Automation Reliability & Maintenance Center

Keep AI Pilot Projects Moving

EP Editorial Staff | February 1, 2022

Don’t bite off more than you can chew by implementing AI solutions that will require costly, time-consuming development and high upkeep costs.

Stories of manufacturing companies getting stuck in the pilot stage of implementing AI (artificial intelligence) solutions are prevalent.

Successful leaders, however, have learned how to stage effective pilot projects that lead to AI deployment across multiple plants and lines, boosting productivity in ways they would have never thought possible a few years ago. Here are some steps that will keep your pilot project moving.

Identify a use case for AI

Identify a small-use case. This pilot project should attack something that has been difficult to achieve and where success would expand current capabilities, increase profitability, and/or earn your organization a competitive advantage. Going after net-new capabilities allows organizations to uncover unrealized value without disrupting the current ways of doing things, thus maintaining excitement for digital transformation. The best pilots occur in processes that are obviously inefficient, where failure entails little loss, but success can mean wholesale transformations. 

Once you have mastered deployment of AI through a pilot project that tackles the most important and challenging aspects of production, you stand a better chance of learning lessons that might scale throughout your organization. Early wins will foster other use cases that will leverage the initial pilot-project knowledge.

Get to value fast

Don’t bite off more than you can chew by attempting to implement AI solutions that will require costly, time-consuming development in the short term and high upkeep costs in the long term. Do-it-yourself AI solutions can require an army of data scientists to roll out successfully. This extra layer of personnel is precisely what can disrupt efficiently functioning processes that are not a good fit for AI solutions. These data scientists, furthermore, will likely not possess domain-specific knowledge that is key to fashioning the most effective AI solutions, thus slowing the pilot project and increasing its chance of failure.

Taking advantage of the latest in AI, sensors, machine learning, and machine reasoning, these solutions can monitor vibrations and conditions, identify breakdowns that could happen in the future, and suggest corrections. Having a platform that’s tailored to industrial operations rather than a generic platform can help reduce the cost and time needed to build new-use cases.

Identify AI champions

People are central to the organizational shift that comes from those capabilities. You’ll need champions in your enterprise who own initiatives when they hit snags or lose momentum and elevate successes along the way. Like anything else in business, pilots collect dust when nobody believes in them or invests the necessary time and resources to make them successful.

A champion doesn’t have to be an executive or plant manager. Champions are simply people who can make a pilot happen, demonstrate its value, and be accountable for the value delivered. The best pilots often emerge as so-called “skunk works,” where small groups bypass organizational hurdles to innovate. When supervisors or leaders of those grassroots teams become champions, pilots don’t sit on the shelf. They achieve lasting impact. EP

Information provided by Prashant Jagarlapudi, Vice President of Products, Savigent, a Symphony Industrial AI company, Woburn, MA,


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