Article by Nadine Soyez

 

I experience that across organisations and teams, the conversation about AI still revolves around prompts, productivity gains, and which Copilot is best. We debate tools, celebrate task automation, and share headlines about 10x efficiency improvements. Then we act surprised when nothing fundamentally changes. 

The problem is that we treat AI as a personal productivity tool when what we actually need is an organisational system. You can equip every employee with the world’s best AI assistant, but if the underlying way work flows through your organisation remains fragmented or misaligned, all you get is faster chaos. Individual tasks may speed up, but the system advantage you could have with AI never materialises.

 

 

Why individual productivity gains don’t translate to system advantage

 

The common approach in AI adoption is like this: provide tools, offer training, encourage experimentation, and assume the collective impact will emerge naturally. It rarely does. When AI is deployed as a personal productivity tool, each employee tries to optimise their own tasks. Marketing writes better copy. Finance generates reports faster. Operations automates routine processes. All of this creates measurable individual efficiency, but the organisation as a whole doesn’t necessarily become faster, smarter, or more effective.

Why? Because work doesn’t happen in isolation. It happens through handoffs, dependencies, approvals, collaboration, and coordination across teams. If only individual tasks accelerate whilst the connections between them remain unchanged, you’ve simply moved the bottleneck somewhere else. Real organisational advantage comes from redesigning the entire system of work, not just automating pieces of it. This means rethinking how information flows, how decisions get made, where accountability sits, and how teams interact. AI needs to become a system, not just a tool. This is clear.

Wait! Sounds nice? Yes, many AI expert talks about this. I prefer an alternative approach because I know people feel overwhelmed when transforming to fast. Think about this: Completely redesigning how your organisation works at once??? Where do you even start? When you have been following me a bit longer, you know I’m a fan of small practical steps and practical frameworks you can follow. Here we go. 

The answer for AI transformation is: Starting small without staying small. Start small but design for scale from day one. Start with one workflow that matters, redesign it properly, and then use that success as a framework for expansion. When AI fails, it is because people either stay trapped in individual productivity experiments that never connect, or they attempt massive transformation programmes that collapse under their own complexity. Neither approach works.

 

 

A practical framework for building your AI ecosystem step-by-step

 

The framework has three phases: Foundation, Expansion, and Scale. Each phase builds on the previous one, but you can start immediately without having everything perfect.

 

Phase 1: Foundation (Months 1-3)

 

The goal here is not to transform your entire organisation. The goal is to prove that AI can deliver system advantage, not just individual productivity gains.

• Select one high-value workflow. Choose a process that involves multiple people, takes significant time, and has a clear business impact. Examples include monthly reporting cycles, customer onboarding, proposal development, or cross-functional approvals.
• Map the current state honestly. Document every step, handoff, delay, and bottleneck. Identify where time actually goes: data gathering, waiting for input, reformatting, coordinating meetings, or rework.
• Redesign the workflow from scratch. Ask what the actual outcome needs to be, then design backwards. Eliminate steps that only exist because information was previously hard to access. Remove handoffs that only serve as checkpoints. Question whether meetings are necessary when AI can provide shared context.
• Establish lightweight governance. Define clear rules for this single workflow: which data can be used, who reviews outputs, where human judgment remains essential, and how quality gets verified.
• Build capability alongside implementation. Train the people involved, not just on using AI tools, but on how to judge outputs, when to trust results, and where their expertise still matters most.
• Measure system impact, not activity. Track whether the entire process becomes faster, whether quality improves, and whether the coordination effort reduces. Individual task completion time is interesting but not sufficient.

 

This phase proves the concept and creates your first reference point for what AI as a system actually looks like.

 

 

Phase 2: Expansion (Months 4-9)

 

Once you have one workflow redesigned and working, the next phase is about replication and pattern recognition.

• Identify three similar workflows. Look for processes with comparable characteristics: similar data needs, similar governance requirements, or similar team structures. This allows you to reuse patterns rather than starting from scratch each time.
• Extract reusable patterns. Document what worked in Phase 1: which governance structures created confidence, which training approaches accelerated adoption, which measurement frameworks proved useful, and which implementation steps were essential versus optional.
• Create a lightweight playbook. Turn your learnings into a simple guide that other teams can follow. This shouldn’t be a 50-page document, but a practical set of principles, questions, and steps that help teams redesign their own workflows.
• Establish cross-workflow governance. As AI spreads across more processes, governance must evolve from workflow-specific rules to organisational standards. Define which tools are approved, which data classifications require what level of oversight, and where human review remains mandatory.
• Build communities of practice. Create forums where teams share what they’re learning, which approaches work, and where they’re struggling. This peer learning accelerates adoption far faster than top-down training.
• Connect workflows strategically. Look for opportunities where redesigned workflows can feed into one another. When customer feedback analysis feeds directly into product prioritisation, or when financial reporting automatically informs strategic planning, system advantage compounds.

 

This phase transforms your initial success from an isolated example into a repeatable capability.

 

 

Phase 3: Scale (Months 10+)

 

Scaling happens when AI moves from a programme to a permanent capability embedded in how the organisation operates.

• Embed AI into operating rhythms. Make AI a standard part of planning cycles, performance reviews, and resource allocation. When leaders routinely ask “have we considered how AI could redesign this?” the capability becomes self-sustaining.
• Expand governance to match maturity. As AI usage deepens, governance must grow more sophisticated without becoming bureaucratic. Add oversight for higher-risk applications while keeping low-risk usage lightweight.
• Develop internal expertise. Build centres of excellence or capability teams that can support others, evaluate new tools, refine best practices, and ensure quality remains high as adoption spreads.
• Measure organisational outcomes. Track whether AI contributes to strategic goals: faster decision-making, reduced operational costs, improved customer experience, or increased innovation velocity. These business outcomes prove AI has moved from a productivity tool to a strategic capability.
• Reinvest in foundations. As AI scales, continuously strengthen vision, roadmaps, engagement, and capability building. The foundations that enabled initial success must evolve to support wider adoption.

This phase is where AI stops being a project and becomes part of how the organisation works.

 

What organisations can do now

 

If the idea of redesigning your entire organisation feels overwhelming, you’re thinking about this correctly. A transformation that starts everywhere usually fails everywhere.

Instead, commit to the framework:

• Choose one workflow this quarter. Select a process where success would be visible, valuable, and prove that AI can deliver system advantage rather than just individual productivity.
• Redesign it properly. Map the current state, eliminate unnecessary steps, rebuild around what AI makes possible, establish governance, and measure whether the entire system improves.
• Document what you learn. Create your playbook as you go, capturing patterns that other teams can reuse without starting from scratch.
• Plan your expansion. Identify which workflows will come next and how learnings from the first will accelerate the second and third.
• Build foundations as you scale. Invest in vision, roadmaps, engagement, and governance alongside implementation, ensuring they grow stronger as AI spreads wider.

 

The organisations that succeed with AI will be those that started small, redesigned effectively, and expanded systematically. For this, I have an AI Use Case and Strategy Sprint, a structured framework that supports you. In 4-6 weeks, you get a working prototype, validated use cases, and a 12-month roadmap ready for execution and scaling in your organisation. If you would like to learn more, please leave a comment or reach out to me.