Whether you use GenAI within your organization or you want to add it to services, where to start is a challenging question. Internally, you could run a small, first use at minimal cost and risk. You might even absorb the cost and risk of a somewhat larger trial. But costs and risks are different when you take a product or service to market.
To learn that clients don’t want the GenAI-assisted solution you took to market by taking it to market is costly. It wastes time and money, incurs opportunity costs, and can damage relationships and brand. There’s a way to prevent this and, ironically, it’s in the very unknowns that worry us about adopting GenAI.
The smart start is precisely with the things you don’t know. State all your guesses and assumptions. Turn some into hypotheses. Then test those to gather evidence for decision making. That’s exactly what DWPA’s GenAI Discovery Project does, and that’s what we’ll describe below.
What Will We Discover?
The term discovery in GenAI Discovery Project isn’t just descriptive – it’s prescriptive. It refers to a particular method for turning hypotheses about markets and customers into facts. It’s part of a larger method called customer development, created by Steve Blank and Bob Dorf to answer the question, “Why do startups with great ideas fail?”
In The Startup Owner’s Manual, Blank and Dorf argue startups risk great ideas by conducting product or service development without also conducting customer development. By conducting them in tandem, startups greatly increase their odds of going to market with a product or service customers want, and are ready to buy.
You don’t have to be a startup to benefit from customer development.
Today’s govcon market for GenAI-assisted products and services is so new, we’re all startups within it. Filled with more questions and hunches than facts, adding GenAI to existing services is sufficiently startup-like to benefit from customer development. That’s why DWPA is using the method to turn assumptions into facts for investment decision making. That’s what our GenAI Discovery Project is.
DWPA’s Discovery Process
We started in August by brainstorming every assumption we could think of about customers and the market. We generated dozens and grouped them in Osterwalder and Pigneur’s Business Model Canvas.
Using that layout, we could see assumptions held about value propositions, customer relationships, customer segments, and channels – all outward facing from DWPA to the market. We could also see assumptions we held about inward-facing parts of the business model: Key activities, resources, and partnerships, revenue streams, and cost models.
Next, we turned assumptions into hypotheses. “GenAI will save time” became, “GenAI will get clients to a pink team draft faster by finding and aggregating content.” We literally reworded select assumptions as testable propositions using measures we could discuss or directly observe. To test, we used several capture and proposal tools on a trial basis, and we interviewed clients about their generative AI experiences.
Here’s where it got interesting. Testing didn’t just confirm or disconfirm hypotheses in a thumbs-up or thumbs-down way. Testing revealed new information which suggested new opportunities for support.
Testing the hypothesis that “GenAI will get clients to a pink team draft faster by finding and aggregating content,” for example, became evidence of several things:
- Clients will, in fact, save time
- We can help them plan time savings in different ways
- We can help them use time savings for different purposes
- We can help vendors to serve them, their clients, or both
With such evidence we could fashion provisional services and validate them with customers – which is the next step of Blank and Dorf’s customer development process.
We Discover Something Unexpected
Our discovery process led to an ahh-ha! moment we didn’t see coming.
The wording of assumptions read like results or outcomes, as they should: Time saved, money saved, the summary of a section, etc. Tests would demonstrate the possibility, and perhaps the probability, of realizing them. But they demonstrated more.
Tests highlighted requirements for realizing a benefit, and also highlighted steps which would logically follow from a benefit. The view into workflows, benefits and risks, option analysis and decision making – all related to use cases GenAI could support – expanded opportunities for support. Not every opportunity would be a value proposition, but some could be. One unexpected value of hypothesis testing was the broadening of our conversation about value propositions.
Earlier this year, there might not have been a single service in your line of work which included generative AI. There might not have been a single customer wondering how generative AI could benefit them. Today, every customer is probably wondering how GenAI might help, and first offers might be under development by competitors. Customer development is a methodical way you can manage risks in a new and emerging market, and capitalize on its opportunities.
To learn more or launch your own discovery project, contact Lou.Kerestesy@DWPAssociates.com.
