Conflict resolution
Case Study 3How I navigated a cross‑company terminology conflict between emerging ML/AI capabilities and established concepts, resulting in a long‑awaited change to a deeply embedded term.
As Power Virtual Agents introduced new ML/AI capabilities, it adopted a technical term that accurately described its emerging functionality and reflected industry uage.
That same term, “entity,” already existed in the Common Data Service, but with a different underlying meaning rooted in established concepts. This became evident during an AI Builder integration which would cause data science “entities” to collide with database “entities” in the same workflow.
The ProblemCustomers working across products encountered contradictory definitions, inconsistent documentation, and mismatched expectations.
Internally, teams had strong, valid reasons for their usage — but no shared conceptual model.
The conflict needed to be resolved without fragmenting terminology, breaking existing implementations, or forcing teams to abandon their conceptual foundations.
My Role
UX Content Designer specializing in terminology, conceptual modeling, and cross‑product alignment.
Identified the conflict and articulated its user impact.
Mapped the conceptual differences across products.
Facilitated alignment conversations across org boundaries.
Proposed a shared conceptual model that preserved accuracy for all teams.
Highlighted the conflict that ultimately updated the deeply embedded term.
My Process
A. Mapped the conceptual models behind each team’s use of the term
Documented how Power Platform’s emerging ML/AI features defined and used the term.
Documented how the Common Data Service used the same term.
Identified the technical, behavioral, and user‑facing differences between the two meanings.
Highlighted where the concepts overlapped and where they diverged in ways that would confuse users.
Created a neutral, visual conceptual model that made the conflict visible and undeniable.
B. Facilitated alignment conversations inside Power Platform
Brought together PMs, engineers, architects, and content designers across Power Platform to align on what our implementation actually represented.
Used the conceptual map to ground discussions in shared reality rather than assumptions.
Helped teams articulate what they needed the term to communicate — and why.
Shifted the conversation from “which definition is correct” to “what users need to understand to succeed.”
Reached internal clarity on how Power Platform would define, frame, and use the term moving forward.
C. Extended the alignment effort across Power Platform and Azure
Once Power Platform had internal clarity, expanded the conversation to include Azure partners.
Shared the conceptual model to show how the term’s meaning diverged across products.
Proposed a terminology strategy that preserved accuracy for all teams in the short term:
keep the term in both places
clarify boundaries through framing, examples, and documentation
avoid introducing competing labels
Ensured neither team had to compromise their conceptual integrity while still reducing ambiguity for users.
D. Surfaced the need for long‑term change and created the clarity that enabled it
Identified that the term’s meaning in Power Platform’s emerging ML/AI space conflicted with its long‑established meaning in CDS.
Articulated the conceptual mismatch in a way that made the user impact visible and actionable.
Demonstrated how the conflict affected documentation, support, and cross‑product workflows.
Provided the conceptual framing that allowed CDS to see the opportunity for alignment.
When CDS reintroduced their platform as Dataverse, they chose to update the deeply embedded term — a change made possible because the conflict had been clearly surfaced, understood, and contextualized.
Our StoryPower Platform teams gained a shared, accurate understanding of the term and its conceptual boundaries, reducing internal confusion and unblocking feature work.
CDS partners were able to see the conceptual mismatch clearly for the first time, thanks to the neutral model and framing you provided.
Documentation, UI text, and support guidance across Power Platform became more consistent and easier for users to navigate.
The clarified conceptual model reduced support friction for customers working across products, especially those integrating Power Platform with Azure services.
When CDS reintroduced their platform as Dataverse, they chose to update the deeply embedded term — a long‑awaited change made possible because the conflict had been surfaced, articulated, and aligned across teams.
The shared terminology framework you created became a reference point for future cross‑company discussions, helping prevent similar conflicts as Microsoft’s ML/AI capabilities continued to evolve.