I have been the buyer reading your case study
I spent much of my career reading vendor case studies as a buyer, with procurement and third-party risk asking questions alongside the technology team. I found the useful stories explained enough of the original situation for us to judge whether the result could apply to our own bank. A logo and a percentage left too much unanswered. I wanted to understand the work behind the claim.
Now that I help enterprises adopt Claude and governed analytics, I hold our own stories to that buyer's standard. A case study should help a prospective customer decide whether an approach could work in their environment. That requires the problem, the constraints, the work done and the evidence behind the result.
Scope the claim before writing the headline
Write the claim as one sentence that includes its scope: for example, an engagement reduced preparation time for a specific weekly review during a stated observation period. That is far more credible than claiming to transform enterprise productivity. Name who measured the result and where the evidence is retained.
If the work only reached a demonstration, call it a demonstration. A planned benefit can be worth describing, but it must look visibly different from an observed outcome. A CFO or audit committee member reading your story will notice the difference even if your marketing team does not.
Show the starting point and what actually changed
Describe the original workflow: the teams involved, the sources, the frequency and why the task was difficult. Include prerequisites such as completed data cleanup, master data management or an identity project. A prospective customer needs to know which of those conditions it already meets. Otherwise a result achieved after substantial preparation can look like something the product delivers immediately after installation.
Then describe the intervention at the right altitude: definitions agreed, sources connected, review steps added, a user workflow redesigned. Do not publish private architecture or invention details to sound technical. A simple diagram showing the boundary between customer systems, the analytical workflow and human approval is enough. If consulting work or another platform supplied an important part of the outcome, say so.
Make the numbers interpretable
State the measurement window, the sample, the comparison method and the exclusions. Say whether time saved includes the effort to verify the answer. If different people ran the old and new processes, say that too. A small pilot can still make a strong story when its limits are explicit.
Avoid converting an observed reduction into annual savings without showing every assumption. In healthcare and life sciences, where buyers are trained to read study design, an extrapolated number with no method is a red flag. A transparent account of ten representative tasks is often more persuasive than a large figure nobody can examine.
Respect permissions and include what did not work
Get the customer's approval for name, logo, quotations and specific results. A public testimonial about an earlier ERP project does not substantiate a later AI claim. If you anonymize, keep the factual basis and remove details that would identify the customer anyway. Label internal use as internal use, and label synthetic walkthroughs as synthetic.
Include a meaningful limitation or adjustment: a metric definition that needed agreement, an integration with narrower access than expected, users who needed a different review interface. Explain how the team responded and what remains out of scope. This gives buyers a realistic view of implementation effort, which is exactly what their third-party risk reviewers will ask about anyway.
End with a next step that matches the evidence
Connect the story to an offer proportionate to its evidence. A workflow case can invite readers to bring a comparable business question to a demonstration. An adoption story can offer a readiness assessment. Keep the case openly readable so buyers can circulate it to colleagues and their risk teams.
Assign an owner and a review date to every case study, and correct claims when circumstances change. Until approved customer evidence exists, publish clearly labeled walkthroughs that show the process without inventing results, quotations or identities. Credibility compounds; one inflated claim discovered in due diligence can undo years of it.