As part of its ongoing Virtual Education series, the Emerging Trends and Technology Network (ETTN) recently hosted an engaging session focused on one of the industry’s fastest-growing topics: using artificial intelligence (AI) to turn engineering data into faster, clearer business decisions.
Industry experts Bob Morreale of The Tuning School, Kannan Parekh of Allian Tech and Kelleigh Shankel of Limelight Logic led attendees through practical examples of how AI can help engineering, product development and operations teams manage increasingly complex data sets without sacrificing human expertise or oversight.
A key theme throughout the session was that aftermarket companies already have access to enormous amounts of information, from warranty claims and installer feedback to validation data and calibration logs. The challenge is no longer collecting data; it’s interpreting it quickly enough to make informed decisions before opportunities are missed.
One of the most impactful demonstrations showed how AI tools can analyze thousands of warranty claims and support tickets in minutes, helping engineers identify recurring failure patterns, prioritize risks and surface operational trends that may otherwise remain hidden across disconnected systems. Presenters emphasized that AI is most effective when used to reduce “interpretation fatigue,” allowing engineers to spend more time validating solutions and solving problems instead of manually sorting spreadsheets.
The session also explored how companies can begin implementing AI immediately using tools many organizations already have access to, including ChatGPT, Microsoft Copilot and other common platforms. Attendees learned that successful AI adoption does not require massive budgets or enterprise-level infrastructure. Instead, presenters encouraged teams to start small by focusing on repetitive, high-volume tasks such as summarizing reports, organizing data, identifying anomalies and visualizing trends.
Another major takeaway centered on responsible AI usage. Speakers stressed the importance of protecting proprietary information, establishing validation checkpoints and ensuring that human expertise remains part of every critical engineering or compliance decision. As several presenters noted, AI can identify correlations and patterns, but engineers still determine root cause and final accountability.
The discussion also highlighted the growing importance of workflow discipline, data governance and cross-team collaboration as AI tools become more integrated into engineering and manufacturing operations.
Want to dive deeper into the demonstrations, workflows and real-world examples shared during the session?