Technology and innovation, and all the wonders and disruptions they bring, surround us. Service operations leaders navigate digital and physical worlds increasingly filled with the solutions that will help them plot their course into an AI future. Smart, connected products have the potential to provide significant market advantages, but new possibilities require new strategies from leadership.

This new report, co-created by Emerj and PTC, will explore a roadmap approach that industry enterprises can adopt to bring about the transformation that has to happen within organizations in order to bring AI to life. It will map the major stages of AI project deployment and discuss what it takes to progress from one to the next - from organizing enterprise data to deploying a successful AI solution. It will also will examine how to move into project execution by reviewing several use-cases that span both the manufacturing process itself as well as what happens to a smart product once it is out in the field

In this report, you’ll gain insight on:

1. Align Your Data and Business Strategies

Adopting AI begins with framing the problem that you are trying to solve. Determine the specific outcome you are looking to achieve and how success is measured.

2. Understand Your Target Business KPIs

After creating a data-informed perspective on an AI project - and aligning that project with business strategy, it is time to capture and formulate the key performance indicators (KPIs) that your project will optimize.

3. Define User Actions and Tasks

Once a strategy-aligned project has been determined, and KPIs have been developed, leaders should consider which actions need to be taken as a result of the insights provided by data analytics.

4. Envision the End Product

Leaders should determine who will use the application and then ask these users how they would use it, and how much time they would need to proactively integrate its information into their processes.

5. How to Inject Human Business Intelligence into Machine Learning Models

Along with an Emerj Interviewee a Tier I Auto Supplier was able to drive the quality improvements it needed and get a deeper insight into statistical controls that supported their quality objectives.

6. How Data Can Predict Operational Failure

An industrial organization that manufactures large commercial fans collaborated with PTC to develop a data process that predicted inadequate functioning