Mitsui: Scoping a Roadmap to Cut Maintenance Costs by up to 30% and Boost Planner Productivity by 70%

Brief

Mitsui’s maintenance, planning, and engineering teams were losing hours every week to manual, repetitive work – hunting for information across scattered systems and building work packs and instructions from scratch – so we ran structured workshops with their teams, working alongside AI Access, to scope three independent AI opportunities that could boost planner productivity by over 70%, cut information search time by up to 90%, and reduce maintenance costs by up to 30%.

Problem

Maintenance planning runs on information – asset histories, work orders, engineering standards, past job notes – and at Mitsui that information lived everywhere except in one place. Planners building a work pack had to hunt across systems and folders for details a colleague had often already found months earlier. Work instructions got written from scratch each time, even when most of the content was identical to the last similar job. None of this shows up as a single dramatic failure. It shows up as planners spending hours searching instead of planning, work packs taking longer to build than the work itself sometimes takes to complete, and institutional knowledge staying locked in people’s heads instead of being reusable. Mitsui didn’t need a new system to replace their people – they needed their people spending time on the parts of the job that actually needed a human.

Solution

We ran structured workshops with Mitsui’s Maintenance, Planning, and Engineering teams to identify where AI could genuinely remove manual effort, then scoped three distinct opportunities in enough technical and commercial detail for Mitsui to make a confident decision on each.

• Scoped with the people who do the work – every opportunity came directly out of sessions with the planners and engineers who’d actually use it, not a top-down guess at where AI “should” help.
• Three separate, independently viable projects – Mitsui isn’t locked into an all-or-nothing bet; each opportunity can be greenlit on its own merits.
• Delivered in partnership – working alongside AI Access kept the governance and change-management side covered while we focused on the technical scoping.
• Value framed the way the business measures it – productivity, search time, and maintenance cost, not abstract AI capability.

Result

The scoping engagement gave Mitsui a clear, costed roadmap across three initiatives, each targeting a specific point of friction in the maintenance planning process. On current estimates, the opportunities identified could boost planner productivity by over 70%, cut the time spent searching for information by up to 90%, and reduce maintenance costs by up to 30%. The roadmap is currently with Mitsui for decision.

Under the Hood