Workflow first
Begin with a defined job and operating consequence, rather than a model or trend.
Applied AI Solutions
Applied AI for business workflows.
Identify, test, and integrate AI use cases with defined value, explicit evaluation, controlled data boundaries, and human oversight.
Assess an AI use case→Quality measured
Decide what good means, how errors will be found, and which cases require review.
Control retained
Set data boundaries, access, human accountability, cost limits, and a safe fallback.
Use cases
Use AI for a specific job, not as a vague capability.
Potentially useful
- Classifying, extracting, or summarising high-volume documents.
- Searching fragmented internal knowledge with cited source material.
- Drafting routine content for a person to review and approve.
- Triaging service, support, or operational requests.
- Assisting a decision where evidence and human accountability remain visible.
Questions to resolve
- What error rate and failure types can the workflow tolerate?
- Which data may enter the system, and where can it be processed?
- How will outputs be evaluated before and after release?
- Who reviews uncertain or high-consequence cases?
- What happens when the model, cost, or provider changes?
Defined starting point
AI Opportunity Assessment
Turn pressure to “do AI” into a grounded decision about where it can help, what it requires, and how it will be controlled.
Request the assessment→You receive
- A use-case inventory tied to real workflows and outcomes.
- Value, feasibility, data-readiness, and risk scoring.
- A recommended pilot with scope and acceptance measures.
- Data boundaries, evaluation approach, human review, and governance needs.
- A pilot architecture, dependencies, and decision to test, defer, or reject.
Risks made explicit
Useful AI requires a plan for when it is wrong.
Control the system
Accuracy, privacy, security, bias, prompt and model changes, cost, latency, access, traceability, and exception handling become engineering and operating requirements.
Control the decision
High-consequence outputs require a responsible person, enough evidence to review, clear escalation, and a workflow that remains usable when the AI service is unavailable.
Pilot to production
Prove usefulness before scale.
Frame
Define the job, users, outcome, constraints, data boundary, and baseline without choosing a model too early.
Evaluate
Create representative examples and measures for quality, failure types, cost, and human effort.
Pilot
Run a controlled workflow with review, logging, access controls, and a clear route for exceptions.
Integrate
Connect the proven capability to the operating process, monitor it, and keep ownership clear.
Decision rule
A successful assessment may recommend that AI should not be used. That is a useful result when the data, workflow, value, or risk cannot support a responsible pilot.
Common questions
Before a model enters the workflow.
How do we choose a first AI use case?
Choose a bounded workflow with enough volume or consequence, accessible data, reviewable outputs, a measurable baseline, and a safe way for people to handle uncertain cases.
Can private business data be protected?
Data protection depends on the providers, deployment, retention settings, access, contracts, and information involved. These boundaries are defined before a pilot is recommended.
How accurate will the system be?
Accuracy is specific to the task and data. JVS defines representative evaluation cases, measures the failure types that matter, and avoids relying on a generic model benchmark.
Do we need to train our own model?
Usually not for an initial use case. Retrieval, structured prompting, smaller task-specific models, or conventional software may be sufficient. The workflow determines the architecture.
When should AI not be used?
When the value is unclear, data cannot be handled safely, errors cannot be reviewed, deterministic rules solve the problem better, or no accountable person can own the outcome.
Workflow and value
What is an applied AI solution?
An applied AI solution adds a tested capability to a business workflow: extracting information, classifying requests, finding source material, or drafting work for review. JVS assesses the task, data, acceptable errors, and operating cost before proposing a pilot.
Choose a bounded task
A document extraction pilot is easier to evaluate when the required fields, input types, and exceptions are known. A knowledge assistant needs an agreed source collection and a way to show where an answer came from. Broad promises to automate an entire department make quality, accountability, and value harder to establish.
Compare with ordinary software
If a deterministic rule, search query, template, or supported workflow tool solves the problem reliably, a model may add cost and uncertainty without improving the result. The assessment compares these options before selecting AI. The useful outcome can be a decision to defer or reject the proposed use case.
Evaluation
How do we know whether the AI is useful?
Use representative examples from the real task, with appropriate permission and sensitive information removed or controlled. Agree how outputs will be checked before relying on a model in an operating process.
Measure the failure types that matter
For extraction, check each required field and the cases the system should refuse or escalate. For retrieval, check whether the answer is supported by the cited material and whether access boundaries hold. For drafting, assess how much review remains. A generic benchmark cannot establish whether your workflow is ready for production.
Count total effort and cost
Measure latency, service cost, human review time, correction effort, and the frequency of escalation alongside output quality. Compare the full workflow with the baseline. Define pilot acceptance criteria and a stopping rule before investing in a larger deployment.
Data and architecture
Where does business information go?
The architecture must make providers, data movement, access, retention, and logging clear. JVS agrees these boundaries with the business before the pilot uses real information.
Control inputs and retrieved content
Restrict source access to what the user or task is permitted to see. Treat uploaded documents and retrieved text as untrusted input. Keep secrets and unnecessary personal information out of prompts and logs. Retrieval permissions should be enforced by the application, rather than left to a model instruction.
Control actions and outputs
Validate structured outputs before they enter another system. Keep consequential actions behind explicit authorisation and human approval where appropriate. Define what happens when a provider is unavailable, a response is malformed, or a model changes. Record enough evidence to investigate a failure without storing unnecessary sensitive data.
Worked example · illustrative
Document triage with human review
Consider a team sorting incoming documents and entering a few fields into a system. This is a possible pilot design, not a claim of an existing JVS deployment.
Proposed intervention
Identify document types, extract defined fields, and suggest a routing category. Validate the fields against known rules and send uncertain, unsupported, or high-consequence cases to a reviewer. The reviewer sees the source document alongside the suggested result and decides what is accepted.
How success would be checked
Build a representative evaluation set before rollout. Measure field accuracy, incorrect routing, exception volume, review time, and total operating cost. Check that failures are visible and that the original manual path remains available. Scale only if the complete workflow improves against the agreed baseline.
Relevant delivery
AI integration inside a working application
In EmailCraft AI, JVS repaired an AI integration as part of wider application recovery. The work also involved the interface, backend, database, and payments. A model connection has value only when the surrounding application works.
What this demonstrates
Hands-on diagnosis and integration within a real product, with React, React Native, and Supabase. The available evidence is functional delivery and an initial pilot release. It does not establish a measured accuracy improvement or revenue gain.
What to bring to an assessment
The task, sample inputs, current output or manual process, who checks it, and what happens when it is wrong. Describe any data restrictions and the intended destination systems. The assessment can then define whether a safe, useful pilot is possible.
Related guidance and evidence
Written by Jashin Sookdhoo · Founder & Principal Consultant · Reviewed 1 October 2026
Start with a workflow worth improving.
Bring the job, the information it uses, the current baseline, and the consequence of a wrong answer.
Assess an AI use case→