If not addressed, they will reduce project ROI and cause early termination.
The focus here is on predictive maintenance projects for the Industrial IoT (IIoT). But it applies to virtually any AI project focused on anomaly detection within time series data.
WHY IT MATTERS: Walking the path to successful AI is a challenging process even for the experienced. Done right will be transformative. It will give your the well earned reputation of a rock star. AI done wrong leads to frustration and workers retreating to the legacy process. Failure will be in the wind. Knowing and managing these seven risk factors will help you find consistent success:
Use Case Risk
Platform Risk
Team Risk
Data Risk
Data Science Risk
Cultural Risk
Leadership Risk
A LITTLE DEEPER: Each risk factor comes from a different part of the organization, a different set of goals for each. Conflict results as their goals are often divergent. You must achieve a balance, a synergistic compromise that achieves real, measurable results.
Use Case Risk - Significant domain expertise is required. Will the assets selected be have a reasonable chance of demonstrating an suitable ROI in the 60-90 day period of the POC?
Platform Risk - Will the platform scale? Will it integrate with other sensor types and workflows for integrated decision making? Security? How survivable is the vendor?
Team Risk - AI solution building requires a multi-disciplinary team with specialists in IT/OT, data science and engineering. Can they work together?
Data Risk - Does the failure condition show in the data? Are there other anomalies in the data that will create excessive false alarms?
Data Science Risk - The data science team does not have the understand of the physics behind the use case to properly model it and create underlying diagnostics.
Cultural Risk - Workers are afraid of change and fearful of losing their jobs.
Leadership Risk - AI can have a slow to start due to setbacks from other risk factors and inherent learning cycles. Does leadership have patience to realize these gains?
THE BOTTOM LINE: AI is not IT. Leading these projects requires a new set of skills. Stay tuned for future blog posts with detail on each of these seven risk factors.
STAY TUNED: We provide more detail on these risk factors and how to mitigate them in your POCs and deployment. We will discuss how Amazon Monitron, as a next generation predictive maintenance platform, was built to address many of these risk factors.
YOUR PERSPECTIVE? Have we left our a risk factor? A platform you recommend? Share it in the comments below. Thank you!
NEXT POST: Scaling Your Predictive Maintenance Program
GetIQ.ai is a blog about building AI solutions for augmenting decision making and empowering people that make them. It's authored by the engineers at DecisionIQ.
ABOUT DECISIONIQ: We are "boots on the ground" factory engineers expert at the adoption of AI and machine learning into operations. As consultants and system integrators we bring our experience in a mix of a well structured programs that enable our clients to produce winning results and maximum ROI. You will learn to use AI to build competitive advantage by increasing plant uptime, quality and yield. With 24 POCs and 16 deployments we have achieved for our client’s ROIs as high as 60x and a cumulative $22 million in savings.
AN AWS PARTNER: We provide capability in design of AI solutions for industrial applications. We work with the AWS Industrial IoT services stack, cloud migrations and Amazon Monitron POCs and deployments. For qualifying customers, many of these capabilities are eligible for AWS subsidized funding.
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