For anyone working in tech, AI isn’t hogging the limelight. It is the light.
But behind the noise of pilots, prototypes and promises, one truth is clear: without fixing the operational foundations first, AI won’t deliver.
Fresh from talking on the topic at Gartner IT Symposium 2025, Dave Stevens, Brennan’s Managing Director, explains why it matters.
Operational Innovation: the real transformation work
ADAPT research reveals 74% of CIOs plan to invest in AI agents in the next 12 months. Yet 76% of CISOs say their organisations are not ready to adopt AI safely. The gap is glaring. Leaders need to see that the path to AI runs not through short-term pilots, but deeper operational reform.
This is the essence of what we call Operational Innovation: the unseen work of governance, architecture, and culture that turns strategic intent into tangible outcomes. And for AI, from shiny experiment into a reliable growth engine.
Operational Innovation is the process of rethinking how organisations operate at a strategic, cultural, and procedural level to reduce friction and achieve outcomes.
Or, in plain speak: if we want to lead through intelligence, we first need to lead through clarity.
Successful AI adoption – be it generative or agentic – is not solely about models or platforms. It’s about the conditions that allow those models to thrive. For Brennan, that means focussing on three disciplines:
1. Data and Identity
AI output mirrors data quality. If knowledge is diffuse and access uncontrolled, models hallucinate. By segregating information into domain-specific libraries, applying metadata, and aligning identity to roles, organisations create precision over guesswork.
One customer, an energy and water ombudsman, faced diffuse knowledge and diffuse answers as their chatbot struggled to direct legitimate complaints. By restructuring content libraries, applying guardrails, and creating specialised AI agents, answers became context-aware, escalations dropped, and staff confidence grew.
2. Governance, Risk, and Compliance
When AI handles huge operational throughput, like safety plans or contract reviews, governance is no longer a checkbox. It’s a process precursor. Without policies, access controls, and quality assurance, scale collapses under risk.
For one large mining organisation, reviewing 1,000-page contracts was slow and error-prone. With AI-driven policies, access controls, and regression testing, reviews became faster, traceable, and compliant, removing manual bottlenecks while reducing risk.
3. Culture as the catalyst
If AI is to transform work, workers must embrace it. Yet adoption is the most neglected piece of the puzzle. ADAPT reports just 23% of organisations have AI training, and only 6% mandate it. Without user-level adoption, AI may remain a curiosity – or worse, a threat.
And it’s not just technical training. It’s about storytelling, sequencing, and trust. At Brennan, Level One service desk staff initially feared AI copilots would replace them. Training reframed AI as an “always-on mentor,” reducing repetition, easing decision fatigue, and lifting confidence. The result was not fewer jobs, but more empowered staff.
Legacy: the unmoved obstacle
Even the strongest AI vision cannot escape gravity. Forty percent of mission-critical applications still run on legacy systems. This is baggage at scale. Even without AI this slows efficiency; with AI, it becomes an adoption inhibitor.
Legacy complicates integration, duplicates data, and introduces fragility. Without simplification, organisations risk embedding AI on shaky ground. As one CIO put it: every $1 spent on transformation incurred a 70-cent complexity tax. Their rallying cry was clear: simplify, then scale, then innovate.
The true cost of dream selling
Another barrier is what Brennan calls “dream selling.” Around 75% of businesses investing in digital transformation fail to realise the promised outcomes. Only 5% deliver on time and on budget.
AI projects framed in aspirational terms but lacking grounding will stall. The antidote? A set of timeless, disciplined questions:
• What problem are we solving?
• What’s the tangible benefit?
• How will we implement it effectively?
Without these checks, organisations risk piling on tools that solve little and add complexity.
Micro Innovations: clearing the path
Not every fix needs to be colossal. Targeted, automated improvements - what we call Micro Innovations – can eliminate persistent inefficiencies that undermine progress.
One example: expired SSL certificates caused weekly outages across customer environments. A simple workflow – tracking expirations, issuing reminders, renewing proactively, billing later – cut outages to near zero a year.
Mundane though they seem, Micro Innovations build the resilience required for larger leaps like AI. They are the housekeeping that clears the path for ambition.
From hype to habit
The novelty of AI is fading. In its place is a mix of pressure and opportunity. Without governance, culture, and simplification, AI will falter. For it to flourish, organisations first need to ‘till the soil’. Operational Innovation is the plough. It’s the unglamorous, disciplined work that makes all technology initiatives – including AI – viable.
Get it right, and AI won’t just deliver experiments. It will deliver outcomes.
To see how we can deliver true performance to your technology mission, visit the Brennan website.
