Artificial Intelligence (AI) will revolutionize manufacturing and supply chain management. From operational gains in production efficiencies to unlocking new markets and sales opportunities, AI, when built correctly, has the power to transform the entire value chain. Sounds great, but the catch is that AI can only be built from a foundation of well-governed, clean, quality data. Contrary to some vendor claims, AI cannot fix bad data. In order to build an AI powered enterprise, programs must get needed resources for data remediation, show value from applying those resources appropriately and overcome multiple barriers to business adoption.
This session will provide concrete steps for tying foundational investments to business outcomes and speeding adoption of AI projects by
- Understanding the nature of “cognitive” versus structured training data
- Identifying areas for prioritization of data remediation
- Installing scorecards and dashboards that measure progress at three distinct levels of detail: data remediation, process improvement and business outcomes
The session will end with target use cases for getting started that meet the criteria of solving immediate business problems; providing clear and measurable ROI and; justifying investment in a data foundation that can be leveraged across multiple initiatives.