A North American Industrial Equipment Manufacturer Reduced Case Resolution Time by 40% With Dynamics 365 AI | Nalashaa Digital

A North American Industrial Equipment Manufacturer Reduced Case Resolution Time by 40% With Dynamics 365 AI

Talk to an Expert
40%
Faster case resolution ~5 days to ~3 days
~30%
Lower average handle time ~22 min to ~15 min
6 min → <30 sec
Time to understand the full case context

Client Overview

The client is an industrial equipment manufacturer that sells and services machinery through a network of dealers and field technicians across North America. Its support team of about agents handles roughly 500 product, warranty, and technical support cases a month.

Its support team uses Microsoft Dynamics 365 Customer Service to manage product, warranty, and technical support cases raised through email, web forms, and phone. Dynamics 365 was already the system of record for every case, customer, machine, warranty, and resolution.

The client wanted to reduce the time agents spent preparing each case for resolution, improve consistency across technical responses, and handle rising support volumes without replacing the existing platform or removing human oversight.

Business Challenges

Cases Were Triaged Manually

Every incoming case had to be read, classified, prioritised, and routed by a person. This meant support work did not begin when the case arrived. It began only after someone had worked out what the issue was and who should handle it.

Agents Spent Too Long Rebuilding the Case History

Before investigating the problem, agents had to gather the machine model, serial number, warranty status, previous repairs, and earlier conversations from multiple records. For complex or reopened warranty cases, several minutes were lost simply reconstructing context.

Technical Answers Were Scattered Across Systems

Product manuals, service bulletins, engineering notes, SharePoint files, and past case resolutions all held part of the answer. Agents searched several sources before finding a likely fix, and different agents could reach different conclusions for the same fault.

Together, manual triage, repeated history reviews, and fragmented knowledge pushed the average resolution time to around five business days. Much of that time was spent finding and organizing information before troubleshooting could begin.

Approaches Considered

As support volumes increased, the client was preparing to add more agents and explore a standalone chatbot. The plan would have increased capacity, but it would not have reduced the sorting, reading, and searching attached to every case. Nalashaa reviewed where time was actually being lost and proposed a different approach.

Client's Initial Plan

Add Headcount and a Generic Chatbot

Additional agents could absorb more cases, but each new hire would still spend the same time reconstructing machine histories, searching technical documents, and manually routing work. Support costs would continue to rise with case volume.

A generic chatbot outside Dynamics 365 could answer basic questions, but it would not be securely grounded in the manufacturer's case records, machine data, warranty information, service manuals, or engineering bulletins.

For technical support, a confident answer based on the wrong machine model, repair procedure, or warranty condition could create more risk than a delayed response.

Nalashaa's Recommendation

Native AI Inside Dynamics 365 Customer Service

Nalashaa recommended using Copilot and Dynamics 365 AI agents inside the platform the support team already used, grounded in trusted Dataverse records and approved SharePoint knowledge.

AI would remove the overhead around each case by summarising the history, retrieving relevant technical guidance, drafting a response, classifying the issue, and routing it to the appropriate specialist.

Agents would review, edit, and approve every suggested response before it reached a dealer, technician, or customer. Existing security roles, permissions, and quality controls would remain in place.

This approach addressed the source of the delay without replacing Dynamics 365, migrating data, or removing human accountability.

Solutions Implemented

Before the project, a typical case took the team about five business days to resolve, and much of that time went to the sorting, reading, and searching described above rather than to the fix itself. The changes below removed that overhead, without changing the platform or taking the agent out of the decision.

01 / Context Automation

AI Case Summaries Replaced Manual History Reading

Copilot generates a structured summary of each case, covering the customer, product, subject, priority, case type, and what has happened across the full interaction history. The source fields were configured to surface the machine model, serial number, and warranty status used by the support team.

Agents now open a case and see the context immediately instead of reconstructing it. What previously required several minutes of reading now takes seconds of scanning.

Copilot Summary
machine · warranty
history · priority
02 / Grounded Search

Grounded Knowledge Search Ended the Hunt for Answers

Copilot retrieves answers grounded in the client's product manuals, service bulletins, engineering notes, SharePoint documents, and the resolutions of similar past cases. An agent can ask a question in natural language and receive an answer with its source attached, allowing the recommendation to be verified before use.

Because every agent draws from the same approved knowledge, similar faults are handled more consistently. The Customer Knowledge Management Agent also helps turn resolved cases into new or updated knowledge articles, allowing the knowledge base to improve as cases are closed.

Manuals Bulletins SharePoint Past Cases
answer + source
03 / Assisted Drafting

AI-Drafted Responses Kept the Agent in Control

Copilot drafts the response using the case context and retrieved knowledge. The agent reads it, edits it, and decides whether to send it. Nothing is sent automatically.

This rule preserved quality and accountability while helping the support team build trust in the system.

Copilot draft response agent sends
04 / Triage & Routing

Automated Triage and Routing Removed the Front-End Delay

The Case Management Agent classifies each incoming case, assigns a priority, and routes it to the appropriate queue or specialist without requiring someone to read it first. Cases now reach the team best placed to resolve them with less delay.

new case classify + route
Specialist A Specialist B
05 / Governance

Human-in-the-Loop and Responsible AI by Design

The AI is grounded in trusted, permissioned sources rather than open-ended information. Existing Dynamics 365 security roles and record permissions continue to govern what each user, and therefore Copilot, can access. Supervisors review AI-assisted resolutions through the normal quality assurance process.

Copilot Supervisor QA
Security Roles
+ Permissions

Implementation Roadmap

1
Discovery and Knowledge Base Clean-Up

The team audited existing knowledge articles, SharePoint content, product manuals, service bulletins, engineering notes, and case resolution history. Duplicate, outdated, and contradictory information was retired, consolidated, or rewritten before AI was enabled.

2
Enabled Copilot Case Summaries

Case and conversation summaries were introduced first because they delivered an immediate, visible time saving without creating customer-facing risk. This helped agents build confidence in the system early.

3
Turned On Grounded Knowledge Search and Draft Replies

Copilot was connected to the cleaned knowledge sources so agents could ask questions in natural language and receive cited answers. Draft response generation followed, with a strict rule that no response could be sent without agent review.

4
Introduced Automated Triage and Routing

The Case Management Agent was configured to classify, prioritise, and route incoming cases. It initially ran alongside manual triage so the team could compare decisions and tune the configuration before relying on it more broadly.

5
Measurement, Tuning, and Rollout

Resolution time, average handle time, first-contact resolution, routing accuracy, and agent adoption were measured against a pre-AI baseline. Prompts, routing rules, and knowledge grounding were tuned using real case data before the solution was rolled out across the wider support team.

Business Impact

40%

Faster case resolution, from about 5 business days to about 3 (within one quarter of full rollout)

~30%

Lower average handle time, from about 22 minutes to about 15 per case handle time

~50%

Faster new-agent onboarding, from about 8 weeks to about 4 to full productivity

6 min → under 30 sec

Time for an agent to understand the full case context

Looking Ahead

With the preparation work removed from the front of every case, the support team gained capacity without replacing Dynamics 365 or increasing headcount at the same rate as case volume.

The same grounded AI foundation can extend to dealer and customer self-service, proactive case creation from connected-machine signals, and supervisor-side quality review across a larger share of cases. It can also help identify repeated faults, documentation gaps, and emerging product issues earlier.

AI did not replace the support agents or the platform. It removed the sorting, reading, and searching that stood between an agent and the technical problem they were hired to solve. Nalashaa continues to tune the knowledge grounding and routing accuracy as products and case patterns evolve.

"My agents used to spend the first few minutes of every case just working out which machine it was and what had already been tried. Now the summary is waiting for them and the fix is already suggested, with the manual or bulletin it came from attached. They spend their time solving the problem instead of hunting for it. And nothing reaches a dealer or customer that a person has not read first."

— Head of Technical Support

Resolve Manufacturing Support Cases Faster With AI

Nalashaa implements AI-powered case management in Dynamics 365 Customer Service, using your case history and technical knowledge to help teams resolve issues faster while staying in control.

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