The best first AI project is rarely the most impressive idea in the room. It is the workflow where a focused system can create visible value, where the organization can provide the right context, and where a person can judge whether the result is good.
That distinction matters. “Use AI in claims” is an ambition. “Review an incoming mitigation estimate, identify documentation gaps, and prepare a structured set of findings for an adjuster” is a workflow. The second version has inputs, a job, an accountable user, and an output that can be evaluated.
Organizations often start with a model or a vendor and then search for a problem. A stronger approach begins with the work itself. Our AI Opportunity & Readiness Assessment uses that workflow-first perspective to separate promising opportunities from expensive distractions.
Start with work people already understand
A first AI workflow should have a recognizable current state. Someone should be able to show how the work arrives, what information is reviewed, which decisions are made, what exceptions occur, and what a completed result looks like.
That does not mean the current process must be well documented. In fact, the most useful discovery often comes from observing the spreadsheets, inboxes, notes, and workarounds that never made it into the official procedure. But the team performing the work must be able to explain what “done well” means.
Look for workflows with several of these characteristics:
- skilled employees spend meaningful time gathering, reading, comparing, drafting, or checking information;
- the work happens often enough that an improvement will compound;
- inputs are available in digital form or can be made available without redesigning the entire organization;
- the output follows a recognizable structure;
- a subject-matter expert can review the output and explain what is wrong;
- a bad result can be contained through human approval, limited permissions, or a clear fallback;
- the organization can measure time, quality, throughput, rework, or another operational outcome.
The strongest candidate is not necessarily the workflow with the largest theoretical return. A narrower workflow with clean evaluation and committed users is often a better first proof than a company-wide assistant with no accountable owner.
Score the opportunity across six dimensions
We recommend evaluating candidate workflows against the same set of questions. A simple score is less important than the discussion the score forces.
1. Operational value
What changes if the workflow improves? The answer should be more specific than “save time.” Identify whose time, how frequently the work occurs, and what the released capacity enables. Other value can come from shorter cycle time, fewer avoidable corrections, more complete evidence, more consistent decisions, or the ability to process work that is currently left untouched.
A useful baseline does not require perfect finance data. A representative sample and a credible range are enough to compare opportunities.
2. Workflow clarity
Can the team define the beginning and end of the job? Can it describe normal cases, exceptions, escalation, and ownership? AI struggles to rescue a process that has no shared definition of success.
If every reviewer performs the task differently, discovery may need to establish a minimum operating standard before automation begins. That is not wasted effort. It prevents the system from encoding conflict and ambiguity.
3. Data and context readiness
List the information a qualified person uses to complete the work. Where does it live? Who owns it? Is it current? Can the project access it legally and technically? Does it contain sensitive or regulated information?
The answer may point to retrieval over approved documents, structured database access, an integration with an existing system, or a simpler rules engine. It may also reveal that the first project should improve data collection rather than add AI.
4. Verifiability
The team needs a way to determine whether the system did its job. Some workflows have objective answers. Others require a rubric and expert review. Both can work, but “the output looks smart” is not an evaluation method.
Build a representative set of examples before the pilot is finished. Include common cases, ambiguous cases, known edge cases, and situations where the system should refuse, escalate, or ask for more information. This evaluation set becomes one of the most valuable assets in the project.
5. Risk and human control
Consider the cost of an incorrect answer, an omitted fact, an inappropriate action, or an unavailable service. Then design the system’s authority to match that cost.
An agent can prepare a recommendation without approving it. It can draft a communication without sending it. It can flag missing evidence without deciding a claim. Human review is not a temporary weakness in the design; in many high-stakes workflows, it is the correct operating model.
6. Adoption and ownership
Who will use the result, and who owns the workflow after launch? The future user should participate in discovery and pilot review. The owning team needs enough capacity to answer domain questions, provide examples, and decide what should happen when the system is uncertain.
A technically successful pilot can still fail if it adds a second inbox, duplicates an existing system, or creates more review work than it removes. Integration and user experience belong in the opportunity score.
Know the common bad first projects
Several patterns repeatedly produce weak first investments:
- The universal company chatbot. The audience, questions, source material, and definition of success are too broad.
- A fully autonomous high-impact decision. The system receives too much authority before its behavior is understood.
- A workflow with no accessible examples. The team cannot build or evaluate against representative work.
- A process nobody owns. Stakeholders want improvement, but no one can make decisions about the workflow.
- A novelty feature disconnected from operations. The demo is interesting, but it does not remove work or improve an outcome.
- A project chosen only because a tool can do it. Tool capability is not evidence of organizational value.
Traditional automation may also be the better answer. If the inputs are structured, the rules are stable, and exceptions are limited, deterministic software can be cheaper and easier to operate. AI earns its place when language, unstructured evidence, variability, or judgment makes conventional rules insufficient.
Define a pilot as an operational slice
A pilot should prove one complete path through the workflow. It should not attempt every integration and exception, but it should be more than a prompt demonstration.
For example, a document-review pilot might:
- accept one representative document type;
- extract a defined set of facts;
- compare those facts with an approved policy or rubric;
- produce structured findings with supporting evidence;
- route the result to a qualified reviewer;
- capture corrections and evaluation results.
That slice tests the difficult parts: context, output structure, evidence, user review, and measurable quality. If it works, the team has a foundation for integration and expansion. If it does not, the organization has learned something specific before committing to a large build.
Decide what success means before building
Choose a small number of measures tied to the workflow. Depending on the job, those may include:
- minutes of skilled labor per case;
- percentage of outputs accepted without material correction;
- recall of important issues or required elements;
- turnaround time from intake to reviewer-ready result;
- percentage of cases correctly escalated;
- cost per completed workflow;
- user adoption and continued use;
- error severity, not only average accuracy.
Record the current baseline using the same definitions. A model evaluation and a business result are related, but they are not the same. A system can produce accurate answers and still fail to improve the operation if it arrives in the wrong place, requires excessive review, or cannot handle the work’s real variability.
The output should be a decision, not an idea list
A useful readiness effort ends with a ranked opportunity, a defined pilot, success measures, known constraints, and a practical architecture direction. It should also be willing to recommend “not yet” or “do this with conventional automation.”
That decision discipline is what turns AI strategy into engineering. If your team has several possible workflows and needs to know where to start, bring us the workflow. We can help map the work, test the assumptions, and define the smallest credible path forward.