Drivers for Success in AI
According to research, despite increased interest in and adoption of artificial intelligence (AI) in the enterprise, 85% of AI projects ultimately fail to deliver on their intended promises to business. AI teams claims a major source of AI challenges is to be found in senior leadership who are lacking to see the value it can bring, however, is this the real problem?
Multiple factors come into play when developing and implementing an AI project: Data, skills, domain understanding, company culture, AI strategy, data strategy, team setup, ... are a just few topics that play a key role in the success of AI projects.
In this session, it will become clear that the major focus should not be on data and technology, but on people and processes. A common mistake. We will find answers on the questions: How to define high impact use cases? How to identify projects that will create the highest impact on company KPI’s, rather than picking projects where one only sees the scope of a technical breakthrough?
You will understand what’s needed to move beyond the typical PPP (Pilot, POC & Prototype) phase while looking at some real examples and common reasons why AI projects fail to deliver.
The successful drivers for AI implementations will be your new guideline when leaving the session.