AI is no longer an experimental add-on. It’s becoming deeply embedded in the way organisations operate today. And with that comes a practical and strategic question: how should AI shape the way we work?
Understanding both the costs and benefits is crucial in this context. While the early gains of AI often appear as efficiency savings, long-term value stems from how businesses create and capture value. But realising that value involves investment in technology infrastructure, new skills, new roles and governance channels to scale responsibly. The clearer you are on where you’re heading, the easier it is to invest at the right level.
For most organisations, your AI journey should begin with improving speed, accuracy and efficiency within your existing processes.
But that’s only the beginning. As momentum builds, leadership teams will face a critical design decision.
- Do we continue enhancing what already exists?
- Do we transform key value streams using AI?
- Or do we go further… reimagining the business with AI at the core?This decision isn’t always easy. But our AI operating model approach makes it easier, helping your organisation understand what needs to shift at each stage to deliver real value.
THE FOUNDATION: ENHANCING OPERATIONS WITH AI
As we already touched on, your journey begins with operational enhancement. AI will start to integrate with your existing workflows to enhance efficiency and drive better results.
This often looks like:
- Automating routine tasks (e.g. invoice processing, financial reconciliations)
- Embedding AI into systems like CRMs to deliver smarter recommendations
- Deploying chatbots for employee support or IT queries
- Using AI-assisted recruitment screening
At this stage, AI is a quiet enabler—working behind the scenes to improve performance without fundamentally changing ways of working. You’re laying the foundations for more strategic change to come.
Operating Model impacts:
- Core processes: repetitive tasks are automated, making workflows faster and leaner.
- People and culture: teams begin to upskill, flexible team models are explored, and teams learn to work alongside AI.
- Technology: the focus is on infrastructure readiness. For example, GPU availability, cloud/on-prem choices, setting up MLOps and AI platforms.
- Information and data: building data governance, quality and access foundations.
- Governance and decision-making: introducing early-stage governance and establishing decision-making frameworks for responsible AI use.
The cost-benefit?
This phase is all about smart, low-risk moves with high-impact returns. With a relatively modest investment, you can unlock quick wins that show off AI’s potential—without needing to overhaul your entire business.
Most of the spend here goes into laying the groundwork: setting up cloud infrastructure, building data pipelines, introducing basic AI tools, and giving teams the initial training they need. Maybe a light touch on process redesign too.
The payoff? Fast, visible improvements in efficiency, accuracy, and cost savings. Less manual work, smoother operations—and all without reinventing the wheel.
THE DESIGN DECISION: TRANSFORM OR RE-INVENT?
Once you’ve laid your foundations, you’ll face a more strategic question.
“Do we use AI to enhance and streamline how we currently deliver value, or do we take a bolder step and create a new operating model with AI at the core?”.
It’s important to note that these are not maturity levels. They reflect different design intents and have varying implications across structure, roles, skills and governance.
Option 1: Transforming with AI
In this transformation approach, your business model remains intact, but AI plays a more significant role in delivering value. It evolves from supportive to collaborative, acting as a ‘co-pilot’, influencing decisions and processes.
You’ll see this in:
- AI-led personalisation across digital channels
- Forecasting and demand planning driven by machine learning
- Semi-autonomous decision-making in risk or pricing processes
Operating Model impacts:
- Products and services: offerings become more innovative and personalised.
- Capabilities: AI capabilities need to be embedded cross-functionally.
- Customers and channels: AI-driven personalisation becomes embedded in digital experiences.
- Organisational design: new roles emerge (e.g., AI product leads). Product, data and business teams start to operate in more integrated, agile ways, often reorganised around value.
- Governance and decision-making: Governance formalises with clearer value-tracking frameworks, outcome metrics and cross-functional oversight.
- People and culture: More agile culture forms, focused on experimentation and cross-functional collaboration.
The cost-benefit?
This stage is where real investment kicks in. It takes more investment—think embedding AI into core systems, building AI capabilities across teams, and getting people, processes, and governance aligned.
The big cost drivers? Hiring AI talent, upskilling your teams and managing change across multiple parts of the business.
Yet the value is high: sharper decision-making, more personalised customer experiences, greater agility and a clear impact on revenue.
Option 2: Reinventing with AI
With this approach, operating models are built around AI as a central principle, often to support new lines of business, products or customer models.
AI shapes how value is created, delivered and captured.
Examples include:
- AI services including dynamic pricing, generative content or autonomous agents
- AI-first customer experiences, with data and intelligence at the core
- Real-time reporting driven by self-learning algorithms
Operating Model impacts:
- Vision and mission: AI becomes a defining principle in purpose and future direction.
- Products and services: entirely new AI-native services are created.
- Culture: creating a ‘safe’ place to innovate and experiment.
- Organisational design: AI hubs emerge, and new leadership roles such as ‘Chief AI Officer’ are integrated into the design.
- Technology: AI becomes the infrastructure, and systems are designed for continuous experimentation and learning.
- Ethics, risk and compliance: responsible AI practices, sustainability and AI governance are deeply embedded in how the business operates.
- KPIs: value realisation metrics evolve to track AI impact directly.
The cost-benefit?
This is where the big transformation happens. This means investing in AI-native platforms, rethinking core processes and structures, and embedding responsible AI from the ground up.
Yes, the costs are higher—but so is the upside. You unlock entirely new value streams, gain first-mover advantage, and build a competitive edge that lasts.
ALL MODELS UNLOCK GROWTH
Each path offers real potential for growth and value. But what really makes the difference is having a clear ambition and a joined-up design. Too often, organisations get stuck in the middle—uncertain about the value they’re aiming for, how much effort it’ll take, or what it’s going to cost to get there.
So what’s next?
- Get clear on your goal
What are you really trying to achieve with AI? Is it about cutting costs, unlocking new revenue or strategic differentiation?
- Understand what’s required
What effort, skills, investment and cultural change will it require? Think infrastructure, people, data and governance readiness.
- Prioritise where to focus
Which parts of the business are ready now? Prioritise the area where AI can deliver the most value the quickest.
Our AI Operating Model approach helps you detangle the impact of AI design decisions. Get in touch if you’re navigating how AI can shape your strategic business decisions.
