Insights

Short reads for leaders

Short articles on enterprise coherence and AI transformation, written for executives. Each one is also shared on LinkedIn.

Enterprise coherence

Why most companies still see no AI impact

The technology is rarely the problem. The system it lands in usually is.

26 October 2026 · 2 min read Process reinvention

Reduce friction before you accelerate

Most cycle time is waiting, not working. Measure that first.

26 October 2026 · 2 min read Governed autonomy

Who decides when an agent acts?

Owning the model is not the same as owning the decision.

26 October 2026 · 2 min read
Enterprise coherence

Why most companies still see no AI impact

26 October 2026 · 2 min read · Erlend Hollebosch

Almost every large organization now uses generative AI. Yet fewer than one in five reports a material effect on earnings. The usual explanation is that the technology is not mature enough. In most cases, that is not the problem.

AI lands in an existing system. In many organizations, that system is fragmented: decisions wait for several committees, work changes hands many times, and every incident adds another layer of control. A copilot makes individual tasks faster, but the case still waits in the same queues.

Agents raise the stakes. They do not just assist, they act. In a coherent organization they amplify value. In a fragmented one they amplify friction, at machine speed.

The first question for an executive is therefore not which AI tool to buy. It is: where does our work lose coherence today, and what will happen when we accelerate it?

Share on LinkedInRead chapter 02: The coherence lens
Process reinvention

Reduce friction before you accelerate

26 October 2026 · 2 min read · Erlend Hollebosch

Most AI business cases measure the time a task takes. But in most processes, the task itself is a small part of the cycle time. The rest is waiting: for information, for an approval, for the next team.

Before you add agents, measure four things for one process: how long a decision takes, how often a case changes hands, which share of the cycle time is pure waiting, and how much work is done twice.

Those numbers show where the value really sits. Remove a handoff, give one person ownership of the whole flow, make an approval proportional to the risk. Often that alone shortens the cycle time noticeably, before any AI is involved.

Then add agents to a process that is ready for speed. That is the difference between automating the old process and designing a better one.

Share on LinkedInTry it in the value simulator
Governed autonomy

Who decides when an agent acts?

26 October 2026 · 2 min read · Erlend Hollebosch

As soon as an agent can act on its own, a new question appears: who is accountable for what it does? Many organizations assume that whoever owns the model also owns the decision. That is a dangerous shortcut.

Settle five decision rights before an agent goes live: who approves its deployment, who monitors its behavior, who can stop it, who owns the handoff between teams, and who is accountable for the business outcome.

The answers can differ by risk. Low-risk agents can act within clear boundaries. For critical decisions, the agent recommends and a person decides.

One principle does not change: final accountability stays singular and human. Autonomy works when everyone knows who decides.

Share on LinkedInRead chapter 07: Decision rights for every agent
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