go do it. I want them to. That’s how they figure out what works.

The second a workflow needs to push or pull from a core system, like Salesforce, then I step in to help. Because one bad connection to a live system could create a stability or a security issue. My job is to work with our engineering and security teams to make sure the plumbing is secure and reliable so people can keep building on top of it.”

Kimber Spradlin, Chief Marketing Officer at Graylog, is coming at this from the other direction. Her team already has custom GPTs. Now she’s moving them toward orchestrated workflows using Claude.

Kimber Spradlin, Chief Marketing Officer at Graylog

“I built a drag-and-drop workflow designer for our team offsite inspired by Liza’s dynamic ‘AI teammate and workflow Ideator’ app. Vibe-coded the whole thing.

The reason I could design the system is because I’d already built the individual piece using custom GPTs. I knew what each step needed because I’d spent time using them one at a time first. Now we’re moving it to a production server and building the actual agents behind each step. We learned a lot from building and using custom GPTs . It was the reason I could see how the pieces fit together.”

Here’s the dynamic AI workflow app that sparked Kimber’s approach. It offers sample AI workflows and AI teammates for each one.

Dominic and Kimber are at different starting points in the same progression. Both are building.

Find Your Starting Point

Your situation is different from the person sitting next to you. Different tools, different constraints, different starting point. A CMO at a 5,000-person company and a VP of Sales at a 50-person startup are both reading this newsletter right now, and their next move isn’t the same.

I built an interactive AI Evolution Framework guide that helps you figure out yours. It has three tabs.

Start with the decision guide on the first tab. Pick what you’re trying to do, and it maps you to the right stage, the platforms that fit, and whether you need to involve your enablement team. Takes about 30 seconds.

The other two tabs go deeper. One breaks down what’s changed inside each stage, including the platforms available now and how to know where you are. The other shows the operating model: what you build, what your enablement team builds, and how the roles work together. The short video below walks through both.

Interactive AI Evolution Framework Guide

See the 90-sec video preview of the interactive guide below.

If you want the quick-reference version, the one-pager maps every platform to every stage across all four ecosystems (Google, Microsoft, Claude, OpenAI) on a single page. Keep it open when you’re deciding what to build next. Here’s the one-pager.

Platform guide: which AI tool to open for each kind of work

What Comes Next

The framework gives you a starting point. These are the decisions that come after.

How fast should teams move from Stage 2 to Stage 3? Some teams I work with are still building their first AI teammates. Others are ready to orchestrate. The temptation is to push everyone toward workflows, but rushing past the teammate stage means you miss the part where people learn to evaluate AI critically. That skill is what makes Stage 3 work.
Where does the enablement team role land in smaller organizations? At a large company, you have an ops team, IT resources, and legal review capacity. At a 50-person company, the person building AI teammates might also be the person managing governance. The roles still apply. The question is how they map when one person is wearing three hats.
Are Custom GPTs a dead end? Ethan Mollick said they are. I get why. AI is moving toward systems that orchestrate themselves, picking tools and skills without you directing every step. But the thinking behind the GPTs is important. The knowledge you put in, the way you learned what AI is good at and where it falls short, the judgment about when to trust the output, all of that carries forward. You can’t set up AI-orchestrated workflows well if you’ve never orchestrated them yourself.

Go Deeper

Your Turn

This is my best thinking right now. It will keep evolving as the platforms advance and as I learn with teams putting this into practice. I want to hear from you.

Where is your team in this progression?
Where are you stuck?
What would be most helpful to go deeper on?

I read every response, and your input directly shapes what I write next. The conversations that feed this newsletter go both ways. Always have.

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: See real examples—a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily 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 organization and corporate-wide events.

Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.