Specsavers, a global leader in optical and audiology services, recently reached a critical technological inflection point within its engineering function. It has a highly motivated engineering workforce benefiting from initial investments in AI. As adoption scaled, the organisation encountered natural friction points common to large, regulated enterprises operating at pace.

They reached out to Daemon’s AI First Squad, a dedicated team of AI-first engineers, delivering high-impact transformations across our clients - fast. Through a deep-dive AI-enabled discovery involving nearly 40 interviews, Daemon’s AI First Squad identified that the barrier was not a lack of ambition, but an environment of fragmented knowledge and legacy Software Development Life Cycle (SDLC) processes.

By pivoting the strategy from "radical innovation" to "foundational integration", Specsavers has moved from a state of "Enthusiastic Frustration" to a clear, evidence-based foundation for an AI-enabled future.

Navigating SDLC complexity

Specsavers started their AI journey with deliberate and controlled investment across their SDLC, deploying both Microsoft Copilot and GitHub Copilot. Teams were encouraged to adopt and use these tools and functions to explore their potential.

However, usage and impact was inconsistent, and leadership struggled to see value relative to the opportunity. A "one-size-fits-all" enablement model failed to meet the specific needs of various teams across the SDLC, leading to fragmented experiences. While teams in selected areas of the business were able to adapt their working practices and gain some benefits, moving towards agentic tooling through process adaptation and the introduction of standards, this was not scalable.

As a global organisation operating across multiple markets, Specsavers must balance speed, safety, and consistency at significant scale.

The hidden barriers

Despite the high levels of enthusiasm, the engineering function faced hurdles that hindered speed.  Daemon’s discovery revealed the root causes were environmental rather than behavioural:

• The knowledge management crisis: Critical information was scattered across multiple sources of knowledge and work item management platforms or locked in the heads of long-serving SMEs. As one Business Analyst noted, "The worst thing by far… is knowing who you need to talk to".
• SDLC bottlenecks: Historical process issues, particularly around PR and route-to-live
  bottlenecks, presented opportunities for optimisation. This created friction that slowed delivery pace and limited teams' ability to fully leverage AI tooling within established workflows.
• The metrics vacuum: There was no standardised definition of "done" across the Software Development Lifecycle. Without a baseline, proving the potential value of AI was impossible, as teams measured success differently.

The prevailing sentiment from the interviewees was "Enthusiastic Frustration": teams were desperate to modernise but felt they were "spending more time talking about the thing than actually doing it".

The approach

Daemon acted as an impartial strategic partner, deploying a reliable, AI-first interview method to accelerate time-to-value. This structured discovery uncovered the 'art of the possible' for Specsavers' engineers.

• Scope: The team conducted extensive video-based and AI-transcribed interviews with
  stakeholders across the entire Software Development Life Cycle, ranging from Principal
  Engineers and Architects to Legal, Product, and Procurement. These sessions covered every stage of value delivery, including Business Analysis, Design, Development, Testing, and Release.
• Method: Rather than a rigid audit, Daemon facilitated open-ended, sentiment-led discussions designed to capture the emotional reality of engineering at Specsavers. By focusing on qualitative pain points and daily friction, not just technical compliance, the team established a psychologically safe space.
• Synthesis: Moving beyond static documentation, the team distilled findings into a strategic Opportunities Runway that prioritised high-impact interventions over theoretical advice. These insights were organised into a clear, phased roadmap; ranging from "Laying the Foundation" (0-3 months) to long-term "Organisational Transformation" (12+ months).

Integration over innovation

Daemon’s AI-first discovery revealed a counter-intuitive insight: Specsavers did not need radical innovation; it needed foundational integration. The strategy was to "fix the plumbing before installing the rocket engine".

• Knowledge as a product: Treat institutional knowledge as a product to be ingested by AI. A SDLC-focused knowledge bot was identified as the highest-leverage opportunity. This advances Specsavers to using AI for architecture archaeology (scanning legacy code and docs to auto-generate system maps).
• Adaptive governance: Streamlining SDLC workflows by embedding automated quality and compliance checks directly into the delivery pipeline. By using AI to enforce rule files and validation at the point of development, governance becomes an enabler of pace rather than a constraint on it, allowing Specsavers to innovate responsibly at speed.
• Standardised quality: Implementing automated quality orchestration to enforce a consistent definition of done. This ensures AI is not just generating code faster, but validating it against aligned quality gates.

Outcomes: Building the evidence for change

Our engagement did not attempt to force immediate ROI; rather, it provided Specsavers with the foundation for a business case. By moving from anecdotal frustration to data-backed evidence, Daemon equipped Specsavers’ leadership with the tools to justify investment in an AI-enabled SDLC.

1. Transformed subjective complaints into quantified pain points:
   • Validated blockers: The discovery identified people feeling stuck to the disconnected data, noting that 24 of 40 interviewees explicitly cited knowledge fragmentation as a critical blocker.
   • Demand vs. Readiness: The data revealed that while ~40% of staff had strong demand for AI, ~60% faced environmental blockers slowing adoption.
2. Quantified these pain points against a maturity and opportunity model to forecast the potential impact of removing them. The roadmap projected that, based on market research and developer practice studies, addressing these foundational issues could lead to a potential maximum impact of up to:
   • 60–80% reduction in information discovery time.
   • 60–80% reduction in governance approval cycle times.
   • 5–25% improvement in delivery predictability.
3. Provided strategic clarity through an evidence-based proposal with a clear path forward:
   • A unified roadmap: A structured 0–12 month plan prioritising quick wins (like the
     Knowledge Bot) alongside long-term governance changes.
   • Defining value: By identifying a metrics vacuum, Daemon helped Specsavers
     understand that they needed to define what value meant before they could measure AI's contribution to it.

The bottom line

Daemon’s AI First Squad provided the strategic scaffolding to Specsavers, aligned to our own “AI First” ethos. Specsavers now possess the quantified evidence required to move from experimenting with tools to building a fully funded, AI-enabled engineering function.

We not only identified Specsavers' systemic blockers but showed them how our AI First approach identifies hidden improvement opportunities, providing the clarity and evidence-based prioritisation required to build a strong foundation for the future.

Phil Peters, Head of Software Engineering at Specsavers, comments;

“This engagement with Daemon has given us fantastic insight into our AI Adoption readiness and has prepared us to start thinking about how an AI Platform can enable an AI-Enabled SDLC that makes AI a true accelerator to delivery rather than just a toolset.”

Karim Allaouat, Senior Consultant at Daemon, comments;

“By letting AI handle the data, we were able to focus on listening and creating a safe space for honesty. It shows that AI-First is as much about people as it is about building software. It was rewarding to demonstrate that an AI-First mindset facilitates honest dialogue just as well as it writes code.”

More about the AI-First Squad

Our Al-First Squad reveals where Al truly impacts, and where it doesn't. Our squad comprise real, experienced software engineers examining how AI can be embedded in various organisations, verticals, and contexts. We help our clients accelerate time-to-market, optimise their entire software delivery pipeline, and transform their internal technology AI capabilities for lasting impact, without introducing risk.

Connect with Daemon today to work with our AI-First Squad. Book a 30 min discovery call.