4. What business outcome do we want to achieve?
As with other technology hype cycles, don’t make the mistake of force-fitting AI into the business rather than letting business needs and goals dictate sensible applications of AI.
The final step in Hodler’s trinity: Always align your AI and ML initiatives with your overall business strategies.
“It makes no sense to invest in an area that does not have a clear impact on business goals,” Hodler says. “If you ignore this, you will never make it to production and be stuck in proof-of-concept purgatory.”
Hodler points to example business goals or impacts such as new business opportunities, decreasing waste or fraud, or accelerating efficiencies. Again, you get to decide internally which goals and impacts matter most – and you should be sure to have more than one perspective in the room.
“If an organization is sussing out ways to effectively implement AI in their business operations, its first step should be to ask business leaders which challenges keep them up at night or which opportunities they think might grow their business substantially – 10x is a good rule of thumb, but not definitive,” says Arti Garg, emerging market and technology director at Cray.
The evaluative work doesn’t stop once you’ve lasered in on a particular goal or problem (or several of them.)
“Once the business problems have been identified, the organization should take a deeper dive to understand what is driving current bottlenecks or preventing progress on high-value opportunities,” Garg says. “If those obstacles can be removed with an AI solution and removing those obstacles would solve the problem, then the project is worth pursuing as an AI project.”
5. Will this actually solve our problem?
By Garg’s count, too few companies ask this question during their AI exploration.
“It is common to identify business problems for which there is a relevant AI project, but where the project will not solve the problem,” Garg says. “In my experience, this is common in AI projects aimed at driving better business efficiency.”
Morten Jørgensen, VP customer solutions at Arundo Analytics, offers another way of framing this question: Is this project actually feasible for us? It’s as important as focusing on business goals for determining AI use cases.
“Our view is that where you start working [on AI] should be driven by a combination of two factors: One, business value – demonstrating that predictive analytics, for example, or AI will yield real value for the business – and two, feasibility – the availability of required data and the ability to use machine learning to solve the problem at hand.”
Garg from Cray points out that when the answer to this question is no, organizational roadblocks are often as much the reason why as of a lack of data or technical expertise.
“The problem is [often] misaligned incentives across business units,” Garg says. “Even if the AI solution offers better insight into the problem, structural barriers will prevent the business from acting on those insights.”
Bonus tip: Ask the above questions during project retrospectives
Brock, the VP of engineering at Very, offers some extra advice for agile teams: Project retrospectives can be especially fruitful times to ask these questions.
“As your team reflects on what happened in the iteration and identifies actions for improvement going forward, you often uncover the largest pain points in your processes, projects, and organization,” Brock notes. “By using these assessments and practicing continuous improvement, the team will begin to naturally focus on problems that can have the highest impact. Take those pain points and look for data, labels, and whether or not the pain point is something that can be solved by [AI and] data science.”
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