Most companies, even leaders, are still relatively early in their A.I. journey. Only about one-quarter of A.I. projects are now in widespread deployment among A.I. leaders. Many A.I. projects are still in pilot or early deployment stages. However, firms are planning to boost their A.I. investments by an average of 8.3% annually over the next three years, bringing their annual A.I. to spend from $38 million currently (or 0.75% of revenue) to over $48 million.
As companies progress in A.I. use, they often shift their focus from automating internal employee and customer processes to delivering on strategic goals. For example, 31% of A.I. leaders report increased revenue, 22% greater market share, 22% new products and services, 21% faster time-to-market, 21% global expansion, 19% creation of new business models, and 14% higher shareholder value. In fact, the AI-enabled functions showing the highest returns are all fundamental to rethinking business strategies for a digital-first world: strategic planning, supply chain management, product development, and distribution, and logistics. The study found that automakers are at the forefront of A.I. excellence, as they rush to use A.I. to deliver on every part of their business strategy, from upgrading production processes and improving safety features to developing self-driving cars. Of the 12 industries benchmarked in the study, automotive employs the largest A.I. teams (557 people on average vs. 370 for all industries) and has the largest A.I. budgets ($59.4 million on average vs. $38.3 for all industries). With the government actively supporting A.I. under its Society 5.0 program, Japanese companies lead the pack in A.I. adoption. Unlike in the U.S., where A.I. is viewed often as a threat to jobs, firms in Japan tend to see A.I. as a way to fill the employment gap caused by an aging population and stringent immigration laws.
To drive A.I. performance, executives should consider these best practices uncovered by the research:
1.Begin with pilots, then scale A.I. applications across the enterprise. Companies starting out should work closely with business teams to identify use cases and demonstrate A.I.’s worth through pilots. But the true value of A.I. can materialize only with widescale deployment when firms can offset their upfront costs with substantial business gains.
2.Lay a firm foundation. Organizations should have the proper I.T. and data management system in place; have a secure and sufficient budget; work through the data security, privacy, and ethical risks of A.I.; develop a clear vision and plan that takes into account AI-driven strategic transformation; obtain senior management support, and have a robust ecosystem of partners and suppliers.
3.Get your data right. Nine out of ten A.I. leaders are advanced in data management. But ensuring your data is in good shape is not enough; organizations should bring in a diverse set of data, such as psychographic, geospatial, and real-time data. The study found that combining different types of data can create a multiplier effect on A.I. returns.
4.Solve the human side of the equation. AI is as much about people as technology. A.I. leaders spend 27% of their A.I. budget on developing and hiring people, almost twice the percentage that A.I. beginners spend. They are also more apt to appoint specialists, such as Chief AI and Data Officers, to lead their A.I. initiatives. They outsource less and build internal teams more.
5.Adopt a culture of collaboration and learning. About 85% of companies that generate large A.I. returns work to ensure close collaboration between A.I. experts and business teams. A.I. leaders are better at providing non-data-scientists with A.I. skills. They also decentralize A.I.'s authority to help ensure that A.I.'s responsibility and expertise are distributed across their organizations.
“As the pandemic propels businesses into a digital-first world, AI will become a key driver of corporate growth and competitiveness. But building proficiency in A.I. is not easy,” said Lou Celi, ESI ThoughtLab CEO and program director for Driving ROI through A.I. “A.I. is not a magic bullet. It can fail to deliver results if the wrong business case is selected, the data is prepared incorrectly, or the model is not built for scale.”