Engineering productivity, product integration, support automation, revenue operations. AI creates value across every function in a technology company — but only when it moves beyond isolated tools into redesigned workflows with proper governance.
Technology companies adopt AI tools faster than almost any other sector. That speed often creates a different problem: fragmented adoption without a coherent operating model.
Engineering teams are using AI coding assistants. Support teams are experimenting with ticket summarisation. Product teams are embedding AI features into the roadmap. Sales teams are trying AI-generated proposals and pipeline analysis. Each team is making reasonable decisions in isolation — but nobody is designing the overall operating model.
The result is shadow AI spreading across the organisation, inconsistent governance, duplicated tool spend, and productivity gains that remain localised rather than compounding across functions. Meanwhile, the strategic questions go unanswered: what does AI mean for your product positioning? How do you protect intellectual property when engineers are using AI coding tools? What happens to your competitive position if AI-native competitors redesign the category?
Technology companies also face a particular governance challenge. Your engineers understand AI better than most — but that familiarity can lead to under-governance rather than over-governance. IP exposure through code generation tools, customer data flowing through third-party models, AI-generated content in customer-facing products without proper quality controls. These risks compound as adoption scales.
The companies getting this right aren't the ones that moved fastest. They're the ones that built a coherent AI operating model — covering internal productivity, product integration, and governance — then moved with confidence.
Mike led enterprise-wide AI transformation at Verimatrix — a publicly listed global SaaS company — under direct ExCom oversight. Not advising on AI adoption. Executing it across engineering, support, sales and operations in a nine-country organisation.
This included converting uncontrolled shadow AI into governed enterprise-wide adoption under an AI Steering Group. Designing a multi-model architecture strategy that avoided unnecessary specialist tool spend. Implementing AI coding assistants with IP protections and code review governance. Building AI-assisted support workflows that reduced ticket volume whilst maintaining quality.
The Responsible AI governance framework was institutionalised across a regulated EU-listed company — not a theoretical exercise, but a working system approved by the board and operational across every function.
AI creates value across every function in a technology company. Where you start depends on your engineering maturity, product strategy, and which governance questions need resolving first.
AI coding assistants, test generation, documentation, debugging support and legacy code comprehension. The productivity gains are real — but they need governance: approved tool lists, IP protections, human review of AI-generated code, and audit trails. The companies seeing compounding returns are the ones that designed the governance alongside the tooling.
Ticket summarisation, knowledge retrieval, response drafting, root cause analysis across support data. AI-assisted support reduces handling time, improves first-line resolution quality, and cuts escalations to engineering. The support data also becomes a feedback loop for product improvement when properly analysed.
Embedding AI into your product as a capability — in-product copilots, intelligent configuration, automated classification, detection enhancement. This creates differentiation and customer value, but raises questions about model reliability, guardrails, customer transparency and inference cost management that need structured answers.
RFP response automation, proposal drafting, pipeline intelligence, competitive battlecards, partner enablement. AI can materially improve sales productivity and win rates — the Verimatrix programme demonstrated a ~10% win-rate improvement from AI-enabled sales workflows.
Competitive analysis, product feedback synthesis, usage pattern analysis, roadmap prioritisation from customer signals. Turning the data you already have into actionable product insight — faster than manual analysis allows.
Enterprise AI workspace design, knowledge assistants, meeting summarisation, document drafting, tool rationalisation. Reducing shadow AI through governed enterprise platforms that give people better tools with proper controls — often saving significant licence costs in the process.
Every technology company has a different engineering culture, product strategy, and organisational readiness for AI. The engagement starts with understanding yours.
Whether you're a SaaS company scaling AI coding tools across engineering, a platform business embedding AI into your product, or a technology firm that needs to convert fragmented experimentation into a coherent operating model — the approach is structured around your specific context.
A focused 2–4 week assessment across engineering, product, support and revenue functions. Maps where AI is already being used (including shadow AI), identifies the highest-value workflow redesign opportunities, and produces a governance risk assessment alongside the pilot recommendation. Technology companies often discover they have more AI adoption than they realised — and less governance than they need.
A 6–12 week pilot in a real operational context — not a sandbox experiment. Typical starting points include AI-assisted engineering workflows with IP governance, support automation with quality controls, or sales enablement with measurable win-rate tracking. The pilot blueprint defines success metrics, governance controls and human oversight requirements alongside the workflow design.
A 3–6 month engagement to move from pilot success into an AI operating model that works across the organisation. This means defining platform strategy (which models, which tools, which boundaries), establishing governance that scales without slowing innovation, and building the human-AI accountability structures that let teams move fast with confidence. For multi-product companies, this often means designing a shared AI platform with function-specific customisation.
A retained engagement — typically 1–3 days per month — providing senior AI oversight at the intersection of product strategy, engineering productivity and governance. Especially valuable for technology companies at a strategic inflection point around AI, where the CTO or CPO needs a thought partner who understands both the technology and the organisational transformation required. Mike can engage credibly with boards, investors and engineering leadership — because he's operated at that level inside a publicly listed technology company.
Structured AI adoption for technology companies — from someone who's delivered enterprise AI transformation inside a global SaaS organisation.