The AI Value Equation: How to decide which automations are worth building

Aug 5, 2026, By Alex Puttonen

Most field service teams have no shortage of AI ideas, but many AI initiatives still fail to deliver measurable value. Flowfinity is helping business leaders decide which projects are worth pursuing by sharing an AI Value Equation used to rank and prioritize AI proposals.

Often, the highest-impact idea is the least feasible, dragged down by messy data or disconnected systems rather than any flaw in the concept itself. The takeaway is to start with whichever use case clears the bar today, and do the data preparation and integration work necessary to put your remaining ideas on your roadmap.

Before you write a single prompt or connect a single workflow, it helps to score potential project ideas using a simple equation that forces the right conversation early. The AI value equation was developed for the University of British Columbia's AI in Digital Media program as a practical framework to compare opportunities, weighing potential impact against feasibility and risk.

Impact asks whether the idea moves a metric your organization actually cares about, such as first-time fix rates, reporting errors, or customer satisfaction. Feasibility asks whether you actually have the data, systems, and resources to build and scale it. Risk asks what happens if AI gets it wrong: a miscategorized support ticket is a minor inconvenience, while a failed safety inspection is not.

Multiply impact by feasibility, then divide by risk. The result isn't a precise financial forecast; it's a heuristic, a structured way to compare ideas on a level playing field instead of chasing whichever one got the most attention in last week's leadership meeting.

A score of 10 or above signals a strong candidate for a pilot. A score in the 6 to 9 range is worth scoping further. Anything at 5 or below usually means the idea needs more groundwork, or should be shelved until the underlying data and processes are ready.

Scoring potential field service use cases

1. AI-generated inspection summaries

Consider a workflow where a technician submits photos, voice notes, and mobile form entries from the field, and AI turns that raw input into a clean, customer-facing report.

Impact scores a 4 out of 5, since this cuts report turnaround from hours to minutes and improves consistency across a team with varying writing skills. Feasibility scores a 5, since the data already exists in structured mobile forms stored centrally and easily accessible by an AI assistant with the right permissions. Risk scores a 2, since a poorly worded draft is easily corrected by human review before it reaches a customer.

That puts the value at (4 × 5) ÷ 2 = 10.0. A strong candidate for an impactful project.

2. AI-assisted dispatch and route optimization

An AI assistant could review job locations, staff availability, and real-time conditions to recommend the day's schedule and alert field techs where to go.

Impact scores a 5, since faster response times and fewer wasted miles are both measurable and visible to leadership. Feasibility scores a 3: most organizations have the job and location data, but integrating live traffic with technician locations via GPS takes more tools and setup. Risk scores a 2 again, since a suboptimal route plan is inconvenient rather than dangerous, and a dispatcher can always override it.

This use case scores (5 × 3) ÷ 2 = 7.5. Worth scoping, and potentially a good second or third project once the team has some demonstrable success under its belt.

Where feasibility kills good ideas

Notice that the higher-impact idea on this list, dispatch and route optimization, scored lower overall. Not because it's a bad idea, but because feasibility dragged it down.

This is the pattern that trips up most field service organizations. The most exciting AI use cases, the ones that sound best in a boardroom, are often the ones sitting on the messiest data or needing integrations with external tools that make them more complex to deliver.

This is also where the equation earns its keep. It doesn't just rank ideas; it tells you why an idea isn't ready, which is usually more useful than the score itself. A low feasibility score is a to-do list: consolidate the data, standardize the inputs, close the integration gaps. Do that work first, and a use case that scores a 5 today might score an 8 in two quarters.

From score to roadmap

Once you've scored your list, the highest-value, lowest-risk item is your starting point, not because it's the most impressive demo, but because it's the one most likely to deliver.

That's also where the Flowfinity no-code platform changes the equation itself. When adding an AI-enhanced step to a workflow doesn't require intense developer resources, the feasibility score goes up. Data that is already structured in a centralized system is immediately usable, and guardrails like role-based access and human-in-the-loop keep risk scores low.

Flowfinity experts are here to help if you want a second opinion on scoring and scoping your AI projects. You may also want to read about structuring a successful AI pilot project or embedding AI directly into your workflows.

Getting started

You don't need to embark on a multi-year AI transformation. Ask: where in your workflows would a technician benefit from some assistance before they make a judgment call? Where is there manual effort spent summarizing, analyzing and generating text records? Start there. Map the process, build it in Flowfinity, test with a small team, and iterate.

Talk to our experts for advice about where in your operations AI can deliver the fastest return and how we can help.