Enterprise AI Part 5

In this week’s Data Diaries, we move from last week’s inference-hub architecture to the question that decides whether any of those controls pay off: who does what work, and where does the human judgment go. Last week we asked where your data lives. This week we ask where your work lives. This is Enterprise AI part 5.

Executive function as it relates to AI, especially agentic AI, comes down to four things: planning, organization, decision-making, and problem-solving. We call those four capabilities POD — plan, organize, decide, solve. McKinsey’s 2024 analysis puts numbers on the pressure. Emerging technologies could automate up to 60% of current tasks, while roughly 9% of workers may change occupations by 2030.

Read that carefully — AI recomposes work at the task level, not the job level. Penn’s lived-experience version makes the same math concrete. The templated tasks inside a job move to machines; the non-templated tasks stay with the human who can still plan, organize, decide, and solve. If it is a template today, a machine does it tomorrow.

So what happens when leaders confuse task redesign with headcount reduction? They de-skill the workforce and starve their own agentic systems at the same time. If you hand planning, organizing, deciding, or solving to a machine, you diminish your own skills and stop being as effective a worker.

The institutional knowledge problem then compounds the damage. Documented corporate memory captures successes; the failures live in mid-career human heads, and AI systems that learn only from the corporate record inherit a survivor bias no amount of retraining will erase.

The honesty test belongs here. I’m not fond of just tossing people overboard to increase your earnings an extra 0.1%, and the cost surfaces later as agentic systems that confidently repeat mistakes nobody alive remembers correcting.

EU markets add a deployment-blocker layer. The Hamburg ruling we covered last week shows how fast a workforce move turns into a courtroom problem, §87 of the German Works Constitution Act (BetrVG) grants works councils co-determination over algorithmic management, and the EU Platform Work Directive transposes by December 2, 2026. Skip the council conversation and you stop your own rollout cold.

Now what do you actually do? Hand off the templated stuff to machines and have people double down on the non-templated stuff. The build/buy/borrow/bot framework still gives you the procurement options.

Build means upskilling your developers, analysts, and domain experts. Buy means hiring scarce specialists — AI security leads, platform architects, risk officers — when speed beats cost. Borrow means partnering with integrators for accelerants like ISO 42001 certification, then pulling the capability back in-house. Bot means automating the clearly templated work — a candidate task for AI under any honest TRIPS assessment.

Tier matters. If you run a small agency, bot heavily on internal templated work and productize the non-templated judgment work that enterprises now need to buy. Mid-market leaders should borrow managed services for capabilities they cannot build, then bot the templated functions where the TRIPS score runs highest.

At enterprise scale, tie every build/buy decision to a TRIPS score, route major deployments through an AI Council that includes a ride-along reviewer, and engage the works council before deployment in any EU market. AI literacy carries the legal weight. EU AI Act Article 4 made AI literacy a duty for providers and deployers on February 2, 2025, and role-based curricula plus documented practice — not e-learning videos — satisfy it.

Measure outcomes, not output volume. Reward the employee who resolves more issues at higher quality, not the one who pushes out more emails.

Next week we move to where your compute actually lives — the power, hardware, and geopolitics that bound every AI program.

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