H1 Trends Brief Powered by Watchtower
When AI becomes an operating model.
The demand now is better, faster, and cheaper, not one at the expense of the others. Andrew Au · Co-CEO, Intercept
Inside This Brief
17%
of marketers expect AI to improve content quality this year. Differentiation matches it at 17%, ROI follows at 16%.
$500M
single-month AI bill at one company after leaving employee usage ungoverned. Token cost is the new constraint.
59%
of buying committees will demand more proof before signing off in 2026. Verifiable evidence is now table stakes.
Section 01 · The Question Has Changed
The question is no longer whether to adopt AI.
The conversation about AI continues to change. The question in the room is no longer whether to adopt it, but whether it is working, driving impact, and becoming part of a system the team can reliably run. Leadership wants to see the return.
We have been here before. In 1990, with companies pouring money into computers and seeing little of it reflected in productivity numbers, Michael Hammer made the case in the Harvard Business Review that the technology was not the problem. Companies were using computers to speed up the way they already worked, paving the cow paths instead of building new roads. The returns only arrived when they redesigned the work around the technology and built a more disciplined way to run it. Ford rebuilt its accounts payable process and cut headcount by roughly three quarters, where automating the old process would have saved a fifth.
The same logic governs AI now. The teams adding a copilot here and a chatbot there, on top of the workflow they already had, keep waiting for the return. The teams redesigning the work around AI, and turning that redesign into a repeatable operating model, are the ones who will see it.
This is the fourth report in our series on how AI is changing B2B marketing, and it captures the biggest shift yet.
AI has moved from something teams use to something teams increasingly run the function on, which makes workflow design and governance part of the work itself.
A year ago, leadership cared about adoption, about which tools the team had, how many people used them, and how fast it was moving. Today, leadership cares about outcomes, and about whether the system producing them can be trusted.
This report lays out where that shift appears in the data, what it means for how marketing teams are run, and what a more deliberate operating model now requires from leadership.
Section 02 · Executive Summary
AI stops sitting on the sidelines.
Adoption is up again this quarter, but the more important change is what marketers now expect from AI, and from the systems forming around it. Acceptable output is cheap to produce, so volume no longer impresses anyone. The bar has shifted to whether AI can match the quality a strong team would produce while costing less and moving faster.
17%
Marketers have stopped measuring AI by whether they are using the tools and started measuring it by whether it improves the business.
The conversation about agents is running well ahead of the systems needed to define, govern, and manage them. Teams use the word "agents" freely, while few have agreed internally on what an agent is, who builds one, or who is responsible for it when it does the wrong thing.
More broadly, AI concerns have moved in the same direction. A year ago, they were ethical and reputational, about copyright, hallucinations, and brand drift. Now they are operational, centered on whether teams have a reliable way to manage what AI is doing inside the funnel.
14%
These shifts share one thing in common. When execution becomes abundant, judgment becomes the scarce resource, and the teams that win are the ones keeping the tightest collaboration between people and AI. That is what it means as AI moves from the edge of the marketing function into its operating model: the advantage shifts to teams that can build a repeatable system for judgment, execution, and accountability.
Section 03 · Lessons from the Past
The work changes when the system changes.
The companies that won the last technology shift were the ones that redesigned the work rather than digitizing the old version of it, then made that redesign durable through the way the work was actually run. The same will hold for AI. Different this time is speed, because the 1990s redesign that took the better part of a decade is now expected inside a planning cycle.
"The gap between what AI can do and what most teams actually get out of it is not a technology problem. It is a design problem, and right now it is the most valuable problem in marketing. The teams that win the next few years will be the ones that stop bolting AI onto the old workflow and start rebuilding the workflow around it."
Shaheen Yazdani · Co-CEO, Intercept
Section 04 · On The Radar
Six signals that show where we’re heading.
Five of the six radar dimensions rose in the first quarter of 2026. The sixth, Job Confidence, fell, and it is the most revealing number in the set.
Individual AI Proficiency climbed back to 3.4 this quarter after three straight reports of decline. As a general observation, AI is being used as a point solution rather than a shared capability, picked up ad hoc by the people motivated enough to teach themselves, rather than encoded into how the team works. The result is pockets of real productivity that never add up to enterprise value, because what one person figures out rarely reaches the person next to them.
The Rebound · Four-Quarter Trajectory
Almost all of this proficiency is self-taught, since structured AI training inside most organizations is still largely missing. The value that does show up, in social content, workflow automation, SEO content, and AI search optimization, stays locked inside those few people and select use cases.
The Signal
AI lands first where the work is repetitive, fast, and modular, which is also where confidence is highest. Confidence drops sharply on anything that crosses team boundaries or asks the tool to exercise judgment, which is exactly where the harder enterprise value sits.
The Implication
The fact that this skill is self-taught says as much about motivation as it does about ability. People are teaching themselves AI because they have a reason to, but self-teaching alone does not create the shared workflows, standards, or repeatability needed for organizational value.
"AI is a reset moment for how people learn, and it rewards a growth mindset. Mindset is fast becoming the real divide."
Shaheen Yazdani · Co-CEO, Intercept
"Organizational AI readiness was never about turning a few people into power users. It is about lifting the whole organization, and that takes executive vision, a culture that builds a growth mindset into the everyday, and the infrastructure underneath it all. This is an organizational transformation, not a tooling upgrade."
David Toto · Managing Director, Intercept
Organizational AI Readiness sits at 3.1 this quarter. Watchtower defines readiness as structural preparedness: the training, governance, and leadership alignment behind running AI. The data says that is the gap. The harder friction is not just getting people to use AI; it is coordinating it, prioritizing the use cases, creating strategic clarity, and putting a workable system around it across the function. The two most-reported blockers this quarter were difficulty prioritizing AI use cases (12.1%) and a lack of strategic clarity (8.5%) — a management problem, not a technology one.
Same Goal · Three Different Bets
How organizations are approaching AI ownership
Give staff the models. Let them figure it out with little central guidance.
Lock everything behind a central AI function that controls access and policy.
Start further back. Fix data estate and infrastructure so the organization is ready to use AI when it scales.
The common thread across all three is that progress stalls wherever ownership is unclear. Until a named owner decides how AI gets prioritized, governed, and resourced, there is no real operating system for the work, only scattered experimentation, and the readiness score stays stuck.
Job Confidence fell to 2.7 this quarter, its lowest point across the four reports, and it is the only dimension that dropped while every other one rose. That gap, between conviction in the technology and confidence in personal relevance, is the clearest signal in the data.
All Six Pillars · Q4 2025 to Q1 2026
Five pillars rose. Job Confidence fell.
The Signal
Underneath the number is a broad discomfort with what comes next, and most of it is simply not knowing. The frontier labs have not been specific about how any of this benefits the people doing the work, and the message that comes through is a vague reassurance that it will all work itself out.
The Implication
Executives owe their teams a concrete account of what AI does to the labor market and to specific roles, and a real plan for reskilling, retraining, and how jobs evolve rather than simply disappear. The conversation that actually settles people is about new value creation, not efficiency.
"Using AI to chase efficiency is short-sighted. The real prize is new value creation, doing the things we could never do before. Keep doing the same work faster and all you get is 'ghost GDP.' Point it at new ideas and new possibilities, and the opportunity in front of us is abundant."
Shaheen Yazdani · Co-CEO, Intercept
Sentiment Toward AI sits at 3.4 inside the marketing teams we track, but the public mood is far colder.
Inside Marketing
3.4
Watchtower sentiment among the marketing teams we track. Trust grows where results are consistent.
Public Sentiment
26 / 46
Only 26% of Americans viewed AI positively; 46% negatively (NBC News poll). 70%+ believe it is moving too fast (Economist / YouGov).
Across North America this spring, commencement speakers who praised AI were booed off the stage, from Eric Schmidt at the University of Arizona to a speaker at the University of Central Florida who called it the next industrial revolution. NPR ran a segment advising 2026 speakers not to raise the subject at all.
The Signal
Inside the teams using AI every day, the same split shows up. People who get reliable results from AI come to trust it, and people fighting tools that work brilliantly one day and break the next do not.
The Implication
Trust in AI grows when the results are consistent. That comes from building AI into a reliable operating system for the work, not from leaving outcomes to individual prompting habits.
Watchtower Signal Meter
Where trust breaks first this quarter
"Treat AI as a point solution and the experience swings wildly from one person to the next. Build agentic systems that give people consistent, reliable outputs without the guesswork, and trust starts to follow. Consistency is how you earn it."
Francis Silva · Chief Technology Officer, Intercept
Organizational AI Adoption sits at 3.3 this quarter. The question is no longer whether to do this, but which of the existing pilots are solid enough to become part of the workflow infrastructure that actually runs the business. Adoption is real, but uneven.
Adoption Depth · Where Workflows Actually Run
Embedded vs. agentic, this quarter
The bigger shift this quarter is the cost of running it all.
The Cost Reality
Cheap tokens were subsidized to drive adoption. As the subsidy fades, the rates are climbing.
$500M
single-month AI bill at one company after leaving employee usage ungoverned
April
when Uber burned through its entire 2026 AI budget, with its own COO questioning whether the spend was producing better products
Cost governance was an afterthought a year ago, and is becoming a hard constraint on how far adoption can go. Some teams are already pulling back on AI subscriptions.
The Signal
Teams have moved on to designing the workflows AI runs inside, and at the same time they are waking up to what those workflows cost. The token rates that made heavy experimentation painless were subsidized to win adoption, and they are going up.
The Implication
The next phase of adoption is about efficiency as much as capability. Design workflows that match the model to the job rather than routing everything through a frontier model. Keep routine tasks on smaller or on-device models and save the expensive frontier calls for the work that truly needs them.
"A lot of what we are doing today is the equivalent of taking a Ferrari to drive one block. You do not need a frontier model to rename a file."
Andrew Au · Co-CEO, Intercept
Future Outlook sits at 3.5 this quarter, the strongest reading in the set, because marketers can see where buying is going even if they cannot yet control it. More of the early buying journey now runs through an LLM before a human is involved, and the question consuming marketers is how to get recommended when it does.
59%
The Signal
Almost everyone is now trying to optimize for how often the models recommend them, because the LLM has become the first gate a buyer passes through. Nobody has a settled playbook for it, and the ground keeps moving, since every new model release can change which brands get surfaced and why.
The Implication
This rewards rapid, disciplined experimentation on two fronts at once.
Two-Front Experimentation
Optimize for today’s models. Feed the next training run.
Front 01 · Today’s Models
Win retrieval-based engines
Real-time engines that read current sources. Reformat existing content for findability and move visibility quickly.
Front 02 · The Next Training Run
Be surfaced by default
Build a base of cited, credible content strong enough that the next generation of models surfaces you without prompting.
"The market is moving toward proof, structure, and machine-readable credibility, and visibility alone will not be enough. The brands that win AI-mediated discovery will be the ones that give machines something worth citing, and buyers something worth trusting."
Shaheen Yazdani · Co-CEO, Intercept
Section 11 · Deep Dives
Underneath the radar.
Agents · Arriving Before The Rules
14% configured prebuilt agents. The rest still debating what an agent is.
Asked about their grasp of AI agents, the largest group of marketers (14%) report they have configured prebuilt agents rather than built their own. The rest split fairly evenly between not knowing who is supposed to build an agent, assuming it has to be IT, calling everything an agent, and admitting they do not really understand what one is. Most teams meet agents through a vendor demo before they have agreed internally on what one is, who can deploy one, or what an agent is allowed to do. A meaningful number have switched on agents they cannot yet govern.
Frictions · Why Progress Fragments
Top pain points: prioritizing AI use cases (12%), strategic clarity (8%).
All the top frictions are organizational rather than technical. The missing piece is less another tool than a clearer system for how AI use cases are chosen, owned, and run. The first move is narrowing to a few high-value workflows and naming who owns the agents before switching them on.
Priorities · What Marketers Expect AI To Improve
Content quality 17% · Differentiation 17% · ROI 16% · Production 12% · Lead quality 9% · GEO 7%.
Judgment has shifted from whether teams are using AI to what AI produced. The case for any new AI investment now has to lead with the business pressure it answers rather than the features it ships with, and every request that reaches leadership needs to tie to a quality number, a differentiation move, or a revenue growth strategy.
Concerns · Where Trust Breaks First
Attribution 14%. Integration, procurement, automation errors, rogue agents all at 11–12%.
The fear has moved from AI itself to losing control of the AI already running inside the function. Attribution standards must be set before AI gets near any workflow that matters, and the line between what an agent can do on its own and where it has to wait for a human has to be drawn before the agent is switched on.
Role Shift · The Marketer Becomes The System Manager
Managing AI agents leads role-change expectations at 18%.
Strategic planning, specialized roles, and technical roles each follow at 10%. The job is shifting from producing the work to judging, supervising, and signing off on it, and the people being asked to do that are the same ones already managing the agents.
Team & Strategy Shift · How The Work Reorganizes
Restructuring, not expansion. 59% of committees will demand more proof.
The headcount signals describe restructuring: shifting budget into AI (19%), adding AI specialist roles (18%), reducing execution headcount (15%). Almost no one expects to simply add people. Meanwhile the buying committee itself is changing — as AI tools move into procurement, IT and security weigh in earlier, and 59% of marketers expect their committee to demand more proof before signing off.
Section 12 · The Leadership Questions
Four questions that cannot be deferred.
Each is worth answering before the end of 2026. Bring one to the next leadership conversation on the calendar, and see how aligned the room actually is.
What does "better" actually mean now?
Leadership is asking for quality, differentiation, and ROI. The team needs to know where it is instead still tracking AI by hours saved, and what proof would show AI is improving the work itself, not just the speed of producing it.
What exactly counts as an agent, and who owns its behavior?
The same word now covers assistants, automations, copilots, and systems that act on their own. When one does the wrong thing, somebody carries the consequence, so marketing, IT, legal, and security have to agree on a single answer before an incident decides it for them.
How will we defend AI investments if attribution keeps getting harder?
Attribution is the strongest concern this quarter. The measurement standards must answer, in a defensible and repeatable way, how AI paid for itself when that question lands on the desk.
What would a credible AI operating system look like by year-end?
And who would own how it is governed, measured, and improved? The standardization question is which workflows need to be repeatable enough that any team member can pick one up without depending on a single internal expert. The ownership question is who is building the operating model itself, separate from the people running the daily work inside it.
Section 13 · Conclusion
The system is the advantage.
When execution is abundant and judgment is scarce, the advantage goes to whoever can build the system that makes good judgment repeatable while running the work inside it. That is the partner this moment calls for, one that operates on the system and in the work at the same time.
"It is not an experiment if you know it is going to work. There is no silver bullet for AI, and the only way through is to experiment quickly and take real risks. The job of marketing leadership is to make that safe for their teams, and it is why we built InterceptOS."
Andrew Au · Co-CEO, Intercept
InterceptOS gives B2B tech marketers the system. Our team runs the work inside it.
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Interactive Assessment
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9 questions drawn from the brief. ~5 minutes. Your responses score your org’s AI operating model against the Watchtower benchmarks. No email required.
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What to Focus On Next
See an operating model run end-to-end.
InterceptOS is the system. Our team runs the work inside it. The Activation flow shows what that looks like in practice.
InterceptOS · Activation Flow Open InterceptOS. See the four-stage flow that takes the operating-model thesis from this brief and turns it into shipped, measured work.Sources
- Q1 FY26 AI in B2B Trends: B2B Marketers Watchtower Research Mastersheet. Source for all quantitative data in this report: executive priorities, agent-understanding signals, concern signals, adoption signals, value signals, role and team shift signals, buying-change signals, and Watchtower radar dimension scores. Data window: January 1 – March 4, 2026. n=11,249–467,863.
- Michael Hammer, "Reengineering Work: Don’t Automate, Obliterate," Harvard Business Review, July – August 1990.
- AI at 2026 commencements: graduating classes booed speakers who praised AI (Eric Schmidt, University of Arizona; University of Central Florida). NBC News and NPR, "Advice for 2026 commencement speakers: Don’t bring up AI," May 2026.
- AI favorability and public sentiment: NBC News poll, February 27 to March 3, 2026 (1,000 registered voters); The Economist and YouGov, May 2026; Pew Research Center, March 2026.
- AI cost and token economics: company $500M single-month AI bill (Fast Company and Yahoo Finance, May 2026); Uber 2026 AI budget exhausted by April (TechCrunch and Inc., June 2026); token pricing and subscription pullback (Tom’s Hardware and The Economist, 2026).