Clients don’t care about Data Analytics AI or operations. They exclusively care about only three outcomes (the 3Es):
- Efficiency, measured in terms of reduced cost or enhanced scale
- Experience, measured in terms of enhanced speed or higher customer satisfaction
- Effectiveness, measured in terms of enhanced revenue or better end outcomes
Today (and for the next decade), it is impossible to deliver these outcomes without Data Analytics & AI.
However, 70%+ of investments in data analytics & AI will fail or under-deliver. Why?
Because – what gets built never gets injected into an operational workflow or never gets adopted by end users or customers. In other words, the AI solution stays unoperationalized. (In another post, I will cover the primary drivers for lack of operationalization with real examples.)
Current categories of solutions in the market don’t address this problem. Typically, solution providers lack the combination of – end-to-end talent stack, technology and methodology to operationalize data analytics and AI.
Over the last three decades, new specialized categories were created to leverage the underlying new transformative technology of that decade. For example, IT became a new category in 1990s; BPO in 2000s; Data Analytics in 2010s. Similarly, a new category is needed to leverage the impact of AI.
We call it Data Analytics & AI Operationalization.
