From AI Ambition to a Practical Pilot: Choosing Your First AI Use Case in Dynamics 365 Finance & Supply Chain Management

The first critical decision in any ERP AI initiative isn’t which agent to build, but which piece of work deserves to become a pilot. This distinction determines whether an initiative produces measurable improvement or simply another demonstration that fails to scale.

Leadership may want greater productivity. Finance may want faster collections. Purchasing may want less time spent reviewing order status. IT may want a solution it can secure, maintain, and support long-term. These are all legitimate priorities, but by themselves, none of them are pilot requirements.

As a Dynamics 365 Finance & Supply Chain Management (D365 F&SCM) solution architect, my role is to translate these priorities into a bounded task, one with identifiable users, reliable information, observable results, and clear limits on what the technology may do.

The guiding principle throughout this process is fit. A strong first use case fits the business problem, the available capability, the organization’s governance structure, and its ability to evaluate the outcome objectively.

A successful pilot doesn’t need to demonstrate everything AI might eventually accomplish. It needs to answer one essential question: Can this capability meaningfully improve this task under conditions the organization is prepared to accept?

Start With the Work Before Discussing the Technology

Consider a request like: “We want AI in accounts receivable.” It expresses ambition but leaves nearly every architectural question unanswered.

Does the team need help identifying overdue accounts? Understanding payment history? Preparing correspondence? Prioritizing follow-up? Resolving disputes? Each of these tasks carries distinct information requirements and different consequences if something goes wrong.

A more useful starting statement looks like this:

Collections coordinators spend significant preparation time reviewing customer financial information before drafting a follow-up. We want to evaluate whether AI-assisted summaries and draft reminders can reduce that effort without compromising accuracy or removing human approval.

Now there’s something concrete to design around.

The user is identifiable. The task has a defined beginning and end. The expected output is visible. The approval boundary is explicit.

Dynamics 365 Finance offers a relevant standard capability here: The collections coordinator summary draws on Finance data — including aged balances, overdue invoices, and payment history — to generate a summary and a draft reminder email. Microsoft describes this draft as a recommendation for customer follow-up, which users can edit before sending.

That makes it a worthwhile candidate for assessment — not an automatic recommendation for every organization.

The next step is confirming whether the preparation effort is actually the root problem.

If coordinators spend most of their time resolving missing remittance information, a faster summary addresses only a fraction of their workload. If the real delay stems from no one owning dispute resolution, generating correspondence won’t establish that ownership.

A well-scoped pilot targets the cause of avoidable effort, not merely the most visible symptom.

Ask Whether AI Belongs in the Solution

Before selecting an AI capability, test the requirement against simpler alternatives. My recommended assessment begins with four core questions:

1. Is information missing or is it difficult to interpret?
   
2. Is the process inconsistent or is the task inherently judgment-based?
   
3. Could an existing workspace, report, workflow, or rule already address the requirement?
   
4. What additional benefit would AI genuinely provide?

These questions prevent a technology preference from quietly becoming an architectural assumption.

For example, if a team needs a consistent list of overdue transactions filtered by agreed criteria, start by assessing the application’s existing inquiry and reporting options. If the team instead needs a concise narrative assembled from relevant information, an AI-generated summary may be worth evaluating.

The same discipline applies to automation. A fixed approval rule should first be assessed as a workflow requirement; it shouldn’t become an AI reasoning problem simply because the organization has an active AI initiative underway.

Your pilot proposal should explain why AI is the appropriate tool for the task, not just why the task itself matters.

Separate Assistance From Authority

The phrase “AI use case” can conceal very different levels of responsibility.

For pilot selection, I recommend distinguishing between four outcome types:

Outcome	What the capability produces	Main evaluation question
Explanation	Guidance or an answer	Does it apply to the user’s actual situation?
Summary	A condensed view of information	Is it accurate and sufficiently complete?
Draft	A proposed message or recommendation	Can a reviewer validate and approve it efficiently?
Action	A change or business operation	Is it authorized, controlled, traceable, and recoverable?

This is a proposed assessment framework, not a Microsoft product classification.

Its value lies in exposing the difference between helping someone understand a transaction and allowing a solution to change it outright.

A summary pilot might require validation against source records. An action-taking pilot needs a far more deliberate design covering execution identity, permissions, approval, failure handling, and recovery.

Don’t let a successful summary demonstration become implicit approval for autonomous execution; these are separate decisions and should be evaluated as such. For a first pilot, I generally favor a task in which the user can inspect the output before it affects a customer, supplier, financial record, or operational commitment.

Establish Eligibility Before Assigning Scores

A scoring exercise can help compare candidates, but certain conditions should function as gates, not scoring factors. I wouldn’t move a proposal into the pilot backlog until the team can confirm:

• A named business owner
  
• A clearly bounded task
  
• An approved way to access the required information
  
• A supported deployment approach
  
• A reliable way to verify the result
  
• An acceptable control boundary
  
• A fallback if the capability proves unavailable or unsuitable

A high productivity score shouldn’t compensate for an unresolved authorization problem. Likewise, a compelling demonstration shouldn’t compensate for the absence of a business owner. Someone has to decide whether the output is acceptable and whether the process should keep using it.

Classify proposals that fail a gate as “not ready” rather than forcing them into a numerical ranking and document what must change before reconsideration.

Compare the Candidates That Are Ready

Once the eligibility conditions are met, apply a structured comparison. The following worksheet is a suggested decision aid, not a Microsoft methodology or product certification checklist.

Criterion	Evidence to collect	A stronger first-pilot candidate
Business value	Baseline effort, delay, and rework	Addresses a recurring, meaningful problem
Task clarity	Current process and proposed boundary	Has a specific start, finish, and output
Data readiness	Required records and known gaps	Uses accessible, maintained information
Verifiability	Review method and authoritative records	Can be checked without reconstructing the entire task
Consequence of error	Financial, operational, and relationship impact	Allows errors to be caught before consequential action
Standard capability alignment	Documented functionality and limitations	Requires limited extension
Delivery effort	Configuration, integration, and testing needs	Fits the available team and pilot window
Operational ownership	Support and change responsibilities	Has committed business and technical owners

If your team uses a numerical scale, define the scoring anchors before evaluating candidates. A score of “high data readiness” should mean the same thing to Finance, IT, and the implementation partner alike.

Keep the underlying evidence alongside the score. The discussion behind the number is far more valuable than a mathematically precise ranking built on untested assumptions.

Confirm Product Alignment Before Committing to Delivery

Once you’ve selected a preferred candidate, validate the capability against your actual environment.

Microsoft’s enablement guidance confirms that Copilot features in finance and operations apps depend on Power Platform Integration and associated Dataverse components. It also clarifies that dual-write doesn’t need to be enabled unless the implementation specifically uses it.

Don’t translate “Dataverse is involved” into an unnecessary synchronization project.

The same documentation identifies feature-specific installations and setup requirements, along with regional and language considerations. It also notes that Copilot and AI agents aren’t supported in developer environments deployed through Lifecycle Services.

Before approving a delivery plan, verify:

• The selected feature’s current availability and release status
  
• Application and environment prerequisites
  
• Required Dataverse apps and configuration
  
• Supported geography and language
  
• Applicable licensing, capacity, and billing
  
• Security and data-processing requirements
  
• Any additional dependencies introduced by customization

Keep these checks tied to the selected feature specifically. Don’t copy another project’s prerequisite list and assume it applies here too.

Turn the Decision Into a Pilot Charter

A pilot charter should be short enough to use and precise enough to prevent scope drift.

Where a restriction depends on permissions, configuration, or operating procedure, state that explicitly and test it directly.

Establish Success Before Generating the First Output

A pilot shouldn’t be judged by whether users enjoyed the demonstration. Agree in advance on what evidence will support a decision to continue. For a drafting or summary use case, I recommend assessing four distinct dimensions.

Efficiency

Measure the entire task, including review and correction time. Compare:

  [
  

  \text{Baseline task time}
  

  \quad \text{with} \quad
  

  \text{AI-assisted preparation time} + \text{review and correction time}
  

  ]

Use representative cases. A complex account with active disputes shouldn’t be compared casually against a straightforward account with a few overdue invoices.

Quality

Classify defects by consequence, not just frequency. A formatting preference isn’t equivalent to an incorrect balance, an omitted dispute, or a misleading statement about payment history. Separate:

• Material factual errors
  
• Material omissions
  
• Unsupported interpretations
  
• Minor wording edits
  
• Tone and formatting changes

This breakdown produces a far more useful result than a single “acceptance rate.”

Control Effectiveness

Confirm that the pilot remains inside its approved boundary throughout testing. Test the actual user roles and legal-entity scope directly. For custom solutions, inspect the identities and connections used by every single action.

Operational Practicality

Assess training, support, availability, cost, and fallback effort realistically. A capability that performs well under ideal conditions may still need redesign if users can’t reliably recognize when manual review is necessary.

Write the Stop Conditions Before the Success Story

Stopping or narrowing a pilot can be a sound architectural decision, not a failure. I recommend pausing when:

• Users can’t reliably verify material statements
  
• Correction effort eliminates the expected benefit
  
• Required information is unavailable or consistently incomplete
  
• The solution exposes information outside the approved scope
  
• Users begin treating drafts as preapproved communications
  
• The task expands beyond its authorized boundary
  
• No owner accepts ongoing support and quality review

A pilot is fundamentally an evidence-gathering exercise. “Not suitable in the current design” is a legitimate, valuable outcome. Documenting that outcome can prevent a far more expensive production mistake down the line.

Finish With a Decision, Not a Demonstration

The pilot should conclude with one of four decisions:

1. Proceed within the tested scope
   
2. Revise the design and test again
   
3. Defer until prerequisites or controls improve
   
4. Stop because the use case doesn’t justify further investment

Proceeding shouldn’t automatically authorize additional users, legal entities, information sources, or transaction capabilities. Each expansion changes the conditions under which the original result was established.

The first AI use case in D365 F&SCM should therefore be chosen for what it allows the organization to learn, not just what it may help users accomplish.

Choose a task tied to a genuine business problem. Confirm that the capability truly fits. Keep the authority boundary explicit. Measure the full effort, including verification. Make the final decision from evidence, not enthusiasm.

That’s how AI ambition becomes a practical pilot — and how a practical pilot earns its place as part of the business, one deliberate step at a time.

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