How to Choose the Right, Best AI Projects

Artificial intelligence has great potential to support the growth of digital businesses by spurring experimentation and innovation, and helping organizations work more efficiently and effectively. But AI is not a magic wand. Many executives are therefore asking themselves: Why doesn’t AI keep everything that IT has promised?

What’s likely slowing down your AI strategy is the fact that companies now need to invest in strategy, not “adhocracy,” to get the most value from AI.

Yes: ten years ago we said you should adopt AI immediately, make mistakes and stumble instead of waiting for someone else to try to assert themselves in the spooky new space. But now we advise business leaders to slow down and first ask the questions that will determine whether an AI project fits into the larger business strategy or serves as the standard that sets it. IT and business leaders need to determine who is in charge, what they need, and how AI will equip them for a prosperous future.

Here are three key questions leaders should consider when approached with new ideas for AI. Data and analytics leaders should be ready to answer these questions, and perhaps even provide answers before the questions are asked.

1. Who will sponsor this AI project and make sure it is important to the organization?

If the answer is “a CxO,” success is much more likely. C-suite executives have access to sources of funding and influence that can be critical. When inevitable obstacles to the success of an AI project—such as integration costs, staffing availability, and security concerns—surface, C-suite leadership can do what needs to be done.

CxOs also know how to convert the CEO’s growth or innovation ambitions into project relevance. We speak to IT leaders who understandably want to pursue AI projects that deliver results—but results aren’t always enough. Value is measured by impact on aspects of the business that receive attention. For example, one client shared that they use AI to categorize millions of images instead of letting humans do it at the end of the year. However, this task wasn’t particularly important to the company, so nobody treated the IT team that automated it as the heroes they deserved to be seen as.

2. Will this decision result in better capabilities, better data, and better alignment?

AI analogies are easy to find. Coming to TV shows, you don’t want to be in a Twilight Zone situation with AI where every story is new and every episode may or may not keep you in your armchair for the entire three acts. No: they want to be Star Trek, where the episodes – or projects in our case – are thematically intertwined.

Executives should insist on enterprise-wide strategies for AI. They have already confirmed that each project aligns the organization for strategic impact, so it is reasonable to assume that more than one department will be committed to the success of each initiative. But workers and business leaders should also be able to see this path to a more effective future.

AI demands commitments from data stewards (management and quality), IT leaders (integration and security), and business leaders (personnel impact and value). Invest appropriately in the promise of a narrative that interlocks with others. If you care about what happens in deep space, you care about the next generation. Cross-timeline interactions are the best.

3. Is this really something we need to use AI for?

This last question is difficult. Some customers tell us they use AI when they want to experiment with something familiar by using new skills. Some just do small tasks with AI to try and get started. But no matter where companies are in their AI journey, it can still pose a challenge.

The average AI initiative that reaches production takes 7.3 months to get there, and 10% of initiatives take at least a year (but less than two years), according to the Gartner AI in Organizations Survey 2021. Vice versa half of such initiatives less than six months.

We recommend this at least to executives Questions: Is there another way to do this without AI? If the answer is no and it’s a strategic project, it’s time to get started.

If the answer is yes, the project can be done in other ways, then the experiential mindset that AI requires should be treated even more importantly than usual. Measurements related to the project should include questions about resource costs, the challenge of starting and accepting it, and any expected ongoing efforts. When AI is optional, you want to be sure it drives the rest of the company story.

By using these questions to design and evaluate AI projects, IT leaders not only have a better chance of success, but they also gain greater support from key stakeholders inside and outside the organization, from employees to board members to customers. Some of these questions may require research and data analysis to answer, but this preparatory work will ensure that only the best AI use cases are pursued, supporting a virtuous circle of AI investments.

Whit Andrews is a Distinguished Vice President Analyst at Gartner, Inc. researching organizational impact, use cases, and business opportunities for AI. Additional analysis on data and analytics trends, including AI, will be presented during the Gartner Data & Analytics Summitwhich takes place from August 22nd to 24th in Orlando, Florida.