Adoption Brief.

The problem

After enterprise software is implemented, customers often struggle to realize value. Implementation consultants must assess whether a customer is actually adopting the system, diagnose process gaps, and decide on follow-up — but the work is largely manual and depends heavily on the consultant knowing where to look.

Three pain points dominate: adoption data is scattered across usage logs, workflow completion, support history, training records, and meeting notes; interpretation is manual; and recommendations depend on individual consultant experience. Turning that into a clear adoption story is slow, inconsistent, and hard to scale across many customers.

The solution

Adoption Brief is an AI feature within an industrial CMMS/EAM platform that generates customer-ready review briefs from adoption signals, workflows, and success criteria. A consultant selects a customer, site, and review period, and the system produces an evidence-backed brief.

The key differentiator is that it measures true adoption, not just usage — evaluating against the customer's own playbook rather than generic benchmarks. Each brief includes an executive readout, workflow status, key gaps, positive signals, and recommended actions. Consultant-in-the-loop tone and sensitivity controls let teams reframe the brief for different contexts while preserving the underlying evidence and recommendations.

How it works

The MVP evaluates three adoption signals — work request triage timeliness, preventive maintenance completion rate, and work order completion quality — comparing each against the customer's configured threshold and classifying it as Healthy, Watch, or At Risk. The system loads a customer playbook and adoption evidence, then generates the brief.

It uses GPT-4.1 (with GPT-4.1 mini as a lower-cost fallback), chosen for instruction following, structured output, and business writing. A key iteration was to stop asking the model to calculate metrics from raw CSV rows and instead feed a pre-calculated Adoption Signal Summary as the source of truth. Guardrails prevent inventing data, overstating confidence, or implying unsupported causes; tone can adapt but truth is not diluted. In manual review across five simulated scenarios (10 outputs), 6 fully passed and 4 were partial passes, with no full failures — remaining issues being minor over-inference on causes and formatting.

Who it's for

The product is B2B, sold to enterprise organizations in asset-intensive industries such as manufacturing, processing, mining, and utilities, where the buyer is a maintenance, operations, reliability, or digital transformation leader.

The feature is for both internal and external users: implementation consultants and customer success teams who drive rollout and monitor for regression, and customer-side maintenance leaders, planners, and supervisors responsible for sustaining value. The most strategically important users are the implementation and customer success teams, since they drive adoption and prove value.

Why it matters

The CMMS market is projected to grow from about $1.29B in 2024 to $2.41B by 2030 (~11% CAGR), with the broader EAM market around 9% and the digital adoption market growing far faster at roughly 23% — signaling strong demand both for maintenance software and for tools that help customers actually realize value from it.

Currently a demo-ready proof of concept wired to the OpenAI API using simulated data, the intended launch is a controlled beta with a small group of trusted internal consultants on real projects. Because real customer and employee-level data would be involved, the plan builds in consent, PII minimization via pseudonymous identifiers, auditability, and PIPEDA and Quebec Law 25 review before any customer data is used.

At a glance

Project

Adoption Brief

Built by

Michael Kainola

One-liner

Adoption Brief is an AI feature for enterprise technology platforms that generates customer-ready review briefs from adoption signals, workflows, and success criteria.