Dear friends,
Last week, I wrote about why working on a concrete startup or project idea — meaning a specific product envisioned in enough detail that we can build it for a specific target user — lets you go faster. In this letter, I’d like to share some best practices for identifying promising ideas.
AI Fund, which I lead, works with many corporate partners to identify ideas, often involving applications of AI to the company’s domain. Because AI is applicable to numerous sectors such as retail, energy, logistics and finance, I’ve found working with domain experts who know these areas well immensely helpful for identifying what applications are worth building in these areas.
Our brainstorming process starts with recommending that a large number of key contributors at our partner corporation (at least 10 but sometimes well over 100) gain a non-technical, business-level understanding of AI and what it can and can’t do. Taking DeepLearning.AI’s “Generative AI for Everyone” course is a popular option, after which a company is well positioned to assign a small team to coordinate a brainstorming process, followed by a prioritization exercise to pick what to work on. The brainstorming process can be supported by a task-based analysis of jobs in which we decompose employees’ jobs into tasks to identify which ones might be automated or augmented using AI.
Here are some best practices for these activities:
Trust the domain expert’s gut. A domain expert who has worked for years in a particular sector will have well honed instincts that let them make leaps that would take a non-expert weeks of research.
Let’s say we’re working with a financial services expert and have developed a vague idea (“build a chatbot for financial advice”). To turn this into a concrete idea, we might need to answer questions such as what areas of finance to target (should we focus on budgeting, investing, or insurance?) and what types of user to serve (fresh graduates, mortgage applicants, new parents, or retirees?) Even a domain expert who has spent years giving financial advice might not know the best answer, but a choice made via their gut gives a quick way to get to one plausible concrete idea. Of course, if market-research data can be obtained quickly to support this decision, we should take advantage of it. But to avoid slowing down too much, we’ve found that experts’ gut reactions work well and are a quick way to make decisions.
So, if I’m handed a non-concrete idea, I often ask a domain expert to use their gut — and nothing else — to quickly make decisions as needed to make the idea concrete. The resulting idea is only a starting point to be tweaked over time. If, in the discussion, the domain expert picks one option but seems very hesitant to disregard a different option, then we can also keep the second option as a back-up that we can quickly pivot to if the initial one no longer looks promising.
Generate many ideas. I usually suggest coming up with at least 10 ideas; some will come up with over 100, which is even better. The usual brainstorming advice to go for volume rather than quality applies here. Having many ideas is particularly important when it comes to prioritization. If only one idea is seriously considered — sometimes this happens if a senior executive has an idea they really like and puts this forward as the “main” idea to be worked on — there’s a lot of pressure to make this idea work. Even if further investigation discovers problems with it — for example, market demand turns out to be weak or the technology is very expensive to build — the team will want to keep trying to make it work so we don’t end up with nothing.
In contrast, when a company has many ideas to choose from, if one starts to look less interesting, it’s easy to shift attention to a different one. When many ideas are considered, it’s easier to compare them to pick the superior ones. As explained in the book Ideaflow, teams that generate more ideas for evaluation and prioritization end up with better solutions.
Because of this, I’ve found it helpful to run a broad brainstorming process that involves many employees. Specifically, large companies have many people who collectively have a lot of wisdom regarding the business. Having a small core team coordinate the gathering of ideas from a large number of people lets us tap into this collective fountain of invention. Many times I’ve seen a broad effort (involving, say, ~100 people who are knowledgeable about the domain and have a basic understanding of AI) end up with better ideas than a narrow one (involving, say, a handful of top executives).
Make the evaluation criteria explicit. When evaluating and prioritizing, clear criteria for scoring and ranking ideas helps the team to judge ideas more consistently. Business value and technical feasibility are almost always included. Additionally, many companies will prioritize projects that can be a quick win (to build momentum for their overall AI efforts) or support certain strategic priorities such as growth in a particular part of the business. Making such criteria explicit can help during the idea-generation phase, and it’s critical when you evaluate and prioritize.
In large companies, it can take a few weeks to go through a process to gather and prioritize ideas, but this pays off well in identifying valuable, concrete ideas to pursue. AI isn’t useful unless we find appropriate ways to apply it, and I hope these best practices will help you to generate great AI application ideas to work on.
Keep learning!
Andrew