How do you prioritize your AI use cases?
Sort by:
Start by asking what problem we’re really trying to solve and what impact solving it would have. Look for use cases that tie directly to strategic goals, create measurable value, and can be scaled. And, certainly don't chase shiny AI demos.
From there, think about feasibility. Do we have the right data, model, talent, and infrastructure? Are there clear success metrics? Ideally, go after smaller, clearly defined projects that move the needle from a value creation point of view.
The best use cases hit a sweet spot: they solve a meaningful problem, put into production and scaled, and create a foundation for bigger AI wins later.
Start with easiest thing you can build, deploy and most importantly measure business impact. Then tackle bigger, more ambitious projects later.
From my experience is based on your business vision and priorities, a good option is to choose the lowest-hanging fruit method, which involves identifying the use cases with the greatest impact on business results with the least effort involved. This means that you may sacrifice, in the short term, an ambitious project that takes too long to deliver results for one or more than one that offers a clear impact in a shorter timeframe. This will build confidence in the technology and allow you to pursue more challenging projects later.
You can create a decision matrix and have stakeholders support you in determining the degree of impact with the required effort.
Take a look at the Gartner template for prioritising AI use cases. Quite reasonable set of simple criteria across impact (value creation - $ topline, $ bottom-line, cash, productivity), feasibility (technical, time, resource) etc, lifespan...