Presidio · From low adoption rate to organizational level Copilot productivity transformation

A Evidence level

Presidio adoption of Copilot streamlines operations

Publisher: Microsoft · Case of the official customer of the manufacturer · Direct sources at the case level

Claim origin: Microsoft & Client Disclosure · Independent verification: No · Accessed: 2026-09-19

Evidence level measures whether a source can be located and reviewed; it does not mean vendor-reported claims were independently audited.

Deep case | Key delivery chain is substantially documented 12 / 12

Business context2 / 2

Transformation workflow2 / 2

Technical workflow2 / 2

Human roles & governance2 / 2

Measured outcomes2 / 2

Source traceability2 / 2

All six dimensions meet the current completeness threshold.

The case of Presidio illustrates that corporate AI failure is often not a model problem: tool adoption has increased from about 60 per cent to over 90 per cent through job training, in-house community and process design.

Business problem

Presidio initially deployed Copilot to 300 sales, marketing, engineering, IT, management and project personnel, with a rate of implementation of only about 60 per cent; the simple distribution tool did not create stable skills, scenes and measurable business returns.

Solution

The company developed more than 60 training videos for alerts, meetings, content and job scenes, built internal collaborating sites to share experiences and set up specific workflows around mail summaries, minutes, technical file transfer client demonstrations, RFP and project management; the engineering team then used custom assistants such as Copilot Studio and Azure OpenAI to build tickets and fees.

Technical architecture & production workflow

Step 01

Mail, meetings and documentation in Microsoft 365

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Step 02

Copilot General Work Portal

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Step 03

Over 60 job training and in-house experience sites

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Step 04

RFP, demonstration, meetings and project management workflow

→

Step 05

Copilot Studio/Azure OpenAI Custom Assistant

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Step 06

Usage rate and time-saving continuity measurement

Key technology & infrastructure components

Microsoft 365 CopilotCopilot StudioAzure OpenAITraining content repositoryCommunity of experience-sharingUse metrics

Human roles & accountability

AI is responsible for digests, first drafts, transliterations and information reorganizations; sales, engineering and project managers review client-oriented content, lead communication and are accountable for delivery commitments, and internal teams maintain training and usage norms on an ongoing basis.

FDE delivery actions

Identification of low adoption rates is not a licensing issue but a capacity issue
Disaggregated training and reusable scenes by job
End-to-end process from technology document to client demonstration
Establishment of internal sharing of success stories and reminders
Validate effects using adoption rates, working hours and RFP cycles

Reusable delivery patterns

The first indicator for AI office deployment should include adoption rates
Training is about the job, not the function of the product.
Generic assistant and custom workflow stratification
Time savings need to be mission-specific and sampled

Business outcomes & delivery results

The Microsoft case reported an increase from approximately 60 per cent to more than 90 per cent, with users saving an average of about 1,200 hours per month; a 90 per cent reduction in RFP processing time, project managers saving an average of six hours per week and a reduction in a three-day mission to two hours.

Evidence boundaries & verification notes

The page clearly recorded the first range of 300 people, more than 60 training videos and changes in adoption rates.
1,200 hours represents the average monthly savings calculated by Presidio.

Primary source: Presidio adoption of Copilot streamlines operations ↗