October, 2026
Your employees are getting more productive with AI. Your company may not be.
Across organizations, AI is making it possible to analyze information faster, automate complex tasks, generate reports in seconds, and accomplish work that once required substantial time and resources.
These are meaningful advances.
But there is an important distinction between making people more productive and making an enterprise more successful.
An organization can become extraordinarily efficient at doing things that no longer matter.
It can automate processes that should have been redesigned.
It can generate better forecasts without reconsidering the assumptions behind them.
It can dramatically accelerate decisions without questioning whether those are the right decisions to make.
And it can measure all of this with increasingly sophisticated dashboards while missing the larger strategic opportunity.
This is where I believe the Chief Analytics Officer role becomes especially important.
At its best, the CAO is not simply the executive responsible for more sophisticated reporting, better models, or faster analytical production. The CAO should help the enterprise see what it cannot yet see.
Where are customer needs changing before those changes become obvious in financial results?
Which emerging technologies, competitors, or market developments could fundamentally alter the economics of our business?
Where are we allocating capital and talent based on assumptions that may no longer be valid?
What opportunities for growth, innovation, pricing, customer retention, or competitive differentiation are hidden within the intelligence we already possess?
And perhaps most importantly: What should we be doing differently because of what we now know?
Answering these questions requires more than technology.
It requires bringing customer, market, operational, and financial intelligence together; challenging established assumptions; evaluating alternative futures; and helping leadership make consequential decisions with greater confidence.
AI makes this possible at a scale and speed we have never experienced.
But AI does not determine which opportunities an enterprise should pursue, which risks deserve attention, or which investments will create lasting economic value.
Those remain questions of strategy, judgment, and leadership.
The greatest opportunity for the Chief Analytics Officer is not to help the organization do yesterday’s work faster.
It is to help the organization recognize, decide, and act on what will create tomorrow’s value.
As AI becomes embedded throughout the enterprise, are we measuring its success primarily by how much work or expense it saves, or by how much new value it helps us create?
AI value creation is the measurable business value an organization gains from artificial intelligence: new revenue, stronger customer retention, smarter pricing, earlier visibility into risk, and better use of capital and talent. It goes beyond the hours or expenses AI saves and asks whether AI is helping the enterprise make better decisions, not just faster ones.
Productivity gains make existing work faster. If that work rests on outdated assumptions, or on processes that should have been redesigned, AI simply helps the organization do the wrong things more efficiently. The gap is visible in the data: McKinsey’s State of AI research found that nearly nine in ten organizations use AI regularly, yet only 37 percent say it has contributed to their EBIT.
The Chief Analytics Officer brings customer, market, operational, and financial intelligence together, challenges established assumptions, and helps leadership evaluate alternative futures. In an AI enabled enterprise, the CAO’s most important job is helping the organization recognize where value will come from next, and act on it with confidence. You can read more about this in what decision grade intelligence means for enterprise growth.
Time and cost savings are a start, but they are not the whole picture. Leaders should also track which decisions AI improved, the revenue it helped win or protect, the risks it surfaced earlier, and the new opportunities it helped the business pursue. If AI only ever shows up as an efficiency number, the larger strategic opportunity is probably being missed.
If your leadership team is asking how to turn AI investment into lasting enterprise value, schedule a consultation to talk it through.
AI is making employees more productive, but that does not automatically make the enterprise more successful. Here is why the Chief Analytics Officer must help organizations turn AI into lasting value.
As AI makes analysis abundant, the Chief Analytics Officer’s role must expand beyond data and dashboards toward building a trusted enterprise system that turns evidence into better, accountable decisions.
More data does not guarantee better decisions. Learn why enterprise decision intelligence helps organizations create clarity, alignment, and strategic action.
