How Field Decision Intelligence Elevates Your Service KPIs

Field service leaders are under pressure from every direction:

• Work in the field is getting more complex.
• Experienced technicians are retiring faster than new ones can ramp up.
• Only the hardest problems make it past self-service and the contact center to your field teams.

That combination is eroding profitability and driving up costs to serve through lower first-time fix rates (FTFR) and longer mean time to repair (MTTR). This is exactly the problem Field Decision Intelligence (FDI) is designed to solve.

At Circuitry.ai, we bring FDI to life through Service Advisor and Parts Advisor – AI Workers that sit inside your existing workflows and help every technician make better decisions on diagnostics, repair, and parts.

In this post, we’ll:

• Define FDI in practical terms
• Explain the five core metrics that can be improved with FDI
• Show how Circuitry.ai’s FDI capabilities can help you improve each of these service KPIs

What is Field Decision Intelligence (FDI)?

Gartner^® defines Field Decision Intelligence as technologies that build on your existing field technician tools and use multiple forms of AI to deliver actionable, contextual insights that inform, guide, and assure the quality of technicians’ work.  You can access the Gartner research note “Exploiting Field Decision Intelligence to Improve Profitability” to learn more.

FDI gives every technician master-level guidance before, during, and after every job.

FDI solutions focus on making sure technicians are:

1. Informed – with the latest history, telemetry, prior conversations, and best practices for that asset and customer.
2. Educated – with context-specific instructions, diagnostics, and troubleshooting steps predicted from job details like model, equipment age, and conditions.
3. Stocked – with the right parts and tools for the most likely resolution paths.

As the job progresses, FDI tools provide real-time situational awareness, recommended actions, and quality checks, so issues are resolved on the first visit.

The result is improved performance across five critical service metrics: FTFR, MTTR, MTBF, CSAT, and CES.

1. First-Time Fix Rate (FTFR)

Definition

FTFR is the percentage of work orders resolved on the first visit, without a return trip, additional parts run, or escalation.

High FTFR means fewer truck rolls, lower cost per job, and happier customers. Low FTFR usually signals gaps in diagnostics, knowledge, or parts planning.

How FDI improves FTFR

FDI improves FTFR by making sure technicians arrive informed, educated, and stocked for the most probable resolution path.

• Better pre-job insight: FDI summarizes prior interactions, error codes, and telemetry, so the technician understands the likely issue before leaving for the job site.
• Guided diagnostics: During the job, the system recommends the next best questions, tests, or checks based on similar historical cases and work orders.
• Right parts, first time: AI predicts the most likely parts needed for a successful repair and flags potential part substitutions.

How Circuitry.ai helps

• Service Advisor gives technicians a guided diagnostic flow, using your historical cases, manuals, and tribal knowledge to recommend the most probable causes and fixes.
• Parts Advisor recommends parts based on the asset, symptom, and prior fixes and warns when a truck is likely under-stocked for the job.

Together, Service and Parts Advisors dramatically increase the odds that the technician has the right plan and the right parts to fix the issue on the first visit.

2. Mean Time to Repair (MTTR)

Definition

MTTR is the average time it takes to restore service, starting when a technician begins work and ending when the service is restored.

High MTTR means extended downtime for customers, more SLA risk, and higher cost to serve.

How FDI reduces MTTR

FDI shortens MTTR by eliminating trial-and-error and information hunting:

• Instant context: Rather than digging through long manuals or knowledge bases, technicians get concise, AI-generated summaries and the top recommended steps for the specific situation.
• Real-time guidance: As the technician performs checks and enters observations, the FDI system narrows down likely root causes and recommends the next step.  
• Automated knowledge retrieval: Diagrams, schematics, and relevant procedures appear automatically when needed.

How Circuitry.ai helps

• Service Advisor uses knowledge-driven conversational guidance retrieval-augmented generation (RAG) across your service manuals, bulletins, prior cases, and IoT data to present step-by-step troubleshooting paths tailored to each asset and symptom.
• Service Advisor captures technician feedback (“this fix worked / didn’t work”) to continuously refine decision paths, focusing future guidance on the fastest resolutions.

The result is fewer dead ends, less time spent searching, and a shorter average repair time.

3. Mean Time Between Failures (MTBF)

Definition

MTBF measures the average time between one failure and the next for a given asset or system. It’s a key indicator of reliability and long-term product performance.

Low MTBF means repeat failures, more site visits, and lower perceived product quality. Gartner explicitly notes that poor field performance can drive down MTBF and increase safety and quality issues.

How FDI increases MTBF

FDI improves MTBF by ensuring the underlying causes are addressed, not just the immediate symptoms.

• Root-cause focused guidance: FDI uses patterns across many similar incidents to recommend fixes that address root causes, not just quick patches.
• Feedback-driven knowledge updates: When technicians correct or improve a procedure in the field, FDI workflows can route that insight to knowledge managers and SMEs, who can validate and update the official fix steps.
• Preventive recommendations: Based on age, usage, and conditions, FDI can suggest additional checks or proactive replacements while the technician is already on site.

How Circuitry.ai helps

• Service Advisor learns which fixes lead to repeat failures vs. long-term stability and adjusts recommendations accordingly.
• Service Advisor prompt technicians to perform preventive checks when it detects high-risk conditions, for example, a pattern of similar failures on the same model or configuration.
• This turns each service call into an opportunity to increase reliability, not just restore service.

Over time, that means longer intervals between incidents and fewer repeat visits for the same problem.

4. Customer Satisfaction (CSAT)

Definition

CSAT measures how satisfied customers are with the service they receive, often through post-visit surveys or NPS-style questions. Gartner points to lower CSAT as one of the risks when field performance falters.

How FDI boosts CSAT

FDI improves CSAT by making the customer experience smoother, more predictable, and more professional:

• Higher FTFR and lower MTTR: Customers care most about “is it fixed?” and “how long did it take?” FDI directly improves both.
• More confident technicians: When techs have clear guidance and answers at their fingertips, they communicate with more confidence and transparency, building trust. 
• Fewer escalations and callbacks: Quality checks and guidance before job close help your technicians avoid repeat visits, which are one of the biggest sources of customer frustration.

How Circuitry.ai helps

• Service Advisor can generate plain-language explanations technicians can share with customers: what happened, what was done, and what to watch for.
• Both Service Advisor and Parts Advisor reduce the situations where techs “go back to the truck” or “call someone else” to hunt for answers.
• Optional integrations can trigger post-visit summaries and recommendations, making the service feel thoughtful and proactive.

That combination usually shows up as higher satisfaction scores and more positive comments in post-visit surveys.

5. Customer Effort Score (CES)

Definition

CES measures how easy it is for the customer to get their issue resolved: “How much effort did you have to put in to get this resolved?” Gartner recommends CES as a key metric to track when investing in FDI and related AI projects.

High CES (lots of effort) means the customer had to call multiple times, repeat information, chase updates, or be on site more than once. Low CES (little effort) is what we all want.

How FDI lowers customer effort

FDI reduces customer effort by streamlining the entire journey:

• Fewer handoffs and callbacks: With better decision support and quality assurance, more issues are resolved in one visit with fewer escalations.
• Less repetition: FDI keeps track of prior conversations, tests, and actions, so the customer doesn’t have to repeat their information multiple times across touchpoints.
• Proactive communication: When the technician knows what’s likely going on before arrival, they can set clear expectations and reduce surprises.

How Circuitry.ai helps

• Service Advisor pulls together conversation history, error logs, and previous work, so the technician shows up with a complete context, not a blank slate.
• Parts Advisor reduces rescheduling due to missing or wrong parts, which is one of the biggest sources of friction for customers.
• Integrated into your CRM/FSM, Circuitry.ai can pre-populate visit notes and summaries so customers feel like the organization remembers them and their equipment.

The customer’s experience becomes: one call, one visit, clear communication, with low friction.

Bringing It All Together

FDI is a practical way to use AI and knowledge to improve the decisions technicians make at every step of the job, and that directly moves the metrics your board and customers care about:

• FTFR – more issues fixed on the first visit
• MTTR – less downtime and faster resolutions
• MTBF – more reliable assets and fewer repeat failures
• CSAT – happier customers and stronger relationships
• CES – lower effort and smoother experiences

Gartner’s research clearly shows that organizations investing in AI-driven technician support, guidance, and oversight will outperform those that rely solely on traditional FSM tools.  

Circuitry.ai’s Service Advisor and Parts Advisor are designed to give you those FDI capabilities quickly, without a multi-year transformation project, so you can start improving your key service KPIs in months, not years.

If you’d like to see how Field Decision Intelligence would impact FTFR, MTTR, MTBF, CSAT, and CES in your service organization, please contact us to schedule an AI Advisory session and see the Service Advisor and Parts Advisor in action.