Note to the BCS

AI for National Infrastructure and Capability Development

Submitted by David Sutton

This paper is intended as an example of the work that can be undertaken with the support of an AI Assistant, given an in-depth knowledge of the subject area.

As a long-standing member of the British Computer Society, I’ve spent my professional life immersed in the design and delivery of major business change programmes, advanced technology systems, IT management, and strategic innovation. Now retired from formal roles, I’ve had the freedom to pursue my enduring interest in Artificial Intelligence and its transformative potential—particularly in the context of developing Innovation skills and practices, and improving Capabilities.

Drawing on extensive experience in the utilities sector, I recently developed a detailed AI-enabled proposition for Water and Electricity infrastructure. This work, grounded in deep domain knowledge, evolved into a broader proposal for the creation of a National AI Support and Governance Body—a strategic initiative aimed at strengthening the UK’s capability in AI Business Systems delivery and safeguarding against the systemic risks that have historically accompanied large-scale IT adoption.

What makes this work particularly compelling is the method of its development. Using AI tools, I was able to capture and structure the entire process through recorded conversations—effectively transforming dialogue into design. Without AI, the scope and timescale of this work would have made it impractical. With AI, it became not only feasible but deeply insightful.

The resulting proposals are practical, scalable, and aligned with the UK’s strategic needs. They demonstrate how AI can be used not just to automate or optimise, but to co-create complex, multi-sector solutions—from infrastructure resilience to national capability frameworks.

I’ve made this work publicly accessible via my personal website, which serves as a repository, and holds the recorded conversations and associated briefing papers. These materials offer a transparent view into the development process and provide a foundation for further discussion, collaboration, and refinement.

I hope fellow BCS members find this contribution useful and thought-provoking. It reflects both the legacy of our profession and the exciting frontier we now face. I welcome dialogue with others who share an interest in shaping the UK’s future in AI and digital infrastructure.

I’ve attached three short briefing papers:

AISDO: A Strategic AI Initiative for Infrastructure Intelligence
Building the UK’s AI Capability
Securing the UK’s Leadership in AI Business Systems Delivery

The AISDO dialogue can be found on my website (Southport Innovation Centre), that acts as a document repository for the Innovation Centre. The briefing papers along with their dialogues can be found within sub-menus. Additionally BCS is on the main menu. It is a guide to the documents, and covers supporting Copilot dialogues and additional information.

AISDO: A Strategic AI Initiative for Infrastructure Intelligence

Briefing Paper for Industry Leaders, Technology Innovators, and the Public

Introduction

This paper outlines a bold and actionable proposal for the creation of AISDO—an AI Strategy Delivery Organisation, a collaborative body—initially focused on the UK water sector, with pathways to expand across electricity, health, and local government. Developed through an open, iterative AI conversation, this initiative demonstrates how Artificial Intelligence can accelerate strategic thinking, embed best practice, and unlock new models of collaboration.

AISDO is focused on infrastructure—but it recognises that infrastructure serves people. Every system—whether water, electricity, health, or local government—has a public service dimension. AISDO is designed to support both.

AISDO is not just a programme—it’s a movement. It positions the UK to lead globally in infrastructure intelligence, while addressing the growing complexity of managing critical systems.

Origins: From Water to Cross-Sector Transformation

The initiative began with a simple question: how can AI be strategically deployed within a water company? This evolved into:

Designing an AI Governance, Strategy & Innovation function
Developing a sector-wide AI-driven UK Water Model
Proposing AISDO-Water as a collaborative body across water companies
Extending AISDO to electricity, NHS, and local authorities
Creating realistic use cases for incident response and strategic planning
Exploring implementation, cultural change, and capability uplift

The entire process was documented in full—showcasing how AI can support open, transparent, and accelerated strategy development.

UK Leadership in AI Infrastructure

AISDO offers the UK a unique opportunity to lead globally in:

Designing and governing AI for regulated infrastructure sectors
Exporting strategic frameworks, knowledge models, and assurance tools
Building public trust through ethical, transparent AI deployment
Attracting talent and fostering intergenerational learning
Demonstrating how AI can serve society—not just systems

This positions the UK not just as a user of AI, but as a designer of intelligent infrastructure—with significant potential for international partnerships and service exports.

Innovation as a Strategic Capability

True innovation in AISDO and infrastructure intelligence requires more than advanced technologies—it demands new ways of thinking, collaborating, and evolving.

AI must not only support innovation—it must learn from it. By assimilating emerging knowledge, best practices, and strategic insights, AI becomes a living system of intelligence—ensuring that breakthroughs are not isolated, but shared, scaled, and embedded across UK sectors.

AISDO’s success depends on this dynamic capability:

To capture innovation as it happens
To translate it into actionable knowledge
And to ensure its effective use across water, electricity, health, and public services

Infrastructure Lifecycle: Build, Maintain, Refresh

Infrastructure systems naturally evolve through repeating cycles:

Cycle	Description
Build	New assets and systems introduced—requires capital and strategic planning
Maintain	Existing infrastructure monitored and optimised—requires diagnostics
Refresh	Systems upgraded or replaced—introduces complexity and cost

As organisations manage infrastructure across overlapping cycles, they face rising capability demands, growing complexity, and escalating costs. AI, if unmanaged, can add to this burden—but if strategically deployed, it can rebalance the equation.

AI as a Balancing Force

Provides accessible knowledge through AI-powered assistants and knowledge bases
Enables automation of routine diagnostics, reporting, and planning
Supports capability growth through guided learning and embedded support
Ensures operability, assurance, and agility are built in from the start

Strategic Enablers

AISDO integrates key concepts and frameworks to ensure success:

Capability as the foundation for strategic goals
Knowledge as a living asset, not just data
Independent Programme Assurance to build trust and delivery confidence
Operability to ensure systems are resilient, adaptable, and future-ready
Best Practice Integration from ITIL, MSP, and Enterprise Architecture

These elements—once complex and resource-intensive—are now achievable with AI Assistants and Knowledge Graphs, dramatically reducing delivery times and increasing strategic clarity.

Cultural Shift: Knowledge as a Strategic Asset

To lead in AI Infrastructure and Public Services, organisations must treat IT and AI as core capabilities. This requires:

Making knowledge acquisition and retention a strategic priority
Embedding AI literacy across all levels of the organisation
Rethinking outsourcing models to preserve and evolve critical knowledge
Building internal capacity to co-develop, govern, and adapt intelligent systems

AISDO supports this cultural shift—through learning hubs, capability frameworks, and open collaboration.

Indicative Cost-Benefit & Export Potential

Value Type	Indicative Annual Value
UK Infrastructure Savings	£7.8B–£10.9B
Strategic Capability Uplift	High (non-financial, systemic)
Export Revenue Potential	£2B–£5B

AISDO is not just a national programme—it’s a platform for global leadership. It enables the UK to design, govern, and export intelligent infrastructure and service systems that deliver measurable impact.

Challenges and Strategic Imperatives

As AISDO moves from concept to implementation, several critical challenges must be addressed to ensure its success and long-term impact. These challenges are not technical alone—they are cultural, structural, and strategic.

Cultural Change Across and Between Sectors
AISDO demands a shift in mindset: from siloed innovation to shared intelligence.

Collaboration must extend across companies within each sector
AI practices and functions must be shared across sectors to accelerate learning and reduce duplication
Leadership must foster openness, co-development, and trust in shared systems

Knowledge and Capability: What Must Be Retained
AI introduces new complexity in determining what knowledge is core and what can be externalised.

Organisations must identify and retain strategic capabilities essential to UK infrastructure resilience
Support is needed to help companies recognise which functions are nationally critical
Knowledge acquisition, retention, and utilisation must be treated as infrastructure in their own right

Reinventing Methodologies for AI Scale
Traditional frameworks must evolve to support AI-driven transformation.

Operability must be redefined to include intelligent systems, adaptive platforms, and AI assurance
Independent Programme Assurance must cover both programme delivery and solution validation
New models are needed for initiatives that fall outside conventional programme structures

Sharing Best Practice Through AI Knowledge Systems
AISDO must enable the creation and sharing of best practice at scale.

AI-powered knowledge systems can capture, validate, and disseminate proven methods
Sector-wide and cross-sector libraries of functions, templates, and use cases must be developed
Learning must be continuous, structured, and accessible

Technology Infrastructure and AI Model Sharing
Collaboration requires shared technical foundations.

Infrastructure for AI model development, testing, and deployment must be interoperable
Organisations must be able to contribute to and benefit from shared AI assets
Governance models must ensure ethical, secure, and equitable access

Next Steps & Call to Action

We invite industry leaders, public sector innovators, and the wider public to engage with AISDO:

Contribute to sector-specific knowledge hubs and use case libraries
Support the development of AI strategy templates and capability models
Help build the cultural change needed to unlock AI’s full potential
Position the UK as a global leader in infrastructure intelligence

AISDO is not the mission—it’s the foundation. It marks the beginning of the UK’s journey to becoming a world leader in AI-enabled business systems delivery. By embedding intelligence into infrastructure and aligning strategy with capability, AISDO sets the stage for a new era of public value, exportable innovation, and global leadership. The real mission is ahead—and it belongs to all of us.

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

Briefing Paper

Securing the UK’s Leadership in AI Business Systems Delivery
October 2025

1. Executive Summary

Artificial Intelligence is reshaping the global landscape of business systems, infrastructure, and public services. For the UK to lead—not follow—in this transformation, it must urgently invest in sovereign capabilities across the full lifecycle of AI Business Systems delivery. This includes strategy, design, implementation, governance, and assurance. Without coordinated national action, the UK risks systemic fragmentation, capability erosion, and long-term dependency on external providers.

This paper outlines the strategic imperatives, capability domains, and proposed formation of a cross-sector support and governance organisation to safeguard and grow the UK’s leadership in this space.

2. Strategic Imperative

AI is no longer experimental—it is foundational. From finance and healthcare to utilities and logistics, AI-enabled systems are driving operational efficiency, predictive insight, and adaptive service delivery. However, the UK’s current approach is fragmented:

AI initiatives are often tactical, short-term, and disconnected from core business strategy.
Programme delivery is frequently outsourced, with limited internal ownership or capability development.
Technical leadership and advanced skills are in short supply, with high demand met by external bodies.
Governance frameworks are inconsistent across sectors, leading to duplication and risk.

To reverse this trend, the UK must treat AI Business Systems as a strategic national capability, embedded across sectors and supported by a unified governance and capability-building framework.

3. Capability Domains for National Development

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

Capability Area	Description
Business & Technology Strategy	Aligning AI adoption with long-term organisational and national goals
Programme Management	Leading complex transformation programmes with strategic oversight
Independent Programme Assurance	Providing external validation, risk mitigation, and futureproofing
Operability & Systems Integration	Ensuring systems work together reliably and adaptively
Enterprise Architecture	Designing scalable, interoperable platforms across business and technology layers
Data, Information, Knowledge & Intelligence	Building semantic, structured, and actionable data ecosystems
Advanced Technical Skills	Developing AI scientists, architects, analysts, and engineers
Business & Cultural Change	Embedding AI into organisational culture, ethics, and workforce development

These domains must be cultivated within UK organisations—not outsourced—through targeted investment, education, and strategic partnerships.

4. Risks of Continued Fragmentation

The UK’s current trajectory presents significant risks:

Loss of strategic control over core business systems and infrastructure
Reduced resilience in the face of technological disruption or geopolitical shifts
Missed economic opportunities in AI-driven innovation and productivity
Erosion of public trust due to inconsistent governance and ethical oversight
Capability drain as domestic talent is underdeveloped or exported

These risks are compounded by the lack of a coordinated national framework for AI Business Systems delivery.

5. Proposed Action: National Support & Governance Organisation

To address these challenges, we propose the formation of a virtual, cross-sector support and governance organisation, focused on:

Capability development across the domains listed above
Strategic coordination between government, industry, academia, and professional bodies
Independent assurance for major programmes and infrastructure initiatives
Ethical and inclusive AI adoption across sectors
Knowledge sharing and best practice dissemination

This organisation would be federated, drawing expertise from bodies such as The BCS, IET, RSE, and others, and would serve as a strategic convenor and capability builder.

6. Cross-Sector Governance Frameworks

Effective AI adoption requires shared governance across key sectors:

Sector	Governance Priorities
Utilities	Infrastructure resilience, predictive maintenance, ethical automation
Healthcare	Patient-centric AI, data privacy, clinical decision support
Public Sector	Transparent service delivery, citizen engagement, inclusive access
Finance & Industry	Risk modelling, compliance, AI-enhanced operations
Education & Research	Talent development, interdisciplinary innovation, ethical frameworks

Joint working practices, independent assurance, and interoperable standards will be essential to reduce risk and maximise impact.

7. Strategic Outcomes

Outcome	Benefit
National Capability	Sovereign control over AI systems and services
Economic Growth	Innovation-led productivity and competitiveness
Organisational Resilience	Smarter, more adaptive operations
Inclusive Opportunity	AI for individuals, communities, and future generations
Global Leadership	Recognition as a trusted, ethical leader in AI systems delivery

8. Call to Action

We invite industry leaders, technical experts, and institutional partners to:

Recognise AI Business Systems as a strategic capability
Invest in UK-based delivery, management, and oversight
Participate in the formation of a national support and governance organisation
Embed AI into the design and culture of organisations
Lead the cultural shift required for sustainable, ethical AI adoption

The BCS IT Leadership Forum

The BCS were holding their annual IT Leadership symposium, where they would decide on key areas of focus for the coming year. I was asked to present my proposals on no more than 4 slides.

Collecting Documents together and creating a Discussion Talk based on them

Building on AISDO “Use Cases” for Water and Electricity

Base on the BCS ITLF Symposium I’m looking at how we can build Process Diagrams for “Use Cases” covered in the AISDO conversation. I’m also considering the skill sets needed by Business Executives based on the talks on IT requirements for MBAs. At the end of the conversation I go back to the Swim Lanes to cover Resilience. What happens if the Knowledge Base has been corrupted or is unavailable. I idea is to emphasise that areas previously not been considered such as Knowledge Bases can have serious implications across an organisation and AI needs to be understood across all levels.

I’m not showing the complete conversation as I needed to rethink the Swim Lanes a few times to cover what I wanted.

Copilot Prompt

Based on the Water and Electricity Incident Response, Use Cases (“WaterAndElectricityUseCases”), can you create Enhanced Business Process Flow Diagrams.

Note: This prompt was amended and updated, a number of times with suggested approaches, to achieve the response below.

Copilot Response

Certainly, David. Here’s the updated version of both Enhanced Business Process Flow Diagrams—now reflecting a Business View swim lane order and incorporating “Build Knowledge Base” into Step 0: Preconditions. This addition reinforces the foundational role of institutional memory and AI training in resilient operations.

💧 Wastewater Incident Response – Business View (Updated with Step 0: Build Knowledge Base)

Swim Lanes (Top to Bottom):

Operations & Governance (incl. Systems & Business Resilience)
Knowledge Base
Problem Support Team
Field Response Team
IT Systems
AI Layer
AI Monitoring System

Step	Governance	Knowledge Base	Problem Support	Field Response	IT Systems	AI Layer	AI Monitoring
0. Preconditions	Establish governance protocols and resilience thresholds	Build and curate domain-specific Knowledge Base	—	—	Maintain sensor network and data pipelines	Train anomaly detection models	—
1. Detect Anomaly	—	—	Receive alert and initial analysis	Dispatch team with AI insights	—	—	Detect anomaly in effluent quality
2. Predict Impact	—	—	Review dashboard: measurements, hypotheses, priority zones	Arrive on site and assess conditions	Host predictive modelling engine	Simulate outcomes and score severity	Predict severity and simulate outcomes
3. Investigate	—	Suggest rare fault conditions and targeted tests	—	Query Knowledge Base for alternative causes	Enable mobile access and digital twin overlays	—	—
4. Diagnose & Resolve	—	Confirm diagnosis and resolution steps	—	Identify root cause (e.g. rare chemical interaction)	Support diagnostics and data integration	—	—
5. Log & Document	—	—	—	Resolve issue with temporary process adjustment	—	—	—
6. Update Logs	—	—	Update Problem Log with new insights	—	Log management and tagging tools	—	—
7. Post-Incident Review	—	Update Knowledge Base with new learnings	Conduct Post-Incident Review	—	Support review dashboards and audit trails	Summarise incident and cross-reference cases	—
8. Generate Follow-Ups	Create work requests incl. Resilience Management review	—	—	—	Trigger work request system	Generate follow-up recommendations	—
9. Automate & Govern	Automate actions (e.g. sensor recalibration); Update Risk Register with Business and Systems Risk entries (include resilience)	—	—	—	Execute automated workflows	Automate routine actions	—

⚡ Electricity Substation Incident Response – Business View (Updated with Step 0: Build Knowledge Base)

Swim Lanes (Top to Bottom):

Operations & Governance (incl. Systems & Business Resilience)
Knowledge Base
Problem Support Team
Field Response Team
IT Systems
AI Layer
AI Monitoring System

Step	Governance	Knowledge Base	Problem Support	Field Response	IT Systems	AI Layer	AI Monitoring
0. Preconditions	Establish governance protocols and resilience thresholds	Build and curate domain-specific Knowledge Base	—	—	Maintain sensor network and SCADA integration	Train fault prediction and grid stability models	—
1. Detect Anomaly	—	—	Receive alert and initial analysis	Dispatch team with AI dashboard and overlays	—	—	Detect voltage and thermal anomalies
2. Predict Impact	—	—	Review sensor data and AI hypotheses	Arrive on site and assess conditions	Host predictive modelling engine	Simulate cascading grid effects	Predict cascading grid effects and severity
3. Investigate	—	Suggest harmonic interference and targeted diagnostics	—	Query Knowledge Base for rare fault conditions	Enable mobile access and overlays	—	—
4. Diagnose & Resolve	—	Confirm diagnosis and resolution steps	—	Identify misconfigured capacitor bank	Support waveform analysis and diagnostics	—	—
5. Log & Document	—	—	—	Reconfigure capacitor bank and stabilise system	—	—	—
6. Update Logs	—	—	Update Fault Log with new insights	—	Log management and tagging tools	—	—
7. Post-Incident Review	—	Update Knowledge Base with new learnings	Conduct Post-Incident Review	—	Support review dashboards and audit trails	Summarise incident and cross-reference cases	—
8. Generate Follow-Ups	Create work requests incl. Resilience Management review	—	—	—	Trigger work request system	Generate follow-up recommendations	—
9. Automate & Govern	Automate actions (e.g. firmware updates); Update Risk Register with Business and Systems Risk entries(include resilience)	—	—	—	Execute automated workflows	Automate diagnostics and patching	—

This addition strengthens the narrative around preparedness, institutional learning, and AI maturity. I can now help you visualize these in PowerPoint or Visio, or build a cross-sector storyboard for stakeholder engagement. Let me know if you’d like a printable summary or editable layout next.

Copilot Prompt

We now have a Business Process Model covering two similar incidents. A question I am looking at is what IT related knowledge should be included on a MBA course. I am concerned that executives are often unclear as to what is required to redesign core business processes, the skills required and their involvement. These two process flows illustrate future business processes that include AI. They also illustrate the wide range of skills require to develop future business processes and strategies. Can you review the process diagrams and provide ownership, governance and skill sets required. It should highlight business involvement, in addition to IT and AI.

Copilot Response

Absolutely, David. The two incident response models—Wastewater and Electricity—offer a rich foundation for identifying the ownership, governance, and skill sets required to redesign and lead future business processes that integrate AI. Here’s a structured analysis tailored for MBA-level understanding, with emphasis on executive involvement and cross-functional capability.

🧭 Executive-Level Analysis: Ownership, Governance & Skill Sets for AI-Enabled Business Process Redesign

🧱 1. Ownership & Governance Structure

Domain	Primary Owners	Governance Responsibilities
Operations & Resilience	COO, Head of Operations, Risk & Resilience Leads	Define resilience thresholds, approve automated actions, oversee risk register updates
Knowledge Base & Learning Systems	Chief Knowledge Officer, Innovation Leads	Curate institutional memory, validate AI training data, ensure post-incident learning
Problem Support & Field Response	Service Delivery Managers, Incident Commanders	Coordinate response teams, validate diagnostics, ensure field data integrity
IT Infrastructure & Systems	CIO, Enterprise Architects	Maintain sensor networks, enable mobile access, ensure system interoperability
AI Strategy & Enablement	Chief Data Officer, AI Product Owners	Oversee model training, simulation logic, ethical AI use, and continuous improvement
AI Monitoring & Automation	Platform Engineers, AI Ops Leads	Manage real-time detection, alerting systems, and automated workflows

🧠 2. Skill Sets Required Across Domains

🔹 Business Leadership & Strategy

Process Redesign & Innovation Thinking
Risk & Resilience Management
Governance of AI-Driven Decisions
Cross-Sector Systems Thinking
Stakeholder Engagement & Change Leadership

🔹 IT & Systems Architecture

Sensor Network Design & Maintenance
Cloud Infrastructure & Edge Computing
Cybersecurity & SCADA Integration
Digital Twin & Mobile Access Enablement
Data Governance & Interoperability Standards

🔹 AI & Data Science

Model Training & Validation (Anomaly, Fault, Prediction)
Simulation & Scenario Modelling
AI Ethics & Explainability
Knowledge Graphs & Semantic Search
Automation & Intelligent Workflow Design

🔹 Field & Operational Intelligence

Incident Command & Diagnostics
Mobile Data Capture & Interpretation
Human-AI Collaboration in Field Settings
Post-Incident Review & Knowledge Capture

📘 3. MBA Curriculum Implications

To prepare executives for leading AI-enabled transformation, MBA programs should include:

🧩 Core Modules

Digital Business Process Redesign
→ Case-based learning using AI-enabled incident flows
AI Strategy & Governance for Executives
→ Ethical, regulatory, and operational implications
Resilience & Risk Intelligence in Smart Operations
→ Linking business continuity with AI automation
Knowledge Systems & Institutional Learning
→ Building and governing enterprise knowledge bases
Cross-Functional Leadership in AI Transformation
→ Role clarity, collaboration, and decision-making across domains

🛠️ Applied Learning

Simulations of AI-enabled incident response
Workshops on redesigning legacy processes with AI
Collaborative labs with IT, AI, and business teams
Board-level scenario planning and governance exercises

🔍 4. Business Involvement: Critical Touchpoints

Executives must be actively involved in:

Defining resilience thresholds and escalation protocols
Approving AI-generated work requests and risk entries
Validating post-incident reviews and learning loops
Championing cross-sector knowledge sharing
Ensuring ethical and transparent AI deployment

Copilot Prompt

Can you consider a further complication to be Wastewater process flow to illustrate the importance of IT Resilience. Let us assume that the Knowledge Base has been corrupted and there is no fall back available. Recovery of the knowledge base takes 2 weeks due to major problems with the Cloud Infrastructure

Copilot Response

That’s a powerful scenario, David—one that vividly illustrates the critical role of IT resilience in AI-enabled operations. Let’s walk through how this disruption would impact the Wastewater Incident Response process, what it reveals about systemic dependencies, and what governance and design lessons it offers for executive education and strategic planning.

⚠️ Scenario Extension: Knowledge Base Corruption & Cloud Infrastructure Failure

🧨 Complication Introduced:

During an active incident, the AI Knowledge Base becomes unavailable due to corruption.
No fallback or offline replica is available.
Cloud infrastructure issues delay recovery for 2 weeks, affecting multiple dependent systems.

🔄 Impact on Business Process Flow

Process Step	Impact of Knowledge Base Failure
Step 3: Investigate	Field team cannot access AI-recommended diagnostics or historical fault patterns. Decision-making reverts to manual expertise and guesswork.
Step 4: Diagnose & Resolve	Increased risk of misdiagnosis or delays. AI cannot validate rare interactions or suggest resolution paths.
Step 5–7: Logging, Review, Learning	No ability to log new insights, cross-reference similar cases, or update institutional memory.
Step 8–9: Follow-Up & Governance	AI-generated work requests are incomplete or missing. Risk Register updates lack historical context. Resilience Management cannot assess systemic learning gaps.

🧠 Lessons for IT Resilience & Executive Awareness

🔹 1. Knowledge Base = Strategic Asset

It’s not just a technical tool—it’s a core operational memory system.
Its failure disrupts diagnostics, learning, and governance.
Executives must treat it as critical infrastructure, with the same priority as SCADA or telemetry systems.

🔹 2. Cloud Dependency = Double-Edged Sword

Cloud enables scale, access, and AI integration—but introduces shared risk.
Without multi-region redundancy, offline access, and failover protocols, even short outages can paralyze response.

🔹 3. Business Continuity Planning Must Include AI Systems

Traditional BCPs often overlook AI-specific components like:
- Model registries
- Knowledge graphs
- Training data pipelines
- AI inference services
Resilience testing must simulate AI and cloud service degradation.

🧩 Required Capabilities & Ownership

Domain	Resilience Responsibilities
Operations & Governance	Define critical AI dependencies in BCPs; ensure fallback protocols and manual override procedures are in place.
Knowledge Management	Maintain offline or edge-accessible snapshots of key knowledge assets. Define recovery SLAs and data integrity checks.
IT Infrastructure	Implement multi-cloud or hybrid resilience strategies. Monitor cloud service health and automate failover.
AI & Data Teams	Design AI systems to degrade gracefully. Ensure explainability and fallback logic when knowledge is unavailable.
Field & Support Teams	Train for AI-degraded scenarios. Maintain procedural knowledge and decision trees independent of AI.

🎓 Implications for MBA & Executive Education

This scenario underscores the need for future leaders to:

Understand AI system dependencies and their role in operational continuity.
Lead cross-functional resilience planning that includes AI, cloud, and knowledge systems.
Ask the right questions:
- “What happens if our AI can’t think?”
- “Do we have a manual fallback for digital knowledge?”
- “Who owns the resilience of our institutional memory?”

Copilot Prompt

Can we take the scenario of the corruption of a Knowledge Base and update the Wastewater Process Diagram.

Copilot Response

Certainly, David. Here’s the updated AI-Supported Wastewater Incident Response Process Flow incorporating the loss or corruption of the Knowledge Base. This version highlights the operational, diagnostic, and governance consequences of that failure, and introduces compensating actions and resilience gaps.

💧 Updated Business Process Flow: Wastewater Incident Response with Knowledge Base Failure

🧭 Swim Lanes (Business View Order)

Operations & Governance (incl. Systems & Business Resilience)
Knowledge Base
Problem Support Team
Field Response Team
IT Systems
AI Layer
AI Monitoring System

⬇️ Process Steps with Failure Path

Step	Governance	Knowledge Base	Problem Support	Field Response	IT Systems	AI Layer	AI Monitoring
0. Preconditions	Establish governance protocols and resilience thresholds	Build and curate Knowledge Base; define fallback access	—	—	Maintain sensor network and data pipelines	Train anomaly detection models	—
1. Detect Anomaly	—	—	Receive alert and initial analysis	Dispatch team with AI insights	—	—	Detect anomaly in effluent quality
2. Predict Impact	—	—	Review dashboard: measurements, hypotheses, priority zones	Arrive on site and assess conditions	Host predictive modelling engine	Simulate outcomes and score severity	Predict severity and simulate outcomes
3. Investigate	Trigger Resilience Protocol: Knowledge Base unavailable	System Failure: No access to diagnostic insights	Revert to manual fault hypothesis generation	Rely on field experience and procedural knowledge	Attempt to access backup or cached data	—	—
4. Diagnose & Resolve	Escalate to Resilience Management	—	Collaborate with field team to test hypotheses	Identify root cause through manual diagnostics	Support ad hoc data collection	—	—
5. Log & Document	—	Unable to log new insights	Capture incident notes manually	Document workaround and resolution	—	—	—
6. Update Logs	—	Queue update for post-recovery sync	Update Problem Log offline	—	Log management tools in degraded mode	—	—
7. Post-Incident Review	Initiate review with resilience lens	Delayed: Knowledge Base recovery pending (2 weeks)	Conduct interim review using field notes	—	Support offline review tools	—	—
8. Generate Follow-Ups	Create work requests incl. Resilience Management review	—	—	—	Trigger work request system	Generate partial recommendations (limited by KB loss)	—
9. Automate & Govern	Update Risk Register with: • Business Risk: Loss of institutional memory • Systems Risk: Cloud dependency	—	—	—	Execute only non-KB-dependent workflows	Automate basic actions	—
10. Recovery & Reintegration	Oversee Knowledge Base restoration and audit	Restore KB and reconcile missed insights	Re-ingest field logs and review notes	Validate field learnings	Sync logs and diagnostics	Retrain AI models if needed	Resume full AI-KB integration

⚠️ Key Implications

Operational Risk: Diagnostic delays, increased reliance on human expertise, potential for misdiagnosis.
Governance Risk: Incomplete learning loop, delayed risk updates, and resilience blind spots.
Strategic Insight: The Knowledge Base is not just a support tool—it is a critical dependency for AI-enabled operations and must be treated as such in resilience planning.

Copilot Prompt

Can we take the scenario of the corruption of a Knowledge Base and list the implications for a hospital where knowledge bases are used across the totality of the organisation and list the areas impacted and the potential impact.

Copilot Response

Certainly, David. Here’s the adjusted scenario and analysis for a hospital-wide Knowledge Base (KB) failure, now reflecting:

Unknown cause and indefinite recovery timeline
Compromised recent backups
Heightened uncertainty and risk across clinical, operational, and governance domains

🏥 Scenario: Hospital-Wide Knowledge Base Failure

Complication:

The central KB is corrupted.
Cause unknown, and recovery timeline uncertain.
Recent backups are also compromised, raising concerns about data integrity and completeness.
AI systems, decision support tools, and documentation workflows are dependent on the KB.

🔍 Impacted Areas & Escalating Consequences

1. Clinical Decision Support

Systems Affected: AI diagnostics, treatment recommendations, drug alerts
Impact:
- Immediate loss of AI-assisted decision-making
- Increased risk of clinical error or delay
- Surge in manual workload and cognitive burden on clinicians
- Erosion of trust in digital systems

2. Electronic Health Records (EHR) Integration

Systems Affected: Smart documentation, historical case referencing
Impact:
- Incomplete patient histories and disrupted continuity of care
- Difficulty in validating prior decisions or treatment pathways
- Potential legal exposure if documentation gaps emerge

3. Pharmacy & Medication Safety

Systems Affected: Drug interaction databases, dosage calculators
Impact:
- Manual cross-checking required for prescriptions
- Increased risk of adverse drug events
- Delays in medication reconciliation and fulfilment

4. Radiology & Imaging

Systems Affected: AI-supported interpretation, historical pattern matching
Impact:
- Reduced diagnostic precision
- Longer turnaround times
- Increased reliance on senior radiologists

5. Pathology & Lab Diagnostics

Systems Affected: AI anomaly detection, test interpretation
Impact:
- Slower identification of critical results
- Risk of missing rare or complex conditions
- Manual revalidation of lab thresholds

6. Emergency & Critical Care

Systems Affected: Triage support, escalation protocols
Impact:
- Delays in risk scoring and prioritisation
- Increased exposure to preventable deterioration
- Reduced ability to coordinate across departments

7. Operations & Scheduling

Systems Affected: Theatre scheduling, bed management
Impact:
- Inefficient resource allocation
- Increased cancellations and bottlenecks
- Difficulty in forecasting demand or capacity

8. Training & Continuous Learning

Systems Affected: Clinical education platforms, onboarding modules
Impact:
- Halted access to learning resources
- Delayed onboarding and skill development
- Loss of institutional learning during crisis

9. Governance, Risk & Compliance

Systems Affected: Incident logging, audit trails, policy updates
Impact:
- Incomplete documentation of clinical decisions
- Delayed incident reviews and regulatory reporting
- Increased exposure to litigation and reputational risk

10. AI Model Performance & Trust

Systems Affected: Inference engines, feedback loops
Impact:
- Degraded AI performance due to missing context
- Inability to retrain or validate models
- Long-term erosion of trust in AI systems

🧠 Strategic Implications for Hospital Leadership

🔹 1. Knowledge Base = Critical Infrastructure

Must be governed with the same rigor as EHR, imaging, and life-support systems
Requires multi-layered resilience, not just backups

🔹 2. Resilience Must Include Unknowns

Plans must account for indeterminate outages and data integrity failures
Offline access, edge replicas, and human fallback protocols are essential

🔹 3. Governance Must Be Cross-Functional

IT, clinical, governance, and AI teams must co-own:
- Recovery protocols
- Manual override procedures
- Communication strategies during uncertainty

🔹 4. Executive Questions to Ask

“What decisions rely on this knowledge base?”
“Do we have a fallback for clinical reasoning?”
“How do we maintain trust and safety when AI is blind?”
“Who leads the response when the cause is unknown?”

🧩 Recommended Actions

Domain	Resilience Actions
Clinical Leadership	Activate manual protocols; reinforce peer review and escalation pathways
IT & Infrastructure	Launch forensic investigation; isolate affected systems; assess backup integrity
Governance & Risk	Update risk registers; initiate incident review; communicate with regulators
AI & Knowledge Teams	Suspend AI inference; document degraded performance; prepare for post-recovery reconciliation
Operations & Communications	Coordinate cross-departmental fallback plans; maintain transparency with staff and patients

IT Governance Proposal

Copilot Prompt:

let us consider the totality of IS/IT delivery and develop a case for a new functional area: If we cover the three main functional domains: business systems and application; IT Operations; Operational Technology; Service Delivery of Outsourced functions. The key processes are: Strategy development for each of the areas; major programmes; infrastructure refresh; strategic enhancements; updates, amendments and enhancements. Given the number of major programme failures, critical system outages, and lack of agility and future proofing, we need to consider a new functional domain responsible safeguarding a business from serious problems and unexpected costs, and ensuring systems are resilient, future proof and can take advantage of new operativities such as AI. This function will be termed IT Systems and Services Governance. The main board will have accountability for the function which will be the responsibility of a new head of IT working alongside the CIO or in smaller organisations working directly under the CIO. Can you define the role and benefits of this new function.

Copilot Response:

Absolutely, David. Here’s a structured definition and strategic case for the proposed IT Systems and Services Governance function—positioned as a critical enabler of resilience, agility, and future-readiness across the full spectrum of IS/IT delivery.

🧭 IT Systems and Services Governance

A new functional domain to safeguard business continuity, resilience, and strategic alignment across all IS/IT domains.

🧱 1. Purpose and Scope

Purpose:
To provide independent oversight, assurance, and strategic coherence across all IS/IT delivery domains—ensuring that systems are resilient, future-proof, and aligned with business value and risk appetite.

Scope of Oversight:

Business Systems & Applications (ERP, CRM, clinical, financial, etc.)
IT Operations (infrastructure, cloud, networks, end-user services)
Operational Technology (OT) (SCADA, IoT, industrial control systems)
Outsourced Service Delivery (MSPs, SaaS, BPO, cloud platforms)

Key Processes Covered:

Strategy development and alignment for each domain
Major programme governance and assurance
Infrastructure refresh and lifecycle planning
Strategic enhancements and innovation enablement
Updates, amendments, and continuous improvement
AI and automation readiness
Resilience, continuity, and risk governance

👤 2. Organisational Placement

Accountability: Main Board (via Audit, Risk, or Transformation Committee)
Responsibility: Head of IT Systems and Services Governance
- Works alongside the CIO (or reports to the CIO in smaller organisations)
- Independent from delivery teams to ensure objectivity
- Collaborates with Enterprise Architecture, Risk, Audit, and Programme Management functions

🧠 3. Core Responsibilities

Domain	Governance Responsibilities
Strategy & Architecture	Validate alignment of IS/IT strategies with business goals and risk appetite
Programme Assurance	Provide independent oversight of major programmes and transformation initiatives
Resilience & Continuity	Ensure systems are designed and operated for resilience, recoverability, and continuity
Change & Lifecycle Management	Oversee governance of updates, enhancements, and infrastructure refresh cycles
Outsourced Services	Monitor service performance, contractual compliance, and risk exposure from third parties
AI & Emerging Tech	Assess readiness, ethical use, and integration of AI and automation into core systems
Risk & Cost Avoidance	Identify systemic risks, duplication, and hidden costs across IS/IT portfolios
Knowledge & Capability	Ensure institutional knowledge is retained and that governance roles are clearly defined (e.g. RACI clarity)

🌍 4. Strategic Benefits

Benefit Area	Impact
Resilience & Risk Reduction	Reduces likelihood and impact of major outages, data loss, or programme failure
Cost Avoidance	Prevents duplication, rework, and hidden costs from unmanaged change or vendor lock-in
Future-Proofing	Ensures systems and architectures can adapt to AI, automation, and evolving business models
Board Confidence	Provides the board with clear visibility and assurance over IS/IT investments and risks
Cross-Domain Coherence	Bridges silos between business systems, IT, OT, and external providers
Governance Maturity	Elevates IS/IT governance to the same level of rigour as financial and operational governance

🧩 5. Why Now?

Rising complexity across hybrid IT, cloud, and OT environments
Increased regulatory scrutiny on digital resilience and third-party risk
High-profile failures of major programmes and critical systems
AI disruption demanding new governance models and ethical oversight
Board-level accountability for digital risk, resilience, and transformation outcomes

Would you like a visual framework or board briefing slide that positions this function alongside CIO, CISO, and Programme roles? I can also help map this to RACI models or maturity assessments for adoption planning.

Copilot Prompt:

which major companies use this approach

Copilot Response:

While few companies publicly label it “IT Systems and Services Governance,” many leading organisations have adopted similar cross-domain governance functions to ensure resilience, strategic alignment, and oversight across IT, OT, and outsourced services.

Here are some notable examples and patterns:

🏢 Major Companies with Similar Governance Functions

1. BP (British Petroleum)

Approach: BP has implemented a federated IT governance model that spans business systems, OT, and cloud platforms.
Notable Feature: Their Digital Security and Risk Committee oversees resilience and third-party risk across IT and OT domains.

2. Siemens

Approach: Siemens integrates IT, OT, and AI governance under its Digital Industries division.
Notable Feature: Uses a central governance layer to align AI, cybersecurity, and infrastructure strategy across business units.

3. HSBC

Approach: HSBC has a Technology Governance and Control function reporting to the board’s Risk Committee.
Notable Feature: Oversees resilience, change assurance, and third-party technology risk across global operations.

4. Unilever

Approach: Unilever’s Digital Transformation Office works alongside the CIO and CDO to govern IT, AI, and business systems.
Notable Feature: Focuses on future-proofing platforms and ensuring ethical AI deployment.

5. Pfizer

Approach: Pfizer’s Enterprise Technology & Performance function governs IT, digital operations, and AI enablement.
Notable Feature: Includes a Digital Governance Council to oversee resilience, compliance, and innovation readiness.

6. Nestlé

Approach: Nestlé operates a Global IT Governance Board that spans infrastructure, applications, and outsourced services.
Notable Feature: Uses a multi-tiered governance model to ensure alignment between business units and global IT strategy.

🧠 Common Elements Across These Models

Board-level accountability for digital resilience and third-party risk
Independent governance functions that sit alongside or report to the CIO
Cross-domain oversight across IT, OT, AI, and cloud services
Focus on assurance, risk, and future-readiness, not just delivery
Integration with enterprise risk, audit, and transformation committees

Would you like a visual comparison matrix showing how these companies align with your proposed function, or a draft charter for introducing this role in a UK infrastructure context?

Copilot Response:

Can you create a visual comparison matrix

NOTE – This was an iterative process as I added further columns as I considered the wider implications of my approach. The corporations’ names have been sanitised.

Copilot Response:

Here’s the fully updated Visual Comparison Matrix, now including a new column titled Top Tier Business Impact. This column reflects each organisation’s ability to govern and adapt resilience, continuity, and systems oversight in response to massive business change (e.g. mergers, acquisitions, divestments) and business continuity events (e.g. pandemics, cyberattacks, climate disruptions).

🧩 Updated Visual Comparison Matrix: IT Systems and Services Governance Across Major Organisations

Organisation	Governance Function	Scope of Oversight	Board-Level Accountability	AI & Resilience Focus	Outsourcing Oversight	Resilience & Infrastructure Lifecycle	Top Tier Business Impact
Your Proposal	IT Systems and Services Governance	Business Systems, IT Ops, OT, Outsourced Services, AI	✅ Yes – via main board or CIO	✅ High – resilience, future-proofing, AI readiness	✅ Yes – strategic oversight and risk governance	✅ Full lifecycle: strategy, delivery, maintenance, refresh, resilience governance	✅ Explicitly includes governance for M&A, divestments, continuity exercises, and post-incident reviews
Bxx	Digital Security & Risk Committee	IT, OT, Cloud, Cybersecurity	✅ Yes – via Risk Committee	✅ Strong – cyber resilience and continuity	✅ Yes – third-party risk governance	⚠️ Partial – focused on cyber and OT resilience	⚠️ Partial – strong on cyber risk, less visible in M&A or continuity integration
Sxx	Digital Industries Governance	IT, OT, AI, Industrial Systems	✅ Yes – via Digital Transformation Board	✅ High – AI integration and OT resilience	✅ Yes – integrated vendor oversight	⚠️ Partial – strong OT lifecycle, less visible in IT refresh	⚠️ Partial – strong in industrial continuity, less emphasis on strategic business change
Hxx	Technology Governance & Control	IT, Cloud, Programmes, Risk	✅ Yes – via Board Risk Committee	⚠️ Moderate – operational risk focus	✅ Yes – strong third-party controls	⚠️ Partial – programme assurance strong, lifecycle refresh less visible	⚠️ Partial – continuity planning strong, but M&A integration governance less transparent
Uxx	Digital Transformation Office	Business Systems, AI, Innovation	✅ Yes – via Transformation Steering Group	✅ High – future-proofing and AI ethics	✅ Yes – aligned with global IT strategy	⚠️ Partial – innovation-focused, lifecycle governance varies by region	⚠️ Partial – responsive to continuity events, less structured for M&A resilience
Pxx	Enterprise Tech & Performance	IT, Digital Ops, AI	✅ Yes – via Executive Committee	✅ High – AI enablement and continuity	✅ Yes – vendor assurance and compliance	⚠️ Partial – strong in digital ops, less emphasis on infrastructure refresh	⚠️ Partial – strong continuity response (e.g. COVID), less visible M&A governance
Nxx	Global IT Governance Board	IT, Infrastructure, Outsourcing	✅ Yes – via CIO and global governance	⚠️ Moderate – lifecycle and continuity focus	✅ Yes – multi-tiered vendor governance	✅ Strong – infrastructure refresh and lifecycle planning embedded	⚠️ Partial – continuity and refresh strong, M&A governance less explicit

This Resilience Life Cycle is only intended as illustrative and to provoke a wider consideration of Resilience. There were a few iterations as I considered the wider implications of Top Tier Business Strategy, not always made available to IT.

Copilot Updated Response:

Certainly, David. Here’s the updated Resilience Lifecycle Process Flow Diagram, now including a new step that reflects Business Continuity learnings from exercises or real-world disruptions—such as COVID-19—requiring changes to business operations, infrastructure, and governance.

🔄 Updated Resilience Lifecycle Process Flow

Swim Lanes:

IS Strategy
IT Strategy
Programme Delivery
Apps Support
IT Operations
External Service Providers (implicit in flow)
IS/IT Governance

🧭 Process Steps by Swim Lane

Step	IS Strategy	IT Strategy	Programme	Apps Support	IT Ops	IS/IT Governance
1. Resilience designed to meet IS and IT strategy	✅ Define strategic resilience goals	✅ Align infrastructure and systems to strategy	—	—	—	Review against Business, IS and IT Strategies
2. Resilience updated to meet programme requirements	—	—	✅ Integrate resilience into programme design and delivery	—	—	Undertake Independent Programme Assurance
3. Resilience updated to meet wider implications from Apps & IT Ops	—	—	✅ Capture downstream impacts	✅ Identify app-level dependencies	✅ Flag operational constraints	Review scope and updates
4. Resilience updated based on External Service Provider requirements	—	✅ Assess service provider capabilities	✅ Include in programme risk profile	—	✅ Validate SLAs and continuity plans	✅ Review third-party risk exposure
5. Resilience updated based on programme review recommendations	—	—	✅ Conduct post-implementation review	—	—	✅ Recommend resilience improvements
6. Resilience updated due to regular enhancements from Apps & IT Ops	—	—	—	✅ Trigger updates from app changes	✅ Trigger updates from infra changes	✅ Monitor and validate updates
7. Resilience updated based on ongoing governance recommendations	✅ Feed strategic insights	✅ Align with evolving tech landscape	—	—	—	✅ Continuous oversight and improvement
8. Resilience updated based on periodic governance reviews	✅ Participate in strategic review	✅ Provide infrastructure roadmap	—	—	—	✅ Conduct formal resilience audits
9. Resilience updated via annual Technology Refresh Strategy	✅ Align with long-term IS goals	✅ Lead refresh planning	—	—	✅ Execute refresh cycles	✅ Validate refresh impact on resilience
10. Resilience updated via long-term Strategic Planning incl. AI	✅ Define future-state vision	✅ Assess AI readiness and impact	—	—	✅ Plan for AI-integrated operations	✅ Ensure governance of emerging risks
11. Resilience updated based on major incidents and post-incident reviews	✅ Review strategic implications	✅ Assess infrastructure gaps	✅ Capture programme-level lessons	✅ Identify app-level vulnerabilities	✅ Analyse operational failures	✅ Lead post-incident governance review
12. Resilience updated due to undisclosed or late-stage massive business change (e.g. sales, mergers, acquisitions)	✅ Reassess strategic risk and continuity posture	✅ Evaluate infrastructure exposure and integration risks	✅ Review programme dependencies and transition plans	✅ Assess app-level impact and continuity	✅ Validate operational readiness and fallback plans	✅ Lead urgent governance review and board-level escalation
13. Resilience updated to reflect Business Continuity learnings from exercises or disruptions (e.g. COVID)	✅ Reframe strategic assumptions and continuity models	✅ Adapt infrastructure and remote ops capabilities	✅ Reassess programme delivery models	✅ Adjust support models for remote and hybrid operations	✅ Reconfigure operational protocols and fallback systems	✅ Integrate continuity findings into governance and resilience planning

🧠 Strategic Insight

Business continuity exercises and real-world disruptions (like COVID) expose systemic vulnerabilities and operational blind spots.
This step ensures resilience is actively updated based on tested scenarios, not just theoretical planning.
It reinforces the role of governance in embedding continuity learning into strategic and operational design.

Would you like this version visualised in PowerPoint swim lanes or adapted into a Business Continuity and Resilience audit framework? I can also help you build a cross-sector scenario planning toolkit based on these lifecycle stages.

Using AI to describe the proposal

The above information was used by AI to generate this analysis of the proposal

Using AI to verify the Proposal against major cyber attacks in the UK

Based on publicly available information from the internet and AI, for illustrative purposes rather than as an analysis of the outages.

Copilot Response for analysis against known outages:

Perfect, David. Let’s build a new comparison matrix that applies the governance approaches we’ve been discussing to the three UK outage cases (NHS, Marks & Spencer, Land Rover). The aim is to show which governance elements—if properly implemented—could have potentially prevented or mitigated the impact of these cyber attacks, given the underlying issues.

🧩 Matrix: Governance Approaches vs. UK IT Outages

Case	Underlying Issue(s)	Governance Approach That Could Have Helped	Potential Preventive/Mitigating Effect
NHS	Supplier warnings on vulnerabilities were issued well in advance, but Security Reviews, Infrastructure Refresh, and Business Continuity did not safeguard services.	– Resilience & Infrastructure Lifecycle Governance (ensuring refresh cycles and patching are enforced) – IS/IT Governance Reviews (validating supplier warnings are acted upon) – Business Continuity Governance (testing continuity against cyber scenarios)	– Mandatory refresh cycles would have reduced exposure. – Governance reviews would have ensured supplier warnings were escalated. – Continuity exercises would have revealed gaps in safeguarding critical services.
Marks & Spencer	Outsourced Help Desk Services; attacker posed as infrastructure staff and obtained passwords/access rights.	– Outsourcing Oversight Governance (vendor risk and identity/access controls) – Programme Assurance (ensuring secure outsourcing models) – Top Tier Business Impact Governance (board-level accountability for outsourced service risks)	– Stronger vendor oversight would have enforced identity verification protocols. – Programme assurance would have required secure access management. – Board-level accountability would have highlighted outsourcing risks earlier.
Land Rover	Cyber attack damaged both IT and OT infrastructure, disrupting business operations and manufacturing plants.	– Cross-Domain Governance (IT + OT) (ensuring resilience across both domains) – Resilience Lifecycle Governance (covering programme delivery, maintenance, refresh) – Top Tier Business Impact Governance (preparedness for operational disruption and continuity)	– Integrated IT/OT governance would have reduced siloed vulnerabilities. – Lifecycle governance would have ensured resilience was embedded across infrastructure. – Business continuity governance would have prepared fallback operations for manufacturing.

🔍 Key Insights

NHS: The failure wasn’t just technical—it was governance. Supplier warnings weren’t escalated or acted upon, and refresh cycles weren’t enforced.
Marks & Spencer: Outsourcing without strong governance created identity and access vulnerabilities. Vendor oversight and board accountability could have prevented social engineering success.
Land Rover: The lack of integrated IT/OT governance left manufacturing exposed. A resilience lifecycle approach would have embedded safeguards across both domains.

🧠 Strategic Takeaway

Your proposed IT Systems and Services Governance function directly addresses these gaps:

Lifecycle resilience (NHS case)
Outsourcing oversight and board accountability (Marks & Spencer case)
Cross-domain IT/OT governance (Land Rover case)

This matrix demonstrates how the function could have prevented or mitigated all three outages by embedding governance at the right level.

Combining AI Proposal for Knowledge Base with IT Governance & Oversight

IT Systems and Service Governance – Executive Summary

Executive Summary

Proposal for IT Systems and Services Governance (Oversight)
with Advanced Operating Model: National Intelligent Knowledge Base

Purpose

The proposed IT Systems and Services Governance (Oversight) function safeguards business continuity, resilience, and strategic alignment across IS/IT domains. It provides independent assurance while recognising the critical oversight role of IS, IT, and OT practitioners—a role often eroded by outsourcing and SLA‑driven management.

The advanced delivery model introduces the National Intelligent Knowledge Base (NIKB) as a strategic national asset. Designed with the future in mind, the NIKB enables revolutionary change in the delivery of national services such as the NHS, Local Government, and Utilities. It facilitates knowledge sharing and cooperation across organisations, resulting in significant cost savings, improved efficiency, adoption of best practice, and increased national capability.

Drivers

Rising cyber breaches and ecosystem failures (e.g., AWS outages, M&S, Jaguar Land Rover).
Unsuccessful large‑scale IT programmes.
Disconnect between corporate governance and IT governance.
Outsourcing reducing national capability and practitioner visibility.
Unpreparedness for Industry 4 and AI disruption.
Need for cross‑sector transformation in national service delivery.

Scope of Oversight

Business Systems & Applications (ERP, CRM, Finance).
IT Operations (infrastructure, cloud, networks).
Data, Knowledge & AI (analytics, generative AI, Dat Lake mirrors).
Operational Technology (SCADA, IoT, ICS).
Outsourced Service Delivery (MSPs, SaaS/PaaS/IaaS).
Ensures practitioner oversight remains central, even in outsourced environments.

Lifecycle Oversight (Example)

Lifecycle oversight ensures that all critical aspects of business systems and infrastructure are continuously aligned with the overarching business strategy. This includes the development and review of systems and infrastructure strategies to support long-term planning and organisational objectives.

Business Strategy Alignment: Ongoing evaluation to confirm that IS/IT initiatives remain closely matched with the organisation’s strategic direction.
Systems and Infrastructure Strategy: Formulation and periodic review of strategies for systems and infrastructure to support current and future business needs.
Long-Term Planning: Proactive planning to anticipate emerging requirements and ensure the sustainability and scalability of technology assets.
Sales, Mergers, Acquisitions: Oversight of technology implications during major business transactions, ensuring seamless integration and risk mitigation.
Outsourcing, Insourcing: Supervision of transitions between outsourced and insourced service arrangements to maintain service quality and practitioner visibility.
Audit Committee Initiatives: Participation in audit-related activities to ensure compliance and address identified risks.
Business Continuity: Assurance that systems and infrastructure have robust continuity plans in place to support uninterrupted operations.
System and Infrastructure Programmes: Oversight of major programmes involving the deployment, refresh, or change of core systems and infrastructure.
Infrastructure Refresh: Coordination and management of regular updates, enhancements, and maintenance for critical infrastructure to ensure reliability and performance.
Significant Periodic System and Infrastructure Updates, Maintenance, Enhancements: Ensuring that all substantial updates and improvements are strategically planned and executed.
Service Management Initiatives: Oversight of initiatives aimed at improving service delivery, support, and management processes.
Major Incidents: Supervision and review of responses to significant incidents to ensure effective resolution and incorporation of lessons learned.

Organisational Placement

Accountability: Corporate Board (Audit, Risk, or Transformation Committee)
Responsibility: Head of IT Systems and Services Governance
Independent from delivery teams, collaborating with CIO, Enterprise Architecture, Risk, Audit, and Programme Management.
Ensures practitioner oversight is valued and integrated into governance, even when delivery is outsourced.

Responsibilities

Validate IS/IT alignment with business strategy
Provide programme assurance
Ensure resilience and continuity
Oversee lifecycle refresh and change management
Monitor outsourced services
Govern AI and emerging tech adoption
Identify systemic risks and hidden costs
Retain institutional knowledge and clarify governance roles
Safeguard practitioner oversight across IS, IT, and OT, countering erosion from outsourcing and SLA‑driven management.

Strategic Benefits

Resilience & Risk Reduction: Prevents outages and programme failures
Cost Avoidance: Reduces duplication and vendor lock‑in
Future‑Proofing: Prepares for AI, automation, and new business models
Board Confidence: Provides visibility and assurance
Cross‑Domain Coherence: Breaks down silos between IT, OT, and external providers
Governance Maturity: Elevates IS/IT governance to board‑level rigour
Practitioner Empowerment: Ensures IS, IT, and OT oversight remains central, even in outsourced environments.

Operating Model

The Oversight Function will operate like Independent Programme Assurance, combining:

Continuous Oversight – ongoing visibility across programmes, infrastructure, AI adoption, and outsourced services.
Periodic Reviews – structured governance reviews at key lifecycle stages.
Point Reviews – triggered by incidents, board changes, M&A, or emerging risks.
Governance Integration – collaborates with IS, IT, and OT practitioners to ensure their expertise is not displaced by external providers.

New Advanced Operating Model: National Intelligent Knowledge Base (NIKB)

The NIKB is a strategic national asset designed to work alongside the Oversight Function. It is built from day one with the strategic goal of enabling:

Revolutionary change in national service delivery (NHS, Local Government, Utilities). – Future Development
Knowledge sharing and cooperation across organisations, breaking down silos.
Significant cost savings and improved efficiency.
Best practice adoption across sectors.
Rapid development of governance and oversight skills at massively reduced cost.
National capability uplift, positioning the UK as a global leader in technology services governance. – Future Development

Strategic Benefits of NIKB

Accelerated Capability Development
Unprecedented National Cost Reductions (Enablement of Industry 4 and AI)
Global Leadership in technology services provision
Holistic Lifecycle View (strategy, delivery, resilience, risk)
Strategic Opportunity Enablement
Public Sector Transformation – NHS, Local Government, Utilities

Strategic Benefits (Oversight + NIKB)

Resilience & Risk Reduction
Cost Avoidance
Future‑Proofing for AI & automation
Board Confidence
Cross‑Domain Coherence
Governance Maturity
Practitioner Empowerment
National Intelligent Knowledge Base – accelerates skills, reduces costs, transforms public services, and positions the UK as a global leader

Why Now

Complexity of hybrid IT, cloud, and OT environments
Regulatory scrutiny on resilience and third‑party risk
High‑profile programme/system failures
AI disruption demanding new governance models
Board accountability for digital risk and transformation outcomes
Unique opportunity for the UK to lead globally through NIKB and strategic oversight integration—transforming national service delivery

Strategic Takeaway

IT Systems and Services Governance (Oversight), enhanced by the National Intelligent Knowledge Base, provides holistic assurance—bridging resilience, programme delivery, AI readiness, and outsourcing oversight. It ensures IT is not just a tactical enabler but a strategic partner, while protecting practitioner oversight and enabling a revolutionary shift in national service delivery across the NHS, Local Government, and Utilities.

Briefing Note

Briefing Note

Proposal for IT Systems and Services Governance (Oversight)
Including Advanced Operating Model: National Intelligent Knowledge Base

Purpose

This governance function is to be established to safeguard business continuity, resilience, and strategic alignment throughout IS/IT domains. It provides independent oversight, while acknowledging the essential contributions of IS, IT, and OT practitioners.

Additionally, this proposal introduces the National Intelligent Knowledge Base (NIKB), a transformative national capability conceived as a strategic asset rather than a checklist or conventional standard. Initially developed to support governance and oversight, NIKB is designed with a long-term goal of enabling a fundamental transformation in public and private sector service delivery within the UK, notably across sectors such as the NHS, local government, utilities, and beyond.

Drivers

Escalating cyber breaches and ecosystem failures (e.g., AWS, M&S, Jaguar Land Rover).
Unsuccessful large‑scale IT programmes.
Strategic disconnect between corporate and IT governance.
Outsourcing reducing national capability and internal practitioner visibility.
Unpreparedness for Industry 4 and AI disruption.
Need for cross-sector transformation in national service delivery.

Scope of Oversight

Business Systems & Applications (ERP, CRM, Finance).
IT Operations (infrastructure, cloud, networks).
Data, Knowledge & AI (analytics, generative AI, Data Lake mirrors).
Operational Technology (SCADA, IoT, ICS).
Outsourced Service Delivery (MSPs, SaaS/PaaS/IaaS).
Ensures internal practitioner oversight remains central, even in outsourced environments.

Operating Model

The Oversight Function will operate like Independent Programme Assurance, combining:

Continuous Oversight – ongoing visibility across programmes, infrastructure, AI adoption, and outsourced services.
Periodic Reviews – structured governance reviews at key lifecycle stages.
Point Reviews – triggered by incidents, board changes, M&A, or emerging risks.
Governance Integration – collaborates with IS, IT, and OT practitioners to ensure their expertise is not inadvertently displaced by external providers.

New Advanced Operating Model: National Intelligent Knowledge Base (NIKB)

The NIKB is a strategic national asset designed to work alongside the Oversight Function. It is built with the future in mind, enabling:

Initial Stage

Knowledge sharing and cooperation across sectors, breaking down silos and enabling joined-up governance.
Significant cost savings, improved efficiency, and accelerated adoption of best practices.
Rapid development of governance and oversight skills at massively reduced cost.
Strategic intelligence across lifecycle stages: Strategic Opportunities, Tactical Benefits, Delivery Approaches, and Risk.

Longer Term Strategy (influencing initial design)

A revolutionary change in the delivery of national services such as the NHS, Local Government, and Utilities.
National capability uplift, positioning the UK as a global leader in technology services governance.
National strategic Industry 4 and AI enablement, positioning the Uk as a leader in industry 4 and AI systems delivery

Strategic Benefits of NIKB

Accelerated Capability Development – reduces reliance on external consultants.
Massive Cost Reduction – lowers training and knowledge transfer costs.
Global Leadership – establishes UK leadership in scalable governance innovation.
Holistic Lifecycle View – integrates strategy, delivery, resilience, and risk.
Strategic Opportunity Enablement – empowers boards to act faster with evidence‑based assurance and adoption of future opportunities (I4 and AI).
Public Sector Transformation – enables revolutionary change in NHS, Local Government, and Utilities.

Strategic Benefits (Oversight + NIKB)

Resilience & Risk Reduction
Cost Avoidance
Future‑Proofing for AI & automation
Board Confidence
Cross‑Domain Coherence
Governance Maturity
Practitioner Empowerment
National Intelligent Knowledge Base – accelerates skills, reduces costs, transforms public services, and positions the UK as a global leader

Why Now

Complexity of hybrid IT, cloud, and OT environments
Regulatory scrutiny on resilience and third‑party risk
High‑profile programme/system failures
Industry 4 and AI opportunities and disruption demanding new governance models
Board accountability for digital risk and transformation outcomes
Unique opportunity for the UK to lead globally through NIKB and strategic oversight integration—transforming national service delivery

Strategic Takeaway

IT Systems and Services Governance (Oversight), enhanced by the National Intelligent Knowledge Base, provides holistic assurance—bridging resilience, programme delivery, AI readiness, and outsourcing oversight. It ensures IT is not just a tactical enabler but a strategic partner, while protecting practitioner oversight and enabling a revolutionary shift in national service delivery across sectors.

IT Governance – AI Conversations

I’ve created a simple Knowledge Base by attaching a number of the documents covered above plus further insights into Governance. I asked AI to create an audio covering the key points.

Town Regeneration, SMEs and VCSE

I volunteered to create a strategy for revitalizing Southport using advanced technology to assist the local council leader a few years ago. This was delayed due to changes in council governance. The idea came from a similar UK town known for its successful rejuvenation. The town was an exemplar of advanced technology led regeneration.

The Innovation Centre was established to encourage innovation and, when appropriate, support regeneration through strategic planning, knowledge sharing, and collaboration. The town has a history of leadership in advanced technology.

Innovation & AI

Communications Paper

Unlocking Continuous Innovation: The Unexpected Benefits of AI Assistants

Author: David Sutton MBCS CITP

October 2025

1. Introduction

Innovation is often portrayed as a sudden flash of insight—a breakthrough moment from a lone expert. But in today’s complex, fast-moving world, this view is increasingly outdated. What organisations truly need is continuous innovation: a sustained, collaborative capability to solve unexpected problems, adapt to change, and unlock new opportunities.

This paper explores how AI Assistants—specifically Copilot—can play a transformative role in enabling continuous innovation. Drawing on recent work across infrastructure, education, and community development, it highlights the unexpected benefits of AI in breaking down silos, linking ideas, and accelerating progress.

2. Rethinking Innovation: From Flash to Flow

Traditional innovation often occurs in isolated pockets—within departments, disciplines, or expert groups. But continuous innovation requires:

Cross-functional collaboration
Reusable methods and frameworks
Rapid iteration and feedback
Knowledge sharing across domains

It must be fostered as a skill, not left to chance. This mindset shaped the design of the Southport Innovation Centre, a concept developed to support inclusive, community-driven innovation.

3. AI as a Design Partner

Using Copilot, the Innovation Centre was designed across multiple dimensions:

Conceptual and technical design of showcases and learning spaces
Detailed planning for large public centres, community-run hubs, and personal “Innovation Centre in a Box” models
Programme management support including costings, strategies, executive summaries, and stakeholder briefings
Technology development: AI-assisted creation of code, music, advertising materials, and educational content
Knowledge infrastructure: A website repository to house documents, conversations, and evolving proposals

The breadth and depth of AI support far exceeded expectations—pointing to how AI will evolve as a co-creator across disciplines.

4. Linking Ideas Across Domains

One of the most unexpected and powerful benefits of using Copilot was its ability to remember conversations and link initiatives. This capability helped break down siloed thinking and foster cross-domain innovation.

Examples include:

Educational crossover: While exploring AI tools like Magic School, Copilot suggested lesson plans aligned with technology and cultural themes from other projects.
Method reuse: Innovation and capability techniques developed for infrastructure were recommended for use in education and community engagement.
Strategic synthesis: Proposals for AI-managed infrastructure led naturally into a broader national strategy for AI-enabled business systems—without needing to restart or reframe the work.

This ability to connect ideas across time and context is a hallmark of continuous innovation—and a glimpse into the future of AI-assisted creativity.

5. Implications for the Future

AI Assistants like Copilot are not just productivity tools—they are innovation catalysts. Their ability to:

Retain context
Suggest cross-domain applications
Accelerate design and delivery
Support strategic thinking and execution

…makes them uniquely suited to help individuals and organisations build innovation capability, not just execute tasks.

As AI continues to evolve, we can expect even deeper integration across disciplines, more intuitive collaboration, and greater support for ethical, inclusive innovation.

6. Conclusion

The journey from infrastructure design to national strategy was made possible by AI—not just through automation, but through connection, continuity, and creativity. The unexpected benefit of Copilot’s memory and contextual awareness was its ability to link ideas, break down silos, and foster innovation as a living system.

This is the future of innovation: not isolated brilliance, but collaborative intelligence—supported by AI, shaped by people, and open to all.

Briefing Paper

AI Assistants and the Future of Continuous Innovation in the UK
Author: David Sutton
Date: October 2025

1. Executive Summary

Artificial Intelligence is rapidly transforming how individuals, organisations, and nations approach innovation. While much attention has been given to AI’s technical capabilities—automation, prediction, optimisation—its role in enabling continuous, cross-domain innovation is less understood but increasingly vital.

This paper explores the unexpected benefits of using AI Assistants, particularly Copilot, in fostering innovation across infrastructure, education, and community development. It draws on recent work designing Innovation Centres, developing national infrastructure proposals, and exploring AI-enabled business systems. The findings point to a strategic opportunity: to harness AI not just as a tool, but as a collaborative partner in building the UK’s innovation capability.

2. Rethinking Innovation: From Flash to Flow

Traditional models of innovation often rely on isolated breakthroughs—moments of insight from domain experts. While valuable, this approach is insufficient for the challenges facing modern organisations and public services. What’s needed is continuous innovation: a sustained, adaptive capability embedded across teams, disciplines, and communities.

Key characteristics of continuous innovation include:

Cross-functional collaboration
Reusable frameworks and methods
Rapid iteration and feedback loops
Knowledge sharing across domains
Strategic alignment with long-term goals

This shift requires not only cultural change but also technological support—and AI Assistants are emerging as a key enabler.

3. Case Study: Southport Innovation Centre

The Southport Innovation Centre was conceived as a model for inclusive, community-driven innovation. Using Copilot, the design process covered:

Conceptual and technical design of showcases and learning spaces
Detailed planning for large public centres, community-run hubs, and personal “Innovation Centre in a Box” models
Programme management support including costings, strategies, executive summaries, and stakeholder briefings
Technology development: AI-assisted creation of code, music, advertising materials, and educational content
Knowledge infrastructure: A website repository to house documents, conversations, and evolving proposals

The breadth and depth of AI support far exceeded expectations—demonstrating how AI can accelerate design, delivery, and strategic thinking.

4. Infrastructure and National Strategy

Building on the Innovation Centre work, further studies explored AI-managed infrastructure across Water and Electricity sectors. These led to:

Detailed proposals for infrastructure monitoring, predictive maintenance, and ethical automation
Strategic frameworks for cross-sector governance and capability development
A national proposition for the UK to lead in AI-enabled business systems delivery

These initiatives were made possible by Copilot’s ability to retain context, generate structured outputs, and support multi-layered planning—functions that would have been prohibitively time-consuming without AI assistance.

5. Unexpected Benefits: Breaking Down Silos

One of the most powerful and unexpected benefits of using Copilot was its ability to link ideas across domains and break down siloed thinking. Examples include:

Educational crossover: While exploring AI tools like Magic School, Copilot suggested lesson plans aligned with technology and cultural themes from other projects.
Method reuse: Innovation and capability techniques developed for infrastructure were recommended for use in education and community engagement.
Strategic synthesis: Proposals for infrastructure led naturally into broader national strategies for AI-enabled business systems—without needing to restart or reframe the work.

This ability to connect conversations, reuse insights, and suggest cross-domain applications is a hallmark of continuous innovation—and a glimpse into the future of AI-assisted creativity.

6. Implications for UK Capability

The UK has a strategic opportunity to lead in AI-enabled innovation—but only if it invests in:

Internal capability development across business, technology, and programme disciplines
Cross-sector governance frameworks to ensure ethical, inclusive adoption
Support structures to help individuals and organisations use AI effectively
Cultural change to embed innovation as a shared, continuous practice

AI Assistants like Copilot can play a central role in this transformation—not just as tools, but as collaborative partners in strategy, design, and delivery.

7. Recommendations

To realise the full potential of AI in fostering continuous innovation, the UK should:

Recognise AI Assistants as strategic enablers, not just productivity tools
Invest in training and support for individuals, SMEs, and public sector teams
Develop shared repositories and knowledge frameworks to capture and reuse insights
Establish cross-sector innovation hubs, supported by AI and aligned with national goals
Promote ethical and inclusive AI adoption, with independent assurance and public engagement

8. Conclusion

This is the future of innovation: not isolated brilliance, but collaborative intelligence—supported by AI, shaped by people, and open to all.

Southport AI and IKB Exemplar

Southport Exemplar Vision: SMEs, VCSEs, AI, and Innovation

1. Purpose

To demonstrate how AI, an Intelligent Knowledge Base (IKB), and collaborative governance can rejuvenate SMEs and VCSEs, positioning Southport as a national exemplar town for inclusive, AI-enabled regeneration.

2. Challenges Facing SMEs and VCSEs

Limited resources compared to large corporations.
Fragmented access to knowledge, training, and digital tools.
Vulnerability to retail decline, tourism shifts, and economic disruption.
Difficulty retaining skills and institutional knowledge.

3. How AI + IKB Can Help

Shared Intelligence & Best Practice: SMEs and VCSEs gain access to pooled knowledge, templates, and case studies.
AI-Driven Local Insights: Visitor flows, spending patterns, and seasonal trends analysed to guide business decisions.
Collaborative Resilience: Shops, cafés, charities, and creative groups co-develop campaigns and avoid duplication.
Digital Transformation at Scale: Shared AI services (analytics, marketing automation, volunteer coordination) reduce costs and barriers.

4. Southport Opportunities

Reimagining the High Street: Curated “Southport Experiences” combining independent shops, food, and cultural events.
Tourism Revitalisation: AI forecasts visitor flows and personalizes offers (family packages, eco-tourism, heritage trails).
Community-Led Innovation: VCSEs collaborate on digital heritage archives, festivals, and skills hubs.
AI-Powered Local Marketing: Coordinated campaigns highlight Southport’s uniqueness — Victorian architecture, seaside heritage, cultural events.

5. Role of the Southport Innovation Centre

Knowledge Repository: Documents governance, AI, and innovation conversations for shared learning.
Practical Laboratory: Provides a safe space to test AI tools and collaborative practices before scaling.
Community Anchor: Focal point for collaboration among businesses, charities, educators, and citizens.
Exemplar Enabler: Ensures Southport’s exemplar status is credible by demonstrating real-world application.

6. AI Training Integration

Current AI training for local businesses should be captured and embedded into the IKB, ensuring:
- Shared learning across SMEs, VCSEs, and community groups.
- Knowledge retention and continuity.
- Scalable impact beyond Southport.
- Strengthened exemplar status through documented, transferable resources.

7. Benefits of Exemplar Status

Economic Renewal: Attracts investment, grants, and partnerships.
Reputation & Visibility: Gains recognition as a living lab for AI-enabled regeneration.
Talent Attraction: Draws young professionals, creatives, and technologists.
Community Empowerment: VCSEs deliver more impactful programmes with shared tools.
Scalable Lessons: Southport becomes a source of national learning for coastal and regional regeneration.

Strategic Takeaway

By embedding AI, an Intelligent Knowledge Base, collaborative governance, and the Southport Innovation Centre, Southport can:

Rebuild its identity around shared experiences and innovation.
Empower SMEs and VCSEs to act like a networked corporation with pooled intelligence.
Position itself as an exemplar town, attracting investment, talent, and recognition.
Provide scalable lessons for national transformation, proving that governance and AI can rejuvenate local economies and communities.

The following discussion document was created directly from the Knowledge Base using standard Microsoft Office 365. It is unedited, so should be treated as an example of auto generated audio from a simple and limited knowledge base.

The question for Southport is, “Can a historic town lead the UK?” The answer is “Yes,” as it draws inspiration from a similar town. The main challenge is not technology or ability, but rather Culture and Belief.

There is an Ethical Charter for Southport Innovation Centre, which includes AI bias and Guardrails, which you can find here

Illustration of using a Knowledge Base

Based on the attached knowledge base what resilience considerations are there during sales and acquisitions

Great question, David. Based on the attached BCS Knowledge Base, resilience during sales and acquisitions (M&A) is treated as a critical governance checkpoint. Here are the key considerations drawn from the document:

🔑 Resilience Considerations in Sales & Acquisitions

1. Strategic Risk and Continuity Posture

Reassess the organisation’s strategic risk profile when ownership or structure changes.
Ensure continuity of critical services (utilities, health, public sector) is not disrupted by integration or divestment.

2. Infrastructure Exposure and Integration Risks

Evaluate how IT, OT, and AI systems will integrate across merged entities.
Identify vulnerabilities in infrastructure lifecycles (build, maintain, refresh) that may be exposed during transition.
Ensure interoperability of platforms and resilience of shared data pipelines.

3. Programme Dependencies and Transition Planning

Review ongoing programmes for dependencies that could be disrupted.
Plan for continuity of delivery models, especially where AI-enabled systems are embedded.
Include resilience checks in programme assurance during transition.

4. Application-Level Continuity

Assess how mergers affect application dependencies (ERP, CRM, clinical, financial systems).
Ensure app-level resilience and fallback plans are in place to avoid service interruptions.

5. Operational Readiness and Fallback Plans

Validate operational readiness for new ownership structures.
Ensure fallback systems and continuity protocols are tested and updated.
Include incident response scenarios for integration failures.

6. Governance and Board-Level Escalation

Lead urgent governance reviews to capture risks and resilience gaps.
Escalate findings to the board to ensure accountability for resilience during M&A.
Update risk registers with business and systems risk entries specific to acquisition impacts.

7. Knowledge Base Integrity

Safeguard institutional knowledge during transitions.
Ensure the Knowledge Base (IKB/AISDO repositories) is updated with lessons learned and resilience adaptations.
Prevent corruption or loss of knowledge assets, which could undermine continuity.

📘 Strategic Takeaway

In the AISDO/BCS framework, sales and acquisitions are treated as resilience-critical events. They require:

Strategic reassessment of risks and continuity.
Integration planning across IT, OT, and AI systems.
Fallback and continuity protocols at programme, application, and operational levels.
Governance oversight with board-level accountability.
Knowledge retention to ensure resilience lessons are embedded and shared.

I can map these considerations into a Resilience Lifecycle Diagram for M&A, showing how governance, IT, applications, and AI layers interact during acquisitions. Would you like me to sketch that visual flow next?