Stratis helps businesses identify where AI creates genuine operational value — and implements it in a way that’s grounded, practical, and measurable. This is not AI as a trend exercise. This is AI deployed where it directly improves profitability and enterprise value: in workflows, decision support, reporting, automation, recruitment, analysis, and operational efficiency.
The leadership team knows AI matters but can’t identify where to start.
Vendors are pitching tools, but nobody is asking whether the business’s processes and data are ready for them.
Or worse — AI has been introduced in isolated pockets with no connection to the operating model, no measurement of impact, and no governance around its use.
The opportunity is real — AI can recover profitability through automation, strengthen enterprise value through scalable intelligence, and free the team from repetitive work that drains margin. But without operational grounding, AI becomes another cost line with unclear returns.
Stratis approaches AI the same way it approaches every intervention — starting with the business, not the technology.
Working on-site inside the business, Stratis identifies where AI creates real value by examining AI readiness and opportunity across all four pillars — Finance, People & Organisation, Commercial, and Operations. Financial processes may benefit from AI-enhanced reporting and forecasting. People management may benefit from AI-driven recruitment and performance analysis. Commercial workflows may benefit from AI-powered customer analytics. Operational processes may benefit from full AI automation of repetitive tasks.
The Systems/Data/AI lens — one of four cross-cutting lenses that include Governance & Decision Rights, Founder Dependence, and Risk & Compliance — is where AI opportunity is most directly surfaced during the diagnostic work. It reveals where the business’s data infrastructure, systems maturity, and existing technology either enable or constrain AI deployment. The other lenses ensure that AI is governed, doesn’t create new founder dependencies, and doesn’t introduce unmanaged risk.
The question is never “where can we use AI?” — it’s “where does AI solve a problem that matters to profitability and enterprise value?”
From there, Stratis designs and implements practical AI use cases: AI process automation for repetitive operational tasks, AI-driven recommendations for decision support, AI-enhanced reporting and analysis, and intelligent workflow automation through BetterOpsAI. Every use case is tied to a measurable outcome — recovered margin, reduced cost, faster throughput, or stronger control.
Antoine’s eight years in software development means AI implementation is led by someone who understands the technology at an architectural level — not as a buyer reading vendor brochures, but as a builder who knows what AI can realistically do and where it falls short.
AI use cases identified across all four pillars — Finance, People & Organisation, Commercial, and Operations — designed and implemented inside the business, not recommended in a report. Each use case grounded in the diagnostic findings from the four lenses, ensuring AI is governed, doesn’t create new dependencies, addresses genuine systems and data readiness, and doesn’t introduce unmanaged risk.
AI process automation deployed through BetterOpsAI for the workflows and tasks where it creates the most value. AI-driven corrective recommendations built into the monitoring and control layer. Measurable improvement in profitability — through reduced manual effort, faster decisions, better consistency, and lower operational cost. Stronger enterprise value — through a business that operates with scalable, AI-enabled intelligence rather than manual effort. And critically, AI that is governed, monitored, and connected to the operating model — not floating in isolation.
Applied AI touches all four pillars — because AI opportunities exist across financial, people, commercial, and operational workflows. The Systems/Data/AI lens is the primary diagnostic driver, but Governance, Founder Dependence, and Risk lenses all shape how AI is deployed responsibly.
It builds on Digitisation & Workflow Redesign — digitised, well-structured workflows are the foundation for effective AI deployment, and the profitability gains from digitisation compound when AI automation is layered on top. It connects to Operating Model, Governance & Control because AI must be governed within the broader operating model to protect enterprise value rather than creating unmanaged risk. And it connects directly to BetterOpsAI, which is the proprietary platform that powers the AI automation, recommendations, and monitoring layer within Stratis engagements.
The first conversation is about understanding your situation — where you are, where you want to be, and what’s standing in the way.