How OEMs Can Evaluate AI Co-Pilots for IoT-Driven Service Operations
AI co-pilots for service operations are moving from experimentation to board agendas. Industrial IoT and predictive maintenance markets are expanding rapidly, with estimates putting the global predictive maintenance market at more than 13 billion USD in 2025 and over 70 billion USD by 2032. Industrial IoT itself is projected to grow from roughly 289 billion USD in 2024 to more than 840 billion USD by 2033.
At the same time, nearly 80 percent of high performing field service organizations already use AI and workflow automation to improve response times and first-time fix rates. AI co-pilots that sit on top of IoT, service, and knowledge systems are a logical next step.
For OEM leaders, the challenge is not whether AI co-pilots will matter. The challenge is how to evaluate them with discipline, avoid hype, and fund what will scale.
This article outlines a structured evaluation approach for AI co-pilots in IoT-driven service operations, written from a decision-maker perspective.
1. What an AI co-pilot means in IoT-driven service operations
In this context, an AI co-pilot is an assistive system that works alongside human planners, dispatchers, and technicians. It uses data from IoT devices, service history, parts inventory, and knowledge bases to:
- Recommend next best actions for faults and alarms
- Guide technicians during diagnosis and repair
- Automate work order creation and documentation
- Assist in scheduling, routing, and resource allocation
AI co-pilots are different from generic chatbots. They are deeply integrated into operational workflows and systems of record, and they need to operate with high reliability, explainability, and domain context.
2. Start with outcomes, not features
Many pilots start by showcasing impressive models without a clear link to business value. OEMs should anchor the evaluation in a small set of measurable, IoT-linked outcomes, such as:
- First time fix rate for connected assets
- Mean time to diagnose and repair
- Service cost per asset or per incident
- Uptime and SLA compliance for critical equipment
- Technician productivity and training time
Industry analyses show that AI in field service commonly delivers improvements in operational effectiveness, customer satisfaction, and productivity, often in the 10 to 30 percent range for specific metrics when implemented well.
Define 3 to 5 priority metrics before you look at any AI co-pilot demos. Those metrics will drive the evaluation criteria.
3. Evaluation framework for AI co-pilots in IoT-driven service
A practical way to evaluate AI co-pilots is to assess them across a set of dimensions. The table below can be used as a scoring template during vendor reviews and pilots.
3.1 Evaluation dimensions and key questions
| Dimension | What to look for | Key questions for vendors and internal teams |
| 1. Business impact and use case fit | Clear link to top 3 service KPIs, with realistic impact ranges and references from similar deployments | Which metrics will this co-pilot move in the first 6 to 12 months, and by how much, based on comparable customers or pilots? |
| 2. IoT and data integration | Ability to consume real time and historical IoT data, work orders, parts, contracts, and manuals | How does the co-pilot connect to our IoT platform, EAM, FSM, and CRM systems, and what integration patterns have you already implemented for other OEMs? |
| 3. Model quality and domain intelligence | Use of domain specific prompts, tools, and reasoning on top of base models | How is the co-pilot grounded in our equipment models, fault codes, and procedures, and how do you prevent generic or hallucinated answers? |
| 4. Human in the loop and UX | Practical experience for dispatchers, planners, and technicians inside their existing tools | How many clicks does a planner or technician need to use the co-pilot on a typical job, and where does it sit in their current workflow screens? |
| 5. Security, compliance, and data governance | Enterprise grade security, data residency options, and clear policies on data usage and model training | Where is data stored and processed, what data leaves our environment, and how do you handle PII, telemetry, and customer specific information? |
| 6. Operations, reliability, and monitoring | SLAs, monitoring dashboards, and incident handling processes for the co-pilot itself | What uptime and response time guarantees do you offer, and how do you monitor model performance, drift, and business KPIs over time? |
| 7. Change management and adoption | Training, playbooks, and change support for service teams and partners | How do you onboard technicians and planners, and what adoption rates have you achieved in similar organizations? |
| 8. Economics and scalability | Transparent pricing connected to value drivers, with a clear path from pilot to scale | How do costs scale with number of assets, users, and transactions, and what ROI have past customers achieved after 12 to 24 months? |
OEM leaders can convert this table into a scored checklist, assigning weightings to each dimension based on strategy. For instance, an OEM with high exposure to regulated industries may assign more weight to security and governance, while one in a competitive service market may focus more on time to value and adoption.
4. Data and IoT readiness checkpoints
An AI co-pilot is only as strong as the data it can reach. Before, or in parallel with, evaluating vendors, assess your own IoT readiness on three levels:
- IoT coverage and quality
- Share of installed base that is connected and sending usable data
- Data latency from device to analytics or event pipeline
- Consistency of tags, fault codes, and equipment hierarchies
- Service and asset data foundation
- Clean mapping between assets, customers, contracts, and sites
- Historical service records with structured fields for symptoms, cause, and resolution
- Parts and inventory data that can be linked to specific equipment and events
- Knowledge and content
- Digital service manuals, SOPs, wiring diagrams, and troubleshooting trees
- Internal knowledge bases, FAQs, and tribal knowledge that can be captured and organized
Companies that have invested in these foundations see faster AI payback in field service and maintenance.
If you are still early on IoT and data standardization, choose a co-pilot that can start with narrower use cases, such as knowledge assistance and work order summarization, while you mature your data estate.
5. Designing a disciplined pilot and evaluation plan
Instead of a loose proof of concept, OEMs should run disciplined pilots with a clear evaluation design.
5.1 Pilot scope
- 1 to 3 asset families with good IoT coverage
- 1 to 2 regions or service partners
- A defined set of use cases, for example:
- Alarm triage and next best action suggestions
- Technician guidance for 10 to 20 common fault patterns
- Automated work order summaries and parts recommendations
5.2 Pilot duration and sample size
Most OEMs can gather meaningful data in 12 to 16 weeks if they pick assets with sufficient volume and incident frequency. Industry case studies suggest that AI in field service begins to show measurable impact within a few months when there is already a digital foundation.
5.3 Pilot metrics and thresholds
You can use a simple metrics table like the one below to track performance.
| Metric | Baseline (before co-pilot) | Target after 3 months | Notes |
| First time fix rate | X percent | X + 5 to 10 percentage points | Track on IoT connected assets in pilot scope only |
| Average diagnosis time | Y minutes | 10 to 30 percent reduction | Measured from alarm creation to decision on action |
| Technician handle time on site | Z hours | 5 to 15 percent reduction | Focus on complex tickets, not quick wins |
| Average time to close work order | A hours or days | 10 to 20 percent reduction | Includes documentation and approvals |
| Technician satisfaction with tools | Survey score | +0.5 to +1.0 on 5 point scale | Measure adoption and perceived value |
These numbers are illustrative. OEMs should use their own history and the vendor’s references to set realistic thresholds. Metrics such as data quality, model usage, and human evaluation of recommendations should also be part of the scorecard.
6. Build vs buy, and the right questions for vendors
Many OEMs are debating whether to build their own co-pilots on top of foundational models, or adopt vendor solutions embedded in existing field service and IoT platforms.
6.1 When building in house can make sense
- You have a strong data and AI engineering team with experience in MLOps and GenAI operations
- Your IoT platform and service stack are already integrated and standardized
- You want deep differentiation in how you diagnose faults and orchestrate service
6.2 When partnering is often more practical
- You rely on commercial FSM, EAM, or CRM platforms that already ship AI co-pilot features
- You want to move quickly on standard use cases and avoid heavy upfront engineering
- You prefer a managed service model for model updates, monitoring, and compliance
Regardless of the path, OEMs should ask vendors a consistent set of questions:
- Which parts of the stack are yours, and which are from hyperscalers or model providers
- How you handle model upgrades, safety, and governance over time
- What your reference customers in industrial and IoT intensive sectors have achieved, with numbers
- How lock in works and what happens if you decide to change vendors or models in the future
Market activity, including investments in field service and AI companies, signals that vendor ecosystems around co-pilots will continue to deepen. OEMs should use this to their advantage and demand transparency and measurable outcomes.
7. Common failure modes to avoid
Several patterns recur in unsuccessful AI co-pilot initiatives:
- Feature first, workflow last
- Impressive demos that do not align with real technician or planner workflows
- Adoption stalls because using the co-pilot feels like extra work
- Weak link between IoT and service data
- IoT events not tied cleanly to assets, customers, and service history
- Co-pilot recommendations lack context and are not trusted
- No clear success criteria
- Pilots are declared successful based on subjective feedback
- No quantified impact on first time fix, MTTR, or cost per incident
- Underestimating change management
- Technicians view the co-pilot as surveillance, not support
- Planners fear automation will deskill their roles
- Incomplete governance
- No clear owner for model performance, drift, and incident handling
- Data governance and security questions are answered informally
Addressing these issues in the evaluation phase prevents expensive rework later.
8. A CEO level summary
For OEMs, AI co-pilots are not side projects. They sit at the intersection of IoT, service strategy, and customer experience.
A disciplined evaluation approach should:
- Start from 3 to 5 service metrics that matter for your installed base
- Use a multi-dimensional framework that covers business impact, integration, model quality, UX, security, operations, adoption, and economics
- Run structured pilots with clear baselines, thresholds, and governance
- Take a pragmatic view on build versus buy, and insist on transparency and references
The objective is not to have an AI co-pilot label in your portfolio. The objective is to improve how your organization responds to signals from the field, supports technicians, and protects customer uptime.
If you are exploring IoT driven service operations or evaluating AI co-pilots for your installed base, our team can support with implementation and strategy discussions. You can reach out to us to begin a focused conversation.
