How to prioritize Artificial Intelligence (AI) projects: 6 tips

How do you decide which Artificial Intelligence projects matter most? When was the last time you re-prioritized your AI projects? Consider this advice
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On-demand, scalable, and economic cloud storage and computation has enabled the efficient processing of huge data sets to draw critical insights using Artificial Intelligence (AI). Launching multiple AI initiatives is par for the course today. After all, some will not succeed. But how do you choose where to devote your resources?

"With so many options, the most difficult part can be deciding where to invest first."

“It is near impossible to name an industry that isn’t implementing AI solutions nowadays given its breadth of applications,” says Jim Radzicki, CTO of Telus International. “But, with so many options, the most difficult part can be deciding where to invest first.” There are so many areas where AI can be applied and demand for intelligent capabilities in the enterprise continues to grow, says Peter A. High, author of Getting to Nimble: How to Transform your Company into a Digital Leader and president of the technology and business advisory firm Metis Strategy.

AI project prioritization is critical. “It ensures that AI is connected to the business’ agenda and priorities,” says Goutham Belliappa, vice president of AI engineering at Capgemini North America. “Through tight governance and monitoring, companies can identify which projects are performing better than others and adjust the prioritization and resources accordingly in an agile manner.”

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

AI project prioritization: 6 strategies

Prioritization provides a framework wherein leaders can review all the options and available resources to determine the order in which AI projects will be implemented. “This type of big picture approach helps businesses achieve long-term success by taking all factors into consideration and making thoughtful decisions as opposed to executing them on an ad hoc basis,” Radzicki says. “Understanding the potential missed opportunities foregone by choosing one project implementation over another allows for better decision-making.”

Consider these six tips for AI project prioritization:

1. Focus on business significance

Buy-in from the board and C-suite is paramount. “If projects are tied to high-priority, tangible business outcomes, then they are more likely to succeed,” says Belliappa. Tie all AI initiatives into the business strategy of the enterprise and divisions of the company.

This “helps ensure that you do not develop AI for AI’s sake,” says High, “which can be another pathway to problems.”

2. Emphasize clear and manageable metrics

Business leaders should set up a formal measurement mechanism to monitor the progress of AI projects and how they are tracking toward those outcomes,” Belliappa says. “Without a baseline or way to measure, you won’t be able to identify ways to improve.”

3. Consider forming an AI committee or board

The most effective AI initiatives are conceived and implemented by a diverse mix of teams, skills and perspectives.

Since AI demand is coming in from across the company, decision making should extend across the company as well. The most effective AI initiatives are conceived and implemented by a diverse mix of teams, skills and perspectives. “That includes consensus on deciding where to start,” says Radzicki. “This means bringing people together from across the organization to thoughtfully discuss and decide what to prioritize in order to meet a company’s holistic needs as opposed to only one small aspect of the business.”

An AI (or innovation) board can oversee project prioritization and resource allocation. “This formalized board will drive decision-making based on the measured and monitored performance of each AI project,” says Belliappa.

A committee that votes on priorities, with input from IT and digital leaders, ensures that the IT team can proceed with greater confidence and transparency, High adds.

[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]

4. Embrace cross-functional teams

“Organizations made up of silos with very little collaboration between teams will lead to inefficiency, delays, and potential for failed implementations,” says Radzicki. “For organizations to find success, tech teams must be thoroughly integrated with leaders across all areas of the business when making decisions about digital transformation. By bringing groups together that may have different objectives and priorities or have a different lens on the business’ needs today and into the future, organizations will make decisions that better position a company for sustained success,” Radzicki says.

5. Create an AI bot onboarding process

“AI roadmaps and a defined AI onboarding process for AI-powered bots, similar to human co-worker onboarding, is key,” says Radzicki. However, those roadmaps must be fluid.

“Longer-term planning and prioritization are important, but so is the ability to change direction when needed,” he adds. “There will be unexpected obstacles and stumbling blocks that will set plans behind, and the market may unexpectedly change, requiring a quick pivot in order to remain competitive.”

6. Don't forget the re-prioritization

The fast-paced nature of digital transformation demands flexibility across the board. Re-prioritizing AI projects can be even more important than prioritizing them.

“When this happens, business leaders need to quickly and clearly communicate the changes with their teams and the entire organization in order for there to be a collective understanding of the change in scope, timeline and steps involved for all stakeholders,” Radzicki says.

[ How does AI connect to hybrid cloud strategy? Get the free eBooks, Hybrid Cloud Strategy for Dummies and Multi-Cloud Portability for Dummies. ]

Stephanie Overby is an award-winning reporter and editor with more than twenty years of professional journalism experience. For the last decade, her work has focused on the intersection of business and technology. She lives in Boston, Mass.

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