The failure mode nobody sees coming — and the three questions that surface it before it costs you.

This episode examines scope drift — the process by which an AI system gradually takes on work beyond its original validated remit, without any single deliberate decision causing the expansion. The argument draws on a pattern observed across enterprise AI deployments: systems built on large language models will attempt tasks they were not designed for, and the resulting degradation in reliability is typically gradual and invisible rather than sudden and obvious.

The claim that context quality matters more than context size is supported by research published in twenty twenty-five and twenty twenty-six on large language model behaviour under varied input conditions. The framing around structured human oversight at defined intervention points draws on the Singapore Consensus on Global AI Safety Research Priorities, published in twenty twenty-six. The figure on organisational AI adoption and scaling comes from McKinsey's State of AI survey, published in twenty twenty-five.

The three governance practices described in the episode — a maintained remit document, periodic usage reviews, and a defined escalation path — are not presented as a complete methodology. They are a starting point for teams that currently have no structured process for detecting when a system's scope has expanded beyond its design. The episode deliberately avoids specifying implementation details, because the right approach will vary considerably depending on the system, the organisation, and the risk profile of the decisions involved.

The core takeaway is a management question rather than a technical one: not whether your AI system was built well, but whether the task it is actually performing today bears a reasonable resemblance to the task it was designed and validated for. For most organisations that deployed AI systems in twenty twenty-three or twenty twenty-four, the honest answer is that nobody has checked.

Chapters

• 0:08 — Cold open
• 0:46 — Why the system never says no
• 2:19 — The signals that are easy to miss
• 3:58 — What governance actually looks like in practice
• 5:39 — Three questions to bring back to your team
• 6:58 — Close

Read the written version · Transcript

Each episode is written from that week's Coinmedia insight and voiced with a synthetic model of Zsófia's own voice. The thinking, the editorial line and the approval to publish are human.

Your AI system reasons confidently from whatever it's given — the problem is deciding what that actually is.

This episode is based on Coinmedia's insight piece 'What Your AI System Can See — and What It Cannot', published in September twenty twenty-six. The piece examines the practical consequences of information boundaries in deployed AI systems — not as a technical problem but as a governance and design problem that sits with the leadership teams running those systems.

The statistic about two thirds of senior technology executives being accountable for AI systems they cannot realistically supervise, and the figure of fifty-four AI agent incidents per organisation per year, comes from a global study of senior technology executives published by IBM. The episode cites these figures as the article presents them; the original research is referenced in the article itself.

The finding that large language models attend more reliably to information at the beginning and end of long inputs — sometimes called the 'lost in the middle' effect — has been documented in published academic research on model behaviour with extended context windows. The episode does not name specific papers; the article from which this episode is drawn does not cite them by name either, and the episode does not go beyond what the source supports.

The three visibility gaps named in the episode — the recency gap, the tacit knowledge gap, and the organisational boundary gap — are framing developed in the Coinmedia article from observed patterns across operational and advisory contexts, not from a single external study. The five questions at the end of the episode are drawn directly from the article's closing section and are intended as prompts for internal conversation, not as a technical audit framework.

Chapters

• 0:08 — Cold open
• 0:51 — Why the boundary that matters most goes undrawn
• 1:50 — Why making the system bigger does not fix this
• 3:16 — Three gaps that appear again and again
• 5:02 — The real danger is misplaced confidence
• 6:17 — What deliberate visibility design actually looks like
• 8:25 — Close

Read the written version · Transcript

Each episode is written from that week's Coinmedia insight and voiced with a synthetic model of Zsófia's own voice. The thinking, the editorial line and the approval to publish are human.

The AI system impressed everyone in the pilot. So why is it sitting unused six months later?

This episode is based on the Coinmedia article “Why Your AI Agent Works in the Demo and Fails in Production”, linked above. It draws on research published by Gartner in May 2026, and on data from Forrester, Anaconda and BCG published in 2026.

The Gartner prediction referenced in the episode: by 2027, 40 per cent of enterprises will decommission AI agents because their teams never distinguished between an agent's capacity to act and the scope of access it should have been granted. The 88 per cent pilot-to-production failure figure comes from Forrester and Anaconda's 2026 joint research.

The three-tier authority model discussed in the episode is a practical framework for leadership teams, not a technical implementation guide. The point is to prompt the governance conversation before a deployment is committed to, rather than after it has stalled.

Chapters

• 0:08 — Cold open
• 0:55 — Why pilots always look better than production
• 2:31 — The structural mistake underneath the failure rate
• 3:30 — A mental model: three tiers of authority
• 5:16 — What the organisations that get it right actually do
• 6:52 — Close

Read the written version · Transcript

Each episode is written from that week's Coinmedia insight and voiced with a synthetic model of Zsófia's own voice. The thinking, the editorial line and the approval to publish are human.