The general consensus is that AI can enhance performance and productivity.

What you won’t hear from the hype merchants is that the process of reimagining critical workflows and then executing change is hard, which is why transformation and adoption matter as much as the technology itself.

Secondly, AI can increase cognitive load.

Lisanne Bainbridge’s seminal Ironies of Automation published in 1983 observed that when users must evaluate uncertain outputs from automated systems, mental effort often goes up.

This ‘automation bias paradox’ means that while automation can reduce physical effort, it may increase mental effort especially when trust in its accuracy is shaky.

Work must be deliberately designed so that humans have clarity about processes, the decision-making logic, and their role in oversight and intervention.

It’s no secret AI is advancing at lightning speed. It’s transforming industries, streamlining workflows, and creating new economic value.

But beneath the excitement lies a problem few acknowledge: AI doesn’t know when it’s wrong.

Unlike a junior analyst who double-checks a figure or an executive who updates their position based on new evidence, AI frontier models such as ChatGPT lack a real-time feedback loop.

When they ‘hallucinate’ or produce incorrect data, they don’t flag it, correct it, or learn from it immediately.

The error simply remains.

Even the most advanced models today including GPT-5 operate as static knowledge systems, frozen snapshots from their last training cycle.

Here’s how they improve:

  • User feedback: Errors are flagged and stored for later review.
  • Data review: Human engineers analyse those issues, identify patterns, and find better examples.
  • Model updates: A new model version is eventually released with corrections embedded.
  • Human feedback loops: Reinforcement-learning techniques encourage the model to favour helpful and accurate responses.
  • External tools: Web search or plug-ins can help reduce reliance on outdated internal knowledge.

In practice, this means AI doesn’t fix itself mid-sentence, mid-task, or even mid-quarter.It requires human judgement to detect, contextualise, and correct its output.

In high stakes environments like finance, medicine, or energy, that gap is more than inconvenient – it can be dangerous.

The real risk isn’t just that AI is sometimes wrong.

It’s that it can be confidently wrong.

AI-generated outputs often sound authoritative whether they’re true or not.

Unlike a human expert who might say, “I’m not sure” or “Let me check that,” AI rarely offers a disclaimer unless prompted.

This can create false confidence in flawed outputs particularly when used by teams without deep domain knowledge.

For executives and directors, this introduces a governance challenge: How do you ensure oversight when the technology is convincing but unaccountable?

I see many organisations eager to deploy AI but underestimating the importance of sharpening the axe before they cut.

Early conversations often revolve around cost. But the true value of AI lies not in automation, but in augmented decision-making which demands human review of AI-generated insights.

We don’t simply drop AI tools into workflows.

We define use cases linked to enterprise strategy with defined value, technical feasibility and map judgement points to identify where human input is essential.

The future of AI is judgement, not just speed

There’s a growing narrative that AI will soon replace humans. This overlooks the nuance of real-world decision making and ignores the reality that boards have a defined risk appetite.

No board wants autonomous agents running unchecked through their operations.

Instead, leaders must think carefully about which parts of a task AI should handle, and how that aligns with their critical business objectives.

Data is only half the story; the other half is context, values, and consequences. AI can support the first, but only humans bring the second and third.

The most powerful AI systems in the years ahead won’t be the fastest or most eloquent.

They’ll be those deployed alongside human professionals who understand their limits and know when to challenge them.

Ironically, the very fact that AI doesn’t clean up its own mistakes may be what saves us. It forces us to remember that critical thinking, not automation, is the cornerstone of sound decisions.

AI is a remarkable tool. It can lift productivity, reduce routine administration, and unlock insights at scale.

But it is not infallible, and it is not self-correcting.

For executives, boards, and frontline staff, the message is simple: don’t rely; review. Build custom AI into the heart of your operations workflows but build in human oversight.

Without this, you risk accelerating in the wrong direction.

It’s time to move past novelty and design for reliability to delight the markets and employees you already serve.

Because in the AI era, it’s not just what’s generated that matters – it’s what’s understood, reviewed, and trusted.