When One Person's Brain Becomes Your Company's Infrastructure

Can we implement an AI workflow if my work team all use the same shared excel sheet?

How do I identify undocumented processes and knowledge gaps before introducing an AI solution?

How do I tell which process or team to start with when implementing AI?

AI implementation is partly a knowledge audit.

In the discovery phase of a recent nonprofit project, we didn't encounter technical constraints or legal issues. Instead, we found a classic organizational problem: key-person dependency. All of us have worked with this person before.

Years of workarounds had turned one person's undocumented judgment into part of the organization's operating system. She couldn't adequately explain her process because she'd never needed to. One person's brain had become infrastructure.

Research suggests this isn't necessarily the key person's fault. Researchers studying what they call the "key people paradox" found a self-reinforcing cycle: key people accumulate knowledge over years, become indispensable, carry more responsibility, have difficulty relinquishing control, and eventually become too overloaded to develop successors or transfer what they know. (1)

I'm seeing versions of this in a lot of AI work. Sometimes it's a person. Sometimes it's the familiar Excel workbook that someone built six years ago, that everyone still uses, and that three other systems now depend on. Sometimes it's a workflow that's partially automated but still requires a human to keep it moving.

These systems work. That's exactly why they survive.

But they raise another question as organizations introduce AI: does the organization actually possess its knowledge in a form that anyone other than the humans carrying it around can use? A 2026 paper on human-AI decision-making describes organizational knowledge as fragmented across software systems, manual documents and tacit human expertise, much of it originally designed for human consumption. (2)

And that's what makes changing them difficult. Introducing AI isn't just a technology decision. It can expose years of workarounds, dependencies and undocumented decisions. Change the workflow and someone may worry about losing control, getting buried in additional work, or breaking something the organization depends on.

That makes process discovery part of AI implementation, not something that happens before it.

A 2026 McKinsey and EuroCommerce survey of European retail executives found that 48% named an organizational issue as the biggest thing stopping AI from scaling, such as process redesign or unclear decision rights, compared with 42% who named a technical one. The report's conclusion is blunt: AI usually stalls because of change management, outdated workflows and missing skills, not because of the algorithms. (3)

The technology may actually be the easier part. But I still find the human element more rewarding. That's where you discover how the work really gets done, and what AI actually needs to make better.

Sources

(1) Findlay-King, L. (2026). The key people paradox: human capital vulnerability in smaller voluntary sport organisations. Voluntary Sector Review. https://doi.org/10.1332/20408056Y2026D000000090

(2) Marx, A. S. R., Avelino, R. M., Netland, T., & El-Assady, M. (2026). Do we have the knowledge we need? Rethinking human-AI decision-making in corporations. arXiv. https://arxiv.org/abs/2606.15575

(3) McKinsey & Company and EuroCommerce (2026, June 10). Rewiring retail in Europe: The AI imperative. Survey of 36 European retail executives. https://www.mckinsey.de/industries/retail/our-insights/rewiring-retail-in-europe-the-ai-imperati