AI has been getting its fair share of inflated and unrealistic expectations due to a lack of broad understanding of this wide-ranging space by software vendors and customers. Software tools can be extremely powerful, however the services, infrastructure, data quality, architecture, talent and methodologies to fully deploy in the enterprise are frequently lacking. This four-part series by Earley Information Science and Pandata will explore a number of issues that continue to plague AI projects and reduce the likelihood of success. The sessions will provide actionable steps using proven processes to improve AI program outcomes.
In the final session the topic is "what's next?"
Many organizations have begun their AI journey and need to bring capabilities to the next level. There may be disconnected approaches and decentralized decision making. Lessons and successful approaches are not leveraged across siloes or repurposed and built upon. In other cases, projects may have shown value in a pilot, but are being held back from full deployments by various constraints.
This final session in our four-part series provides several approaches for:
- Determining priorities for your efforts
- Installing metrics to monitor progress and impact
- Structuring governance and decision making
- Engaging in appropriate risk and change management
Be sure to check out all sessions in this series:
- Designing AI Programs for Success - Part 1: Why AI Projects Fail – 3 Key Ingredients to Success
- Designing AI Programs for Success - Part 2: Organizational Readiness
- Designing AI Programs for Success - Part 3: Why You Need Ontology and Information Architecture for Artificial Intelligence to Succeed
- Designing AI Programs for Success -Part 4: Beginning (or continuing) the AI journey – how and where do you focus your resources?