Mayfield just released their 2026 CXO Survey on agentic AI adoption. The headlines are impressive: 42% of enterprises have agents in production, 91% plan to increase budgets, fastest enterprise technology shift in a decade.
But two things stood out to me as counter-intuitive, and a recipe for future headaches. The efficiency ceiling and governance is somebody else problem mentality.
The Efficiency Ceiling
Let us tackle what I mean by efficiency ceiling first. Looking at the Mayfield data agents are deployed as cost reduction / efficiency gains: developer productivity leads at 70%. Then customer service automation, IT operations, data analysis. Cost reduction dominates at 72%.
I can understand why. The ROI on an efficiency agentic use-case is easier and gets approval with very few questions asked. Who doesn’t want to save money. But think about what that means. If your primary use case is making existing processes faster and cheaper, you have a finite return. Once you’ve automated a workflow, you’re done. You can’t cut the same cost twice.
The survey is full of efficiency language: reducing wait times, accelerating review cycles, cutting cognitive load. These are real gains. But they are one-time gains. They don’t compound.
Efficiency plays are good for learning, but do they teach the right thing? Deploying agentic solutions to create new revenue streams is a completely different beast. It comes with greater uncertainty on ROI, different metrics, unknown reception by consumers etc. So it makes sense that enterprises are not building that, but they are currently locked into a finite bottomline growth game (or cutting game if you want).
The Compliance Debt
Now this is where the real headache starts.
84% of CXOs say security and compliance is non-negotiable when selecting AI vendors. Yet 60% report having no formal AI governance framework. Budget allocation? Governance isn’t a top investment priority.
This is completely backwards. You are telling me security and compliance is a top-priority when it is somebody else building your solutions, but internally you have no clue and you are not allocating budget to solve the problem? This simply won’t end well. The EU AI Act enforcement for high-risk systems begins August 2, 2026. That is like tomorrow in corporate timelines. Penalties run up to €35 million or 7% of global annual revenue. And like GDPR, it applies extraterritorially — if you have EU operations or EU customers, you’re in scope.
Now somebody else doesn’t have a problem your have. Because the act covers both providers and deployers, and all of those checkboxes for security and compliance now become your responsibility as the deployer.
High-risk systems under the Act include hiring algorithms, credit scoring, medical diagnostics, educational assessment, biometric systems. These aren’t edge cases. These are core enterprise functions that many of the 42% already in production are likely touching. You, as the deployer has independent obligations around human oversight, risk management, and transparency. Vendor compliance certificate does not make you compliant.
The Math isn’t Mathing
Taken together what I see is a typical short-terms optimization game. Cut costs, worry about tomorrow another day.
Enterprises are deploying agents primarily for efficiency gains that have a natural ceiling. At the same time, they’re accumulating compliance liabilities that will come due in months, not years.
What happens when the efficiency gains plateau and the compliance bills arrive?
And yet it makes perfect sense. Business cases are approved on ROI, use-cases that are easy to grasp wins, compliance wasn’t really part of the use-case as it was just a test, however, it made it into production. Who would risk their neck for not having immediate savings, because a compliance committee had to figure things out first.
A Decade Old Problem
Data readiness is cited as the #1 blocker in the survey, as it has for at least the last decade. And yet it still isn’t a budget priority.
Sorry to say, but fixing your data foundation, fixes like 70% of your problems.
Good data governance solves problems up and down the value chain. If your data is clean, documented, and properly governed, your AI governance problem becomes marginal. You know what data trained your models, you can trace decisions back to sources, you can demonstrate compliance because the foundation is already there.
It also unlocks the revenue plays. The reason enterprises are stuck in efficiency mode is that revenue-generating agents require trusted data about customers, markets, and operations. If your data is a mess, you can’t build agents that make decisions about what to sell, who to target, or how to price. You are simply cutting yourself short and limiting what problems you can tackle by not cleaning up your mess.
So data readiness is the lever that moves both problems. It makes governance tractable. It makes revenue use-cases possible.
But cleaning up data is no fun. There’s no demo, no headline, no “we deployed 50 agents” press release. It’s unglamorous infrastructure work. And so it sits at the bottom of the priority list while enterprises deploy agents on top of data foundations that can’t support what comes next. Because let us be honest, nobody ever got that VP of AI promotion by yelling: Stop! we need to sort out master data management and setup a governance board.
Don’t let 2026 be the year where you continue this game of jenga. Take a hard look at your portfolio: are you having the right balance between efficiency and moonshots, do you allocate budget to fixing the foundation, and do you have an eye towards governance. If not, what you’re putting into production has a shelf life you’re not accounting for.