Two methods, set out in full.

I. BOCA — Business Opportunity and Commercialization Assessment

Before the fruits of innovation become actionable in a commercial sense, you have to understand the scope, scale and quality of the opportunity that successful development would create. BOCA blends legal, business and scientific criteria into a single assessment, producing both quantitative and qualitative reads on a given invention.

Stage one: the litmus test

From a business perspective the first question is whether a technology has the potential to generate profit at all. To ask it, most variables have to be normalised or assumed favourable. Two inputs:

• Market size. Is there adequate overall revenue potential, demand and sustainable growth?
• General market attributes. At a high level: pricing, operating and manufacturing costs, key competitors, known drivers.

Market size, general growth rate and basic dynamics — cost of goods sold, typical R&D investment rates, sales and general administration — give a very high-level view of how much revenue an early entrant might generate and share over a period of years. This is deliberately generalised. It describes the opportunity available to any entrant in the abstract, not to you.

As such, stage one provides a fundamental litmus test for going forward: is there enough revenue potential here to proceed, and do I understand the fundamentals of the market well enough to judge whether this technology, or this entrant, is likely to succeed? If the answer to either question is no, the analysis has done its work — most of the cost of a poor commercialization decision is incurred after this point.

Stage two: contextualisation

Beyond general market opportunity, a set of factors guides both the quality of the opportunity and the vectors around which a functional business plan can be built. Add specific known projections — real operating, research and sales costs — and the known hurdles that define product development or market entry, such as regulatory requirements for biomedical technologies. With that financial contextualisation begun, the opportunity takes closer shape.

With this early work in place you can make an informed, nuanced decision not only about paths to success but about investment potential (by assigning value), strategic partnering and revenue sharing.

The contextual factors

Eight non-market factors that drive more informed value and strategic decisions. Each carries the questions to ask — and, where artificial intelligence and data assets have changed the analysis, a note on what has changed.

1. Intellectual property

   Examine the prior art for potential freedom to operate. What potential claims exist? How crowded is the field? How much of the technology might be embodied in patent claims? How much of those claims would be embodied in the product? How much dependency is there, and what would it cost?

   Where AI changed this. Freedom to operate is no longer answered by the patent landscape alone. For anything with a model in it, the exposure has moved to the inputs: what the training data was, whether it was licensed for this use, whether the outputs are protectable at all, and whether the model weights are owned or merely licensed on terms that can change. A clean patent position over a model you do not control, trained on data you cannot document, is not freedom to operate. It is worth requiring training-data provenance in writing — and treating “we cannot reconstruct it” as a finding in its own right, rather than an inconvenience.

2. Known or potential revenue models

   Who is the customer for the proposed product? Are there market intermediaries or established channels for delivery? Does the industry contain dynamics that limit or control pricing? Are there clear ways to improve on the established models?

   Where AI changed this. Per-seat and per-hour pricing assume human effort is the scarce input. Where a model does the work, the cost floor collapses toward compute and the pricing question becomes what the output is worth rather than what the effort cost. Two consequences worth testing: a defensible model prices scope expansion rather than efficiency, because efficiency gains are competed away and passed to the buyer; and any revenue model resting on a third-party model provider inherits that provider's pricing power, which has moved quickly in both directions. It is useful to model the business at a compute price ten times higher, and again at one tenth; a business that works only at today's price is not yet a business.

3. Cost of capital and established risk parameters

   Does this industry have established capitalisation parameters? Is risk quantified across the industry, and within the stage of market entry under consideration?

   Where AI changed this. Capital intensity has bifurcated. Applications built on someone else's model can reach revenue on far less capital than the sector's historical parameters suggest, which makes comparables misleading on the low side; anything requiring its own training runs is far more capital-intensive than the same comparables suggest on the high side. It matters to establish which of the two you are before borrowing an industry benchmark.

4. Capabilities

   Does the team have a particular — potentially unfair — advantage that indicates a higher likelihood of success? Are the capabilities and relationships of the firm and its members sufficiently different from the competition's to make them more successful?

   Where AI changed this. This is the factor that has moved furthest. Advantages that consisted of doing skilled work faster than competitors have largely evaporated; the controlled studies find the largest gains accrue to the least skilled operators, which compresses exactly the advantage a strong team used to hold. What survives is proprietary data, regulated access, distribution, and institutional relationships a model cannot hold on your behalf. When a team's stated advantage rests on expertise alone, the question worth asking is what remains of that advantage when a competitor's junior staff can perform at a comparable level.

5. Established pricing

   Is pricing within the target industry or product sector static, understood, or subject to change? Can the firm's approach or capabilities create new profit or competitive opportunities within current pricing dynamics?

6. Novelty of the planned value proposition

   Does the proposed product approach create, in the aggregate, a clearly higher value proposition than the competition's?

7. Visibility into the value chain

   Is the path to market sufficiently clear? Is it possible to connect with players in the existing or proposed value chain in ways that provide a competitive edge?

8. Fragmentation and dominance

   Is the market open to new entrants? Can a new entrant make and sustain a place that will endure competition? Is current consolidation or fragmentation an opportunity in itself?

II. The Appreciative Inquiry Summit

BOCA answers whether something is worth doing. It does not, by itself, bring a room full of stakeholders to commit to doing it — that is a different kind of work, with a different lineage.

Appreciative Inquiry originates with David Cooperrider and Suresh Srivastva at the Weatherhead School of Management, Case Western Reserve University; the founding paper is Cooperrider and Srivastva, “Appreciative Inquiry in Organizational Life,” Research in Organizational Change and Development (1987). David Cooperrider and Ronald Fry developed it at Weatherhead into the body of practice taught there today, structured around the 4-D cycle — Discovery, Dream, Design, Destiny. The large-group form, the AI Summit, is set out in Ludema, Whitney, Mohr and Griffin, The Appreciative Inquiry Summit (2003). I hold the Weatherhead certificate in Appreciative Inquiry and Positive Business and Society Change, and this is the method behind the convening work in CF-08 and CF-09. The lineage is stated once, here, because none of the underlying method is mine.