A feasibility study is an analysis of whether a proposed project or business is viable. The classic framework — market analysis, technical feasibility, financial projections, operational plan — hasn't changed fundamentally in decades. But the world it needs to analyze has changed enormously. AI is compressing competitive cycles, disrupting cost structures, and making historical data less predictive than it used to be.

Here's how I'm updating the framework for clients at Claryon.

What's broken in the traditional approach

Projection horizons are too long. A five-year financial projection for a technology business in 2026 is mostly fiction. Not because the math is wrong, but because the market assumptions underlying it will be wrong by year two. The traditional feasibility model was designed for slower-moving industries — manufacturing, real estate, established retail. Applying it unchanged to technology-adjacent businesses produces false precision.

Competitive analysis misses AI disruption. Traditional competitive analysis catalogs current competitors and their market share. It misses the more important question: which parts of this business model are most exposed to AI automation, and how fast is that automation arriving? A business that looks competitively strong today can be structurally disrupted within 18 months if its core value proposition is AI-replaceable.

Cost assumptions don't account for AI price curves. AI capabilities are getting cheaper every six months at a rate that has no historical parallel. A feasibility study that prices out technical implementation based on current vendor costs will be significantly wrong within a year. The framework needs to model cost-curve scenarios, not fixed-cost assumptions.

The updated framework: five additions

1. AI disruption mapping. Before market analysis, identify every function in the proposed business and assess its AI disruption exposure on a 1–5 scale over a 24-month horizon. This doesn't replace market analysis — it informs it. A function rated 4 or 5 requires either a defensibility strategy or a plan to automate it before competitors do.

2. Scenario-based projections with explicit assumptions. Replace single-line projections with three scenarios — conservative, base, and aggressive — each with explicit, testable assumptions. Review those assumptions at 90 and 180 days after launch. The projection isn't the goal; the assumption audit is.

3. Time-to-validation metric. How long will it take to know whether the core hypothesis is true? This is the most important number in the feasibility study for fast-moving markets. If the validation period is 18 months, the study needs to ask whether the market assumption can stay stable for 18 months. If not, the project needs to be restructured to validate faster.

4. Talent availability analysis. In technology-dependent projects, talent is often the binding constraint — not capital, not market demand. The feasibility study should explicitly assess whether the required skills are available in the target market, at what cost, and what happens to the plan if key hires take 6 months longer than projected.

5. Exit or pivot criteria. Define in advance what evidence would indicate the project should be stopped or pivoted. This turns the feasibility study from a one-time document into an ongoing decision framework. It also forces the analysis to be honest about what can go wrong.

What doesn't change

The fundamentals of rigorous analysis haven't changed: clear problem definition, honest market sizing, conservative financial assumptions, and clear accountability for each claim in the document. AI doesn't make feasibility studies obsolete — it makes good ones more valuable, because the cost of acting on a bad one has gone up.

If you're commissioning a feasibility study for a project in the Arab market, the above framework is what we apply at Claryon. The goal is always the same: not to predict the future, but to make the risks legible before you commit resources to them.