HBR describes a project that after  4 years and $62mill achieves nothing and makes the point that "low-hanging fruit" should be the focus rather than big "moonshot" projects.

The lure of AI can be seductive; leapfrog analogue competitors and transform the business. But, as always, data is key and the quantity and quality beyond the capabilities of most enterprises. If you can only access 20% of your data you will not be able to manage "moonshot" scope AI. And  that is the case for most companies and organisations- all that nasty unstructured data hidden in data silos and multiple legacy systems inherited with each M&A.

Try smaller projects and the results may well make a huge difference in three areas.

  1. Process Automation
  2. Cognitive Insight
  3. Cognitive Engagement

To do so you will need to:-

  • Understand your customers and future behaviour
  • Understand the technologies
  • Create a portfolio of projects
  • Launch pilots
  • Focus on low hanging fruit
  • Scale up

Follow the advice in Harvard Business Reviews' "Artificial Intelligence for the Real World"  January-February 2018  article below.