Key Stats at a Glance
Legal & General, a FTSE 100 financial services leader, sought to scale its business while modernising its technology division. Previous change programmes had struggled to deliver agility at scale, leaving the organisation with complex operating structures and limited flexibility.
To succeed, Legal & General needed more than an external blueprint. It required internal capability: leaders who could confidently design and manage their own operating model transformation.
Hudson & Hayes was engaged to partner with the leadership team and embed capability in operating model design, ensuring Legal & General could continue to evolve independently and sustainably.
Opportunities included:
Hudson & Hayes enabled Legal & General to shift from dependency on consultants to true self-sufficiency in operating model design. By embedding capability, not just delivering solutions, the organisation can now continue evolving its structures with confidence.
The result is a more agile, scalable operating model, positioned to meet the demands of a dynamic market and sustain long-term business growth.
For more than a decade, Robotic Process Automation (RPA) has been the backbone of back-office efficiency. By mimicking keystrokes and clicks, RPA freed employees from repetitive, rule-based tasks like invoice entry and claims processing. It delivered measurable cost savings and faster processing times. But RPA has limitations: it breaks when processes change, struggles with unstructured data, and ultimately only scratches the surface of what’s possible with automation.
Today, a new wave is taking shape. AI Agents and Agentic AI are redefining how organisations think about digital transformation. The shift is not just from faster scripts to smarter bots — it’s from automation as task execution to automation as orchestration.
This journey can be mapped as an automation maturity curve with three stages:
Organisations that understand and climb this curve will move from incremental savings to step-change strategic value.
RPA thrives in high-volume, structured processes. It is best suited for tasks that follow clear rules and rarely change. Examples include:
The value case for RPA is straightforward: efficiency and cost reduction. By removing repetitive keystrokes, organisations gained speed and accuracy. However, fragility is its main weakness. A small change in process or system layout can break an RPA bot. And because RPA relies on structured inputs, it cannot handle the unstructured data that dominates knowledge work — such as emails, free-text fields, or conversations.
RPA is therefore the entry-level stage of automation maturity. It is useful, it delivers savings, but it is not transformative.
Where RPA mimics clicks, AI Agents understand context. Powered by large language models (LLMs) and integrated with enterprise tools and data, AI Agents can:
The breakthrough is in handling unstructured data. Emails, documents, and chat logs can be understood and acted upon. Unlike RPA, AI Agents are not brittle — they adapt to new inputs, guided by human oversight.
This makes AI Agents a bridge between efficiency and intelligence. They do not just automate tasks, they augment knowledge work. For employees, this means less time searching for answers or updating systems, and more time solving higher-value problems. For customers, it means faster, more personalised service.
As an example, a service desk agent supported by an AI Agent can resolve routine tickets instantly while escalating complex cases to human experts. The result is not only efficiency but a better end-user experience.
The frontier of automation is Agentic AI. Instead of following a single-task instruction, Agentic AI systems can:
Imagine the goal “optimise the procurement cycle.”
This is not just automation, it is orchestration. A network of AI agents collaborates to achieve an outcome, with minimal human input. Humans set direction, provide oversight, and make final calls.
The business value is transformational. Instead of speeding up existing processes, Agentic AI enables organisations to reimagine how work is organised. Procurement, supply chain, clinical scheduling, or customer onboarding can shift from sequential, human-driven tasks to parallel, AI-driven ecosystems.
The three stages form a maturity curve:
Importantly, each stage does not replace the last. They build on one another. RPA still has its place for structured processes. AI Agents elevate knowledge work. Agentic AI unlocks orchestration and adaptive decision-making.
Leaders must assess where they are today and design a roadmap for progression. A balanced portfolio will combine all three, applied to the right contexts.
Why does this matter now?
For organisations, the message is clear. Automation is no longer just a tool for cutting costs. It is a strategic lever for redesigning operations.
The companies that win will not be the ones who “just add AI.”
They will be the ones who climb the automation maturity curve, using the right tool for the right context:
We are moving from automation as cost-cutting to automation as strategy. The question is no longer “What can we automate?”
It is “How do we design our operating model for a world of autonomous, multi-agent systems?”
Artificial intelligence (AI), especially with recent advances in generative AI (GenAI), offers huge potential, from driving breakthrough innovation and productivity to transforming how organisations operate. But turning this promise into reality takes more than ambition. Moving from experiments to large-scale execution requires strong foundations and an operating model built to support and scale AI effectively.
So what does an AI-ready operating model look like? And what foundational capabilities do you need before pushing ahead with major transformation? These are the questions we hear most from clients.
Implementing AI at scale isn’t like other tech transformations. It brings new complexities, from ethical considerations and responsible deployment to managing a workforce that might feel uncertain or unprepared. Without the right structure, these challenges can slow progress or even derail it.
That’s why a capabilities-driven target operating model is so important. It brings together the diverse capabilities needed to deliver your AI strategy and ensures functions across the organisation work seamlessly together. No single team can do this alone. Just as critically, your people need to feel engaged, empowered, and part of the journey. True transformation happens when your workforce sees themselves as part of the change and part of the future.
From a strategic perspective, a capabilities-driven approach gives you clarity on which use cases are feasible and builds a strong foundation for AI to become a true business asset. It also helps you avoid common pitfalls, like low adoption, lack of buy-in, and difficulty measuring outcomes and ROI.
A capability-driven target operating model focuses on two main areas: We categorise AI capabilities into two main groups: foundational capabilities, which are key to unlocking the potential of AI tools, and transformational capabilities, which enable long-term value creation through the development and application of AI technology.
These capabilities lay the groundwork for an AI-driven transformation. They include the technology, governance, and processes needed for sustainable AI adoption.
A robust approach defines a clear vision, aligns talent and partners, ensures ethical and transparent governance, prepares your workforce for responsible AI use, integrates AI into operations, and continually improves model performance.
These capabilities help scale AI beyond individual use cases, embedding it into core processes to drive continual innovation and efficiency.
This means exploring new opportunities, measuring impact against strategic and ethical goals, managing risk proactively, and strengthening data engineering and infrastructure. It’s also about developing talent, delivering solutions, managing change effectively, and building strong business cases. Vendor management and enterprise architecture play key roles in creating scalable, compliant AI solutions.
Once the target components are identified and evaluated, the next critical question is how to effectively design and implement the operating model. This model should align with your strategic goals, existing capabilities, and AI maturity. However, there are three key considerations that apply to all.
The foundation begins with your vision. Key questions to consider include: What business goals are you aiming to achieve, and how can AI help make them a reality? Where and how can AI be leveraged to boost productivity or cut costs? Which existing capabilities can AI enhance or replace to drive revenue growth?
AI implementation isn't a standalone strategy; it’s a tool to help achieve your objectives. By adopting a capability-driven approach, you can identify your unique strengths, determine how to capitalise on them, and decide how much to invest in each capability area. Having an experienced, trusted partner to guide you in this journey will prove to be invaluable.
Evaluating your AI maturity is a crucial first step in prioritising AI capabilities, defining your ambitions, and setting the course for transformation. Have you identified the right capabilities to unlock real business value from AI? From there, you can consider how to implement AI ethically, integrating that mindset into your operations and processes. Do you have the talent to tailor models and structure data for specific use cases? How confident is your workforce in its ability to leverage AI to create value?
These strategic assessments will not only highlight the capabilities necessary to achieve your goals but also reveal how they align with your organisation’s structure and help you develop a roadmap for effective implementation and growth.
Developing and refining AI capabilities is a continuous journey, not a one-time goal, although you can focus on advancing to the next stage on the AI maturity scale. A key aspect of this journey is enhancing existing skills and identifying new ones required to execute an AI-driven strategy. Equally important is figuring out how these new capabilities will be integrated into daily operations. This may necessitate a rethink of organisational structure, as well as adjustments to job roles, responsibilities, and descriptions as you transition to AI-powered ways of working.
If you’re serious about scaling AI in a way that’s sustainable, strategic, and people-first, the operating model you build around it matters just as much as the tools you choose.
Capability-led design gives you more than just structure - it gives you the clarity to prioritise, the focus to scale, and the confidence to bring your people with you. It helps you move beyond scattered use cases toward a connected, organisation-wide approach that’s aligned to business goals and grounded in reality.
Whether you're just starting to explore AI, or you're looking to mature your implementation and build long-term value, the key is designing a model that fits your vision, your pace, and your people.
If you'd like to talk about what that could look like in your organisation, we're always up for a conversation. Get in touch with our Consulting Director, Abz; Abhiram.adi@hudsonandhayes.co.uk
As organizations navigate the complexities of digital transformation, one question repeatedly arises:
“How do we shift from functional design to horizontal design when designing our Operating Model?”
Traditionally, functions like Finance, Procurement, Operations, and Marketing operate within their own silos, each developing its own operating model. However, to drive efficiency and enhance customer experience, organisations must transition to a horizontal, customer-centric design that integrates all functions seamlessly.
This transition is particularly critical as organizations incorporate AI into their end-to-end processes. AI’s potential can only be fully realized if it is embedded in a structure that fosters cross-functional collaboration, data-driven decision-making, and seamless process integration.
Even if different teams work independently, aligning on a shared set of design principles ensures consistency across the organization. This alignment fosters interoperability, clarity in decision-making, and a uniform approach to AI-driven transformation.
To break down silos, organizations need a high-level Operating Model Blueprint—a single-page visual representation of how various functions interact. This helps teams drill down into their specific designs while maintaining a unified, enterprise-wide perspective.
End-to-end processes like Source-to-Pay (S2P) or Order-to-Cash (O2C) serve as the foundation for horizontal design. They ensure visibility across functions, clarify handoffs, and eliminate inefficiencies in workflows spanning multiple departments. This approach forces an organization-wide mindset, promoting AI’s role in optimizing these processes.
Understanding the organization’s core business capabilities—both shared and function-specific—prevents redundant efforts and encourages resource optimization. A well-defined capability model helps leaders identify synergies across teams, ensuring AI investments deliver enterprise-wide benefits.
Governance should be an enabler, not a bottleneck. Establishing a governance framework ensures alignment between teams, facilitates collaboration, and prevents duplication of AI-powered initiatives. It also creates clear communication channels to sustain horizontal integration.
At some stage, all functions must come together—whether at the start of detailed design or during execution. Convergence helps prioritise initiatives based on enterprise-wide value, rather than individual departmental gains. AI implementation particularly benefits from this approach, ensuring resources and technologies are deployed strategically.
Transformational change is most effective when it complements ongoing efforts. Instead of disrupting current initiatives, organizations should focus on enhancing existing processes and integrating AI solutions where they add the most value. This collaborative mindset fosters a smoother transition to a horizontal model.
The shift to a horizontal design is not just a structural change—it’s a strategic necessity in today’s AI-driven world. Organizations that successfully transition can achieve:
With AI at the core, organizations must ensure their operating models evolve to support digital transformation, intelligent automation, and business process optimization. A siloed approach will only limit AI’s impact, while a horizontal, integrated structure will enable long-term, scalable success.
Transitioning from a functional to a horizontal operating model is challenging, particularly in large, complex organizations. However, businesses that embrace a cross-functional, AI-integrated approach will be better positioned to drive operational excellence and unlock future growth.
A common challenge in transformation projects is aligning local autonomy with centralised change. This dynamic often plays out across regions, NHS Trusts, business units, and global operations, where local decision-making power can sometimes conflict with broader organisational goals. Addressing this issue requires a thoughtful and strategic approach.
In decentralised structures, local teams often hold significant decision-making authority and manage their own P&Ls. While this autonomy allows for flexibility, it can lead to resistance when centralised changes are introduced, especially if local priorities seem misaligned with the overarching strategy. Balancing these two forces is essential for successful transformation.
✅ Document Decision-Making Rights
Many organisations lack clarity on who holds decision-making authority at different levels. By mapping these rights, gaps and misalignments can be identified. For example, a Telco aiming to empower Product Owners discovered that authority and accountability were not properly aligned. Addressing these gaps allowed the organisation to design an operating model that facilitated faster and more effective decision-making.
✅ Identify True Influencers
Formal structures don’t always reveal the real decision-makers. Informal influencers often hold significant sway within organisations. In a commercial real estate project, one country head, despite lacking formal authority, consistently influenced the direction of others. Recognising and engaging such individuals is critical to driving alignment and progress.
✅ Tie Outcomes to Incentives
Resistance to change often stems from misaligned incentives. For instance, a system rollout negatively impacted local P&L for two years, leading to pushback from leaders whose bonuses were tied to short-term performance. Aligning incentives with long-term success reduced resistance and supported the transformation.
✅ Co-Create Non-Negotiables
Decision principles provide clarity during transformation, but their strength comes from collective creation. Collaborative workshops ensure that stakeholders agree on non-negotiables. While disagreements are common, these principles anchor decision-making and provide a reference point for resolving future conflicts.
✅ Co-Design the Future State
Stakeholder involvement is crucial during the design phase. In a project with NHS Trusts, CFOs frequently asked, “Who on my team has inputted into this?” Incorporating input from key stakeholders not only ensured alignment but also avoided resistance caused by a lack of ownership. When operating models are designed without collaboration, adoption often fails due to the “not invented here” mindset.
✅ Create a Shared Vision
Communicating the broader impact of change is essential, especially when it seems to negatively affect certain teams. In one instance, a smaller market resisted adopting a system that increased their workload. By showing how their efforts contributed to regional success, alignment was achieved. The key was articulating the shared vision in a way that connected to individual contributions and value.
With the rapid advancements in AI and increasing cost pressures, organisations need to rethink their operating models to remain competitive. Balancing local autonomy with centralised change is rarely a straightforward process. It requires clear strategies, collaboration, and alignment to ensure the organisation is positioned for long-term success.
#OperatingModelDesign #Transformation #Leadership #FutureProofing
Hudson&Hayes partnered with a large media client undergoing a merger to design and deliver a comprehensive operating model for the newly formed agency.
Hudson&Hayes provided expertise in Operating Model Design best practices and collaborated with the client to design a new agency operating model with agency leadership. We helped the client define the capability model and identify new, existing, and overlapping capabilities.
Following the high-level design, we supported the client in understanding implementation considerations for local markets (adapting the model to their local situations) and global teams (creating new global discipline groups and re-framing global enabling functions) as well as alignment with the group/parent company. The challenge was to facilitate the development of the new operating model while refining it to align with the agency's vision and value proposition.
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