Most technology companies are experimenting with AI. Far fewer are changing how work actually happens.
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.
What makes technology companies different
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.
More on the context
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.
Enterprise AI transformation delivered inside a technology company
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.
Why this matters for technology companies
AI-native product advisory
Where AI creates measurable value in technology companies
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.
Engineering productivity
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.
Customer support and experience
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.
Product AI integration
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.
Revenue and sales operations
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.
Product and market intelligence
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.
Platform strategy and internal productivity
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.
What happens when engineering actually ships faster, not just codes faster.
Engineers individually faster with AI tools. Team throughput flat. Cycle times stuck. Tech debt mounting. No audit trail for any of it.
A mid-market SaaS company, three hundred engineers across multiple product lines, had done what most technology companies do: let engineers adopt AI tools individually. The tools worked. Individual productivity went up. But cycle times hadn't moved. Code reviews were still the bottleneck. Documentation was still the thing nobody did. And nobody could tell the board how much of the codebase had AI-assisted elements, or whether any of it created IP risk.
The company changed how engineering workflows operated, not the tools, but the structure around them. Productivity improved by roughly an hour per developer per day, worth about £1.2M in annual capacity. Feature cycle times dropped 25%. Thirty-one percent of merged code had AI-assisted elements, every line tracked, cleared, and documented. Zero IP incidents.
The engineers were already faster. What changed was how the team shipped.
What an engagement looks like
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.
AI Training Workshops
Want to upskill your team before scoping a broader engagement? Our workshops give technology teams practical AI skills across engineering, product, support and revenue workflows, moving beyond individual tool use to integrated team practices. A half-day, full-day or adoption workshop with no consulting commitment, and a natural first step before a formal assessment.
AI Adoption Radar
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.
Pilot Programmes
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.
Scale & Operating Model
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.
Fractional AI Leadership
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.
What it looks like day to day
In a technology company, a Business AI Operating System gives product, engineering, sales and support the same picture of customers and roadmap, takes on the preparation and first drafts that slow teams down, and gives each leader a dashboard of what needs a decision.
See one working →Start a conversation about your AI programme
Structured AI adoption for technology companies, from someone who's delivered enterprise AI transformation inside a global SaaS organisation.