Eric Zie

Part of the IT Leaders 100 - a list of the most influential IT leaders in the UK in 2026.

Eric Zie is Founder of GoCodeGreen and Chief Research Officer at Charsfield Research & Advisory, focused on the environmental impact of software, digital services, and AI. He helps organisations measure, manage, and reduce digital emissions - now central to regulation, procurement, and responsible technology investment – and his work spans industry, research, and policy.

GoCodeGreen delivers standards-aligned measurement platforms, while CR&A leads research including sustainable AI under Eureka / ITEA. Eric is former Vice Chair of techUK's Climate Council, Chair of the Tech Nature and Biodiversity Group, a Fellow of the British Computer Society, partner to the UN ITU, and Visiting Professor.

What's the most exciting part of your current role?

The most exciting part of my role is operating at the intersection of research, engineering, and real-world impact - where we are no longer theorising about sustainable digital, but proving it with data.

Through Charsfield Research & Advisory (CR&A), I'm advancing applied research into how software, cloud, and AI systems can be measured and optimised at a lifecycle level. That research directly informs the capabilities we deliver through GoCodeGreen (GCG), where organisations are now achieving 25–50% reductions in digital emissions and 15–25% improvements in operational efficiency.

What makes this particularly compelling today is AI. Most organisations are scaling AI without understanding its environmental or economic consequences. We are changing that - bringing measurement, governance, and optimisation into AI development and deployment, not as an afterthought, but as a design principle.

The real excitement comes from seeing this translate into action. Engineering teams are prioritising carbon alongside performance. CIOs are making decisions based on evidence, not assumption. And for the first time, we have a clear pathway to align digital growth with environmental responsibility - at scale, and with measurable outcomes.

What do you do in week one of a new project to avoid pain in month six?

Week one is where you decide whether the next six months will be controlled or reactive. Most of the pain organisations experience later - rework, cost overruns, performance issues - comes from not establishing clarity early.

The first step is to define the system properly. What is in scope, what is not, and what is the functional unit we are measuring and optimising? Without that, data becomes inconsistent and decisions become subjective. In parallel, we establish a baseline using available data - architecture, usage patterns, cloud configuration, and delivery processes. It does not need to be perfect, but it must be structured and representative.

We then align engineering, architecture, and product teams around a shared set of performance indicators- cost, carbon, and system efficiency- so trade-offs are explicit from the start. This is where we introduce low-carbon non-functional requirements and ensure they are embedded into backlog and delivery governance.

Critically, we identify the highest-impact optimisation opportunities early and map them into the delivery pipeline. That avoids the common scenario where sustainability or efficiency is treated as a later remediation exercise. If you get week one right, month six becomes a story of measured improvement, not expensive correction.

What’s the part of your estate you most want to simplify in 2026?

It's not my estate that needs simplifying - it's how we consistently apply proven patterns across our clients at scale.

In 2026, the focus is on removing variability in how organisations approach digital and AI optimisation. Today, too much depends on individual teams, tooling choices, or maturity levels. The result is inconsistency - different interpretations of performance, fragmented measurement, and missed opportunities to drive systemic improvement.

What we are simplifying is the translation of insight into action. Through CR&A research and GoCodeGreen delivery, we are codifying repeatable patterns - across architecture, engineering, cloud configuration, and AI usage - that are already proven to reduce emissions by up to 50% and improve cost efficiency by 15–25%.

The goal is to make these patterns portable, measurable, and embedded directly into delivery pipelines. That means standardised baselines, consistent lifecycle measurement, and pre-defined optimisation pathways that teams can adopt without starting from scratch.

Simplification, in this context, is about scale with integrity. If we can make the right way the easy way - grounded in data, aligned to standards, and built into how teams already work - we remove friction, accelerate adoption, and avoid the inefficiencies that come from reinventing the same solutions repeatedly.

Excluding smartphones, what piece of technology could you not live without and why?

It would be the AI agents that operationalise my research and make it usable by others.

The value is not in AI as a concept, but in its ability to take complex, evidence-based knowledge - spanning digital lifecycle assessment, engineering optimisation, and sustainability - and translate it into structured, actionable outputs at scale. What previously relied on a small number of specialists can now be distributed across engineering, architecture, and product teams in a consistent and governed way.

These agents act as an extension of capability. They interpret data, identify optimisation pathways, and generate backlog-ready actions aligned to standards and real-world constraints. That changes the pace of delivery. Instead of insight sitting in reports, it becomes embedded directly into decision-making and execution.

For me, that is the shift that matters. It is not about automation for its own sake, but about ensuring that good thinking - tested, evidence-based, and repeatable - can be applied widely and quickly.

Without that, scaling impact remains slow and dependent on individuals. With it, we can move from isolated improvement to systemic change across digital and AI systems.

What excites you most about the future of IT?

IT is finally being treated at the same level of accountability, measurement, and optimisation expected in energy, transport, and finance.

For years, digital was seen as intangible. Software, cloud, and AI now have clear energy, carbon, and cost profiles, and we have the methods to measure them at a product level and act on the results. This changes the role of IT leaders. Decisions are no longer based purely on performance and delivery speed, but on how efficiently systems operate over time.

The real shift is the convergence of engineering, data, and responsibility. We are seeing the emergence of standardised measurement, lifecycle thinking, and AI-driven optimisation that can translate insight into action at scale. That creates a pathway where digital growth does not have to mean increased impact or cost.

What makes this genuinely exciting is that the solutions already exist. Organisations are achieving material reductions - 30–50% in emissions and meaningful cost savings - through better architecture, engineering discipline, and operational control. The future of IT is not just faster or more intelligent systems. It is systems that are designed, built, and run with precision - where performance, cost, and environmental impact are managed together as a single outcome.

What talent are you quietly proud of that would never show up on LinkedIn?

Determination. It doesn't show up on LinkedIn, but it underpins everything.

The ability to stay with a problem long after the initial interest fades, to keep pushing when the answer isn't obvious, and to carry ideas through from concept to real-world impact.

Much of the work I do - whether in research, building new capability, or shifting how organisations think about digital and AI - takes time to land. It involves resistance, complexity, and often working ahead of where the market is ready. Determination is what keeps that moving. It's the difference between having a good idea and actually seeing it adopted, embedded, and delivering results.

It also shows up in the detail. Going deeper than most are willing to go, challenging assumptions, and not accepting surface-level answers when the data doesn't hold. That persistence is what turns theory into something practical and repeatable.

It's not visible, but it's consistent - and without it, none of the outcomes would exist.

What’s your most-used keyboard shortcut or command that you wish everyone knew?

I don't rely on shortcuts. I rely on the keyboard for what it was built for - writing.

Most of my work starts as thinking made visible. Books, research papers, frameworks, and action plans. The keyboard is where ideas are tested, challenged, and refined until they become something others can use. That's the real leverage - not speed of navigation, but depth of thought translated into clear, structured output.

In a world increasingly driven by prompts, summaries, and shortcuts, I think there's real value in doing the hard work of writing properly. It forces clarity. If you can't explain it, you don't understand it. And if you don't understand it, you can't apply it at scale.

That's how the work behind CR&A and GoCodeGreen has been built - through structured writing that connects research to engineering reality and then into delivery.

So my "command" isn't a shortcut. It's the discipline of sitting down, thinking deeply, and writing something that leads to action.