Many organizations have begun tiptoeing into artificial intelligence (A.I.) and machine learning, figuring out what to spend and who to hire in order to make apps, services, and internal processes “smarter.” It’s a difficult and often confusing journey, and some organizations are better at it than others.
What differentiates the organizations getting A.I. “right”? McKinsey and MIT’s Machine Intelligence for Manufacturing and Operations (MIMO) recently studied 100 businesses in sectors from automotive to mining, and used the data to determine best practices for adopting A.I. The Harvard Business Review breaks down the report in exhaustive detail, but here are some of the ways that certain organizations manage to stand out in their A.I. and machine-learning work:
What differentiates the organizations getting A.I. “right”? McKinsey and MIT’s Machine Intelligence for Manufacturing and Operations (MIMO) recently studied 100 businesses in sectors from automotive to mining, and used the data to determine best practices for adopting A.I. The Harvard Business Review breaks down the report in exhaustive detail, but here are some of the ways that certain organizations manage to stand out in their A.I. and machine-learning work:
- Governance: Smart companies continually refine and adjust their A.I. and machine-learning process. In addition, they also put a lot of internal emphasis on resourcing and guiding A.I. projects to success.
- Deployment: Broad deployment of A.I. and machine-learning processes is key; narrowing the scope of these technologies means that fewer parts of the company will ultimately benefit from them.
- Partnerships: Partnerships with academics, third-party vendors, and consultants can help accelerate the adoption and iteration of A.I. and machine learning.
