Addressing Organizational Resistance to Predictive Analytics and Machine Learning

Many who work within organizations that are in the early stages of their digital transformation are surprised when an accurate model — built with good intentions and capable of producing measurable benefit to the organization — faces organizational resistance. No veteran modeler is surprised by this because all projects face some organizational resistance to some degree. This predictable and eminently manageable problem simply requires attention during the project’s design phase. Proper design will minimize resistance and most projects will proceed to their natural conclusion – deployed models that provide measurable and purposeful benefit to the organization. Keith will share carefully chosen case studies based upon real world projects that reveal why organizational resistance was a problem and how it was addressed.

• Typical reasons why organizational resistance arises.
• Identifying and prioritizing valid opportunities that align with organizational priorities
• Which teams members should be consulted early in the project design to avoid resistance
• How to estimate ROI during the design phases and achieve ROI in the validation phase
• The importance of a ‘dress rehearsal’ prior to going live.