HARTING Unifies Customer Service with Dynamics 365: Copilot and Autonomous Bots Still Planned

For enterprise IT teams, the case offers a useful modernization pattern—but not a measured return-on-investment study. Its strongest lesson is about organizing service work before adding more AI.

A shared service model, without erasing regional needs​

According to NTT DATA’s customer case study, industrial connectivity manufacturer HARTING faced different regional tools, manual request handling and information silos. Its rollout used pilot regions, process standardization and staged deployment to establish global consistency while retaining regional flexibility.

That balance matters. A global service platform should make information portable without assuming every market operates identically. The analytical takeaway is that standardizing case ownership and handoffs can be more important than making every regional workflow look exactly the same.

What is running—and what is still coming​

NTT DATA describes the implementation using the wording “Microsoft Dynamics 365 Customer Service Workspace Contact Center.” Its account separates the capabilities as follows:

These are deployment descriptions from the implementation partner, not an independently audited inventory. The account does not specify product versions, licensing, rollout dates or the configuration behind its AI-assisted workflows.

The distinction is nevertheless valuable: agent assistance is not the same as autonomous resolution, and a roadmap should not quietly become a list of completed features.

What Microsoft’s documentation adds​

Microsoft’s Dynamics 365 documentation helps explain the mechanics behind this kind of project without establishing which settings HARTING uses.

Automatic record creation rules can turn incoming activities into Dataverse records, including support cases. Microsoft lists both email and phone-call activities among the supported inputs. For email rules, a queue is required, and the rule must be activated before it processes matching activity. Microsoft also explicitly states that the modern automatic record creation feature is not supported in on-premises environments.

Separately, Microsoft describes unified routing as two stages:

1. Classification: Rules or machine-learning models add information about the incoming work.
2. Assignment: Requests are prioritized and matched to representatives using factors such as skills, availability and workload.

This provides a practical reading of the case study: creating a ticket and finding the right person to handle it are separate operational problems. Automating the first does not automatically solve the second.

A concrete warning for administrators​

Microsoft also documents a duplicate-case risk when emails are processed across multiple queues. One supported mitigation is to funnel relevant messages into a dedicated queue monitored by a single automatic record creation rule, using Exchange mail-flow rules, Power Automate or a plug-in. That is general platform guidance—not a claim about HARTING’s architecture.

For administrators evaluating a similar consolidation, this creates a useful test scenario: send one request to several departmental addresses and confirm that the resulting case count matches the intended policy. A unified interface is less impressive if the same customer problem arrives wearing three ticket numbers.

Treat the benefits as claims, not benchmarks​

NTT DATA reports faster responses, reduced handling time and lower costs, but supplies no numerical baseline, measurement period or before-and-after results. Those benefits should therefore remain attributed claims rather than benchmarks for another organization’s business case.

A sensible evaluation would ask for:

• Case creation accuracy, including duplicate and missed requests.
• Routing accuracy and reassignment rates.
• Response and resolution times by channel and region.
• Reopened cases and customer satisfaction alongside handling time.
• Clear separation between workflow improvements and any later Copilot contribution.

These are evaluation recommendations, not reported HARTING results.

The broader lesson is straightforward: build reliable intake, ownership, shared history and measurement first. HARTING’s case describes that foundation; its planned AI expansion is the next chapter, not evidence that human service agents have already left the building.