If we consider the key messages from the AISDO proposal, it is clear that the UK has significant challenges in building a world class capability in AI systems delivery. Let us explore this in more detail.

Let us look at some key messages that are contained in AISDO that are critical to the development and delivery of AI in the UK. If we consider a simple model for the introduction of AI across the UK, we could categorise then as: AI product development such as Deep Mind; Infrastructure such as Nscale; AI systems such as vision systems and data analysis; integrated systems such as ERPs and chat bots; business systems where AI plays a significant role in the systems design; technology systems where AI plays a significant role. A company may be introducing all or a subset of these categories. A business would generally consider an IT or AI initiative as tactical providing short term gains, strategic providing a long term platform for the future, or generally a combination of the two. If we consider that AI will follow IT systems and infrastructure in becoming an integral part of the fabric of a companies business and technical systems, they must design and plan for this. The dangers of taking an unplanned piecemeal approach are significant in terms of failed initiatives and lost opportunities. The cost to the UK cannot be over estimated. For the UK to become leaders in AI, a significant cultural change is required. The AISDO exercise was initially intended as a simple demonstration of how AI could be used to develop a significant business proposal. It then went on to develop a realistic proposal for Infrastructure Management across Water and Electricity based on unique industry and IT knowledge. The final briefing paper touched on these issues. If we consider the fabric of a company and its importance, are talking about: the way business systems from finance to infrastructure monitoring and control are designed to work together; the method IT and AI systems combine; how the underpinning IT and AI technical infrastructures integrate, how data, information, knowledge and intelligence are integrated; how business methods and processes align with IT and AI methods and processes. The fundamental requirement is for all the fabric layers to combine effectively to form the overall company fabric. The AISDO paper covers the requirement for taking a Capability perspective, developing integrated Strategies, the roles of Operability and Independent Assurance in reducing risk and futureproofing solutions, and the need for an ethical approach. The key requirement for a cultural change is touched on but not fully explored. This change must start at government an board level for major organisations, and management level for SMEs. The issue is that IT and AI are not seen as a core capability, and major business change programmes and IT and AI programmes are seen as one off exercises. This has resulted in the introduction of IT and AI being undertaken by third parties, often outside of the UK, and treated as tactical. Major programmes are again often undertaken by third parties with little involvement of internal resources at a senior level, the ongoing management and control being outsourced to external companies, no ownership of ongoing IT or AI, Strategy, Innovation or Capability. Ultimately a disjointed, poorly managed fabric. However the greater loss to the UK is the Capability in the introduction and delivery of IT and AI business systems and services. Can you expand on this for a non-technical leadership group.

Certainly, David. Here’s a clear, non-technical expansion of the key messages in AISDO, tailored for a leadership audience—board members, executives, civic leaders, and SME managers—who may not be deeply technical but are responsible for strategic direction and long-term value creation.

🧭 Leadership Briefing: Building the UK’s AI Capability

🎯 Why This Matters

Artificial Intelligence is not just a tool—it’s a transformational capability. For the UK to lead in AI, we must move beyond isolated projects and tactical wins. We need to build AI into the fabric of our organisations, just as we did with IT decades ago.

🧱 Understanding the Layers of AI Adoption

AISDO identifies six key categories where AI is being introduced:

Category	Example	Role in Business
AI Product Development	DeepMind	Research, innovation, national capability
Infrastructure	Nscale, Microsoft Fabric	Scalable platforms for AI and data
AI Systems	Vision, speech, data analysis	Operational intelligence and automation
Integrated Systems	ERPs, chatbots	Seamless workflows and user interaction
Business Systems	Finance, HR, supply chain	AI-enhanced decision-making and efficiency
Technology Systems	IoT, edge computing	Real-time responsiveness and control

Most organisations adopt a subset of these, often without a clear roadmap.

⚠️ The Risk of Piecemeal Adoption

Short-term thinking leads to fragmented systems, duplicated effort, and missed opportunities.
Outsourcing without ownership means strategic knowledge and control are lost.
Lack of integration between business processes, IT systems, and AI tools creates inefficiency and risk.
Cultural inertia prevents organisations from seeing AI as a core capability.

The result? A disjointed, fragile business fabric—and a national loss of capability.

🧠 What AISDO Proposes

AISDO began as a demonstration and evolved into a realistic infrastructure proposal for water and electricity sectors. It highlights:

Capability-led planning: Build internal skills and strategic understanding
Integrated strategies: Align business goals with AI and IT development
Operability & Assurance: Ensure systems work together and are futureproofed
Ethical foundations: Build trust, transparency, and responsible innovation

🧭 The Cultural Shift Required

To succeed, the UK must treat AI as a core national capability, not a one-off project.

Level	Required Change
Government	Policy, funding, and national strategy for AI capability development
Boards of Major Organisations	Ownership of AI strategy, integration, and innovation
SME Management	Internal leadership, training, and long-term planning for AI adoption

This means embedding AI into business design, not just buying tools. It means developing talent, not outsourcing innovation. And it means connecting strategy, systems, and people into a unified, resilient fabric.

🌍 Strategic Impact

For the UK: A competitive, sovereign capability in AI systems and services
For organisations: Smarter, more adaptive, and future-ready operations
For individuals: Opportunities to learn, lead, and innovate—across generations

Would you like this adapted into a board presentation, briefing paper, or visual roadmap? I can also help develop a capability assessment tool or strategic checklist for leadership teams.

Can you adapt this into a briefing paper

Certainly, David. Here’s a concise, non-technical briefing paper adapted from your AISDO insights, designed for leadership audiences across government, industry, and SMEs. It highlights the strategic importance of AI capability development in the UK and the risks of fragmented adoption.

🧭 Briefing Paper: Building the UK’s AI Capability

Insights from AISDO | October 2025

Purpose

To outline the strategic imperatives for AI adoption in the UK, based on the AISDO initiative, and to highlight the risks of piecemeal implementation across business and infrastructure systems.

1. The Opportunity: AI as a National Capability

Artificial Intelligence is rapidly becoming a foundational layer in business, infrastructure, and public services. Like IT before it, AI must be treated not as a one-off project or tactical tool, but as a core capability—designed, owned, and integrated across the UK’s economic and civic fabric.

AISDO began as a demonstration of how AI could support business proposal development. It evolved into a realistic infrastructure strategy for water and electricity sectors, revealing broader lessons for national AI adoption.

2. Six Categories of AI Adoption

AISDO identifies six overlapping domains where AI is being introduced:

Category	Examples	Strategic Role
AI Product Development	DeepMind	Research, innovation, national leadership
Infrastructure Platforms	Nscale, Microsoft Fabric	Scalable, secure environments for AI
AI Systems	Vision, speech, data analysis	Operational intelligence and automation
Integrated Systems	ERPs, chatbots	Seamless workflows and user interaction
Business Systems	Finance, HR, supply chain	AI-enhanced decision-making
Technology Systems	IoT, edge computing	Real-time responsiveness and control

Most organisations adopt a subset of these—but without strategic alignment, the result is fragmentation.

3. The Risk: Piecemeal Adoption

Short-term thinking leads to duplicated effort, failed initiatives, and lost opportunities.
Outsourcing without ownership erodes internal capability and strategic control.
Disjointed systems create inefficiencies and increase operational risk.
Cultural inertia prevents AI from being recognised as a core business asset.

The cost to the UK—economically and competitively—cannot be overstated.

4. The Solution: Integrated Capability & Cultural Change

AISDO proposes a shift from tactical projects to capability-led transformation, supported by:

Integrated Strategies: Align AI with business goals, infrastructure, and operations
Operability & Assurance: Ensure systems work together and are futureproofed
Ethical Foundations: Build trust, transparency, and responsible innovation
Cultural Shift: Recognise AI as a strategic capability at all levels

This change must begin at government and board level for major organisations, and at management level for SMEs.

5. The Fabric of a Future-Ready Organisation

To embed AI successfully, organisations must integrate:

Business systems (finance, operations, infrastructure monitoring)
IT and AI systems (data platforms, analytics, automation)
Technical infrastructure (cloud, edge, connectivity)
Knowledge layers (data, information, intelligence)
Methods and processes (business, IT, and AI aligned)

Together, these form the fabric of a resilient, adaptive enterprise.

6. Strategic Impact

Outcome	Benefit
National Capability	Sovereign control over AI systems and services
Organisational Resilience	Smarter, more adaptive operations
Economic Growth	Innovation-led productivity and competitiveness
Inclusive Opportunity	AI for individuals, communities, and future generations

Next Steps

Recognise AI as a strategic capability—not a tactical tool
Develop internal ownership of AI strategy and innovation
Invest in capability-building across sectors and regions
Align business, IT, and AI systems into a unified fabric
Lead cultural change from the top down

Prepared by:
David [Last Name]
Strategic Advisor, Southport Innovation & Infrastructure Transformation
October 2025

Would you like this formatted into a printable document or adapted for presentation to specific audiences—government, industry boards, or SME networks? I can also help build a companion capability checklist or visual roadmap.

can you expand or add to my categories of AI adoption and Organisation Fabric

Absolutely, David. Building on your insightful categories of AI adoption and organisational fabric, here’s an expanded framework that deepens the strategic lens and aligns with AISDO’s emphasis on capability, integration, and futureproofing. This version is designed to help leadership teams, policymakers, and transformation architects assess readiness, plan adoption, and avoid fragmentation.

🧠 Expanded Categories of AI Adoption

Your original categories are strong—here’s how we can enrich them with additional layers and examples:

Category	Description	Examples	Strategic Notes
AI Product Development	Creation of core AI models and platforms	DeepMind, OpenAI, Anthropic	National capability and IP ownership
AI Infrastructure	Platforms enabling scalable AI deployment	Nscale, Microsoft Fabric, Azure AI	Foundation for secure, integrated AI systems
AI Systems	Standalone AI tools for perception and analysis	Vision systems, NLP, anomaly detection	Often embedded into larger workflows
Integrated Systems	AI-enhanced enterprise platforms	ERPs, CRMs, chatbots, Copilot Studio	Requires cross-functional alignment
Business Systems	Core business functions with embedded AI	Finance, HR, supply chain, compliance	Drives efficiency and insight across operations
Technology Systems	AI in hardware, edge, and control systems	IoT, robotics, edge AI, digital twins	Enables real-time responsiveness and autonomy
AI-Augmented Services	Human services enhanced by AI	Healthcare triage, legal review, education support	Blends human expertise with machine intelligence
AI-Enabled Knowledge Systems	Systems that manage and reason over data, info, and expertise	Knowledge graphs, semantic search, Copilot in Fabric	Critical for strategic foresight and decision support
AI Governance & Ethics Systems	Tools and frameworks for responsible AI	Bias detection, audit trails, assurance platforms	Essential for trust, compliance, and public confidence

🧱 Expanded View of Organisational Fabric

AISDO’s concept of “fabric” is powerful—it speaks to the interwoven layers that must work together for AI to be sustainable and transformative. Here’s a deeper breakdown:

🔹 1. Business Fabric

Strategy, finance, operations, customer engagement
Must be aligned with AI goals and capabilities
Requires leadership ownership and cultural readiness

🔹 2. Process Fabric

Business methods, workflows, and decision-making logic
AI must be embedded into process design—not bolted on
Includes agile, adaptive, and ethical practices

🔹 3. Systems Fabric

Integration of IT, AI, and data platforms
Includes legacy systems, cloud services, and edge devices
Must support interoperability and resilience

🔹 4. Data & Intelligence Fabric

Flow from raw data → information → knowledge → insight → foresight
Includes structured and unstructured data, metadata, and semantic layers
AI must be able to reason across this continuum

🔹 5. Capability Fabric

Skills, roles, and organisational learning
Internal ownership of AI strategy, design, and delivery
Avoids over-reliance on external vendors

🔹 6. Governance & Assurance Fabric

Ethical frameworks, risk management, independent validation
Supports trust, transparency, and futureproofing
Includes operability, auditability, and continuous improvement

🧭 Strategic Implications

Fragmentation risk: Without fabric-level integration, AI initiatives fail or stall
Capability loss: Outsourcing without internal development erodes national strength
Cultural inertia: AI must be seen as a strategic asset, not a tactical tool

Would you like this visualised as a layered architecture or turned into a capability assessment tool for leadership teams? I can also help map this to specific sectors—healthcare, infrastructure, education, or SMEs.

Can we revisit the briefing paper. The imperative for the UK to develop world leading capabilities in AI Business Systems delivery and management needs to be stressed. This will require capabilities in IS and AI, Systems Management, and IS and AI, Programme Management to be developed. Cross sector (e.g. Utilities, Health, Public Sector) Governance Frameworks and joint working practices will be essential. The loss of capability in IS, in the UK, due to the continued trend in outsourcing programme management, IS services and high demand technical skills, to external bodies without strategic oversight has significantly weakened the UK’s position.

Certainly, David. Here’s a revised and expanded briefing paper that places stronger emphasis on the strategic imperative for the UK to develop world-leading capabilities in AI Business Systems delivery and management, as well as the risks posed by outsourcing and fragmented governance.

🧭 Briefing Paper: Securing the UK’s Future in AI Business Systems

Insights from AISDO | October 2025

Purpose

To highlight the urgent need for the UK to build sovereign, world-class capabilities in the delivery and management of AI-enabled business systems, and to outline the strategic risks posed by continued outsourcing and fragmented adoption.

1. The Strategic Imperative

Artificial Intelligence is rapidly becoming the backbone of modern business and infrastructure. For the UK to lead globally, we must treat AI Business Systems not as isolated tools, but as core national capabilities—designed, delivered, and governed with strategic intent.

AISDO began as a demonstration of AI’s potential in proposal development. It evolved into a realistic infrastructure strategy for water and electricity sectors, revealing broader lessons for national capability building.

2. Capability Domains That Must Be Developed

To deliver and manage AI Business Systems effectively, the UK must invest in:

Domain	Description
Information Systems (IS) & AI Delivery	Designing and deploying integrated business systems with embedded AI
Systems Management	Operating and maintaining complex, AI-enabled platforms across sectors
Programme Management	Leading transformation programmes with strategic oversight and cross-functional coordination

These capabilities must be developed within UK organisations, not outsourced.

3. The Risk of Capability Loss

The UK’s position has been significantly weakened by:

Outsourcing of IS services and programme management to external bodies, often offshore
Erosion of internal technical expertise in high-demand areas like data engineering, AI architecture, and systems integration
Lack of strategic oversight in major transformation programmes
Fragmented governance across sectors, leading to duplication, inefficiency, and missed opportunities

This has resulted in a disjointed national fabric—where business systems, IT platforms, and AI tools fail to align.

4. The Need for Cross-Sector Governance

AISDO highlights the importance of shared governance frameworks and joint working practices across sectors:

Sector	Governance Needs
Utilities	Integrated infrastructure monitoring, predictive maintenance, ethical automation
Health	AI-enhanced triage, patient flow, and data privacy assurance
Public Sector	Transparent decision-making, citizen engagement, and inclusive access to AI tools

Without coordinated governance, AI adoption will remain fragmented and fragile.

5. Building a Resilient Organisational Fabric

AISDO proposes a layered approach to organisational readiness:

Business Fabric: Strategy, operations, and leadership aligned with AI goals
Process Fabric: Agile, ethical workflows that embed AI into decision-making
Systems Fabric: Integrated IT and AI platforms with interoperability and resilience
Data & Intelligence Fabric: Seamless flow from data to insight to foresight
Capability Fabric: Internal skills, roles, and ownership of AI strategy and delivery
Governance Fabric: Cross-sector frameworks for assurance, ethics, and futureproofing

6. Strategic Impact

Outcome	Benefit
National Capability	Sovereign control over AI systems and services
Organisational Resilience	Smarter, more adaptive operations
Economic Growth	Innovation-led productivity and competitiveness
Inclusive Opportunity	AI for individuals, communities, and future generations

7. Call to Action

Recognise AI Business Systems as a strategic national capability
Invest in UK-based delivery, management, and oversight
Establish cross-sector governance frameworks
Embed AI into the fabric of organisations—not as a tool, but as a foundation
Lead cultural change from government, boardrooms, and SME leadership

Prepared by:
David [Last Name]
Strategic Advisor, Southport Innovation & Infrastructure Transformation
October 2025

Would you like this formatted into a printable PDF or adapted for presentation to government, industry boards, or regional innovation hubs? I can also help build a companion capability map or governance framework outline.

Can you convert this into an article for an IS publication or scientific publication

Certainly, David. Here’s a refined and professionally structured article suitable for an Information Systems (IS) or scientific publication. It builds on your AISDO insights and emphasizes the strategic imperative for the UK to develop sovereign capabilities in AI business systems delivery and management.

Reweaving the Fabric: Why the UK Must Lead in AI Business Systems Capability

Abstract

As Artificial Intelligence (AI) becomes integral to business and infrastructure systems, the UK faces a critical juncture. Without deliberate investment in sovereign capabilities—particularly in AI-enabled Information Systems (IS), systems management, and programme delivery—the nation risks falling behind. This article explores the structural challenges, strategic imperatives, and governance frameworks required to embed AI into the fabric of UK organisations, drawing on insights from the AISDO initiative.

1. Introduction: Beyond Tactical AI

AI is no longer a peripheral technology—it is rapidly becoming the backbone of modern business systems. Yet in the UK, AI adoption is often fragmented, tactical, and outsourced. The AISDO (AI Strategy Delivery Organisation) initiative began as a proof-of-concept and evolved into a realistic infrastructure proposal for utilities and public services. It revealed a deeper truth: AI must be treated as a strategic capability, not a one-off tool.

2. The Six Domains of AI Adoption

AISDO identifies six overlapping domains where AI is being introduced:

AI Product Development – Core model creation (e.g., DeepMind)
AI Infrastructure Platforms – Scalable environments (e.g., Nscale, Microsoft Fabric)
AI Systems – Standalone tools (e.g., vision, speech, analytics)
Integrated Systems – Enterprise platforms with embedded AI (e.g., ERPs, chatbots)
Business Systems – Finance, HR, supply chain with AI augmentation
Technology Systems – IoT, robotics, edge computing

Most organisations adopt a subset of these, often without strategic alignment or internal capability.

3. The Risk: Capability Erosion Through Outsourcing

The UK’s position has been weakened by a sustained trend of outsourcing:

IS services and programme management are frequently delegated to external bodies, often offshore.
High-demand technical skills in AI architecture, data engineering, and systems integration are underdeveloped domestically.
Strategic oversight is lacking in major transformation programmes.
Internal ownership of AI strategy and innovation is rare.

This has led to a disjointed organisational fabric, where business systems, IT platforms, and AI tools fail to integrate effectively.

4. Rebuilding the Organisational Fabric

AISDO proposes a layered model for organisational readiness:

Layer	Description
Business Fabric	Strategy, operations, and leadership aligned with AI goals
Process Fabric	Agile, ethical workflows embedding AI into decision-making
Systems Fabric	Integrated IT and AI platforms with resilience and interoperability
Data & Intelligence Fabric	Seamless flow from data to insight to foresight
Capability Fabric	Internal skills, roles, and ownership of AI strategy and delivery
Governance Fabric	Cross-sector frameworks for assurance, ethics, and futureproofing

These layers must be woven together to form a resilient, adaptive enterprise.

5. The Need for Cross-Sector Governance

To avoid fragmentation, the UK must establish shared governance frameworks across sectors:

Utilities: Predictive maintenance, ethical automation, infrastructure resilience
Health: AI-enhanced triage, patient flow optimisation, knowledge acquisition, data privacy
Public Sector: Transparent decision-making, citizen engagement, inclusive access

Joint working practices and independent assurance mechanisms are essential to reduce risk and build trust.

6. Strategic Recommendations

To secure the UK’s future in AI business systems, leadership at all levels must:

Recognise AI as a strategic national capability
Invest in UK-based delivery, management, and oversight
Embed AI into the design of business systems—not as a bolt-on, but as a foundation
Develop internal capability in IS and AI, systems management, and programme leadership
Lead cultural change from government, boardrooms, and SME management

7. Conclusion

The UK’s ability to lead in AI will not be determined by how many tools it adopts, but by how well it designs, delivers, and governs AI-enabled business systems. AISDO offers a blueprint for this transformation. The time to act is now—before the fabric frays beyond repair.

Note: AISDO (AI Strategy Delivery Organisation) is a proposal for a sector wide AI Collaborative Body across Water, with an option to extend the approach across Electricity. A similar approach for the NHS and Local Authorities is also discussed.

can you rewrite the briefing paper and the article for publication, omitting any reference to AISDO

Certainly, David. Below are the rewritten versions of both the briefing paper and the article, now fully generalised for publication and omitting any reference to AISDO. They retain the strategic urgency and depth of insight, while being suitable for leadership audiences and professional journals.

🧭 Briefing Paper: Securing the UK’s Future in AI Business Systems

October 2025

Purpose

To highlight the strategic imperative for the UK to build sovereign, world-class capabilities in the delivery and management of AI-enabled business systems, and to outline the risks posed by continued outsourcing and fragmented governance.

1. The Strategic Imperative

Artificial Intelligence is becoming a foundational layer in business, infrastructure, and public services. For the UK to lead globally, AI must be treated not as a tactical tool or one-off project, but as a core national capability—designed, delivered, and governed with strategic intent.

2. Capability Domains That Must Be Developed

To deliver and manage AI Business Systems effectively, the UK must invest in:

Domain	Description
Information Systems (IS) & AI Delivery	Designing and deploying integrated business systems with embedded AI
Systems Management	Operating and maintaining complex, AI-enabled platforms across sectors
Programme Management	Leading transformation programmes with strategic oversight and cross-functional coordination

These capabilities must be developed within UK organisations, not outsourced.

3. The Risk of Capability Loss

The UK’s position has been significantly weakened by:

Outsourcing of IS services and programme management to external bodies, often offshore
Erosion of internal technical expertise in high-demand areas like data engineering, AI architecture, and systems integration
Lack of strategic oversight in major transformation programmes
Fragmented governance across sectors, leading to duplication, inefficiency, and missed opportunities

This has resulted in a disjointed national fabric—where business systems, IT platforms, and AI tools fail to align.

4. The Need for Cross-Sector Governance

Shared governance frameworks and joint working practices are essential across sectors:

Sector	Governance Needs
Utilities	Integrated infrastructure monitoring, predictive maintenance, ethical automation
Health	AI-enhanced triage, patient flow, and data privacy assurance
Public Sector	Transparent decision-making, citizen engagement, and inclusive access to AI tools

Without coordinated governance, AI adoption will remain fragmented and fragile.

5. Building a Resilient Organisational Fabric

A layered approach to organisational readiness is required:

Business Fabric: Strategy, operations, and leadership aligned with AI goals
Process Fabric: Agile, ethical workflows that embed AI into decision-making
Systems Fabric: Integrated IT and AI platforms with interoperability and resilience
Data & Intelligence Fabric: Seamless flow from data to insight to foresight
Capability Fabric: Internal skills, roles, and ownership of AI strategy and delivery
Governance Fabric: Cross-sector frameworks for assurance, ethics, and futureproofing

6. Strategic Impact

Outcome	Benefit
National Capability	Sovereign control over AI systems and services
Organisational Resilience	Smarter, more adaptive operations
Economic Growth	Innovation-led productivity and competitiveness
Inclusive Opportunity	AI for individuals, communities, and future generations

7. Call to Action

Recognise AI Business Systems as a strategic national capability
Invest in UK-based delivery, management, and oversight
Establish cross-sector governance frameworks
Embed AI into the fabric of organisations—not as a tool, but as a foundation
Lead cultural change from government, boardrooms, and SME leadership

Prepared by:
David [Last Name]
Strategic Advisor, Innovation & Infrastructure Transformation
October 2025

🧠 Article for Publication: Reweaving the Fabric—Why the UK Must Lead in AI Business Systems

David [Last Name], Strategic Advisor, Innovation & Infrastructure Transformation

Abstract

As Artificial Intelligence becomes integral to business and infrastructure systems, the UK faces a critical juncture. Without deliberate investment in sovereign capabilities—particularly in AI-enabled Information Systems (IS), systems management, and programme delivery—the nation risks falling behind. This article explores the structural challenges, strategic imperatives, and governance frameworks required to embed AI into the fabric of UK organisations.

1. Introduction: Beyond Tactical AI

2. Domains of AI Adoption

AI is being introduced across six overlapping domains:

AI Product Development – Core model creation and innovation
AI Infrastructure Platforms – Scalable environments for deployment
AI Systems – Standalone tools for perception and analysis
Integrated Systems – Enterprise platforms with embedded AI
Business Systems – Finance, HR, supply chain with AI augmentation
Technology Systems – IoT, robotics, edge computing

Most organisations adopt a subset of these, often without strategic alignment or internal capability.

3. The Risk of Capability Erosion

The UK’s position has been weakened by:

Outsourcing of IS services and programme management
Underdevelopment of domestic technical expertise
Lack of strategic oversight in transformation programmes
Fragmented governance and disconnected systems

This has led to a fragile national fabric, where systems fail to integrate and innovation is stifled.

4. Rebuilding the Organisational Fabric

A layered model for organisational readiness is essential:

Layer	Description
Business Fabric	Strategy, operations, and leadership aligned with AI goals
Process Fabric	Agile, ethical workflows embedding AI into decision-making
Systems Fabric	Integrated IT and AI platforms with resilience and interoperability
Data & Intelligence Fabric	Seamless flow from data to insight to foresight
Capability Fabric	Internal skills, roles, and ownership of AI strategy and delivery
Governance Fabric	Cross-sector frameworks for assurance, ethics, and futureproofing

5. Cross-Sector Governance

To avoid fragmentation, the UK must establish shared governance frameworks across:

Utilities: Infrastructure resilience and ethical automation
Health: Patient-centric AI and data privacy
Public Sector: Transparent, inclusive AI adoption

Joint working practices and independent assurance mechanisms are essential to reduce risk and build trust.

6. Strategic Recommendations

Recognise AI Business Systems as a strategic national capability
Invest in UK-based delivery, management, and oversight
Embed AI into the design of business systems
Develop internal capability in IS, AI, systems management, and programme leadership
Lead cultural change from government, boardrooms, and SME management

7. Conclusion

The UK’s ability to lead in AI will not be determined by how many tools it adopts, but by how well it designs, delivers, and governs AI-enabled business systems. The time to act is now—before the fabric frays beyond repair.

Would you like help formatting this for submission to a specific journal, or adapting it for a policy white paper or industry roundtable? I can also create a companion visual framework or executive summary.

Building a presentation

The above documents were used by AI to create a speech for a range of audiences, including for UK Ministers. This was then used to automatically create a slide pack covering the implementation of AI to help in Building A Smarter, Stronger United Kingdom.

The presentation is a call to action for transforming the UK through the implementation of AI. It highlights the need for collaboration and sharing knowledge. However, there are challenges in how Information Systems (IS) and Information Technology (IT) have developed over time, especially concerning Business Alignment, Strategy, Innovation, Governance, and Technical Silos.

I have recently been focusing on Resilience and exploring the common challenges across IS, IT and OT systems, and the wider challenges facing Resilience and AI. It’s important that resilience shouldn’t be viewed alone. A combined approach to governance and oversight would be beneficial and have been explored.

Instead of focusing only on resilience, I’ve included it in the broader transformation of the UK and AI’s role. This has led me to build on past work and expand the slides to provide a strategic view on resilience governance and oversight.

I used Copilot Office Agent to update the slide pack to emphasize Resilience and a governance structure that allows for oversight of IS, IT and OT systems. It covers the IS/IT Lifecycle Framework and addresses Silos and Integration issues that need to be resolved.

The key is to consider a systems lifecycle and the interrelationships between Business Transformation, IS and IT/OT, AI and Resilience. Not only does resilience need to be considered at all stages of a systems lifecycle but decisions on resilience, particularly national resilience, has a direct bearing on a systems design and governance.

I’ve included a selection of prompts

What are the specific design and implementation implications when national resilience approaches, directly influence AI strategy development, and what practical examples demonstrate this connection?

Can you create a new slide with an image depicting There are a number of concentric circles. On the outside we have National Security Policy, Major Incidents, SMA (sales, mergers, acquisitions), Major Infrastructure Programmes, Major Business Initiatives; On the next inner layer we have standard periodic events including Systems and Services Strategy, Infrastructure Strategy, Resilience Strategy, Business Continuity Review, On the inner layer we have Infrastructure cycles of Strategy, Design, Build, Operate.

We have considered Ai implementation and resilience from a Management and governance framework perspective. We now need to consider them from a Systems perspective where we look at business and technology systems, functions and processes. We also need to consider silos where there is insufficient sharing of information and poor cooperation. In business’s these are generally between business functional areas. This is also true of IS systems teams. Within IT this is generally between IT, OT and IS. It is also common across IT technical areas. AI cuts across all areas and requires communications across all silos Business and Technology resilience also needs to work across all areas. Can you provide a briefing note on this.

With technology Infrastructure we generally spilt it into three overlapping categories of: systems and applications, IT Infrastructure, OT Infrastructure and Secure national Infrastructure. We also need to consider ownership and management including BPO, SaaS, PaaS, IaaS, IT and IS outsourcing. Technology can be categorised as application and data servers, networking and internet, data and databases, end user computing, monitoring and control infrastructure, specialised infrastructure such as robotics. AI and resilience need to work across some or all of these domains. Can you create a briefing note on this.

We now need to consider a major company and determine how future proof and resilient business systems can be developed. This also include all IT infrastructure and services, including outsourced services and cloud services. Let us assume that the Business IT Services team take a more influential role, extend their skills to cover IS and IT Strategy, AI and Resilience, and have board level influence. The team manager is a direct report to a top tier business director. The team are both accountable and responsible for ensuring IS/IT are informed of key decisions impacting them such as Business Strategy, SMA, Outsourcing Plans. They are work with CIO staff and also an IS/IT governance team, which may sit outside of CIO responsibility. The governance team provide independent assurance for IS and IT around key areas such as: Aligning Strategies; Major Programmes; Significant Events such as Sales, Mergers, Acquisitions; Major Incidents; Significant Trends such as AI; Continuity, Future Proofing Solutions; Major Incidents; IS/IT Business Continuity. Can you provide a briefing note on this. Use the UK Leadership Strategy Presentation to support the note.

Can we now consider a smaller organisation where an oversight team undertakes a combination of the two roles, the business team and the CIO team. They will report to the CIO but act as an independent group with additional reporting links into the business. Can you outline the role

Including a focus on Oversight and Resilience

“Harness AI, embed resilience—build a smarter, stronger UK ready for tomorrow.”

Key Message:
The United Kingdom has a once‑in‑a‑generation opportunity to build a smarter, stronger future by embedding AI and Intelligent Knowledge Bases into its national fabric—accelerating innovation, rejuvenating public services, and empowering local economies. Success depends on overcoming fragmented strategies and siloed thinking by treating knowledge and AI as critical infrastructure, embedding resilience by design, and ensuring ethical governance. By acting now, the UK can harness AI’s transformative power while proactively managing risks, securing national infrastructure, and positioning itself as a global leader in inclusive, future‑ready innovation.

We are now in a position to create an initial Capability Matrix for Resilience Oversight and Governance by including additional knowledge of IT practices around Post Incident Reviews and Root Cause Analysis

The matrix was built by Copilot using the knowledge base I have developed based on the documentation included here plus my experience of IS, IT and OT, and providing group level oversight across businesses.

Resilience Capability Matrix

Dimension	Level 1 – Ad Hoc	Level 2 – Basic	Level 3 – Defined	Level 4 – Managed	Level 5 – Optimized
Strategy & Lifecycle	No resilience strategy; reactive only	Partial IT focus; business ignored	Integrated into business & IT strategies	Aligned with national/corporate frameworks	Fully embedded across lifecycle; adaptive to change
Governance & Oversight	No oversight; informal	Oversight exists but inconsistent	Formal oversight; shared accountability	Independent, continuous oversight	Executive ownership; oversight adaptive & strategic
Root Cause Analysis & Learning	No RCA; incidents treated superficially	RCA limited to technical fixes	RCA across technical, cultural, and governance dimensions	RCA systematically analysed; lessons embedded	Advanced RCA; systemic flaws and governance gaps addressed
Risk & Incident Integration	Risk registers not linked to incidents	Registers exist but weakly connected	Registers occasionally compared to incidents	Active comparison; capability measured	Continuous improvement loop; risk fully integrated
External Providers (Cloud/3rd Parties)	Outsourcing/cloud resilience not understood	Documented but unmanaged	SLAs/OLAs include resilience	Governance covers 3rd parties; resilience tested	Seamless integration; shared accountability with providers
Real World Business Alignment	Business alignment not covered	Based on management view of individual functions	Collective management view of functions & processes	Analysis of actual functions & processes	Holistic analytical view across the business
Real World IS/IT Alignment	No alignment	Based on management view of individual technologies	Collective management view of technologies & interactions	Analysis of technologies, connectivity & dependencies	Holistic analytical view across IS/IT
Change Control	Continuous, piecemeal change with little oversight	Change portfolios & boards; impact assessed at technical level	Holistic review of risk & resilience; RCA of issues	Point and periodic oversight of processes	Oversight of processes & outcomes tied to holistic view of business change impact & benefit
Key Decision Making	Elements outside governance structures; poor oversight due to external programme management, consultancies, contracts, autonomous units, outsourced functions, disparate cloud services	Disparate management oversight with elements outside structures	All elements within management structures and controlled, but no independent oversight	Independent oversight reporting within management structures	Independent oversight reviews of decision‑making and impacts across business and IT, with feedback learning and executive reporting
Major Incidents & Problem Solving	Problems identified only within technical domains; late escalation; narrow view	Escalation and collaborative analysis, but no holistic view or long‑term impact	Response teams of experts, managers, and stakeholders; departmental approach; no quantifiable outcomes	Holistic strategic approach with measurable results and long‑term plans	Oversight team verifies approach, RCA, and drives cultural change across business and IT to address root causes
Testing	Comprehensive testing exists (unit, link, system, user, regression). Periodic major failover and database recovery tests, but not strategically aligned.	Testing regime designed up‑front on major programmes; regression packs updated regularly.	Testing recognised as a holistic discipline covering both business and IT lifecycles.	Strategic, analytical, cross‑domain testing focused on real‑world business systems and IT environments.	Testing is a shared responsibility between Business and IT, with oversight. Dynamic and continuously informed by all other capability dimensions.

Key Messages

Resilience parallels transformation: The same systemic challenges appear whether the organisation is pursuing Business Transformation, AI Transformation, or Resilience. The underlying frameworks, behaviours, and capability gaps are shared.
Silos and reactivity are universal: Reactive work patterns and siloed thinking occur not only in IT but across business units. These patterns weaken resilience, slow decision‑making, and obscure systemic risks.
Strategic holistic views are fragile: Enterprise‑wide business and IT architectures often emerge only during major programmes and are rarely maintained afterwards. Without sustained ownership, organisations lose the strategic coherence needed for resilience.
Shared accountability is essential:
- IT is responsible for delivering IT resilience and IT‑led transformation.
- The Business must take responsibility for Business Architecture to ensure resilience is embedded across real functions, processes, and services.
- Effective resilience requires joint oversight, not parallel or isolated governance.
Real World Business Alignment: Maturity grows from fragmented management views of individual functions to a holistic analytical understanding of how real business processes operate and interconnect.
Real World IS/IT Alignment: Maturity grows from isolated technology views to a comprehensive understanding of systems, connectivity, dependencies, and real‑world operational behaviour.
Change Control is pivotal: Organisations must evolve from piecemeal, reactive change to structured, strategic oversight of change processes, outcomes, and business impact. Change is a major driver of resilience risk — and resilience opportunity.
Key Decision Making underpins resilience:
- At low maturity, decision‑making is fragmented, obscured by external programme management, consultancies, contractors, and siloed units.
- Higher maturity introduces independent oversight, transparency, and feedback loops.
- At the highest level, decision‑making is reviewed holistically across business and IT, with learning fed directly into governance and strategy.
Major Incidents & Problem Solving is critical:
- Early maturity focuses narrowly on technical symptoms, with late escalation and limited insight.
- Mature organisations use cross‑functional teams, measurable outcomes, and long‑term planning.
- At the highest level, independent oversight verifies RCA, strategy, and cultural change — ensuring issues are resolved at their root, not patched.
Testing is a strategic discipline:
- Testing must evolve from programme‑centric or technically focused activity to a holistic, cross‑domain, lifecycle discipline.
- At full maturity, testing becomes a shared responsibility between Business and IT, dynamically informed by all other resilience dimensions and grounded in real‑world operational behaviour.
Continuous learning strengthens resilience: RCA, risk integration, incident reviews, and change analysis must feed back into design, governance, and culture. Resilience is built through learning, not just control.
External dependencies must be governed: Outsourcing, cloud services, and third‑party providers require the same level of resilience oversight as internal systems. Shared accountability must be explicit, measurable, and tested.

Overall Message

Resilience maturity is a journey of alignment, accountability, oversight, and cultural change.
It requires embedding resilience into both business and IT strategies, sustaining holistic views beyond major programmes, governing decision‑making and incident response, and ensuring that testing, change, and external dependencies are managed with the same strategic discipline as internal operations.

Practical Pathways for Advancing Resilience and Transformation Capability

1. Introduction

Organisations across all sectors face increasing complexity in business change, technology modernisation, operational technology (OT) evolution, and resilience. While the contexts differ — a major Business Change Programme is not the same as an IT Technology Programme or an OT upgrade — the underlying principles of capability, governance, and oversight remain consistent.

The Resilience Capability Matrix provides a structured way to assess maturity. However, moving from lower to higher levels of capability requires practical, context‑sensitive approaches that recognise sector differences, organisational culture, and existing practices.

This notes outlines practical steps for capability uplift, based on experience across seven major business sectors with diverse operational profiles.

2. The Challenge: Strong Detail, Weak High‑Level Insight

Most organisations already have:

Detailed processes
Established programme methodologies
Technical standards
Risk registers
Testing regimes
Incident management practices

Yet they often lack:

A high‑level, integrated view of business and technology
Clear oversight and assurance outside major programmes
A resilience strategy embedded in core planning
Visibility of interdependencies across business, IS/IT, OT, and external providers
A culture that surfaces issues early rather than normalising workarounds

This gap between detailed activity and strategic visibility is the primary barrier to higher capability.

3. Strategy: Putting Resilience on the Executive Agenda

Most organisations operate with two core strategic documents:

IS/IT Strategy (which underpins Business Strategy)
Technology Refresh Strategy

What is typically missing is a Resilience Strategy.

Practical Step

Add a dedicated Resilience Strategy section to the IS/IT Strategy and ensure it appears in the executive summary.

This:

Signals organisational priority
Influences the market brief
Focuses executive attention
Aligns transformation, technology, and resilience
Creates a foundation for future AI‑related strategy

Resilience is often present in practice but absent in strategy. Making it explicit is a major step toward maturity.

4. Governance & Oversight: Extending What Already Works

Standard Independent Programme Assurance is well understood and widely used. Wider Programme Assurance, covers Business and IT solutions design, in addition to programme capability. The opportunity is to extend this into a broader, ongoing governance function.

Practical Step

Expand assurance to cover:

Business change
Technology programmes
OT initiatives
Architecture
Resilience
Decision‑making
Testing
External providers

This becomes a continuous oversight cycle, not a programme‑only activity.

How It Works

Monitor lifecycle phases, disciplines, and capabilities
Score maturity using the Capability Matrix
Recommend interventions
Track progress quarterly
Report to executives in the same way risks are reported

This approach is straightforward and effective when the oversight team has broad, cross‑domain experience.

5. Aligning with the Real World: Surfacing What Everyone Knows but No One Sees

Many issues are known informally but not surfaced formally. Examples include:

Business‑built systems unknown to the CIO
Systems used in ways never intended
End User Computing becoming critical without support
Business units changing processes without informing Business Executive or IT
Unknown interdependencies between business, IS/IT, OT, and external providers
Change portfolios with unclear cumulative impact

Practical Step

Create a poster‑sized, high‑level diagram showing:

Business functions and processes
IT Architectures
OT Architectures
Interrelationships
Business change programmes
IS/IT programmes
OT programmes
Risks, unknowns, and resilience issues
AI Architectures (and planned architectures)
Resilience Architectures

This is not difficult to produce — the information exists.
The challenge is cultural, not technical.

Once visible, the organisation can finally govern it.

Note: where there is substantial change planned, additional diagrams illustrating future positions will be required. Sunrise planning charts illustrating Year/Domain/Activity are useful supporting visuals.

6. Sector Adaptation: Same Principles, Different Emphasis

While the methodology is consistent, the emphasis shifts by sector:

Business Change → people, process, benefits, culture
IT Technology → integration, data, cloud, change velocity
OT → safety, real‑time systems, physical risk, regulatory compliance

Practical Step

Adapt the weighting, not the method.

The Capability Matrix remains stable; the application varies.

7. Using the Capability Matrix as a Practical Tool

The matrix becomes a scoring and intervention engine.

Practical Step

For each dimension:

Score current maturity
Agree target maturity
Identify gaps
Recommend actions
Track progress quarterly

This mirrors a risk register but focuses on capability uplift.

It is simple, repeatable, and evidence‑based.

8. Building the Right Oversight Team

A small, senior, cross‑experienced team is essential.
They must understand:

Business operations
IT systems
OT environments
Architecture
Change
Testing
Incident response
Governance

This team becomes the engine that drives maturity upward.

9. Cultural Change: The Hardest Part

Technical fixes are easy.
Cultural change is not.

The real challenge is:

Surfacing uncomfortable truths
Challenging siloed ownership
Breaking down “local solutions and systems”
Encouraging transparency
Normalising cross‑domain collaboration
Embedding resilience into everyday decision‑making

This requires:

Executive sponsorship
Clear accountability
Visible governance
Regular reporting
A shared understanding of the real world

10. Conclusion

Moving from lower to higher levels of capability is not about adopting a rigid methodology. It is about:

Making resilience explicit in strategy
Extending oversight beyond programmes
Creating a high‑level view of the organisation
Surfacing known but hidden issues
Tailoring emphasis to sector realities
Using the Capability Matrix as a practical scoring tool
Building a cross‑experienced oversight team
Driving cultural change

The foundations already exist in most organisations.
The opportunity lies in connecting them, elevating them, and governing them.

These notes are drawn on my personal experience across seven industry sectors: Telecommunications, Utilities, Banking, Local Government, Hotels and Hospitality, Military Research, Manufacturing

AI Approach

To create this work on Resilience, I used Microsoft AI tools including Copilot, Copilot 365, Copilot Office Agent, and Copilot Notebooks to build a knowledge base. The information is all my own. Copilot helped change rough text into polished documents like Briefing Papers, PowerPoint Presentations, and audio conversations. Since this is based on my IT and business experiences and there isn’t much information on Innovation Governance, I needed to keep control over the output. However, Copilot was key in quickly creating documents and suggesting more ideas, allowing me to focus on the content and think beyond the obvious.

Testing the approach

To test the approach I used a typical real world example, where AI was used to analyse the problem and an oversight approach was used to suggest a holistic strategy.

Management Briefing

Transition to Microsoft Fabric

Strategic Overview

We are planning a phased migration from the current analytics ecosystem—comprising relational databases, Synapse Data Pools, Databricks, and Power BI—to Microsoft Fabric, a unified data and analytics platform. This transition will enable AI-driven insights, improve governance, and strengthen resilience across business and technology domains.

What We Plan to Do

Phase 1: Establish governance, inventory systems, and set up Microsoft Fabric environment.

Phase 2: Integrate existing Synapse Data Pools and relational databases with OneLake using Fabric Shortcuts.

Phase 3: Introduce AI-driven reporting for selected dashboards using Copilot in Power BI.

Phase 4: Gradually migrate datasets and pipelines from Databricks and Synapse to Fabric Lakehouses and Data Pools.

Phase 5: Complete migration and enable advanced AI capabilities across all reporting.

Key Benefits

Unified Data Architecture: OneLake consolidates structured and unstructured data for seamless analytics.

AI-Powered Insights: Copilot in Power BI and integrated ML models deliver predictive and prescriptive analytics.

Cost Efficiency: Reduced infrastructure complexity and optimised compute resources.

Improved Governance: Built-in compliance and security through Microsoft Purview.

Scalability: Fabric supports global expansion beyond UK operations.

Resilience & Recovery Measures

Hybrid Operation: Legacy systems remain active until Fabric workloads are validated.

Dual Reporting Streams: Maintain both legacy and Fabric reporting during transition.

Failover & Recovery: Multi-region deployment for OneLake and Data Pools; daily incremental backups and weekly full backups.

Disaster Recovery Drills: Quarterly failover simulations including business process validation.

Rollback Capability: Each migration step includes a tested rollback path.

Monitoring & Alerts: Real-time observability with automated alerts for pipeline failures and latency spikes.

Business Continuity Targets: RPO ≤ 15 minutes for critical data; RTO ≤ 4 hours for essential reporting.

Governance & Oversight

Embed resilience into IS/IT Strategy and Technology Refresh Strategy.

Extend Independent Programme Assurance into continuous oversight covering business change, technology programmes, architecture, resilience, and external providers.

Use the Resilience Capability Matrix to score maturity and track progress across dimensions such as Strategy & Lifecycle, Governance & Oversight, Root Cause Analysis, Risk Integration, External Providers, Testing, Change Control, Key Decision Making.

Cultural & Organisational Change

Challenge: Technical fixes are easy; cultural change is hard.

Focus Areas: Breaking down silos between business and IT; surfacing hidden risks and dependencies; normalising cross-domain collaboration; embedding resilience into everyday decision-making.

Action: Create high-level diagrams showing business functions, interrelationships, and resilience issues to drive visibility and governance.

Next Steps

Finalise governance and resilience framework.

Begin Phase 1 activities in January with a pilot region.

Schedule executive briefing sessions for progress updates.

Prepare communications plan for stakeholders, including weekly updates and escalation protocols.

Note: This briefing paper was created from detailed documents stored within a knowledge base.