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September 24, 2020

Five Ways to Drive ROI with AI

Oliver Peckham

(Michael-Traitov/Shutterstock)

AI is often touted as the way of the future for enterprises in all industries – but ensuring that the return on investment (ROI) from an AI implementation actually comes to fruition can often be a trickier thing. A group of AI-oriented companies – Appen, Cognizant, Cortex, Dataiku, DataRobot (which recently commissioned its own ROI study), and Deloitte – partnered to commission a study from ESI ThoughtLab that benchmarked 1,200 organizations to identify the factors that drive ROI from AI. The result: a roadmap for success in enterprise AI.

In addition to data on AI investments and returns, the cross-industry survey collected detailed data on how and why the 1,200 organizations had implemented AI. Using that data, combined with an AI maturity framework, input from an advisory board of AI experts, and in-depth interviews with AI leaders, ESI ThoughtLab arrived at a series of conclusions about the current state of AI in business.

Two-thirds of executives, ThoughtLab found, believed that AI would be “critically important for their businesses” – but this was at odds with a somewhat meager 1.3% average return on AI investments, with the average firm taking 17 months to break even and 40% of AI projects showing no positive returns. That 1.3% average return, however, is higher (1.5%) for firms that have scaled AI farther across their enterprises and higher still (4.3%) for AI leaders.

But what constituted an AI leader? What factors drove higher ROI from smarter AI implementations? ThoughtLab put together five core principles that characterized the most successful AI implementations across its 1,200 recipients.

  1. Begin with pilots, but then scale AI across the enterprise. Successful implementations had started with specific use cases that called for AI, demonstrated its value in those pilot cases, and then scaled it across the enterprise.
  2. Lay a firm foundation. Successful AI implementations began with strong IT and data management systems already in place in most cases; many also had substantial available budgets, had considered the ethics and privacy issues in AI, and developed a clear vision and plan.
  3. Get your data right. ThoughtLab found that nine of ten leaders were advanced in data management, including rich data formats like psychographic, geospatial, and real-time data.
  4. Solve the human side of the equation. Leaders spent 27% of their AI budget on people – nearly twice the amount spent by others. Leaders were also more prone to appointing high-level staff (such as chief AI or data officers) to oversee AI efforts.
  5. Adopt a culture of collaboration and learning. 85% of firms with large returns ensured close collaboration between their AI and business experts, and almost nine of ten provided non-data scientists with the skills to use AI independently, decentralizing AI within the workplace.

“With the excitement about AI, there is a tendency to propose new AI initiatives to tackle problems,” said Peter Henstock, lead for machine learning and AI at Pfizer. “Without understanding the underlying technology, it is easy to waste money solving the same problem repeatedly. As AI begins to cascade through the organization, there needs to be a strategy guided by deep knowledge of the problem domain and machine learning field.” 

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