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Achieving Return on AI Projects

Companies embarking on AI and data science initiatives in the current economy should strive for a level of economic return higher than those achieved by many companies in the early days of enterprise AI. Several surveys suggest a low level of returns thus far, in part because many AI systems were never deployed: A 2021 IBM survey, for instance, found that only 21% of 5,501 companies said they had “deployed AI across the business,” while the remainder said they are exploring AI, developing proofs of concept, or using pre-built AI applications. Similarly, a VentureBeat analysis suggests that 87% of AI models are never put into production. And a 2019 MIT Sloan Management Review/Boston Consulting Group survey found that 7 out of 10 companies reported no value from their AI investments. This makes sense: If there is no production deployment, there is no economic value.

But other companies have achieved economic return on their AI investments. Their strategies for finding value include establishing close relationships between the data group and interested business units, selecting projects with tangible value and a clear path to production, lining up trust from key stakeholders in advance of development, building reusable AI products, selectively employing “proof of concept” projects, and establishing a management pipeline or funnel leading projects toward production implementation. We describe each of these approaches below.

Six Strategies Toward Value

AI projects typically are led by the company’s data science group, and that group is tasked with both executing the projects and taking responsibility for their achievements. These six strategies can help guide the data science team toward a greater chance of success in these cross-unit projects.

Focus on partnerships with AI-friendly business units. It is widely known that any technology project benefits from partnerships with the business functions or units that will use the new system. With AI projects, however, it is important to work with business unit leaders who understand the technology and its potential. Indicators of likely support include leaders who are familiar with data and analytics, have curated data, and even have an analytical team within their organization.

At BMO Financial Group, where one of us (Ren Zhang) is the chief data scientist, support for AI within the bank’s different business units is closely correlated with how much data is available within that unit. The bank’s digital unit, for example, has large volumes of clickstream data from customers and welcomes AI and analytics to make sense of the data and to personalize customer interactions. The bank’s financial crimes unit also has data on customer and employee behaviors and is always interested in using the latest AI tools to identify and stop criminal activity. Both of these departments also are subject to industry trends that help prioritize the adoption of AI: For the digital unit, it’s the increasing customer demand for personalized experiences, and for the financial crimes unit, it’s the rise of cyberattacks and digital fraud. These groups have been enthusiastic internal customers of AI.

But other groups within the bank are naturally more conservative in their embrace of AI. The commercial bank, for example, serves fewer customers than the consumer bank and prefers a personal touch over more automated processes and interactions. Executives in the credit risk function are supportive of using data and analytics for better credit decisions, but that aspect of the business is heavily regulated. Complex machine learning models may become more desirable after the necessary transparency is solved to satisfy regulators and the complexity is justified by clearer possibilities for incremental gain.

Continue reading: https://sloanreview.mit.edu/article/achieving-return-on-ai-projects/

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