When AI becomes an operating model.

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 now limits how far adoption can go.

59%

of marketers expect their buying committee to demand more proof as AI tools and agents become common.

Quick takeaways

• Workflow design now sits at the center of AI strategy. Token costs are climbing as the subsidies fade. The fix is matching each task to the right-sized model. One company spent $500M on AI in a single month. Uber used up its 2026 Claude Code budget by April.
• “Agent” is being used before anyone agrees what it is. Only 14% of marketers have configured prebuilt agents. The rest split four ways. They do not know who should build one, assume IT owns it, call everything an agent, or admit they do not understand what one is.
• 59% of marketers expect their buying committee to demand more proof. Half say the biggest change in B2B buying by 2026 is the need for content AI can read. Teams need to reformat for today’s models and feed the next training run at the same time.

At a glance The six-pillar radar

Where the six pillars stand in H1 2026.

Jump to any pillar for the full section, or scan the cards below. Job Confidence is the outlier and the only pillar that fell this quarter.

Chapter 1

Where things stand.

Leadership has stopped asking about AI adoption and started asking about return. The next three sections cover that shift, the executive summary, and what history says comes next.

Sections 1, 2, 3

The question now is whether AI is working.

The question in the room has moved past adoption. Leaders want to know whether AI is driving impact and becoming part of a system the team can reliably run. They want to see the return.

We have been here before. In 1990, companies were pouring money into computers and seeing little of it in their productivity numbers. That year, Michael Hammer argued in the Harvard Business Review that the problem lay in how companies used the technology. 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. Teams that add a copilot here and a chatbot there on top of their existing workflow keep waiting for the return. It will go to teams that redesign the work around AI and turn that redesign into a repeatable operating model.

This is the fourth report in our series on how AI is changing B2B marketing.

AI has moved from something teams use to something teams increasingly run the function on. Workflow design and governance are now part of the work itself.

A year ago, leadership tracked adoption through the tools the team had, how many people used them, and how fast use was growing. Today, leadership cares about outcomes, and about whether the system producing them can be trusted.

The data that follows shows where that shift appears and what a more deliberate operating model now asks of leadership.

AI moves into the operating model.

Adoption is up again this quarter. The more important change is what marketers now expect from AI and from the systems forming around it. Because acceptable output is cheap to produce, volume alone impresses no one now. The bar has shifted to whether AI can match the quality a strong team would produce while costing less and moving faster.

17%

expect AI to improve content quality. Differentiation from competitors ranks equally (17%), followed by marketing ROI (16%) and lead quality (9%).

Q1 FY26 Watchtower Mastersheet n=11,249–467,863

Marketers now measure AI by whether it improves the business.

Teams now use the word “agents” freely. Few have agreed internally on what an agent is, who builds one, or who is responsible when one does the wrong thing. The systems to define and govern agents have not caught up.

AI concerns have shifted the same way. 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%

name attribution as the top concern this quarter. Integration reliability, procurement slowdowns, automation errors, and rogue agents all cluster at 11–12%.

Q1 FY26 Watchtower Mastersheet

With AI producing more of the execution, human judgment becomes the scarce input. The teams that win will keep people and AI working closely together. AI is moving from the edge of the marketing function into its operating model. The advantage goes to teams that build a repeatable system for judgment, execution, and accountability.

Returns come from redesigning the work.

The companies that won the last technology shift redesigned the work instead of digitizing the old version. Then they built that redesign into how the work was run. The same will hold for AI. What differs this time is speed. A redesign that took the better part of a decade in the 1990s is now expected inside a planning cycle.

Chapter 2

The six-pillar radar.

The Watchtower radar tracks six pillars from quarter to quarter. This chapter gives the overview. A deep dive on each pillar follows.

Section 4

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 to 2.7, its lowest reading in the series.

Watchtower

is Intercept’s proprietary intelligence engine, tracking sentiment and intent across a windowed sample of 20 million people globally. Each pillar is scored on a 5-point maturity scale.

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Chapter 3

Underneath each signal.

Each of the six pillars has its own section that opens with a 30-second summary. Pillar 3, Job Confidence, is the one to read in full.

Sections 5 through 10

5

Pillar 1 Watchtower

Individual AI Proficiency Self-taught, one person at a time

3.4

vs Q4 2025

↑ rebound after three declines

In 30 seconds

Proficiency climbed back to 3.4 after three straight declines. Most of the gain is self-taught and tied to specific use cases. It stays with the individuals who learned it. Enterprise value stays out of reach until the skill becomes a shared capability.

As a general observation, AI is being used as a point solution. People motivated enough to teach themselves pick it up ad hoc. It is rarely built into how the team works. The result is pockets of productivity that never add up to enterprise value. What one person figures out rarely reaches the person next to them.

Structured AI training is still largely missing inside most organizations. That is why almost all of this proficiency is self-taught. 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 on repetitive, modular work that moves fast. Confidence is highest there. It drops sharply on work that crosses team boundaries or asks the tool to exercise judgment. That is where the harder enterprise value sits.

The Implication

A self-taught skill says as much about motivation as about ability. People teach themselves AI because they have a reason to. Self-teaching alone does not build the shared workflows and standards that make the work repeatable across a team.

6

Pillar 2 Watchtower

Organizational AI Readiness Nobody owns it yet

3.1

vs Q4 2025

↑ modest rise

In 30 seconds

Readiness sits at 3.1. The gap is coordination, led by difficulty prioritizing use cases (12.1%) and a lack of strategic clarity (8.5%). Until a named owner runs AI across the function, the score stays stuck.

Organizational AI Readiness sits at 3.1 this quarter. Watchtower defines readiness as structural preparedness, meaning the training, governance, and leadership alignment behind running AI. The data puts the gap there. Getting people to use AI is the easier step. The harder part is coordinating it and putting a workable system around it across the function. The two most-reported sticking points this quarter were difficulty prioritizing AI use cases (12.1%) and a lack of strategic clarity (8.5%). Both are management problems.

The outlier The only pillar that fell

7

Pillar 3 Watchtower

Job Confidence The exception in the rebound

2.7

vs Q4 2025

↓ lowest reading in the series

In 30 seconds

Job Confidence dropped to 2.7, the lowest reading across four reports and the only pillar to fall this quarter. The clearest signal in the data is the gap between belief in the technology and confidence in one’s own place in it.

Job Confidence fell to 2.7 this quarter, its lowest point across the four reports. Every other dimension rose.

The Signal

Underneath the number is a broad discomfort with what comes next, and most of it comes from not knowing. The frontier labs have not said specifically how any of this benefits the people doing the work. What comes through instead 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. They also owe them a plan for reskilling and retraining that shows how jobs will evolve. What settles people is a conversation about new value creation and where the work goes next.

8

Pillar 4 Watchtower

Sentiment Toward AI Adopted faster than it’s trusted

3.4

vs Q4 2025

↑ inside marketing

In 30 seconds

Marketing sentiment is 3.4. Public sentiment is far colder, with 26% positive and 46% negative. Inside teams, trust grows where AI gives consistent results and breaks down where tools are unreliable.

Sentiment Toward AI sits at 3.4 inside the marketing teams we track. Outside marketing, the public mood is far colder.

The Signal

The same split appears inside teams that use AI every day. People who get reliable results from AI come to trust it. People whose tools work well one day and break the next do not.

The Implication

Trust in AI grows when results are consistent. That consistency comes from building AI into a reliable operating system for the work. Outcomes left to individual prompting habits are inconsistent.

9

Pillar 5 Watchtower

Organizational AI Adoption Workflow design becomes strategy

3.3

vs Q4 2025

↑ uneven but real

In 30 seconds

Adoption sits at 3.3 and is uneven. 36% have AI embedded in day-to-day workflows, and 13% run agentic workflows in production. The bigger shift this quarter is cost. Token rates are climbing as the subsidies fade.

Organizational AI Adoption sits at 3.3 this quarter. Teams are now deciding which existing pilots are solid enough to become part of the workflow infrastructure that runs the business.

The Signal

Teams have moved on to designing the workflows AI runs inside. They are also learning what those workflows cost now that subsidized token rates 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. Keep routine tasks on smaller or on-device models and save expensive frontier calls for the work that needs them.

10

Pillar 6 Watchtower Strongest reading

Future Outlook AI recommendations are the first gate

3.5

vs Q4 2025

↑ highest in the set

In 30 seconds

Future Outlook sits at 3.5, the strongest reading. More buyers now start their research with an LLM. 59% expect their committee to demand more proof, and 50% say the biggest change ahead is content AI can read.

Future Outlook sits at 3.5 this quarter, the strongest reading in the set. Marketers can see where buying is going, even if they cannot yet control it. More of the early buying process now runs through an LLM before a human gets involved. The question marketers keep asking is how to get recommended when it does.

59%

of marketers expect their buying committee to demand more proof before a decision. Half (50%) say the biggest change ahead is the need for content AI can read.

Q1 FY26 Watchtower Mastersheet

The Signal

Most marketers are now trying to optimize for how often AI models recommend them, because more buyers start their research there. No one has a settled playbook yet. Every new model release can change which brands get surfaced and why.

The Implication

This rewards rapid, disciplined experimentation on two fronts at once.

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Chapter 4

What to do about it.

This chapter holds the deep dives, four leadership questions for your next meeting, and the closing argument for why the system is now the advantage.

Sections 11, 12, 13

Underneath the radar.

Each of these six panels looks deeper at a data point behind the six-pillar view. Open the ones you want.

Four questions to answer before the end of 2026.

Bring one to your next leadership meeting and see how aligned the room is.

1

What does “better” mean now?

Leadership is asking for quality, differentiation, and ROI. The team needs to find where it still tracks AI by hours saved. It also needs to decide what proof would show that AI is improving the quality of the work as well as the speed.

2

What 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. Marketing, IT, legal, and security have to agree on who that is before an incident decides it for them.

3

How will we defend AI investments if attribution keeps getting harder?

Attribution is the top concern this quarter, at 14%. When leadership asks how AI paid for itself, the measurement standards must give an answer that is defensible and repeatable.

4

What would a credible AI operating system look like by year-end?

It also asks who would own how the system is governed, measured, and improved. On standardization, the question is which workflows need to be repeatable enough for any team member to pick up without relying on one internal expert. The ownership question is who is building the operating model itself, separate from the people running the daily work inside it.

The advantage goes to teams that build the system.

As AI takes on more execution, the advantage goes to whoever can build the system that makes good judgment repeatable and run the work inside it. That is the partner this moment calls for, one that works on that system and in the daily work at the same time.

Intercept gives B2B tech marketers the operating model. Our team runs the work inside it.