Got 100 AI Ideas? Here’s the Framework for Finding the Gold

Originally posted on Medium

In this blog series, I’m sharing practical tools, frameworks and lessons learned for accelerating successful AI projects. The first of this series is to help your team get from the initial ideation phase to a clear priority of projects and roadmap.

In our world, we hear a lot of innovative ideas on how to use generative AI. Check out my first blog for more info: Who Are the Google Cloud Generative AI Field Solution Architects? Generative AI has immense potential, but navigating the flood of possibilities can be overwhelming. Many customers I work with have identified dozens, even hundreds, of potential use cases, only to stall when deciding where — and how — to begin.

This post introduces a practical framework to get you started.

1) Find Your “Why” with the Best, Better, Good Methodology

While many frameworks exist, the key is identifying what’s most important and why — making sure to the right stakeholders in the process. Here’s a simple method to help.

I created the Best, Better, Good Methodology to nail down the core mission of what we are trying to accomplish, so we don’t get lost in fuzzy goals, lack of shared vision, and constraints too early. I find that it’s most effective thinking on a 2 year time horizon, with the ideas building a foundation for what could follow. Start with Best, because it focuses on the ultimate change you want to drive before identifying limitations.

Best (The Dream): Starting with the end user in mind, think about the best-case scenario. What does that look like, what elements does it include? This forces ambition and clarifies the North Star. Work back from here and identify critical blockers in accomplishing this. Note these potential blockers, and prioritize investigating their feasibility early on to understand the true constraints. This gives you the vision.

Better (The Strategic Tradeoff): If your Best scenario has roadblocks that are insurmountable, move to Better. What in Best could you pragmatically sacrifice while still achieving significant impact? What about Best is not doable or requires too many tradeoffs, and are there high-value compromises? Follow the same due diligence as the Best scenario. This clarifies your core priorities.

Good (The Must-Have Core): Repeat the same process for Best and Better. What’s the essential, must-have baseline? If forced to strip back further, what must remain to deliver core value? This reveals your non-negotiables.

This structured cascade doesn’t just produce a list — it stress-tests your vision. It illuminates the consistent, non-negotiable elements across all three levels. That’s your true ‘Why’ — the core value proposition to build upon. With this clarity, you’re ready to frame the conversation around specific AI opportunities.

2) Frame the Conversation Correctly

From the context of your Best, Better, Good exercise, start your project ideation process. There are two critical elements in which to frame your brainstorming.

• What is most painful and important for us today? This could be an experience your customers hate, a competitive offering that is taking away marketshare, or tedious internal tasks that suck up valuable employee time.

• Where is generative AI best fit for use cases?

Let’s dig into the second point more. Generative AI can do a lot of amazing things, and gets better everyday, but there are areas where the effort is currently too high or the system complexity will jeopardize your pilot in the first phases with no results.

Frame your thinking about where generative AI fits in a few categories:

• Do we have data that will provide additional value beyond the foundational model’s general knowledge? If yes, you are strategically positioned to add value to a process.

• Can generative AI automate answering frequently asked questions currently handled manually at scale? If so, even if the accuracy of a foundational model doesn’t answer with total precision today, this is a great area to focus because of the multitude of prompt engineering, training and tuning options available.

• Creative work, generating new content and images/video

• Searching across lots of information, summarizing insights, and finding specific points quickly

3) Prioritize: Business Impact to Complexity Mapping

Out of this exercise, you should now have a sense of a) what is truly important and b) technical feasibility. These become your work use cases. Write them all out.

Next, map the use cases to a Business Impact to Complexity chart. This is a simple framework that helps us prioritize the roadmap. I’ve put a slight spin on it here, for generative AI use cases.

Map the use cases to a chart with Business Impact (Low to High) on the Y-axis and Implementation Complexity (Low to High) on the X-axis.

For example, for my dream Dog Wearables project:

In this scenario, I’d start with the Remote Treat Dispenser (low complexity, high impact) to prove a quick win and learn valuable lessons. This initial project might involve developing sensor integrations or user interaction patterns via an app, creating foundational components that will eventually help build toward the Bark Translator (high complexity, high impact).

Questions to ask during the mapping exercise:

• Do we have the necessary, accessible, and high-quality data required for this specific use case?

• Can this use case standalone, or does it require extensive reliance on other systems and integrations to be able to pilot successfully?

• Is this a problem generative AI can/should solve, or should we consider other methods?

• Is this measurable? How specifically are we defining business impact (e.g., KPIs like % time saved, error rate reduction, revenue generated)? Does this impact align to our “Best” goal?

• Who will do this? Do we have the skillset in-house, do we need additional expertise?

While technical deep dives, governance, and change management are crucial next steps, this framework provides the essential starting point. By systematically identifying your ‘Why,’ framing the potential accurately, and prioritizing strategically, you can cut through the noise and build a focused, actionable AI roadmap.

Please leave comments, questions, tell me where you’re struggling with AI projects, or recommend future post topics!