10 Insights into the Emerging Use of AI

The truth is AI is still a dumb infant—but that means a lot of businesses are becoming hesitant first-time parents. A study surveying over 3,000 executives and 160 case studies globally has taken a pulse on early AI usage. In an article for Harvard Business Review, McKinsey’s Jacques Bughin, Brian McCarthy, and Michael Chui derive 10 insights about AI from the study that you need to know:

  1. Most businesses are not using AI yet.
  2. AI can potentially boost your top and bottom line.
  3. Without support from leadership, your AI transformation might not succeed.
  4. Partner up for capability and capacity with AI.
  5. Do not put technology teams singularly in charge of AI initiatives.
  6. Take a portfolio approach to exploring AI.
  7. Machine learning cannot solve everything.
  8. Digital capabilities come before AI.
  9. Taking an offensive digital strategy helps incumbent companies stave off being disrupted.
  10. The biggest challenges are people and processes.

The Early Years

Only 20 percent of businesses in the study are using AI “at scale or in a core part of their business,” though 41 percent are experimenting in some way. This means that it is okay if you are not in the game yet, but you should probably start soon. And although the data is still a little rough, it has been shown that 30 percent of early AI adopters have increased their revenues, their market shares, and/or their number of products and services.

Like with virtually any other initiative, leadership support will play a big role in getting AI efforts off the ground. However, when it comes to actually implementing AI, a majority of early adopters have bought their solutions, as opposed to building them in-house. This indicates there is value in finding an AI-savvy partner, or in outright acquiring someone who already understands it. In any case, it should be a diverse team of business and technical leaders who oversees AI initiatives, so that both business priorities and technological capabilities are fully understood.

About developing a “portfolio” approach to AI, the authors describe how you should consider AI according to different time periods:

Short-term: Focus on use cases where there are proven technology solutions today, and scale them across the organization to drive meaningful bottom-line value.

Medium-term: Experiment with technology that’s emerging but still relatively immature (deep learning video recognition) to prove their value in key business use cases before scaling.

Long-term: Work with academia or a third party to solve a high-impact use case (augmented human decision making in a key knowledge worker role, for example) with bleeding-edge AI technology to potentially capture a sizable first-mover advantage.

The data suggests that a full digital transformation seems to be the best foundation for approaching AI transformation. Those businesses that have made the digital shift are better equipped to understand and benefit from the AI shift. Because ultimately, it is what employees understand that will determine success. This additionally means that ongoing education and upskilling for the workforce will be critical.

For additional details, you can view the original article here:

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