AI Issue Space& PTSD.

01 / The starting point

Start with a familiar complaint

“We need more AI talent.”

That sounds like a diagnosis. It also points straight toward a solution: hire people, buy training, or bring in consultants.

But what does “AI talent” mean here? Who is struggling to do what, in which workflow, and under which constraints?

A team may lack technical expertise. It may also lack access to usable data, a clear business objective, time from domain experts, or agreement about who can approve changes. Hiring another specialist could help. The same specialist could also inherit the same unresolved difficulties.

Before committing to a remedy, we need to unpack the complaint.

02 / The issue space

Six lenses for looking more closely

The AI Issue Space grew out of my research on AI transformation and organizational competences. It offers six lenses through which to examine a situation:

1. 01

   Domain-specific aspects

   Does the team understand the work, its context, and what a useful result would look like?

2. 02

   Technical and technological aspects

   Are data, infrastructure, integration, or security constraints getting in the way?

3. 03

   Managerial leadership

   Are objectives, resources, responsibilities, and leadership support clear enough to act?

4. 04

   Organizational intelligence

   Can the organization assess its capabilities, learn from experience, and adapt its routines?

5. 05

   Relationships and networking

   Can people, teams, and external partners coordinate effectively, including around human–AI interaction?

6. 06

   Ethical and wider impacts

   Whose work, agency, opportunities, or rights could be affected, and how?

One complaint can span several lenses. “We need more AI talent” might reveal a connection between domain knowledge, learning routines, and coordination. The useful question becomes more specific: How can we bring technical and domain expertise together in the actual workflow?

You can refine each lens for your own context. Their value lies in helping you notice relevant questions and connections. The map remains open to issues it has not yet captured.

Looking through six lenses does not require giving each one equal attention.

In our talent example, suppose specialists are already available, but nobody has defined which task the AI system should improve. Clarifying the objective may deserve attention before expanding the team.

That priority is a judgment about this situation. It needs a reason, evidence, and a willingness to be challenged. The loudest complaint is a starting point for inquiry; it may not identify the most consequential constraint.

A question to carry with youWhat deserves attention first, and what supports that judgment?

Every allocation of attention and resources has consequences.

Bringing in an external specialist might accelerate delivery while leaving less expertise inside the organization. Asking domain experts to support an AI project might improve its relevance while reducing the time available for their existing work.

These consequences can fall on different people. A gain recorded in one department may create additional work in another.

A question to carry with youWhat do we gain, what might we lose, and who carries the consequences?

The dimensions interact. Our interpretations of those interactions also differ.

An engineer may hear “talent shortage” and think of missing technical skills. A manager may see slow delivery. A domain expert may see a project that has misunderstood the work. Each explanation directs attention toward different evidence and different remedies.

This is why sensemaking matters. Put those explanations into conversation. Make assumptions visible. Explore how one difficulty may be producing or reinforcing another, and identify what evidence would help distinguish competing accounts.

Agreement can help, but a clearly stated disagreement is useful too: it tells us what we still need to investigate.

A question to carry with youHow do these issues shape one another, and why do we explain them differently?

The situation will move, and our interpretation needs to move with it.

A pilot may initially depend on a few specialists working closely together. As it expands, coordination, staff learning, and responsibility for outcomes may become more pressing. A decision that made sense during experimentation may need revisiting in everyday operations.

Make that revisiting intentional. Changes in scope, data, participants, or observed outcomes can provide reasons to reopen the discussion. Set a review point before the original diagnosis becomes an unquestioned story.

A question to carry with youWhat change would make us reconsider our priorities, trade-offs, and explanations?

Put it into practice

Take it back to the map

Choose one sentence people keep repeating about your AI project. Walk it through the six lenses. Then use PTSD to discuss what matters now, what your choices imply, how you understand the connections, and when to think again.

Leave with a sharper question, an assumption to check, or a small next action whose consequences you can observe.

That is the habit I hope this map encourages: enough curiosity to look beyond the first explanation, and enough humility to redraw the picture.