Make everyday work take less work.

Start with a repetitive task where the input, desired output, and person responsible for the decision are reasonably clear. Examples include preparing a document summary for review, extracting fields from incoming records, helping staff find an answer in approved material, or suggesting how to route a support request.

Compare the proposed workflow with the current process. If the current task is already fast, inexpensive, and reliable, AI may add complexity without enough value. If the bottleneck is missing information or unclear ownership, an AI interface will not fix the underlying process.

A useful first project has representative examples, someone who can judge the output, and an agreed way to handle uncertainty. These conditions make it possible to assess value before expanding access.

Rules-based automation is a good fit when the decision can be described reliably: route an approved invoice, synchronize a record, or notify an owner when a condition is met. It is easier to explain and test than a probabilistic decision when the business logic is fixed.

AI becomes more relevant when information is unstructured or language varies. Reading documents, finding related passages, and drafting a summary can benefit from a model. A practical application often combines both approaches: AI prepares a suggestion, deterministic rules check required fields, and a person approves the consequential action.

We do not assume that every workflow needs an autonomous agent. The right degree of automation depends on what a mistake could do and how quickly it can be detected and corrected.

Identify the approved sources, who owns them, and which users may see which records. For knowledge search, document freshness and access permissions matter as much as the model choice. A user should not receive confidential information through an assistant that they could not otherwise access.

For document processing, assemble examples of the variations the system will encounter: different layouts, incomplete scans, unfamiliar terminology, and missing fields. Separate development examples from evaluation examples so that a convincing demonstration is not the only measure of quality.

Before connecting production data, agree on the provider, retention settings, permitted data use, logging, and access boundaries. The application’s configuration and contracts determine those arrangements; “private AI” on a slide is not a specification.

Define a small evaluation set that represents real work. For extraction, check important fields and exception handling. For summaries, check factual support, omissions, and whether the output preserves the distinctions a reviewer needs. For knowledge answers, check whether the cited material actually supports the answer.

Measure the whole workflow, including review and correction. A quick draft is not necessarily a time saving if staff must repeatedly repair it. Consider output quality, completion time, review effort, response latency, and the cost per completed task.

Keep a path for “not enough information.” Where the output could affect a consequential decision, make the review step clear and test it as part of the product. A user should understand whether they are seeing a suggestion, a source record, or an approved result.

An implementation can include use-case discovery, data preparation, model or provider integration, retrieval from approved sources, output validation, a user interface, and integration with the destination system. The visible assistant is only part of the work.

Production planning also needs access controls, failure handling, usage limits, evaluation procedures, and an owner for ongoing changes. If a model, prompt, or source document changes, there should be a way to check that important behavior has not regressed.

Salt’s clinical-intake project illustrates a bounded use of AI: recordings and transcripts feed structured summaries for clinician review. The application also includes workflow status, configurable templates, and facility administration. The AI capability sits inside a larger product.

Pause if nobody can define acceptable output, the source information cannot be accessed lawfully, or there is no owner for reviewing exceptions. It is also worth reconsidering when a conventional workflow change would solve the problem more simply.

For healthcare and other sensitive settings, identify the relevant data-handling and approval requirements with your responsible specialists. An AI feature, a cloud provider, or a software vendor does not by itself establish compliance for the whole business process.

Agree how prompts, models, evaluation data, and approved sources are versioned and released. Test changes against representative tasks, monitor response quality, latency and usage costs, and keep a rollback path. Assign owners for content updates and provider changes so an AI feature remains supportable.

For an existing custom ML model, we can scope model-serving integration and monitoring after reviewing its artifacts, dependencies, and evaluation evidence. Model training, specialist validation, retraining infrastructure, and 24/7 operations are separate scope decisions; do not assume they are included in an AI assistant project.

Define what the system is allowed to read, suggest, and change. Apply access controls to the underlying data, make supporting sources available where useful, and require human approval for consequential actions. Test missing information, unsafe disclosures, and failure cases alongside successful examples.

For example, document automation can flag uncertain fields for review instead of silently sending them to an ERP. Keep a record of the source, reviewer, and approved output where required. Legal assessments, formal compliance opinions, and independent certification are not implied by these engineering controls.