Strategy, governance, enterprise AI training, and engineering from one team — so AI moves past the pilot and into the systems your people work in every day.
Book an AI adoption consultation
A working session on where AI fits in your organization, what it would take to deliver, and what to do first.
Most enterprise AI stalls after the proof of concept, because nobody owns the path from demo to daily use. The program is built around that handover: every workstream ends in a system people actually work in, with an owner, a runbook, and a support line.
Strategy that never meets an engineer produces slideware; engineering without a strategy produces orphaned tools. The same team that writes the AI adoption roadmap also ships the software, so the plan is costed against what it actually takes to build.
Access control, data residency, human review, audit trails, and model evaluation are designed in at the start rather than retrofitted after a security review blocks the rollout. Governance is what makes AI deployable in a regulated business, not a tax on it.
Enterprise AI training is run for the people who will operate the systems after we leave — not a generic literacy course, but working sessions on the tools, prompts, guardrails, and escalation paths your teams will actually use.
Each use case carries a baseline before anything is built, so the change is measured against how the work was done before rather than against a vendor benchmark. What cannot be measured does not enter the roadmap.
AI systems fail in ways ordinary software does not: prompt injection, data leakage through context, over-permissioned agents. Profitech red-teams what it builds, so adoption does not quietly widen the attack surface.
Under pressure to show an AI strategy, without a reliable way to separate the use cases that pay from the ones that demo well.
A costed, sequenced AI adoption roadmap tied to business outcomes, with the governance story ready for the board and the regulator.
Fielding AI requests from every department while carrying the integration, security, and platform debt those requests create.
A reference architecture, evaluation harness, and rollout pattern that new use cases plug into instead of each one becoming a bespoke project.
Volume-heavy processes — documents, approvals, tickets, reconciliation — where headcount is the only lever currently available.
Automated workflows with human review at the points that matter, and the measurement to show what changed against the original baseline.
Asked to sign off on AI systems whose behaviour, data flows, and failure modes nobody has written down.
Documented data lineage, access boundaries, retention rules, human-in-the-loop checkpoints, and evaluation results for every deployed system.
The consulting layer: finding where AI earns its place in your organization and in what order to build it.
Role-specific training so the systems are used well after handover, rather than quietly abandoned.
The controls that let a regulated organization put AI into production and keep it there.
The engineering that turns the roadmap into systems people log into on a Monday morning.
The build workstream draws on the same capabilities we deliver standalone — AI agents, workflow automation, enterprise search and RAG, and AI cybersecurity.
Understand how the work is done today before proposing to change it. We interview the teams doing the work, inventory the data and systems behind it, and record a baseline for every candidate use case.
Turn the inventory into a sequence. The first build is chosen to be genuinely useful and genuinely finishable, because the fastest way to lose an adoption program is to open with an eighteen-month platform.
Ship the first use cases into production against your real data and systems, in short increments the business can see. Security review and evaluation run alongside the build rather than after it.
Move ownership across. Training is delivered to the people who will run the system, the internal champions are set up, and the next wave of use cases enters the same pipeline.
Not sure which phase you are starting from? The AI readiness assessment takes a few minutes and points at the right entry point. To sketch the commercial case first, use the ROI calculator.
Results are measured against the baseline recorded during the assessment, so the comparison is with how the work was done before — not against a vendor benchmark. See selected work for systems already in production.
An AI adoption program is a structured engagement that takes an organization from scattered AI experiments to systems in production that people use daily. It combines AI strategy and roadmapping, governance and security design, engineering delivery, and enterprise AI training — on the basis that any one of those alone tends to stall. Profitech runs all four with a single team.
Traditional AI consulting usually ends at the recommendation. This program is accountable through to production: the same team that writes the roadmap builds and deploys the systems, trains the people who will run them, and hands over ownership. The roadmap is costed by the engineers who have to deliver it, which tends to change what goes into it.
The assessment and planning phases typically run four weeks, and the first use cases usually reach production within eight to twelve weeks of kickoff, depending on data readiness and integration complexity. Adoption then continues in increments. We give a firm timeline after the assessment, once we have seen the real systems rather than a description of them.
No, and organizations that wait for that rarely start at all. Data readiness is part of what the assessment measures, and the roadmap is sequenced around it — early use cases are chosen to work with the data you actually have, while any groundwork needed for later ones is scheduled explicitly rather than assumed.
An executive sponsor who can make sequencing decisions, a technology or data lead for architecture and integration, the operational owners of the processes in scope, and a risk or compliance representative. The program is designed so those people contribute at defined points rather than being pulled into continuous workshops.
Access control, data classification, retention, and residency are designed in during the planning phase, not added after a security review. Profitech also red-teams the systems it builds for prompt injection, data leakage through context, and over-permissioned agent behaviour, because AI systems fail in ways that conventional application testing does not cover.
Training is role-specific rather than generic literacy. Executives get capability, risk, and realistic time horizons. Practitioners get hands-on sessions on the systems they will operate, including where to escalate and how to tell when an output is wrong. Engineering teams get RAG, agent design, evaluation, and prompt security. Internal champions get runbooks and office hours.
It depends on the number of use cases, integration depth, and how much governance groundwork is needed. Scope is set after the assessment, so the figure is based on your real systems rather than an estimate made before seeing them. Tell us what you are trying to change and we will come back with a firm proposal.
Bring the processes you want to change. We will tell you which are worth automating, what it takes to build them, and what has to be true for your organization to adopt them.
Book an AI adoption consultation
No obligation, and no deck. A working conversation about your systems and where AI genuinely fits.
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