In recent years, the vast majority of the enterprises that invested in Artificial Intelligence (AI) capabilities fell into one of two categories: those who used AI applications successfully to improve operations or cut costs and those who were participating in what Goutham Belliappa, vice president of AI engineering at Capgemini North America calls “AI theater:” They implemented AI models “to create some buzz in the marketplace, but they didn’t go through the hard work of tying their AI capabilities to business value,” Belliappa says.
Today, companies stand on the precipice of a new era. “AI is on the cusp of a tremendous economic impact that will disrupt every industry in the same way that software was positioned about thirty years ago,” says Brian Jackson, analyst and research director at Info-Tech Research Group. “AI’s rapidly growing capabilities are being applied to solve problems in far more efficient ways than we were able to do previously.”
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How to develop an Artificial Intelligence (AI) strategy
As a result, forward-looking IT leaders are revisiting and rethinking their AI strategies for the future. Belliappa has been working on revising client AI roadmaps to increase revenues through personalization, dynamic pricing, and the creation of new data-enabled revenue streams.
Meanwhile, data and AI products have matured and become mainstream. “The challenge is integrating these AI and data products into a company’s operations, commerce, or other products,” says Belliappa, who notes that data and AI strategies from just a year or two ago are now outdated.
“If a company is not considering how its strategy should incorporate AI and how AI might disrupt their industry, then it will only be a matter of time until they find themselves playing catchup with another competitor that has done that work,” agrees Jackson of Info-Tech Research Group.
Consider these ten questions to ask now about your AI strategy:
1. How much revenue do we drive from AI or AI-embedded products?
“Many firms are adopting machine-learning capabilities to assist with some business processes; for example, using chatbots to triage incoming customer support cases,” says Jackson. “That’s great and useful but unlocking the real potential of machine learning and the value it can create won’t be harnessed until it’s adapted into the core value proposition of a business.”
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
2. What role do we see ourselves playing in the AI marketplace?
Enterprises should decide where they fit in terms of the risks and rewards of AI, says Jackson. AI leaders will hire data scientists to create their own AI IP to drive business growth, or even sell AI services to others. Early adopters may not develop their own AI algorithms but will be quick to integrate AI solutions by partnering with AI leaders to drive efficiency and revenue growth. Those with the least risk tolerance will simply want to adopt AI features built into the software and cloud products they already use, Jackson says.
3. What outcomes do we seek?
This may sound obvious. However, many organizations are still pursuing AI for AI’s sake. "Artificial intelligence has gathered such momentum as a concept that many business leaders end up understanding that they need it even before they understand what they need it for," says Vara Kumar, CTO and cofounder of Whatfix. Kumar advocates conducting a thorough audit of an organization’s technical processes.
“Organizations get tunnel vision in attempting to understand what types of opportunities AI can unlock and then map them into organizational goals, versus starting with the organizational goal first and mapping to how AI can help,” says Sam Babic, senior vice president and CTO at enterprise content management and process management software maker Hyland. “This seems like a nuance, but the latter enables the organization to more quickly focus on the requirements necessary to accomplish the goal versus getting lost in a sea of possibilities.”
[ Want best practices for AI workloads? Get the eBook: Top considerations for building a production-ready AI/ML environment. ]
4. What ethical risks should we be monitoring and mitigating?
“With the automated decision-making that comes with adopting AI comes the risk of ingraining a systemic bias into your operations,” says Jackson. “Consider if the decisions you want AI to make will have an impact on people’s lives and where human judgment must be included in the process. There are many efforts underway around the world to issue guidance on AI ethics.”
As part of this, today’s organizations need to ensure they have diverse teams working on AI initiatives to enable their ongoing improvement and efforts toward zero bias, says Michael Ringman, CIO of Telus International.
5. Do we have the capabilities and infrastructure to deliver on our AI plans?
“Organizations must be realistic about what their AI approach is going to be,” Jackson says. “If they are lacking in IT capabilities, such as cloud infrastructure and data warehousing, then there is no skipping straight to the path of AI leader.”
Shane Nolan, head of technology for foreign direct investment agency IDA Ireland, recommends conducting a capability gap analysis, data preparation, and “building AI solutions around readily available data sources – not aspirational ones.”
Let's examine five more important questions to ask:
