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OpenAI pushes “practical adoption” focus for 2026 as user frustration with AI performance grows

ended 21. January 2026

OpenAI has signaled a strategic shift from flashy demos toward making artificial intelligence genuinely useful in everyday life, the company said in a blog post outlining its progress and goals for 2026. The update comes as many users grow weary of AI systems that demand extensive correction rather than delivering fast, reliable results.

OpenAI reported that its annualised revenue is estimated to have exceeded $20 billion in 2025, more than three times its 2024 figure, as demand for its AI services continues to surge and use moves from experimentation into daily workflows across research, learning, work, wellbeing and decision support.

Despite AI's rapid adoption and impressive revenue growth, user feedback from lived experience reveals a significant disconnect between expectation and reality. An independent survey from transcription company rev.com also reported widespread user frustration

  • 42 % of users say AI produces inaccurate or misleading content frequently enough to be a real problem,
  • heavy users are three times more likely to encounter hallucinations and far more likely to lose time rewriting prompts and fact‑checking to get acceptable outputs.

This frustration, often called ‘AI fatigue’,  is more than just an emotional response. It's an increasing burden on professionals who rely on AI for critical tasks, as they spend more time correcting AI mistakes than trusting the tools to perform accurately. This is shaping OpenAI’s 2026 agenda. 

The company says its priority will be narrowing the gap between what AI can do and how it is actually performing for end users especially in areas like healthcare, science, and enterprise, where meaningful accuracy and reliability matter most.

We want your views:

  • Thinking about your industry, which specific tasks fall short in enhancing human productivity?
  • Has an AI mistake hurt a peer’s credibility or brand? What was at stake? What was the outcome?
  • Do you worry about missing AI hallucinations? How much extra time do you spend on fixes? Do you over-correct more than necessary “just to be sure”? How much time is wasted doing that?
  • How would your organisation handle an AI error that damages trust? Do you have a policy to correct it and limit reputational damage?

As OpenAI pushes for progress, the real question is: Can AI truly augment human decision-making, or will we end up spending more time fixing it than trusting it?

5 responses from the Newspage community

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Colette Mason
AI Ethics Consultant at Clever Clogs AI
There is no silver bullet that the tech world can fire. OpenAI and other developers are experimenting with multiple ways to reduce these hallucination errors. One approach is to ground models in external, validated, up‑to‑date sources so answers are tied to facts rather than old patterns in general training data. Another is to use ongoing human feedback to guide models toward more reliable outputs, but this raises concerns around user consent and data security.

These measures can reduce but not eliminate inaccurate responses and make AI more dependable in practice. Even with better grounding and firmer guardrails, current systems will still produce plausible but incorrect answers, meaning human oversight and verification will remain necessary for the foreseeable future.
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Kate Underwood
Founder & Chief People Strategist at Kate Underwood HR and Training
AI is brilliant until it makes up “the law” and you’re the one paying for it. From a small business owner seat, AI fatigue isn’t feelings. It’s risk. I don’t have a legal team on standby, so if a tool hallucinates an HR rule and I copy-paste it into a contract, disciplinary letter or redundancy chat, that can turn into a claim, a payout, and a reputation wobble. Where it falls short? UK people-stuff that needs to be bang on: holiday pay, sickness, maternity rights, reasonable adjustments, even “nice” wording that actually reads like a threat. One wrong line and your team stops trusting you. Yes, I worry about missing mistakes. So I double-check, then rewrite “just in case”. The time-saver becomes a time thief.
Use AI for a first draft only. No staff personal data in. And nothing goes out unless you can back it with ACAS and your own policies. If you couldn’t defend it under pressure, don’t send it.
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Patricia McGirr
Founder at Repossession Rescue Network
AI fatigue is not about novelty wearing off, it’s about trust being quietly drained. OpenAI’s shift from demos to usefulness is overdue. For consumers and professionals alike, the real cost of AI today is not subscription fees, it’s the unpaid labour of checking, correcting and second-guessing outputs. When people spend more time fixing mistakes than acting on answers, productivity claims collapse. In high-stakes areas like healthcare, finance and decision support, accuracy is not a feature, it’s governance. Hallucinations are not harmless quirks. They damage credibility, slow judgement and shift risk onto the user with no clear accountability. OpenAI can grow revenue fast, but trust grows slowly. If AI requires constant supervision to be safe, it isn’t augmenting human judgement, it’s outsourcing risk to the very people it claims to help.
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Tracey Cole NLP
In this fast-paced world, people are turning to AI for stress management and coaching, listing its convenience and chat format as time-saving and efficient. You can log on in a break and receive an answer worthy of any psychology text. However, therein lies the problem. AI quickly jumps onto your own perspective and takes the lead from you, it is neither nuanced nor personalised. It may give the user the quick dopamine fix they seek, but you can almost guarantee they'll need to be back to their AI bot the next day - unlike professional coaching and therapy, which can listen to your intonations, use a sensory acuity to calibrate your body language and linguistically call into conversation your choice of words. AI falls short in all these regards.
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Rohit Parmar-Mistry
Founder at Pattrn Data
I’ve spent over a decade implementing automation in the real world, and the fatigue OpenAI describes is entirely self-inflicted. Businesses have been treating AI like a senior consultant when, in reality, it behaves like an overconfident junior intern, eager to please, but prone to hallucinating facts just to finish the task.

The productivity gains we were promised are currently being eaten alive by a "verification tax." When professionals have to spend hours fact-checking an AI’s work, they haven't automated a process; they’ve simply shifted their job description from creator to editor-in-chief of a liar.

Real-world implementation isn't about what the model can do in a flashy demo; it's about trust. If I have to check your work three times, I might as well do it myself. Until AI stops guessing and starts serving, it remains a liability for serious enterprise. We need tools that work for us, not homework we have to mark.