From Striker to Goalkeeper: How GenAI Is Changing My Role in Consulting
A friend recently offered me a metaphor that perfectly captures how GenAI is reshaping my work in economic consulting.
He said:
“You used to play striker. Now you’re goalkeeper.”
For most of my career, my consulting projects have started with a blank page.
Clients come to me with a complex, ill-defined problem — a water security challenge, a regulatory submission, an investment case that doesn’t yet have a story.
My role has been to frame the problem, define the economic logic, and build the structure to builds on and recommend from the technical and financial evidence. In other words, I’ve been the one running up the field to score the first goal.
The Blank Page Is Gone
That process is changing. When I start now, it’s no longer a blank page. My first move is to open a GenAI chat window and begin laying out the problem — narrating my thinking as a kind of stream-of-consciousness transcript.
I describe what the client is trying to achieve, what the constraints are, and what the shape of the solution might look like — not the solution itself, but the field of play it will occupy.
The AI helps translate that loose reasoning into an early working draft: structured, articulate, and coherent. It’s not a finished product, but it’s a powerful scaffold. It accelerates the start, forcing clarity around assumptions and dependencies, and gives everyone — economists, engineers, policy analysts, and client teams — a tangible place to begin.
My Role Has Shifted
That change in starting point means my role has shifted too. I’m still the one defining the problem, but instead of playing striker — driving everything forward from the front — I’ve become more of a goalkeeper.
My job now is to keep the bad ideas out.
To filter out reasoning that doesn’t hold, to spot gaps in evidence, to guard the intellectual rigour that sits behind every business case or regulatory model.
The work is not in producing the first draft anymore. It’s in discerning — knowing what to trust, what to test, and what to throw away. It’s in recognising when a line of reasoning is clever but wrong.
Why This Is Ideal for Clients
From a client’s perspective, this shift is mostly good news.
Faster clarity.
The early phase of a project — where we frame the problem, align definitions, and scope the analysis — can take weeks. With GenAI, that framing happens in hours. Clients see an initial structure much sooner, which makes it easier to test assumptions, see trade-offs, and refine direction before we invest heavily in modelling or drafting.
More disciplined thinking.
AI forces precision. It mirrors back gaps and inconsistencies instantly. That helps us sharpen reasoning earlier and spend more of the client’s budget on substance rather than structure. The output becomes more coherent, less dependent on who can “write well,” and more focused on the actual economics and logic of the case.
Better use of expertise.
By removing much of the blank-page labour, the client’s money goes where it adds value — into expert judgment, stakeholder negotiation, and rigorous technical work. The AI scaffolds the communication; the people do the real problem-solving.
Transparency of reasoning.
Because the early AI drafts are effectively a transcript of my thinking, clients can see the logic unfold in real time. They get visibility on how assumptions evolve, which makes collaboration smoother and decisions more defensible.
The Risks and Boundaries
But the model has limits — and ignoring them risks both quality and trust.
The illusion of completeness.
AI-generated drafts often sound authoritative. That can tempt everyone — consultant and client alike — to move too quickly from “first framing” to “final logic.” It takes discipline to remember that what looks polished may still be wrong.
Loss of originality.
When every consultant starts from a similar AI-generated base, there’s a risk of convergence — of everyone solving problems in the same way. Strabo Rivers’ edge has always been our ability to see problems differently. Maintaining that distinctiveness requires constant vigilance.
It also means really leaning on the technical specialists — hydrologists, engineers, environmental scientists, and financial modellers — to provide deep, evidence-driven insights. We cannot replace that with AI. These insights are what ground the logic of a business case in reality and keep it from becoming a purely narrative exercise - skiming across the surface.
Data security and confidentiality.
GenAI tools must be used carefully within strict client and government confidentiality constraints. Not all information belongs in an AI prompt, and our responsibility is to protect client data as rigorously as we protect our own analysis.
Erosion of human trust.
Ultimately, clients hire Strabo Rivers for judgment. If they sense that AI is making the calls rather than supporting the thinking, that trust can erode quickly. Clear disclosure and thoughtful use are essential to avoid that.
The Balance
Used well, GenAI amplifies what clients already value: clarity, speed, and analytical depth. Used carelessly, it risks producing elegant nonsense.
At Strabo Rivers, our aim is to sit firmly on the right side of that line — combining human judgment and economic insight with the power of new tools, to produce sharper, faster, and more resilient solutions.