DIGITAL STRATEGY & CONSULTING
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Traditional business cases are built on a simple assumption about time.
A problem is defined, a solution is approved, and outcomes are measured against original intent. That logic holds when execution is linear and change is slow enough to manage through formal checkpoints.
AI doesn’t operate that way.
Once a system is in motion, the most important decisions have already been made, defining what the organization will remain accountable for as conditions change.
Learning happens during execution. Models update while initiatives are still underway. Inputs shift. Teams adjust prompts, data sources, thresholds, and workflows continuously. By the time leadership asks whether the investment is still worth it, the system being evaluated often no longer resembles the one that was approved.
When things look healthy, the instinct is to keep going. But health alone doesn’t explain intent. And intent is what matters when it’s time to decide whether to scale, extend, or walk away. That decision depends on knowing which outcomes were deliberately chosen, and which ones simply emerged.
In that context, a business case can’t function as a static artifact. It has to act as a constraint; something durable enough to hold while the system changes.
This is where value realization becomes essential. Not as a post-hoc explanation of results, but as a way to design economic intent that survives acceleration.
The problem emerges when leadership needs to make a second decision.
At that point, the organization can describe what the system is producing, but not what it is economically accountable to. Different teams surface different indicators. Efficiency, experience, throughput. None of those answers are wrong, but they aren’t sufficient to support a directional call.
This is where value realization starts to wash away because the organization can no longer explain why the current behavior still justifies continued investment (or under what conditions it wouldn’t).
Late-stage ROI doesn’t resolve that uncertainty. When you measure after the original intent has already blurred or shifted, the data doesn’t resolve the decision, it only creates more arguments. What’s missing is defensibility.
The bar for approval has moved, even if most organizations haven’t formally acknowledged it.
Not long ago, investment discussions centered on possibility. Could this open new revenue? Could it improve experience? Could it create strategic advantage? Optimism was a necessary ingredient in making progress.
Today, scrutiny is less interested in what might be possible and more concerned with what can be defended once the system is live. Leaders are being asked to stand behind decisions longer, in environments that change faster, with fewer opportunities to renegotiate intent after the fact.
This has created a tension that many organizations feel but rarely name.
Strategy still demands ambition. Finance still demands discipline. Neither side is wrong. The problem is that AI collapses the distance between aspiration and accountability.
When results surface before economic boundaries are clear, optimism starts to feel exposed. Success without explanation can create as much discomfort as failure. This is why defensibility has replaced excitement as the real requirement for approval.
Defensibility means being able to explain, in plain terms, why an investment made sense before outcomes appeared and why it continues to make sense as conditions evolve.
Optimism still matters. It just no longer carries the decision on its own.
Outcome metrics can confirm that something happened. They can even show improvement. What they can’t do is explain whether the system behaved in line with the assumptions that justified the investment in the first place or whether success arrived by a path no one intended to take.
AI doesn’t optimize toward a single outcome in a single way. It explores, compensates, and finds workarounds. Two initiatives can land on similar results while relying on very different dynamics. One may reinforce the original strategy. The other may quietly undermine it. Looking only at outcomes, they appear identical.
This is where leaders get stuck.
They’re asked to decide whether to scale, extend, or replicate an initiative, but the only evidence available explains what happened, not what was meant to happen. The organization can describe results, but it can’t defend intent.
A defensible business case exists to close that gap.
It establishes the economic logic that gives outcomes meaning — what value the system was designed to create, which signals indicate progress versus drift, and which tradeoffs were acceptable as learning unfolded.
Without that structure, justification becomes retrospective. Proof, in this environment, is the ability to explain why outcomes still justify the decision that produced them.
Most AI initiatives stall because teams make the right decisions at the wrong moments.
Early on, there’s pressure to be precise. Forecast the impact. Lock the use case. Commit to numbers that feel defensible in an approval meeting. This precision is comforting, but premature. It freezes assumptions before the system has had a chance to learn what actually matters.
Later, when learning has reshaped the system, accountability arrives. Leadership wants clarity on value, ownership, and outcomes. By then, the initiative has evolved through dozens of small adjustments that were never economically re-anchored. Precision shows up too early and accountability shows up too late.
User research is one of the few ways to keep economic intent from becoming theoretical. Interviews, usability testing, and behavioral analysis surface where value actually forms, where friction lives, and which “improvements” won’t matter because users won’t adopt them. Those signals should shape the economic assumptions up front.
AI initiatives often begin with strong beliefs about what users will do once the system is deployed. Research challenges those beliefs while change is still cheap, so learning starts in the right direction instead of finding value accidentally.
In practice, it clarifies three things early:
You can see this discipline in mature AI-driven products at companies like Google and Airbnb, where personalization and automation are grounded in observed behavior before models are optimized. The systems still learn, but they learn toward outcomes that were economically meaningful from the outset.
Strategic design is what turns those insights into boundaries the organization can actually govern. It makes assumptions visible, assigns decision ownership, and defines when intent needs to be revisited as the system adapts.
Economic intent exists to remove ambiguity before momentum makes ambiguity expensive. It clarifies what tradeoffs are acceptable, what outcomes justify continued investment, and where learning is allowed to change the system versus where it shouldn’t. Those decisions are rarely revisited later, which is exactly why they need to be made early.
When intent is explicit, teams don’t need to renegotiate meaning every time the system adapts. They know what success is supposed to look like, how learning should be interpreted, and which signals matter enough to trigger intervention. Debate doesn’t disappear, but it has boundaries.
This is why value realization is a continuity problem.
As our CEO, Eric Dean has noted in a recent CMSWire, value conversations tend to fall apart when movement is mistaken for progress. Without intent, activity multiplies while meaning thins.
Economic intent prevents that drift. It gives learning structure without slowing it down and it turns early economics into a constraint that holds while everything else moves.
In an AI economy, organizations don’t get many chances to renegotiate intent.
Systems learn and scale faster than approval cycles, governance forums, or strategy refreshes can keep up. Once deployment begins, assumptions harden into behavior. By the time outcomes drift, the system is already operating on a different logic than the one that justified it.
Rapid Economic Justification (REJ) exists to meet that reality.
REJ is a discipline for surfacing economic intent early enough to matter. It forces assumptions around what value the system is meant to create, for whom, under what conditions, and at what cost.
Those questions are often answered implicitly, if at all. REJ makes them explicit by design.
The value of that explicitness shows up once learning begins. Teams can move quickly without drifting into outcomes that look impressive but no longer align with the original rationale. As systems evolve, the economic story evolves with them. That continuity is the point.
REJ protects value realization by preserving narrative coherence as systems adapt. It ensures that results can be explained in terms of intent, not just performance. Leaders don’t have to reverse-engineer meaning from dashboards. They can point back to decisions that were made deliberately, before momentum took over.
This is also where REJ connects directly to the earlier disciplines in this piece.
User research informs REJ by grounding assumptions in real behavior rather than speculation. Strategic design structures REJ so intent doesn’t collapse under scale. Together, they turn economic framing into something durable.
What REJ ultimately provides is decision clarity.
AI shortens the distance between decision and consequence. Questions arrive sooner and scaling decisions can’t wait for perfect data. In that environment, the ability to explain what was intended, how learning was expected to unfold, and why the economics still justify continued investment is what leaders are judged on now.
This is why defensible narratives function as a competitive advantage.
In constrained environments, when the economic story is clear, decisions move faster. When it isn’t, even strong results stall under scrutiny.
User research and strategic design are what make that clarity possible.
Research anchors intent in real human behavior rather than assumptions. Strategic design ensures that intent survives scale, governance, and organizational complexity. Together, they produce business cases that leaders can stand behind without over-promising and without backfilling explanations later.
Defensible business cases protect innovation. They give organizations the confidence to move quickly without losing their footing, because the logic behind the decision is clear before momentum takes over.
This is where proof actually lives now.
Not in retrospective metrics or reconstructed narratives, but in the ability to stand behind how value was meant to be realized before the system began adapting. Proof is knowing what outcomes mattered, why they mattered, and which signals were supposed to guide learning as conditions changed.
In an AI economy, results will always arrive quickly. The organizations that endure will be the ones that can explain those results without hesitation.
That work doesn’t happen after systems learn. It has to happen before.
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