AI tools promise everything, but their real-world results often have difficulty living up to the hype and high cost that comes with them ie. AI product lifecycle predictions providing only marginally different suggestions than current predictive analytics. Despite these limitations executive boards continue to view AI as a magic bullet to optimize and automate the workforce for the sake of efficiency.  

Service leaders will discuss lessons learned from actual AI business cases leaving you with takeaways on: 

• Building a more tangible AI ROI to understand and better communicate the actual benefits and weaknesses from a cost, efficiency, and support perspective
• Creating a better semantic data layer for AI, and LLMs to draw from, by identifying critical structured and unstructured data from work logs, sensor data, equipment manuals, etc.
• Understanding how various AI tools use data and come to their answers in order to know their strengths and weaknesses in different use cases
• Improving communication with the board by creating realistic AI expectations and implementation timelines 

As costs continue to rise due to labor shortages, fuel costs, equipment costs, and overall inflation, it is important to find efficiencies and savings without impacting service delivery. The most effective way to find efficiencies is to standardize service delivery across customers so any improvement found with one customer can immediately be implemented with others. However, a variety of factors including region, culture, product, and more, make each customer different. Despite these differences it is still important for leaders to standardize workflows where possible in order to measure and optimize for the best outcomes. 

This discussion will unpack the ways in which organizations are creating standardized but flexible delivery models that work across OEM, contract manufacturing, and third‑party ecosystems, without alienating customers or field teams. 

• Defining a core service-delivery blueprint that is adaptable by customer or region without losing measurability or control
• Finding practical levers to drive standardization in the real world: system‑based rules, outcome‑based KPIs, and contract language that nudge customers and suppliers toward common ways of working
• Linking standardized processes to quality, level of effort, uptime, and cost by using consistent data capture, sourcing choices, and master‑data discipline to reduce rework and enable future automation and AI