Organisations face a difficult dilemma: Should they accelerate AI adoption despite imperfect data, or should they wait until their data is fully optimised?
The instinctive response is to fix data first. After all, AI relies on clean, structured, and reliable data to deliver results. However, this approach comes with risks. Data perfection is an endless pursuit, and waiting too long to address every data challenge can stall AI initiatives—leaving organisations lagging behind competitors who take a more pragmatic approach.
The reality is, AI and data quality must evolve together. The organisations that succeed in AI transformation are not the ones that delay adoption but those that strategically integrate AI while continuously improving their data foundations.
Every organisation faces data quality issues. While resolving these challenges is important, insisting on perfect data before AI deployment presents three major risks:
Data governance programmes struggle to secure funding because they don’t deliver immediate revenue or visible quick wins. As a result, they are often deprioritised in favour of more tangible initiatives.
By the time data issues are fully addressed, competitors will have already implemented AI, gaining operational efficiencies and market advantage. In a fast-moving environment, waiting for perfection can be a costly mistake.
The phrase is too vague to act on effectively. Without a structured framework, data governance efforts can become resource-intensive, slow-moving, and misaligned with business goals.
Rather than treating AI adoption and data governance as separate projects, organisations should take a structured approach that allows them to balance immediate AI-driven impact with long-term data improvement.
Instead of postponing AI initiatives, businesses should establish a data improvement workstream alongside AI deployment. AI can actively contribute to better data management rather than waiting for a “clean slate.”
A structured roadmap ensures AI and data quality improvements align with strategic objectives. Key steps include:
Senior leaders, eager for AI-driven efficiencies, may push for rapid implementation. A structured approach ensures AI adoption aligns with corporate goals while maintaining realistic expectations about data readiness.
AI is not just dependent on clean data—it can also enhance data integrity. AI-driven tools can:
By using AI as part of data management, organisations can refine data while implementing AI, rather than delaying transformation efforts.
The notion that “AI can’t work until data is fixed” is outdated. A layered approach—one that unlocks AI’s value now while progressively improving data quality—is the most effective path forward.
It’s not about rewiring the entire house before installing smart lighting. Businesses can modernise where it makes sense while strengthening the foundation over time.
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.
Organisations are increasingly recognising AI's potential to enhance operational efficiency. However, many implementations deliver only marginal gains because AI is often treated as an auxiliary tool rather than a core enabler. A more transformative approach involves embedding AI at the heart of process design, enabling a fundamental shift in efficiency and effectiveness.
The first step is developing a strong understanding of AI and automation technologies, including:
By establishing this knowledge base, organisations can identify high-impact opportunities and redefine processes with AI at their core.
Rather than layering AI onto existing workflows, companies should rethink processes from the ground up, focusing on:
A phased implementation ensures smoother transitions and continuous value realisation. This includes:
Traditional onboarding involves manual data entry and fragmented communication. AI can streamline this by:
Meetings often suffer from inefficiencies in scheduling, note-taking, and follow-ups. AI can enhance productivity through:
To achieve meaningful efficiency gains, organisations must:
Organisations looking to achieve breakthrough efficiency should prioritise AI-driven process design. By embedding AI as a foundational element, adopting a structured methodology, and iterating based on real-world insights, companies can unlock significant operational advantages and drive sustainable 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
AI is changing how organisations operate, bringing both tremendous opportunities and significant challenges. On 22 October 2024, Hudson & Hayes leaders, along with the Digital and AI Community, outlined guiding principles for AI adoption. This collaborative session, led by David Gerouville-Farrell, formed the foundation for Cutting Through the Noise: A Business Leader’s Guide to AI. These principles provide the structure necessary for organisations to realise AI’s potential while effectively navigating its challenges.
AI should support your core business goals, not stand as a separate objective. To deliver tangible value, set AI goals that address specific needs, such as improving customer and employee experiences, improving profitability or creating a competitive edge.
Avoid AI for the sake of AI. Focus on measurable business benefits to ensure every initiative contributes directly to organisational growth.
AI’s use brings ethical concerns like data privacy, fairness, and accountability to the forefront. Create a governance framework that suits your organisation’s specific risk profile. Governance should vary depending on whether your organisation primarily consumes AI (uses external AI products) or builds AI (develops custom solutions). For example, a company that builds AI might emphasise governance around model transparency and ethical training data, while a consumer might prioritise privacy and data protection policies.
AI requires a robust infrastructure. Ensure AI solutions integrate well with existing systems and that interoperability supports a smooth user experience.
Even with AI, there’s always a user at the other end. Involve users in the design phase and focus on creating solutions that address their needs effectively, improving their overall experience.
Many teams lack foundational AI knowledge or expertise in tools. Invest in training programmes from basics to tools like Microsoft Co-Pilot, and consider bringing in partners to support growth.
AI can spark excitement and concern. To manage expectations, develop a communication plan for each stakeholder group, keeping everyone informed as the technology evolves.
AI should enhance, not replace, human work. While some roles may shift, communicate these changes transparently and focus on how AI can augment existing roles.
Moving to a product-focused model can help your organisation keep pace with AI’s rapid development. For example, shifting from traditional to agile workflows may increase your AI adoption speed and responsiveness to market changes.
Ensure AI delivery is accessible to all teams. Develop a clear model aligned with change processes to avoid bottlenecks and make AI adoption practical for every part of the organisation.
Adopting these guiding principles helps organisations navigate AI’s complexities. They offer a pathway for business leaders to integrate AI in ways that are ethical, aligned with strategy, and value-driven.
How AI is Transforming Healthcare Diagnostics
Healthcare systems worldwide, including the NHS, are under immense pressure to improve productivity, reduce waiting times, and deliver better patient outcomes. Diagnostics, a critical component of patient care, is one area where AI is poised to make a transformative impact. From streamlining administrative tasks to enhancing diagnostic accuracy and speed, AI offers numerous opportunities to optimise processes and improve patient care.
However, barriers such as disparate systems across healthcare providers and unclear decision-making structures can hinder the widespread implementation of AI. Despite these challenges, the need to improve efficiency and care quality has never been greater. Now is the time to adopt AI to drive meaningful change in healthcare diagnostics.
In this blog, we’ll explore the key steps in the diagnostic process, highlight specific AI use cases that can help transform healthcare diagnostics, and outline critical success factors for successful AI implementation.
A typical diagnostic pathway in healthcare involves several key stages:
Let’s explore how AI can be applied at each stage of the diagnostic process to enhance efficiency and improve patient outcomes:
For AI to be successfully integrated into healthcare diagnostics, several critical success factors must be considered:
As healthcare systems worldwide, including the NHS, continue to face increasing demand and rising expectations, AI offers a clear path to improving diagnostics efficiency and patient outcomes. From automating appointment scheduling to assisting with image analysis, AI has the potential to revolutionise healthcare diagnostics. By addressing key barriers and ensuring that critical success factors are met, healthcare providers can unlock the full potential of AI and provide faster, more accurate care for patients.
The procurement landscape is rapidly evolving, with increasing pressure on organisations to reduce costs, streamline processes, and make smarter purchasing decisions. As traditional procurement methods struggle to keep up with growing complexities, AI and automation are emerging as powerful tools that can transform the way procurement operates.
AI in procurement offers more than just cost savings—it brings a new level of insight and efficiency. From predicting demand to automating supplier management, AI-driven tools allow procurement teams to focus on strategic activities while leaving repetitive tasks to intelligent systems.
This white paper explores how AI and automation can help procurement teams achieve their goals. We delve into specific use cases, such as predictive analytics for spend forecasting, automating supplier onboarding, and AI-driven decision-making in sourcing. Additionally, we outline the critical success factors for implementing AI solutions and provide a roadmap to realising the full benefits of these technologies.
As organisations navigate economic uncertainty and increasing global competition, AI in procurement provides a clear path to optimising processes, reducing risks, and driving long-term value.
The employability sector is facing growing demands to improve outcomes, increase participant engagement, and reduce the administrative burden on staff. AI and automation are providing solutions that optimise processes, free up resources, and ultimately enhance the participant journey. Below, we outline seven key use cases showing how AI is making an impact in employability.
Engagement is crucial for job success. AI enables personalised communication at scale, combining data analytics with AI to boost participant engagement. Automated systems can adjust messaging based on real-time feedback and engagement, helping participants stay on track and increasing their chances of success.
Missed appointments and rescheduling can be a drain on both participants and staff time. AI-driven scheduling tools automate the process, ensuring participants are reminded of their commitments and offering optimised appointment times based on availability and preferences. This reduces wasted meeting slots and improves efficiency across the board.
Participants often have recurring questions about job applications, CV building, interview preparation, or local service providers. AI-powered digital assistants can handle these FAQs, providing instant answers and resources. This not only saves time for staff but also ensures participants receive timely support.
AI can track participant behaviour and predict engagement levels, flagging early signs of disengagement. By analysing data, such as communication patterns and attendance records, AI identifies at-risk participants so that coaches can intervene earlier, improving retention and outcomes.
Using machine learning, AI can assess the likelihood of a participant achieving a job outcome. By assigning a job outcome score based on various data points, AI allows staff to focus their efforts on those participants who need the most support. This insight can also help in forecasting programme success and tailoring interventions more effectively.
In employability programmes, meetings and appointments generate significant amounts of notes and paperwork. AI can automate note-taking and update records in real time. For example, an AI-driven meeting assistant can take notes and enter data directly into the employability platform, saving time and reducing the administrative load for staff.
Post-placement support is essential to ensure participants remain in work. AI-powered systems can automate regular check-ins with participants who have found employment, offering support and identifying potential issues early. These automated systems can trigger human intervention if needed, helping sustain long-term job retention and success.
These seven AI use cases highlight the transformative impact that automation and AI can have in the employability sector. By streamlining admin tasks, predicting engagement, and enhancing participant support, AI helps organisations improve outcomes while freeing up time and resources to focus on what truly matters—supporting participants on their journey to sustainable employment.
The employability sector faces growing challenges in meeting the demands of participants, providers, and funders alike. With increasing administrative burdens, tighter budgets, and the pressure to deliver better outcomes, organisations are seeking innovative solutions to optimise their processes and enhance participant engagement.
AI and automation present a powerful opportunity to transform the employability landscape. By integrating these technologies, providers can streamline administrative tasks, personalise participant support, and make data-driven decisions that improve overall outcomes.
This white paper explores how AI and automation can help employability providers reduce admin, increase participant engagement, and improve job outcomes. It outlines key use cases, including automating participant communications, predictive tools to identify high-impact factors for job success, and optimising case management. Additionally, we examine the critical success factors necessary for a smooth AI implementation and provide actionable insights for realising the full benefits of these technologies.
How AI and Automation Can Help Optimise Costs in Higher Education
Higher education institutions in the UK are facing unprecedented challenges. With Brexit making it more difficult to attract international students—who often contribute significantly to university revenues—the sector is feeling the strain. Adding to this pressure, the government has made it clear that it won’t be offering financial bailouts to universities, forcing institutions to take decisive action to manage their costs. As a result, cuts must be made, and universities are under increasing pressure to streamline their operations without compromising on the quality of education.
The Slow Road of Traditional Cost-Cutting Measures
In response to financial strain, many universities are resorting to organisational redesigns, such as restructuring departments and cutting underperforming courses. While these measures can lead to cost reductions, they often take a long time to materialise. Courses may need to be wound down over several years, leading to a delayed realisation of benefits and return on investment (ROI).
Given the urgency of the current situation, relying solely on traditional approaches may not be sufficient to safeguard the financial sustainability of universities.
The Fast-Track Solution: AI and Automation
This is where AI and automation come into play. By integrating these technologies into university operations, institutions can accelerate cost-saving measures while unlocking long-term value. AI and automation provide immediate relief by reducing the administrative burden, freeing up staff capacity to focus on higher-impact work, and improving operational efficiency.
The key benefits of implementing AI and automation include:
Critical Success Factors for AI Implementation
While AI and automation present exciting opportunities, their success hinges on certain critical factors:
Conclusion: The Way Forward
With Brexit-related challenges and limited government support, it is a critical time for UK universities to rethink their cost structures. While organisational redesigns and course reductions are traditional approaches, they may take too long to deliver the necessary impact. AI and automation offer a faster, more sustainable solution, providing immediate cost relief and enabling universities to focus on what they do best—delivering high-quality education.
By embracing AI and automation, universities can streamline their operations, enhance student experiences, and unlock long-term savings, all while positioning themselves for a successful future in an increasingly competitive landscape.
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