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.
- Process Automation
- Cognitive Insight
- 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.
