12 practical AI use cases for business—and how to choose your first

A company does not need an AI strategy covering every department before trying something useful. Start with work people already do: reading a customer request, finding the relevant policy, preparing a reply, or turning scattered notes into a handoff. These jobs combine language with business context. AI can help organize that information; the company still owns whether the result is correct and what happens next.

A good candidate has three properties. The input exists in an approved form, such as a document packet or a permitted export. The output has an identifiable reader and a completion check. Someone can catch a bad result before it reaches a customer, changes a record, or commits money. High frequency helps, but a frequently repeated task is still a poor first pilot when its rules are undocumented or nobody can review it.

Research supports looking at specific tasks rather than assuming universal improvement. The NBER working-paper version of Generative AI at Work studied 5,179 customer-support agents and reported 14% more issues resolved per hour on average, with much larger benefits for less-experienced workers and minimal effects for the most experienced. That is evidence from a particular assisted support setting, not a forecast for your company or a result measured in Kona.^[1]

The table describes useful operating patterns, not a claim that every organization needs all twelve. A service firm may start with proposals and handoffs; a distributor may get more value from receivables and inconsistent exports. Begin with the row where your existing information and reviewer are strongest. The deeper guides below provide complete worked examples for the first seven workflows.

Ask the process owner to nominate three recurring tasks. For each, define one work unit: one ticket, one meeting pack, one proposal, or one weekly export. Record the current volume and the end-to-end time required to finish it. Comparing minutes per draft with hours per completed job will make almost any pilot look better than it is.

Before scoring, require an owner, approved data access, a usable source of truth, and a way to review the result. If a task fails one of these gates, first repair the underlying process. Do not compensate for missing access permission with an attractive benefit score. For a first pilot, keep sending, posting, changing permissions, approving discounts, and moving money outside the AI step.

Then compare repetition, time or quality pain, source readiness, and ease of inspection using a simple low/medium/high judgment with a sentence of evidence. A weighted spreadsheet is optional. Its weights are your managerial choices, not a scientifically validated AI-readiness model. The point is to explain why this task deserves a small test before another one.

Writing, research, and customer service do not have the same correctness requirements. An MIT report on a controlled writing experiment describes faster, better-rated work, but it explicitly notes that the assignments did not require precise factual accuracy or company-specific context. A polished proposal or policy answer still needs evidence and commercial review. Better prose does not verify a price, entitlement, or legal obligation.^[2]

Company-level benefit is another separate question. NBER research using Danish survey and administrative data, revised in March 2026, describes widespread AI adoption and task reorganization without detecting corresponding changes in earnings or recorded hours above its study bounds. This does not establish that AI has no value. It is a reason to measure what happens after a draft becomes faster: review, rework, training, integration, and how the team actually uses recovered capacity.^[3]

Treat the examples in this article as editorial methods you can test. None is a Kona customer case study, an industry benchmark, or a guaranteed improvement. If a pilot creates time for better customer follow-up, record that use of capacity. Do not call it payroll savings unless a real staffing or spending decision produces that cash effect.

Consider a fictional 24-person maintenance company. Its operations lead proposes three pilots: turning meeting notes into action registers, preparing overdue-invoice follow-ups, and drafting employee-policy answers. Meeting notes already have an owner and an approved storage location. Invoice exports omit credits and disputes. Two policy files contradict each other. Meeting follow-up is the best initial test because its information and review path are ready, not because the other tasks lack potential.

The team uses a fictional monthly volume of 60 meeting packs. Its baseline is 15 minutes per completed pack, including corrections: 60 × 15 = 900 minutes. A scenario with AI-assisted drafting and review takes 10 minutes per pack plus 120 minutes of monthly setup, sampling, and maintenance: 60 × 10 + 120 = 720 minutes. The scenario recovers 180 minutes, or 3 hours per month. These are illustrative inputs, not measured results; actual pilot timings must replace them.

The trial notes say, “Ravi will check the supplier response; the installation date is still being discussed.” A weak output says, “Ravi: complete installation by Friday.” The reviewed output is, “Ravi: check the supplier response; due date not agreed. Installation date: unresolved.” The reviewer corrects the commitment before it reaches the action register. Saving a few minutes does not justify turning a discussion into a delivery promise.

The process owner measures the same completed unit in both groups. If required corrections consume the apparent saving, or unsupported commitments appear, the team revises the workflow rather than scaling it. Meanwhile, finance reconciles credits and disputes so that a later receivables pilot starts from usable records. This sequencing creates useful progress without handing the model authority to resolve the underlying data problems.

Write a one-page workflow contract. State which records may be used, their as-of dates, and what the model must produce. Make “unknown” a valid answer. For a proposal, request an evidence column next to each customer claim. For an action register, distinguish a confirmed commitment from a suggested next step. For a spreadsheet, preserve original rows and separate proposed changes from applied ones.

Build a small, representative review set containing normal work, missing information, ambiguous wording, and deliberately difficult examples. A support set should include an expired policy and an escalation request. A knowledge set should include an answer the permitted documents cannot supply. OpenAI’s evaluation guidance recommends task-specific success criteria and representative, edge, and adversarial cases; its example numerical thresholds are not mandatory standards for a small business.^[4]

Ask reviewers to record errors by consequence, not merely whether they liked the output. An awkward sentence and an unsupported refund promise are different defects. Re-run the same reviewed examples when a source, prompt, model, or connected tool changes. Keep a versioned output so an improvement in one category does not quietly remove another required field.

Use approved excerpts or narrow exports instead of whole customer databases, mailboxes, or personnel files. A meeting action pilot rarely needs payment details or an employee’s personal documents. Check the actual service terms, access controls, retention, and connected sources for your deployment. A business subscription or a “not used for training” statement alone does not establish that content is never retained.

Treat retrieved text and attachments as information, not permission to take new actions. A malicious or simply irrelevant instruction inside a supplier file should not authorize sending data or changing a record. OWASP describes direct and indirect prompt injection and notes that retrieval does not remove the risk. Enforce access and action limits in the product and connected accounts as well as in the written brief.^[5]

Preparation and execution are different stages. An AI-generated collection email can be useful before anyone sends it. A drafted onboarding checklist does not create accounts or approve access. Add consequential actions only after the process has a responsible approver, appropriate permissions, a record of what was approved, and a practical way to detect and recover from mistakes.

In the first few days, document the current workflow and assemble the approved test packet. Time the manual baseline through final review, not just typing. Agree how you will identify missing facts and material errors. A two-week window is a suggested planning frame; low-volume or seasonal workflows may need longer to produce a useful comparison.

Next, test AI assistance on comparable work while a person retains the final decision. Record preparation time, generation and waiting time, review, corrections, and administration. Keep rejected outputs in the evaluation record so the average is not calculated from successful drafts alone. Compare similar complexity and give completed cases enough follow-up time to reveal reopenings or missed actions.

At the review, choose expand, revise, or stop. Expand only if the finished work meets the agreed quality threshold and the net benefit is useful after recurring costs. Revise when a specific input, instruction, or source gap explains the failure. Stop when review is too expensive, the risk is not controllable, or a straightforward process change solves the problem more reliably. A well-run pilot can create value by ruling out the wrong use case.

The related guides below cover customer-service drafts, knowledge answers, meeting actions, receivables, spreadsheet checks, sales proposals, and onboarding in detail. Each offers a different fictional source packet, reviewer correction, and downloadable worksheet. If you already have a procedure or a measurement method, adapt it rather than introducing a second process just for AI.

In Kona, start with a short description of the job and supply only authorized inputs. Ask for the deliverable, its assumptions, and its review checklist. Workspace can help prepare business documents, analysis, and workbooks; the available connected context depends on your account and permissions. Sending messages, posting ledger entries, provisioning employee accounts, or other external actions require a separately supported and authorized execution path. The prepared brief below does not run a task or upload company data until you choose to proceed.