AI implementation for regulated teams
AI implementation services: a roadmap from readiness to production
What an AI implementation partner should do at each stage, from readiness to monitoring, how to judge one before you sign, and what Kriv AI builds today.
AI implementation services take an AI use case from idea to a system your staff use every day: checking readiness, choosing the use case, arranging data and access, running a pilot with a named owner, evaluating it against measures you set, moving it into production and monitoring it afterwards. The work ends with your team owning it.
what ai implementation services are
What AI implementation services are, and who they are for
AI implementation is the work between deciding to use AI and having a system that runs inside a real workflow, with an owner, rules and a record of what it did. Implementation of AI is the part that touches your systems, your data and your people.
Who this is for: organisations with a use case in mind, or licences already bought, that need it built into a workflow security, compliance and the business owner will sign off. Who it is not for: teams wanting AI research, a product for resale, or a large contractor bench on a multi-year programme. With no budget owner or executive sponsor yet, the first job is internal, and a discovery call will say so.
| Part of the work | What it covers in practice |
|---|---|
| Integration | Connecting the model or assistant to the systems it must read from or write to, with the narrowest access that works. |
| Governance | Who owns the system, which actions need a person's approval, what is logged, and how changes are approved. |
| Adoption | Training the people who use or supervise the system, and handing over the documents they need to run it. |
ai implementation roadmap
An AI implementation roadmap, stage by stage
An AI implementation roadmap is a sequence, not a calendar. Each stage ends with something written down, and the next starts only when that output exists. How long each stage takes depends on your data, systems and review process, so it is agreed in the scope, not promised up front.
Our guide to why enterprise AI pilots stall before production covers the gap between pilot and production in more depth.
1. Readiness
Inventory where AI is already used, which policies apply, what data candidate use cases would touch and what logging exists. Output: a short written assessment of what blocks each use case.
2. Use-case selection
Rank candidates by value, risk and how well the current process is documented. Pick one, and write down what the system may do, must not do, and which decisions stay with a person.
3. Data and access
Decide which systems the AI may read or change, and under whose credentials. Grant the narrowest access the job needs and agree what may be sent to a model and where activity is logged.
4. Pilot with a person accountable
Build the smallest version that runs on real inputs in the real workflow, not a curated demo, with one named person on your side who owns the result and approves sensitive actions.
5. Evaluation against measures you define
Before the pilot starts, you set the measures, for example accuracy on a sample of your own cases, exception handling, or reviewer time spent checking output. A no-go is a valid result.
6. Production
Widen use one team at a time, with approval steps on irreversible actions, change control for prompts, models and tools, and a runbook for operators.
7. Monitoring
Review what the system did, not only whether it ran: sampled outputs, exceptions, reviewer overrides and changes in its inputs. Decide in advance who reviews and what triggers a pause.
strategy versus plan
AI implementation strategy versus an AI implementation plan
The terms get used interchangeably. They answer different questions, and you need both.
AI implementation strategy: which problems, and which not
An AI implementation strategy decides where AI is worth applying, which use cases come first, which are ruled out, and who owns AI decisions. It is short, and it says no to things.
AI implementation plan: one use case, written down
An AI implementation plan takes one use case through the roadmap above: owner, data and access, pilot scope, the measures it is judged against, approval steps and handover. A plan that cannot name the owner and the measures is not ready to build against.
AI implementation in business: where to start
Start where the work is reading and sorting rather than deciding: an inbox one person triages, documents retyped into another system. A person can check the output against a process that already exists. Leave final decisions on money, eligibility, coverage and clinical matters with a named person, and design the AI to prepare that decision.
choose an ai implementation partner
How to choose an AI implementation partner or consultant
Ask these questions of any AI implementation company, consultant or agency before you sign.
Red flags in an AI implementation proposal
A delivery date or results figure promised before anyone has seen your data. A pilot that runs only in the vendor's environment on sample data. No named owner on your side. A claim that the system makes you compliant. Vague answers about logging and approvals. No clear answer to what you keep if the engagement ends.
AI implementation consultant, specialist, agency or company: what the labels mean
The labels overlap, so ask what each delivers. An AI implementation consultant or AI implementation specialist can mean an individual who scopes and builds; an AI implementation agency or company can mean a team, a reseller or a template shop. AI integration consulting and AI engineering consultants connect models to existing systems; AI transformation consulting describes organisation-wide change. Judge each by the questions above.
| Question to ask | What a good answer looks like |
|---|---|
| Who does the work after the sale? | A named person, ideally the one on the first call, not an unnamed team you meet after signature. |
| How will the pilot be judged? | By measures you set before it starts, on your own cases, with a no-go treated as a valid outcome. |
| What is logged, and who can read it? | A specific answer: which actions are recorded, where, and how your security or audit team queries them. |
| What do we own at handover? | The working system, its documentation and runbook, and the trained people to operate it without the partner. |
regulated industries
AI implementation in regulated industries
Kriv AI's niche is healthcare, life sciences, financial services and insurance, where an AI system can touch patient, client or claims data and someone outside the project will ask how it works.
In these teams governance shapes the implementation from the first call, not just before launch. We write the rules to the policy and frameworks you already answer to, such as HIPAA, 21 CFR Part 11, SR 26-2, the NAIC Model Bulletin or the NIST AI RMF. We implement technical controls aligned to your requirements; we are not a compliance certifying body, and we do not certify compliance.
A point for banking teams: SR 26-2, the model risk guidance, states that generative AI and agentic AI models are not within its scope. Your own risk management should still govern them.
For health systems choosing an outside firm, our guide to choosing a health system AI advisory firm covers the healthcare-specific questions.
where kriv fits
Where Kriv AI fits as an AI implementation partner
Kriv AI sends a senior practitioner who both scopes and delivers: no handoffs, no bench, no discovery theater.
Our method runs in five chapters, Assess, Govern, Build, Validate and Operate, each leaving a written artifact your team keeps. More on our AI consulting services page, our agentic AI automation page and our AI automation page.
Partner credentials, in the exact wording each program allows: Claude Partner Network, Select partner · Services Track (program details at claude.com/partners; written confirmation from the Claude Partner Network team available on request; more on our Anthropic partner credentials). OpenAI Partner Network · Select tier. Solutions Partner for Cloud & AI Platforms · Partner ID 7036589. Databricks partner. AWS Marketplace seller, 59 live listings. We are also a Zapier Silver Solution Partner.
Our Claude Code training delivery record: for a multi-billion-dollar distribution enterprise with 2,000+ associates across 122 locations, we delivered 12 live training sessions, 26 training documents and 5 role-specific starter kits.
The honest gap: that record is training work. We do not yet have a named case study for an agentic build or a governance engagement, so we will not quote implementation results we cannot show.
1. Readiness and use-case selection
Where AI is already used, a ranked list of use cases worth doing and the governance gaps that would block them, as in our AI readiness and governance assessment.
2. Agentic builds on your own platforms
Governed agents built to one pattern: trigger, context, a language-model reasoning step, action, then logging and approval, each with a written scope of what it may read, change and must escalate.
3. Workflow automation with an AI step
Automation on Zapier, Make, n8n or Power Automate with approval gates on sensitive steps and run history, handed over so your team can run and change it.
4. Governed deployment and training
Access model, usage rules, logging and rollout for Claude, OpenAI models or Microsoft Copilot, with role-based training for users.
how an engagement runs
How an AI implementation engagement with Kriv AI runs
Every engagement is scoped and priced after a short discovery call, and the length of each step is agreed in the written scope.
1. Discovery call
The problem, the data involved, the regulator or auditor you answer to and who will own the result. If we are not the right fit, you hear it here.
2. Readiness review
What you already have: AI tools in use, policies, data access and logging. The output is a short written assessment of what blocks the use case.
3. Written scope
Named deliverables, the measures the pilot will be judged against, and the sign-off points, scoped and priced after the discovery call and readiness review.
4. Build
The pilot, then wider rollout in stages with a review gate between them, plus the owner, scope, rules and change-control documentation your compliance or risk team will want. A formal sign-off pack can be prepared on request.
5. Handover
Your named owner takes over with the runbook, documentation and trained staff.
get started
Get started
Bring the use case, the data it touches and the name of whoever signs it off. A short call shows whether you need a readiness review, a scoped build, or nothing from us yet. Book an AI implementation discovery call, or see how engagements are scoped and priced.
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Straight answers
Frequently asked questions about AI implementation services: a roadmap from readiness to production
What are AI implementation services?
AI implementation services take a chosen AI use case into daily use: readiness, use-case selection, data and access, a pilot with an accountable owner, evaluation against your measures, production and monitoring. Unlike strategy work, implementation touches your systems and data, and ends with a working system, documentation and trained people your team owns.
What is an AI implementation roadmap?
An AI implementation roadmap is the ordered sequence a use case moves through: readiness, use-case selection, data and access, pilot, evaluation, production and monitoring. Each stage ends with a written output, and the next starts only when it exists. A good roadmap sets order and sign-off points; stage length depends on your data and review process.
What is the difference between an AI implementation strategy and an AI implementation plan?
A strategy decides where AI is worth applying across the organisation, what comes first, what is ruled out, and who owns AI decisions. A plan covers one use case in detail: the owner, data and access, pilot scope, the measures it will be judged against, approval steps and handover. You need the strategy to choose, and the plan to build.
What does an AI implementation consultant or specialist do?
They scope a use case, connect a model or assistant to the systems it needs, set access and approval rules, run the pilot, and hand the result to your owner with documentation. At Kriv AI, a senior practitioner who both scopes and delivers does this work, from the discovery call to handover.
How much do AI implementation services cost?
Cost depends on the use case, the systems involved and the review your organisation requires, so there is no honest list price for implementation work. At Kriv AI every engagement is scoped and priced after a short discovery call, and the written scope names deliverables and sign-off points before work begins.
What is AI integration consulting?
AI integration consulting connects an AI model or assistant to the systems it must read from or act on, such as a CRM, an EHR, a document store or a ticketing tool. The work is access design, error handling and logging. Kriv AI builds on platforms you already license, such as Power Automate, n8n or Zapier.
Is AI transformation consulting the same as AI implementation?
Not quite. AI transformation consulting describes organisation-wide change: operating model, roles and portfolio. AI implementation is narrower and concrete: one use case at a time, built into a real workflow, with an owner and measures. Transformation without implementation produces plans; implementation without a strategy produces scattered pilots. Kriv AI's work is implementation-first, with readiness and governance around it.
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