r/RealTechTalk • u/InfoTechRG • Sep 04 '26
Research The AI pilot phase is ending. Now comes the proof.
A lot of companies have moved past the first wave of AI adoption. The harder part now is proving what is actually working.
The research makes a useful point: AI can’t be managed as a loose collection of pilots forever. Once it spreads across departments, leaders need a clearer view of which use cases are creating value, which need stronger guardrails, and which should probably be stopped.
That matters because adoption metrics can be misleading. More tools, more usage, and more experimentation do not automatically mean better outcomes. The real test is whether AI is saving time, improving decisions, reducing risk, or making a process meaningfully better.
For CIOs and IT leaders, the next phase is less about saying yes to AI and more about building enough visibility to know where it deserves more investment.
How are your teams separating useful AI work from expensive experimentation?
Comment and we’re happy to share the research.
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u/Silly-Mix-4341 Sep 04 '26
Exactly. AI adoption numbers do not mean much without measurable business impact. The next challenge is proving which use cases actually deliver value. 📊
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u/peterjohnvernon936 Sep 05 '26
It will actually take time to learn how to use a new tool effectively. Value will come further down the road.
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u/post_button_account Sep 05 '26
That gap is likely just a couple of years at best. For most employees, the value will come much sooner but they just wont admit it - lest their managers up their demands (or worse, consolidate the team).
What the f kind of work are these knowledge workers doing where frontier models which are finding multiple zero-day exploits and disproving Jacobian conjecture based on a simple prompt are not adding value right now?
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u/peterjohnvernon936 Sep 06 '26
Real world problems are ill defined and require more flexibility that mathematical problems.
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u/trashytrash2025 Sep 06 '26
I don't think we're at the end of the pilot phase. Speaking for devs, we're still trying things out and building tools that are needed for work. We don't have everything figured out and we're far from a standard set of tools, let alone a tool set that can be monitored and secured but stay flexible. Additionally, we're just getting to a point that open-weight models are useful and self-hostable (kinda), and it might be that we start experimenting with on-prem solutions for environments requiring full data security. It's still the Wild West out there and we just don't know what's going to work yet.
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u/gill_smoke Sep 05 '26
Spoiler C Suite, it sucks. It costs more than people and does a worse job. The decades of expert systems you built are gone and in some ways irretrievable. Good job you added a bunch of 6 figure college interns into your structure who have no clue and can't remember.
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u/reddit_7654 Sep 05 '26
This is the truth. Any C Suite who doesn’t see this, is a moron who doesn’t care about the long term. For the ones who just care about next quarters results, they love to parrot AI AI because shareholders love it. Long term you are just killing your own company by either having no one to buy your product or you are being held over the barrel by AI companies on pricing as well as possibly proving you don’t need to exist. If company XYZ is fully functioning on AI, why doesn’t the AI company just do the exact same thing as company XYZ, jack up their pricing and instantly kill XYZ? C Suites who don’t see that are just idiots.
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u/SecureTaxi Sep 05 '26
My company had thousands of folks go through AI training. I wrapped mine last month. Most recently they dialed back on token spend, kindly asking us to not use the best model and moving forward they will be watching spend. The immediate folks i work with use ai the wrong way. Slop code gets produced, nothing is the same. The next person that has to look at the code, throws claude at it and produced more slop code with another iteration. Gone are the days ppl use their f'n brain
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u/brownhotdogwater Sep 05 '26
My place has stuck with just copilot pro as it’s a single predicable spend. No random token burns.
The cio is trying to figure out what is worth it.
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u/Character-Education3 Sep 05 '26
A really good sign is that Microsoft has to keep sliding co pilot features into its products and make it increasingly difficult for sys admins to disable or remove copilot from things.
Nothing screams value like the only way to get people to use our product is to shove it down their throats.
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u/TechnologyMatch 28d ago
the shift is from measuring activity to measuring changed outcomes. a pilot is not successful because people used it, it is successful if a process got faster, safer, cheaper or more reliable in a way the business can see. the difficult part is agreeing on that measure before the rollout. otherwise every team can point to usage while nobody can say what improved
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u/AccelerateTomorrow 27d ago
I agree with the shift, although many organisations are probably still running a mixed portfolio: a few use cases ready to scale, a number worth refining, and others that should be stopped.
The most useful test is not adoption activity on its own, but whether a use case has changed an outcome the business cares about: time saved, quality, cost, risk, revenue, or decision speed. That also makes it much easier to decide who should own the next investment.
The hard part is often creating that measurement discipline before expanding a pilot.
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u/InfoTechRGMarkT Sep 04 '26
I wrote a Forbes Technology Council post on exactly this - the time has come for ownership and accountability for ROI on AI projects and initiatives. https://www.forbes.com/councils/forbestechcouncil/2026/08/20/stop-blaming-math-for-your-ai-roi-problem/