AI Training
Hands-on programmes built around your processes, documents, systems and decisions.
Leadership learns where to invest and what to control. Business teams turn recurring work into repeatable AI-assisted workflows. Technical teams learn how to build and run them safely.
Discuss your AI training programme
The adoption gap
In many companies, the people who understand AI best do not sit in a dedicated team. They are developers, project managers, product leaders and business specialists who started testing tools on their own.
We use training to establish a common level of judgement across the organisation and identify the people who can help carry the work forward.
Useful experiments stay isolated.
Teams apply different rules to the same information.
Quality depends on the individual user.
Early adopters become an unofficial support desk.
Role-specific programmes
The shared sessions establish common rules and language. The applied work changes with the audience, because an executive, an operator and a technical lead do not need the same level of detail.
CEOs, CIOs, CTOs and functional leaders
We examine the company's own processes to identify credible opportunities, challenge the business case and decide where process redesign, better data or new software is required.
A shortlist of use cases, clear ownership and the decisions required to pursue them.
Sales, service, finance, HR, legal and operations
Teams work on recurring tasks from their own environment. They learn to frame the task, provide context, inspect sources and decide when a person must take over.
Tested workflows and practices that colleagues can understand and reuse.
Developers, data teams, product managers and technical leads
Participants address access rights, data quality, architecture, evaluation, testing, monitoring and integration with existing systems.
An approach to test, evaluation criteria and a clear view of the work required before deployment.
Across the programme
Most companies already have potential AI Champions. They have tested several approaches, learned from failed outputs and started helping colleagues informally.
We identify them during the initial discussions and involve them across the programme. They help select relevant examples, connect the training to the way work happens and surface barriers that a central team may not see.
Built around the work
The material comes from interviews, representative documents and the systems people use today. This keeps the sessions close to the decisions participants are expected to make.
Working sessions show where time is lost, which data is available and which controls already exist. We use that evidence to choose exercises that matter to each audience.
Training brief and use-case selectionParticipants complete a task, compare the output with their current method and examine where the result needs correction, additional context or human review.
Tested workflows and review rulesSelected workflows run on a controlled scope. The comparison can use time spent, processing cost, error rate, throughput or quality, depending on the task.
Baseline, shared practices and next decisionSelected practitioners
Stratos brings together former founders, C-level operators, CTOs and data & AI specialists. The team assigned to each programme depends on the audiences and use cases in scope.
CTO Senior Advisor
Florian has led technology and product teams for ten years, with a focus on ERP. He brings practical experience in AI-assisted delivery, process change and leadership development.
CTO Senior Advisor
Alexis is an experienced software leader who works hands-on with AI-assisted development. He helps teams move from individual experiments to shared, production-ready workflows.
.png)
Partner CTO
Pierre-Emmanuel has led work across data architecture, cloud migrations, business systems and product roadmaps.
Data and AI Architect
Soufiane designs cloud data platforms, data pipelines and Generative AI integrations for production environments.
Applied work
A useful exercise has a real input, a defined output, an accountable reviewer and a way to judge the result.
Account research, meeting preparation, summaries, qualification and sales-to-delivery handovers.
Request triage, routing, draft responses, document classification, extraction and exception handling.
Reconciliation, reporting, planning, scheduling and document-heavy controls.
Coding agents, document intelligence, data architecture, model evaluation and production monitoring.
From training to delivery
Training often exposes work that a better instruction cannot solve: unnecessary handovers, information split across systems, unreliable data or missing access rules.
The use case, baseline and constraints identified during training become the implementation brief, so the team does not restart the analysis from scratch.
Questions
We begin with the roles, processes and business outcomes in scope. Shared sessions establish common rules and language. Applied work is adapted to the decisions, documents and systems used by each team.
The format depends on the audiences and the outcome. It can combine an executive workshop, role-specific sessions, hands-on use-case work, office hours and ongoing support for AI Champions.
Yes, where access and security conditions allow it. We agree the data boundaries before the sessions and use sanitised or representative material when sensitive information should remain outside the training environment.
We evaluate selected use cases against an agreed baseline. Depending on the work, that may be time spent, processing cost, error rate, throughput or output quality. We also review whether teams continue to use the workflow and whether the AI Champions can support it.
Work with Stratos
We will start with the roles, processes and business outcomes that matter most, then shape the programme around the work your people need to handle.
