Choose enterprise search when people need trustworthy answers from company knowledge. Choose an assistant for interactive drafting and analysis, workflow automation for a defined sequence, and an agent when the next step depends on what it finds. Consider a custom implementation when existing tools cannot meet your data, access or handoff requirements.
The best AI solution is the one that improves a specific piece of work and remains manageable after launch. A persuasive demonstration is a starting point. Your decision should rest on representative inputs, visible evidence and a clear owner for the result.
Match the solution to the task.
Describe the work without naming a technology: “Find the current renewal clause and prepare a summary for the account owner.” Then separate finding, interpreting, preparing and acting. This reveals whether you need one capability or several working together.
| Business need | Start by evaluating | Check before choosing |
|---|---|---|
| Find answers across internal documents. | Enterprise search | Source references, current versions and permission-aware results. |
| Draft, summarize or explore a question with a person. | AI assistants | Useful context, editable outputs and an obvious review step. |
| Extract fields or compare document details. | Document intelligence | Missing fields, inconsistent formats and links to source passages. |
| Repeat a known sequence across tools. | Workflow automation | Triggers, exceptions, duplicate prevention and approval gates. |
| Select tools and adapt steps within a bounded task. | AI agents | Allowed actions, stopping rules, run history and human handoff. |
These categories overlap. An assistant can use search, while an agent can operate inside a fixed workflow. A stable calculation or simple routing rule may work better as ordinary software. Use AI where interpreting variable language or documents adds value.
Decide whether to configure a tool or build around your workflow.
Try an existing product first when the task is common, the necessary data already lives in that product, and its access and review controls fit your team. A built-in drafting assistant or a configured support tool may cover the requirement with less ongoing work.
A custom AI solution becomes worth evaluating when information spans several systems, the handoff is specific to your operation, or access must follow detailed business boundaries. Compare a configured product, a custom implementation and your current process against the same examples.
- Fit: Which requirements are essential, and which merely reflect how the team works today?
- Integration: Can the proposed system read and update the required sources through supported interfaces? Check integration and model options against the exact systems involved.
- Ownership: Who can change the workflow, export the information and operate it if the supplier relationship changes?
- Total effort: Include configuration, data preparation, review, training and maintenance alongside subscription or build costs.
For example, a standard document summary may fit an existing tool. Preparing a matter brief from several repositories with matter-specific permissions and a designated reviewer calls for a closer look at the full legal workflow.
Define inputs, permissions and review before a pilot.
Write down a small operating agreement. It gives your team and prospective supplier the same definition of an acceptable system.
- Inputs: Name the source owner, document types, expected languages, update frequency and information that must stay out.
- Access: Specify who may retrieve each source, which tools may be used and whether an action can read, prepare or change a record.
- Review: Assign a person to inspect consequential outputs. State what they must see before approving a connected action.
- Exceptions: Define what happens when sources disagree, information is missing, a tool fails or the request exceeds the agreed scope.
- Data handling: Agree hosting, retention, deletion and model-provider terms with the people responsible for your information.
The security and architecture discussion should follow this real use case. The voluntary NIST AI Risk Management Framework offers a broader reference for considering trustworthiness during AI design, use and evaluation. It does not certify a particular implementation.
Use a pilot worksheet with clear acceptance criteria.
Select representative tasks before tuning the system. Include routine examples, unusual formats, outdated sources, ambiguous requests and cases that should be declined or handed to a person. Keep a separate set for the final assessment so repeated tuning does not become the entire test.
Record the current process as your baseline. Agree the acceptance threshold and reviewer for each criterion before running the pilot. The worksheet below contains illustrative evaluation questions, not Strivo performance results or universal targets.
| Criterion | Evidence to collect | Decision to record |
|---|---|---|
| Output quality | Reviewer checks facts, omissions and source support for each example. | Which errors require rejection? |
| Access boundaries | Test users with different permissions and requests for restricted material. | What must never be returned or changed? |
| Workflow reliability | Test missing inputs, failed connections and repeated requests. | When must the system stop or escalate? |
| Team effort | Measure completion and review time against the current process. | Does the benefit remain after corrections? |
| Operating cost | Record usage, model calls, hosting and support effort. | Is the expected workload affordable? |
Use the findings to choose: expand, revise or stop. A pilot that exposes poor source quality can still be useful if it prevents an unsuitable rollout. Ask to inspect failed cases as well as polished examples.
Understand what changes cost and delivery time.
A credible estimate follows scope. Important variables include the number and quality of sources, integration availability, access rules, document complexity, model and hosting choices, testing depth and reviewer availability. Usage volume and ongoing changes affect operating costs after launch.
Strivo describes an approximately 30-day first scoped implementation. Treat that as the starting offer for an agreed scope. Confirm deliverables, dependencies, exclusions, acceptance checks and the support arrangement before comparing proposals. A first workflow and a company-wide rollout are different commitments.
Keep ownership after launch.
Assign an operational owner, source owners and reviewers. Keep a record of important runs and failures, subject to your data-handling agreement. Give users a straightforward way to flag an incorrect answer, pause a workflow and return to the manual process.
Recheck representative examples when models, source collections, permissions or connected tools change. Review whether the system still helps the people doing the work. For enterprise operations, ownership across teams matters as much as the first successful demonstration.
Bring one workflow to the next conversation.
Prepare a short brief: the task, its users, the source systems, a few representative examples, the required review and the outcome you want to measure. Include the reason your current tools fall short. This makes a vendor discussion concrete and makes competing proposals easier to compare.
Explore AI solutions for business to connect the task with the right capability, or review the Strivo platform to see how knowledge, tools and approvals can work together.