What it takes to put AI into field operations and get to first results in ten weeks.
The complexity of your assets keeps growing. Expertise sits unevenly across your engineers, and upskilling takes time. Knowledge is scattered across manuals, tickets, telemetry and memory. And your scarce resources become a real bottleneck for growth and business continuity.
Customers, meanwhile, expect faster fixes and better uptime.
The good news is that AI can do a lot of heavy-lifting: from pre-visit diagnosis, to resolution, follow-up and closure.
Developing a robust, scalable solution however is the hard part. It requires a mix of business and technical knowledge.
On 16 October we will show how to get from AI ambition to operational reality in 10 weeks.
What you will see
- Examples of systems that pulls together live measurements, service history and knowledge documents, then walks a technician through diagnosis and repair.
- How they were built and what needs to happen before writing a single line of code. This is usually what decides whether these systems scale beyond a pilot.
- How it can scale. From a single use case that shortens resolution times, to a full end-to-end customer support system that spans operations, supply chain, customer services, and more.
Who it is for
Companies with large teams of field service engineers maintaining complex assets: utilities, telecoms, infrastructure networks, building technology, industrial equipment and energy systems.
- Directors, VP, or Heads of field operations, service delivery, or customer operations.
- Technical leaders responsible for getting reliable results from AI.
- Knowledge & domain owners whose expertise the AI depends on.
Practical details
- Date: Friday 16 October
- Time: 11:00–11:45 CEST
- Where: Online
Format: 30 minutes of presentation and demo, 10 minutes Q&A.