5 BEST PRACTICES TO BE SUCCESSFUL AT AI

AI technologies could lead to a substantial performance gap between AI front-runners (who fully absorb AI tools across their enterprises) and laggards (nonadopters or partial adopters).

AI is the defining innovation of our time and will fundamentally reshape our future. It will have massive economic implications.

PWC estimates AI could contribute up to $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. And in Notes from the AI frontier: modeling the impact of AI on the global economy, experts from McKinsey & Company suggest that AI technologies could lead to a substantial performance gap between AI front-runners (who fully absorb AI tools across their enterprises) and laggards (nonadopters or partial adopters).

McKinsey quantifies the rewards flowing to front-runners as a potential doubling of cash flow by 2030. This growth is at the expense of non-adopters who “might experience around a 20 percent decline in their cash flow from today’s levels.”

While there are many challenges to AI adoption, most organizations recognize the opportunity that AI can bring. According to a recent research report conducted by MIT Sloan Management Review and BCG, nine out of ten respondents agree that AI represents a business opportunity for their company, yet seven out of ten companies reported minimal or no impact from AI so far.

As organizations continue to adopt AI and scale the technology across the enterprise, they need an enterprise-grade platform that will help them move from “experimental AI” (AI that isn’t value-oriented or trustworthy) and translate data to value across the entire AI lifecycle, from creation to consumption.

To be successful at AI and drive high performance, executives should consider five best practices

1. Begin with pilots, but then scale AI across the enterprise. Companies starting out should work closely with business teams to identify use cases and demonstrate their value through pilots. Be sure to try different use cases, since some AI solutions will fail. Once pilots succeed, it is essential to follow through. The full value of AI can only materialize when firms offset their upfront costs with substantial business gains from widescale deployment. Indeed, three-quarters of organizations with large ROI have scaled AI across business units.
2. Lay a firm foundation. The most important lesson is having a proper IT and data management system in place (60%). That must be followed by having a sufficient budget (49%); considering the data security, privacy, and ethical risks of AI (31%); developing a clear vision and plan (24%); obtaining the support of senior management (21%); and leveraging the ecosystem of AI partners and suppliers (14%). Defining the business case is vital for driving ROI: 77% of firms generating the highest returns from AI do this well.
3. Get your data right. Nine out of ten AI leaders are advanced in data management. Ensuring your data is in good shape is not enough; to drive higher AI performance firms should bring in richer sets of data, such as psychographic, geospatial, and real-time data. At the same time, companies should integrate fast-growing data formats into their AI applications, such as high-dimensional, video, audio, and image.
4. Solve the human side of the equation. AI is as much about people as it is about technology. AI leaders spend 27% of their AI budget on people, almost twice the amount that AI beginners and implementers spend. AI leaders are also more apt to appoint specialists, such as Chief AI and Data Officers, to oversee AI initiatives. They outsource less, build internal teams more, and develop HR plans to address jobs that may be disrupted.
5. Adopt a culture of collaboration and learning. About 85% of firms that generate large AI returns work to ensure close collaboration between AI experts and business teams. Nearly nine out of ten AI leaders excel at providing non-data scientists with the skills and tools to use AI on their own. They also decentralize AI authority to help ensure that AI expertise and responsibility are well distributed across their organizations.