Problem Seen – A common mistake made by many companies in their first AI project is to focus on data first, prediction second and business problems third.
They fill a data lake to the brim with diverse information from many unrelated sources and then try to discover how AI might be able to produce business value from that data.
In addition to being inefficient and ineffective, for companies using personal data from EU citizens and residents, this is also illegal.
Lesson Learned – AI projects should first define a measurable business problem, like the cost of customer attrition.
Then they should identify, qualify, and certify relevant customer-touching business process data and regulation-compliant customer data.
And finally, they should specify and model an actionable predictive decision that can solve the given business problem using the defined business and customer data.
3.
Start at the Glass Problem Seen – In many AI projects, the people who will use the new application have little or no visibility or input into the solution until it is implemented and deployed.
As a result, the AI team builds an application that is usually disruptive – and not in a good way – to established business processes and best practices; and user acceptance of the application is thus low and slow.
Lesson Learned – AI doesn’t take jobs; it takes tasks.
And successful, profitable initial AI solutions should focus on automating low value business process steps that require high human effort.
4.
Prepare to Evolve Problem Seen – Most initial AI solutions solve yesterday’s problems.
That is, by the time the solution is delivered, related business conditions and priorities have changed, creating a gap between solution function and business needs.
AI, just like human intelligence, is dynamic and it is never finished.
Many executive AI newbies are dismayed to hear that a predictive model is correct three times out of four, not realizing that this is actually a great result in many cases.
Lesson Learned – Successful AI projects depend on agility and flexibility in goal setting, resource allocation, and ongoing optimization.
Going from 75% accuracy to 95% requires continuous improvements in data quality and availability, choice of machine learning tools and techniques, and target application goals and functionality.
And, predictive models can be brittle; they might work well today but, due to changing goals and business conditions, not work at all tomorrow.
To avoid an AI train wreck in your first project and beyond, study the problems seen and lessons learned by others who have already tried, failed, learned, and then succeeded.
Don’t be rigid, don’t be afraid, and, most importantly, don’t believe the AI hype.
Instead, be practical, be realistic, and be patient.
About the Author Tim Negris is SVP of Marketing and Business Development at Boston-based Rulex Inc.
, a provider of proprietary enterprise AI technology for Supply Chain, Financial Services, Energy Management, Public Utilities, Healthcare, and IoT applications.
He is a long-time software industry veteran who has played a pivotal role in introducing many new advanced technologies to the world.
Tim has been a prolific thinker and writer on a wide variety of advanced technology subjects, as a regular contributor to several large tech publishers and as a guest lecturer at Stanford University.
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