/ Requirements and coordination · PUBLISHED CASE

Uni Automation India

Technical documentation accelerated before broader workflow automation

An industrial sensor manufacturer introduced AI into documentation and requirement analysis while developing a broader coordination platform.

Industrial sensors and controllers150+ peopleSource pages 16–17

MCCIA-PUBLISHED CASEANALYSED BY DOTSUPERNOT PRESENTED AS A DOTSUPER CLIENT

/ THE SHORT VERSION

An industrial sensor manufacturer introduced AI into documentation and requirement analysis while developing a broader coordination platform.

01 / BUSINESS CONTEXT

The work before the tool.

Uni Automation handles customised industrial sensor and controller projects that create substantial documentation, requirement analysis and cross-functional coordination work.

THE OPERATING CONSTRAINTManual preparation and comparison of customer documents consumed engineering and management time. As projects scaled, coordination across teams became harder to track consistently.

02 / THE INTERVENTION

What changed in the workflow.

ChatGPT was introduced for document summaries, requirement extraction, technical comparisons and communications. The business also began developing a customised CRM and task-management platform.

DEPLOYED VS. PLANNED

Document assistance was in daily use. The publication identifies the CRM and task-management platform as work in progress with a future deployment horizon.

03 / REPORTED OUTCOMES

The numbers, with their labels attached.

Up to 90%faster task completionReported by the organisation in the MCCIA publication. Not independently audited.

30–40%productivity improvementReported by the organisation in the MCCIA publication. Not independently audited.

100%team AI adoptionReported by the organisation in the MCCIA publication. Not independently audited.

Before and after, based on the published case narrative
Workflow area	Before	After
Customer requirements	Long documents read and compared manually	AI highlights specifications and supports a first comparison
Documentation	Repeated drafting across projects	Structured drafts are prepared for engineering review
Coordination platform	Work spread across teams and tools	A central platform was being developed, not yet presented as fully deployed

04 / HUMAN CONTROL

Capability matters when people stay in control.

Engineers reviewed extracted requirements and technical comparisons. Managers remained responsible for project commitments and task ownership.

05 / WHAT A SIMILAR BUSINESS SHOULD TEST

Transfer the pattern, not the conclusion.

01
Separate a deployed document workflow from a planned enterprise platform.
02
Use representative customer documents to test recall and missed requirements.
03
Adoption is stronger when the people doing the work help define the workflow.

06 / SOURCE AND LIMITS

What this page can and cannot establish.

This analysis summarises outcomes published in MCCIA’s AI: Compilation of case studies (2026), printed pages 16–17. The work was conducted through the MCCIA Applied AI Studio. The report credits the Studio team managed by Neeraj Thakur, with Gauri Kale and Ismail Patel. The featured organisation is not presented as a dotSuper client.

01“Up to” represents a peak reported improvement, not a typical result across all tasks.
02The case combines deployed AI use with a platform that was still under development at publication time.

Read the original MCCIA publication

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