TL;DR: The market average in AI isn't a neutral point of comparison, it's a collective diagnosis of where initiatives stall. 88% of organizations already use AI in at least one function (Stanford HAI, 2026), while 88% of proofs of concept never reach deployment at scale (IDC/Lenovo, 2025). That means aiming for the average is aiming for the exact point where the budget disappears and the business doesn't change. This benchmark shows where the market really stands, how to compare yourself honestly on the 5-stage scale, and what separates those who cross into production from those who keep running pilots indefinitely.
The market average is a worse place than it looks
In a meeting, a CDO showed us a slide almost every company has in some version. It was a bar chart of "% of AI adoption by area", nearly all green, with the caption underneath: "we're above the industry average". The conversation that followed was uncomfortable, because when we asked how many of those use cases ran in production with someone depending on the result, the honest answer was: none.
That's the problem with an AI maturity benchmark that stops at adoption. Being above average on "how many areas are testing AI" says almost nothing about what the company can actually do with it. It says you experiment as much as everyone else. And the market average today is a sea of experimentation that doesn't turn into results.
The gap between two numbers tells the story. 88% of organizations already use AI in at least one function (Stanford HAI AI Index, 2026), which makes adoption practically universal. In the same market, 88% of AI proofs of concept don't reach deployment at scale (IDC/Lenovo CIO Playbook, 2025). Almost every company has entered experimentation, but very few have made it to the other side, into production.
For a CDO, this changes which questions are worth asking. "Are we adopting AI?" always gets the same answer: yes, everywhere, often without you knowing exactly where. What separates those who move forward from those who stay in pilot mode is a different question: "how much of our AI is already a real part of the business, and how much is pilots consuming budget without changing anything?".
What the numbers say about the gap between adoption and production
When you read the market statistics side by side, they stop being loose data points and become a collective diagnosis. The whole industry is piled up at the same point in the journey, and that point comes before production.
Getting started has become trivial: a subscription, an API, a marketing pilot. 88% of companies already use AI in at least one function (Stanford HAI, 2026), a figure that echoes what McKinsey reported the year before (also 88% in at least one function, McKinsey 2025).
Reaching production remains just as hard. Gartner projected that at least 30% of generative AI projects would be abandoned after proof of concept by the end of 2025 (Gartner, July 2024). IDC, together with Lenovo, was harsher about the scaling bottleneck: 88% of AI POCs don't advance to deployment at scale (IDC/Lenovo, 2025). And when Gartner looked at who actually made it across, the picture was stark: only 1 in 9 companies runs AI in production (Gartner, 2026). The other eight are stuck in the same pilot cycle.
The cost of stalling halfway isn't abstract, and it has been measured. An S&P Global Market Intelligence survey of more than a thousand companies showed that 42% abandoned at least one AI initiative in 2025 (S&P Global, reported by CIO Dive, 2025). The average cost per abandoned initiative was US$7.2 million, and the median time to abandonment was 11 months (S&P Global, 2025), which means almost a year of team time, budget and leadership attention consumed before the numbers come out negative.
Add up the effects. The pressure to invest is high, and the return is slow to show up. Deloitte found that 72% of CIOs invested in AI over the past year without being able to show measurable P&L impact (Deloitte, 2025). The money goes in, the pilot runs, the result doesn't arrive, and next quarter's report repeats the promise.
The honest reading of these numbers, for a CDO, is that the market's bottleneck lies in the crossing far more than in adoption. Almost everyone is already in, but crossing into production is still rare, and the average, precisely because it's an average, ends up stuck on this side of the chasm.
Trust in data is what holds production back the most
The adoption and abandonment numbers explain the "how much". The most useful data point for a CDO explains the "why", and it points straight at your territory.
The 2026 AI Index reports the barrier organizations cite most: 74% name model inaccuracy as their number 1 risk, up 14 points from the previous year (Stanford HAI, 2026). As AI leaves the experiment and touches real decisions, tolerance for error collapses and distrust rises.
This is the kind of problem you don't solve by switching models. Perceived inaccuracy comes from messy data, poorly mapped processes and a lack of checks before the answer reaches the end user. It's a matter of data quality and governance, exactly a Chief Data Officer's territory. In a pilot environment, nobody depends on the result, so an approximate answer gets a pass. When the system goes into production and someone starts depending on what it delivers, tolerance for error drops and distrust shows up. That boundary, between pilot and production, is largely the boundary of trust in data.
How to compare yourself honestly: the 5-stage scale
A benchmark is only useful if the scale measures what the company can really do. Counting how many areas "use AI" only measures activity, which is why almost every company looks good on that cut. The scale that truly separates is maturity by stages, because it asks how deeply AI is woven into the business: into data, processes, decisions and culture.
We detail this scale in The 5 stages of AI maturity: where your company stands. Here, the point is to use it as a comparison tool. A summary of each stage, with the observable signal that places you there:
- Stage 1, Curiosity: people use AI tools on their own, with no policy and no coordination. Nobody in the company knows how many tools are in use, or with what data.
- Stage 2, Experimentation: the company tests on purpose, with pilots and proofs of concept. There are isolated wins, but nothing critical in production that a customer or process depends on.
- Stage 3, Integration: at least one AI system runs in production that day-to-day work depends on, connected to real data. If it goes down, someone complains.
- Stage 4, Scale: the company runs AI repeatably, with governance and measured ROI. When a new use case comes up, the foundation is already in place.
- Stage 5, Transformation: part of the revenue or business model depends on AI solutions competitors can't copy quickly.
Cross the scale with the market numbers and the benchmark comes into focus. If universal adoption at 88% (Stanford HAI, 2026) corresponds to "someone uses AI in some area", that's Stage 1 or 2. If only 1 in 9 companies runs AI in production (Gartner, 2026), Stage 3 is already a minority. Scale and Transformation, stages 4 and 5, are the exception within the exception.
The honesty of the comparison comes down to a simple rule: the highest stage where you can frankly answer "yes" is where you are, not the highest stage you've invested in. Companies declare themselves at Stage 4 because they bought Stage 4 tools, while operating at Stage 2. A true benchmark ignores what you bought and looks at what supports customers today.
Why being "average" means being stuck in pilot mode
The problem with the average doesn't show up on the slide. Since most of the market is stalled between experimentation and integration, the statistical average lands right at the bottom of the valley, still far from the exit. Tying with the average means tying with companies that didn't make it across either.
The difficulty of getting out of this valley isn't the technology, which accounts for the smaller part of the problem. A successful pilot needs a good idea and a demo that convinces the committee. Production at scale requires a different foundation: reliable data, integration with legacy systems, governance, security and people who trust the result enough to let the machine run. That's why Gartner saw at least 30% of GenAI projects abandoned after the POC (Gartner, Jul 2024) and IDC measured 88% of POCs failing to reach scale (IDC/Lenovo, 2025). The bottleneck is almost never the model, and almost always the data quality and governance that should be supporting the operation.
For the CDO, what this benchmark says in practice is simple. If your internal report says "we're at the industry average", the likely operational translation is: you're at Stage 2, with pilots running and little in production, and the cost of that position is what S&P measured: US$7.2 million per initiative you eventually abandon after a median of 11 months of trying (S&P Global, 2025). The average looks like a safe harbor until you see the bill.
There's a time effect that makes the math worse. While most companies tie at the average, the 1-in-9 minority running in production (Gartner, 2026) keeps accumulating what never shows up on an adoption slide: cleaner data because the systems demand it, governance that unlocks the next use case, and organizational trust that reduces friction on the following initiative. Every month that passes, the distance between those at the average and those ahead grows a little.
For a CDO, what the benchmark makes clear is that comparing yourself to the average is the wrong question. The average is stuck in pilot mode, and what counts is having a concrete plan to get out of it and keep going.
What to measure beyond position: the five fronts
Your stage tells you where you are. The five fronts below tell you what's holding you back, and that's where an honest diagnosis goes deeper, because a company can be advanced on one front and crawling on another.
- Strategy: is there clarity about where AI creates value, or are initiatives scattered, with each area pulling in a different direction?
- Data: is your data accessible and reliable, or locked in silos? This is the front most connected to the 74% who cite inaccuracy as their main risk (Stanford HAI, 2026).
- Governance: do the rules for autonomy and risk exist before the incident, or only after?
- Talent: does the team know how to build and run AI, or does it depend one hundred percent on vendors?
- Technology: does the infrastructure scale, or does every project start from scratch?
For a Chief Data Officer, data and governance tend to be the pair that most often brings operations down in Brazil. That's where a pilot that worked in a controlled environment starts to come apart when it meets day-to-day reality. Assessing maturity only by stage, without opening up these fronts, hides the real bottleneck.
Quick diagnosis: where you stand relative to the market
Answer in your head. The highest stage where you honestly say "yes" is your real position, and the right-hand column shows how the market is distributed there.
| Question | If "yes", you're at... | Where the market is |
|---|---|---|
| People use AI, but nobody controls what or with which data? | Stage 1: Curiosity | Most companies, within the 88% that "adopted" |
| You have pilots, but nothing critical in production? | Stage 2: Experimentation | The largest concentration in the market |
| At least one AI system runs in production that day-to-day work depends on? | Stage 3: Integration | About 1 in 9 (Gartner, 2026) |
| Launching a new use case reuses infrastructure and governance that already exist? | Stage 4: Scale | A minority within the minority |
| Part of your revenue or model only exists because of AI? | Stage 5: Transformation | A rare exception |
The rule for reading the benchmark: if your honest answer stops at "I have pilots, but nothing critical in production", you're exactly where the market average is. And being tied with that average means being parked at a point in the journey that consumes budget without changing the business.
Frequently asked questions
What is an AI maturity benchmark? It's a comparison of what your company can do with AI against what the market can do, measured by maturity stages rather than by how many areas use tools. It measures how deeply AI is integrated into data, processes, decisions and culture, and places you on a scale that runs from casual curiosity to business transformation.
How do I know if my company is above or below average in AI? Look at your stage, because that's what reveals what you can actually operate. The number of areas that adopted some tool only measures activity. With 88% of companies using AI in at least one function (Stanford HAI, 2026) and only 1 in 9 running it in production (Gartner, 2026), being "average" usually means being at stage 1 or 2, with pilots and little that a process or customer depends on. Above average starts at Stage 3.
Why is being at the market average in AI a bad thing? Because the average is stuck before production. Most companies are stalled in experimentation, so tying with the average means tying with those who didn't make it across. S&P Global measured the cost of this limbo: US$7.2 million per abandoned initiative, after a median of 11 months (S&P Global, 2025).
How many companies really have AI in production? Few. Gartner estimates that only 1 in 9 companies runs AI in production (Gartner, 2026), and IDC together with Lenovo measured that 88% of proofs of concept never reach deployment at scale (IDC/Lenovo, 2025). Getting started has become trivial; reaching production is still rare.
What holds back the move from pilot to production the most? Trust in data and the foundation that supports the operation. 74% of organizations cite model inaccuracy as their number 1 risk, up 14 points in a year (Stanford HAI, 2026), and that inaccuracy usually comes from messy data and poorly mapped processes, with the model itself accounting for a small slice of the problem. That's why the bottleneck shows up mainly in data and governance, and rarely in a lack of cutting-edge technology.
Conclusion: the next step isn't beating the average
An AI maturity benchmark is only useful when it measures what the company can operate. Measured by adoption, almost everyone looks good, and that illusion is exactly what keeps the market piled up before production. The numbers S&P, Gartner, Stanford HAI and IDC reported over the past 12 months converge on the same diagnosis: the market average has stalled at Stage 2, with real costs for anyone who stays there longer than they need to.
For a CDO, the right move isn't trying to beat the average, it's to stop using it as a reference. Find your real stage honestly, identify which front, probably data or governance, is blocking progress, and work one step at a time.
If you want an independent diagnosis of where your company stands relative to the market, without the bias of the green slide, that's exactly what the Sciensa AI Maturity Assessment delivers: an honest reading of your maturity across the technical, data, governance and business fronts, with a roadmap of what to prioritize to get out of the average and into production.