Eqwitty AI ReadinessAI readiness assessment for manufacturing
Manufacturers sit on more machine data than almost any other sector and often use less of it. Sensor feeds, quality logs and maintenance records live in systems that were never designed to talk to each other, and the people who understand the process rarely sit next to the people who understand the models.
The diagnostic asks manufacturing organizations about data access, use-case payoff and operating model in shop-floor terms, so that a plant manager can answer alongside the head of digital.
Where AI value shows up first in manufacturing
Early wins are usually predictive maintenance on critical assets, automated visual quality inspection, demand and inventory forecasting, and energy optimization across lines.
How the questions change for manufacturing
These questions are asked in the manufacturing edition instead of the standard wording. The pillar, section and scoring are pinned, so your score stays comparable with organizations in other industries.
- Infrastructure for AIWhere does your AI actually run — and are your factory-floor systems connected to it?Factory-floor systems (the machines and controls on the line) are often kept separate from office IT. Running AI right on the line matters when decisions must be instant or the network is kept offline for safety.Answer options are rewritten for this industry, from “No AI running on the shop floor yet” to “Runs across the line and the cloud, scaling automatically, with factory systems safely connected”.
- Operating Models & EcosystemsWhen you need a new AI capability, what's your usual approach — build it yourselves or buy it in? (Manufacturers often buy off-the-shelf for common needs like camera-based quality checks, and build for what makes their plant special.)There's no right answer — but having a default beats arguing it out in committee for every single shop-floor pilot.Answer options are rewritten for this industry, from “We've never thought of it as a choice” to “A clear written approach, including how we size up shop-floor vendors”.
- AI Use-Case PortfolioIn the last year, what was the biggest single payoff from one of your AI tools? (In manufacturing this is often less unplanned downtime, fewer defective products, higher output, or lower energy use.)Tie it to a shop-floor number the COO already trusts. "Saved engineering hours" on its own does not count here.Answer options are rewritten for this industry, from “We don't have one live yet” to “$10M or more (e.g. a big cut in unplanned downtime across plants)”.
- Data FoundationsWhich of these do you have up and running for your data today? Tick all that are really in use. (Includes manufacturing extras like live data from the production line and a virtual model of the line.)Tick only what's genuinely in everyday use — not what's planned or still in a trial.Answer options are rewritten for this industry, from “None of these yet” to “”.
Pillars this edition emphasizes
All nine pillars are scored for every organization. These are the ones where the manufacturing edition changes what is asked.
One scoring model, comparable across industries
The industry edition changes wording, examples and answer options, never the maths. Every organization is scored on the same nine weighted pillars and lands in one of the same five levels, which is what makes peer benchmarks meaningful. The full method is on the methodology page.
Other industry editions
Find out where you stand
A 15-minute diagnostic, then your nine-pillar score, peer benchmarks and a 90-day plan you can act on Monday. One-time payment of CA$19.80 per report.