Artificial intelligence (AI) is spurring the next wave of the digital revolution. As we enter the cognitive age, forward-looking companies are using AI to transform every aspect of their business—from customer engagement and R&D to cybersecurity and back-office operations. For these firms, AI is more than a technology, it is a new way of doing business and galvanizing strategic, operational, and financial performance.
To help executives drive ROI from AI, ESI ThoughtLab, together with a group of AI leaders, including Appen, Cognizant, Cortex, Dataiku, DataRobot, Deloitte, and Publicis Sapient, conducted a benchmarking study of 1,200 organizations across 12 industries and 15 countries. The survey examined AI investments, plans, practices, and performance results.
Artificial intelligence is attracting growing amounts of corporate investment, however, delivering ROI on AI can be elusive for the uninitiated and a slog even for experienced firms. The returns generated by AI pales against returns on other corporate investments. This begs the question: Is the buzz around AI justified?
The answer is yes, but only if you have patience. Our research shows that AI is a slow-burning process that takes expertise, time, and scale to unlock its full potential and ROI. AI beginners and early implementers essentially show flat results. It is not until they scale AI more widely across their enterprises that the ROI rises to 1.5% on average for firms that are advancing in AI to 4.3% for leaders.
To generate strong ROI from AI, firms need to first lay the groundwork and put the right processes in place. AI overperformers (with ROI over 5%) excel at building a solid foundation for AI. Most overperformers have made significant headway in setting up business cases, implementation plans, and systems for measuring and monitoring AI performance. Overperformers also understand that the real value of AI is not in the models themselves, but in a companies’ abilities to harness them and scale them across the company. Seventy-six percent of overperformers have been successful at scaling AI across businesses units.
Overperformers also know that it takes money to make money – they spend about twice as much as underperformers on AI. They also invest bigger portions of their budgets on next-gen tools, such as machine learning, deep learning, computer vision, and natural language processing.
ROI overperformers understand the importance of employing the right talent and developing in-house AI skills. Eighty-three percent of overperformers are successful at developing and acquiring the right people vs. 9% of underperformers. In most firms there are two types of AI users – data scientists and business analysts (which Gartner refers to as “citizen data scientists”). While business analysts lack the technical training of pure data scientists, they compromise a large and growing segment of AI users. Overperformers are better at training and enabling their non-data-scientists to act as data scientists (88% vs. 2% of underachievers).
Firms seeing high ROI are far more apt to foster a culture of learning and collaboration. Eighty-five percent facilitate coordination between AI experts and business teams (vs. 9% of underperformers). They also work closely with those teams to identify use cases and demonstrate AI’s worth through pilots.
Dr. Daniel Miles is Senior Vice President and Principal at ESI. He leads economic analysis projects across a variety of sectors and industries. Additionally, Dr. Miles is the Chief Operating Officer for ESI ThoughtLab, Econsult Solutions’ thought leadership arm. He is also the firm’s lead EB-5 economist.