Driving AI adoption with robust evaluation

Ford’s story provides a valuable case study for AI adoption and highlights a pattern that may become common across organisations:

1. Adopt AI systems on the expectation of significant increases in effectiveness and/or efficiency for some set of tasks
2. Adjust the human workforce that performed these tasks on the basis that the expectation will be fulfilled
3. Discover through experience that the expectation of AI systems is not fulfilled for one or more of the tasks
4. Adjust human workforce to ‘work alongside’ the AI systems and attempt to regain the effectiveness achieved prior to AI adoption

Rather than being driven by the expectation of AI systems, organisations need to be driven by the reality.

Adoption (or not) of AI systems should be informed by robust evaluation, performed in parallel with existing systems.

Organisations can then make informed decisions about the tasks for which AI systems can increase effectiveness and/or efficiency, and the governance structures and processes that will be required.

Read the BBC article on Ford at: https://www.bbc.co.uk/news/articles/cgrkd41n2v9o