How to Choose Your First AI Project
Artificial intelligence (AI) is poised to transform every industry, just as electricity did 100 years ago. It will create $13 trillion of GDP growth by 2030, according to McKinsey, most of which will be in non-internet sectors including manufacturing, agriculture, energy, logistics, and education. The rise of AI presents an opportunity for executives in every industry to differentiate and defend their businesses. But implementing a company-wide AI strategy is challenging, especially for legacy enterprises.
My advice for executives, in any industry, is to start small. The first step to building an AI strategy, drawn from the AI Transformation Playbook, is to choose one to two company-level pilot AI projects. These projects will help your company gain momentum and gain firsthand knowledge of what it takes to build an AI product.
5 traits of a strong AI pilot project
Tapping the power of AI technologies requires customizing them to your business context. The purpose of your one or two pilot projects is only partly to create value; more importantly, the success of these first projects will help convince stakeholders to invest in building up your company’s AI capabilities.
When you’re considering a pilot AI project, ask yourself the following questions:
Does the project give you a quick win? Use your first AI pilot project to get the flywheel turning as soon as possible. Choose initial projects that can be done quickly (ideally within 6-12 months) and have a high chance of success. Instead of doing only one pilot project, choose two to three to increase the odds of creating at least one significant success.
Is the project either too trivial or too unwieldy in size? Your pilot project does not have to be the most valuable AI application as long as it delivers a quick win. But it should be meaningful enough so that a success convinces other company leaders to invest in further AI projects.
In the early days of leading the Google Brain team, I faced widespread skepticism within Google about the potential of deep learning. Speech recognition was much less important to Google than web search and advertising, so I had my team take on speech as our first internal customer. By helping the speech team build a much more accurate recognition system, we convinced other teams to have faith in Google Brain. For our second project, we worked with Google Maps to increase data quality. Each successful project increased the momentum in the flywheel, and Google Brain played a leading role in turning Google into the great AI company it is today.
Is your project specific to your industry? By choosing a company-specific project, your internal stakeholders can directly understand the value. For example, if you run a medical devices company, building an AI+Recruiting project to automatically screen resumes is a bad idea for two reasons: (1) There’s a high chance someone else will build an AI+Recruiting platform that serves a much larger user base and will outperform what you could do in-house and/or undercut you; (2) This project is less likely to convince the rest of your company that AI is worth investing in than if your pilot project applied AI to medical devices. It is more valuable to build a healthcare-specific AI system — anything ranging from using AI to assist doctors with crafting treatment plans, to streamlining the hospital check-in process through automation, to offering personalized health advice.
Are you accelerating your pilot project with credible partners? If you are still building up your AI team (see Step Two of the AI Transformation Playbook), consider working with external partners to bring in AI expertise quickly. Eventually, you will want to have your own in-house AI team; however, waiting to build a team before executing might be too slow relative to the pace of AI’s rise.
Is your project creating value? Most AI projects create AI value in one of three ways: reducing costs (automation creates opportunities for cost reduction in almost every industry), increasing revenue (recommendation and prediction systems increase sales and efficiency), or launching new lines of business (AI enables new projects that were not possible before).
You can create value even without having “big data,” which is often overhyped. Some businesses, such as web search, have a long tail of queries, and so search engines with more data do perform better. However, not all businesses have this amount of data, and it may be possible to build a valuable AI system with perhaps as few as 100-1000 data records (though more does not hurt). Do not choose projects just because you have a lot of data in industry X and believe the AI team will figure out how to turn this data into value. Projects like this tend to fail. It is important to develop a thesis upfront about how specifically an AI system will create value.