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Problem solving Resources

Use AI to fix Failure Demand

Failure demand is, in essence, the additional (and unnecessary) workload created when an organisation fails to provide a product or service accurately or completely at the first point of contact. In large citizen-facing organisations—government agencies, healthcare systems, or large federated enterprises—failure demand often arises from structural and procedural issues that, if left unmanaged, create spirals of repeated contacts, rework, complaints, and escalations.

Below are common causes of failure demand in large federated organisations, along with ways in which AI can help alleviate or prevent these issues.

1. Fragmented Information and Siloed Systems

Cause:

• Multiple disconnected databases or information systems mean that staff can’t easily access the correct, up-to-date information about a citizen or case.

• Different departments or agencies have their own processes, making it difficult to get a single, integrated view.

How AI Helps:

1. Data Integration & Master Data Management

• AI-driven data integration or entity resolution can match and merge records across siloed systems, providing a single source of truth.

2. Knowledge Graphs

• These can unify information from various internal and external systems, surfacing the relevant data to the front line or self-service portals in real time.

2. Repeated or Escalated Inquiries

Cause:

• Citizens have to call multiple times or contact different departments because they never receive the correct answer or a complete resolution on the first attempt.

• Instructions or next steps are unclear, requiring additional clarifications.

How AI Helps:

1. Natural Language Processing (NLP) for Triage

• AI-based chatbots and virtual assistants can quickly assess the request and route it to the correct team, reducing misrouted calls.

2. Automated Knowledge Bases

• AI can suggest the next best action or provide consistent answers to common questions, reducing inaccurate or incomplete information.

3. Lack of Process Visibility (for Both Staff and Citizens)

Cause:

• Citizens have little visibility into the status of their application, request, or case.

• Staff themselves may struggle to track cases as they move through different departments, leading to delays and confusion.

How AI Helps:

1. Predictive Tracking and Alerts

• AI can monitor case progress and send automatic notifications to both citizens and staff about status changes, required documents, or impending deadlines.

2. Process Mining and Workflow Optimisation

• AI-driven process mining tools analyse workflow logs to identify bottlenecks or high-friction steps, prompting proactive solutions.

4. Overly Complex or Confusing Service Design

Cause:

• Citizens are forced to navigate confusing online portals, physical forms, and long instructions, which leads to errors or incomplete submissions.

• Lack of standardisation across departments can create additional steps and inconsistencies.

How AI Helps:

1. Personalised Digital Assistants

• Virtual agents that guide citizens step-by-step, ensuring forms and data are filled correctly and explaining next steps in simple language.

2. Adaptive User Interfaces

• AI can tailor the user experience based on the user’s profile, automatically simplifying the path or adjusting the language for clarity.

5. Inconsistent Communication or Messaging

Cause:

• Different channels (phone, email, web chat, social media) give conflicting information or instructions.

• Citizens receive either no response or delayed responses, leading to additional follow-ups.

How AI Helps:

1. Omni-channel Response Orchestration

• AI models can be trained on policy guidelines and knowledge bases to ensure consistent, channel-agnostic responses.

2. Sentiment Analysis and Real-time Alerts

• Monitoring digital communications can quickly highlight negative or confused user sentiments, prompting staff to intervene before citizens need to escalate.

6. Manual, Repetitive Tasks Leading to Errors

Cause:

• Staff spend time on repetitive data entry and manual verification processes, which are prone to human error.

• A single mistake can lead to multiple follow-up calls and corrective work.

How AI Helps:

1. Optical Character Recognition (OCR) and Automated Data Entry

• AI tools can accurately parse large volumes of forms, extracting data and populating systems automatically.

2. Robotic Process Automation (RPA)

• Combining RPA with AI (“Intelligent Automation”) can handle repetitive workflows, flags issues automatically, and hand off only exceptions to human staff.

7. Limited Staff Training or High Staff Turnover

Cause:

• In large federated organisations, staff turnover can be high, or training may be inconsistent.

• Knowledge retention is poor, meaning new or rotating staff do not always have the expertise to handle calls correctly.

How AI Helps:

1. Real-time Call Guidance

• AI-driven recommendations can guide agents during phone or chat interactions, suggesting answers based on historical successful interactions.

2. Machine Learning for Training Gaps

• Analysis of interactions can highlight patterns of agent errors or knowledge gaps, guiding targeted staff training efforts.

8. Reactive Instead of Proactive Approach

Cause:

• Processes are often designed to react to incoming inquiries rather than preventing confusion or mistakes in the first place.

• Citizens only discover requirements (e.g., missing documents, extra steps) after they have already submitted something incorrectly.

How AI Helps:

1. Predictive Analytics

• By analysing historical data, AI can forecast which cases might lead to repeated follow-ups or escalate, prompting proactive outreach.

2. Proactive Communication

• Automated notifications (e.g., reminders, deadline notices) reduce the likelihood of citizens missing requirements and calling back to ask for clarifications.

9. Inability to Identify Root Causes

Cause:

• Without an organised way to analyse large volumes of calls, emails, and visits, it is difficult to understand why so many follow-ups or escalations happen.

• Root-cause analysis often requires manual effort, which is time-consuming and prone to oversight.

How AI Helps:

1. Text and Speech Analytics

• AI can analyse phone transcripts, chat logs, and emails to uncover themes, common queries, or shared blockers driving repeat contacts.

2. Topic Clustering

• AI clustering techniques group citizen complaints or issues, helping leadership see broader trends and attack the underlying causes.

10. Poor Feedback Loops Between Front-Line and Policy/Process Owners

Cause:

• Front-line staff and citizens encounter the same problems repeatedly, but those issues are not effectively communicated upstream to the departments that design the processes.

• This results in short-term fixes (workarounds) rather than systemic changes (resolutions to root causes).

How AI Helps:

1. Closed-Loop Feedback Systems

• AI-driven dashboards can aggregate real-time data on contact types, resolutions, and user satisfaction, automatically flagging major process issues.

2. Continuous Improvement Recommendations

• Machine Learning (ML) algorithms can recommend policy or process changes based on patterns and outcomes, pushing insights directly to policy owners.

Key Takeaways

1. Integration and Data Sharing

• Breaking down organisational silos is essential to reducing failure demand. AI can help by unifying and analysing disparate data.

2. Personalisation and Proactivity

• AI can provide personalised guidance and proactively alert citizens (and staff) to potential issues, cutting down on repeated contacts.

3. Automation of Low-Level Tasks

• Robotic Process Automation (RPA) and intelligent document processing reduce human error and free staff for more complex, value-adding activities.

4. Insight Generation

• Text analytics, speech analytics, and clustering methods can reveal hidden causes of frequent failures and drive continuous improvement.

By applying AI methods to target these root causes—fragmented data, repeated inquiries, manual errors, and slow feedback loops—large citizen-facing and federated organisations can decrease failure demand, improve citizen experiences, and allow staff to focus on more valuable, mission-critical tasks.

Categories
Problem solving Feature Resources

Top 20 Problem Solving Methods and Tools

Here is a list of 20 problem solving methods, techniques, tools, and approaches commonly used in startups and small to medium-sized enterprises (SMEs) to help you tackle your own problems:

1. Lean Startup Methodology

  • Focuses on building a minimum viable product (MVP), iterating quickly based on customer feedback, and reducing wasted resources.

2. Agile Framework

  • An iterative and flexible approach to project management, especially suitable for startups to adapt and pivot quickly.

3. Design Thinking

  • Emphasizes empathy for the customer, rapid prototyping, and iterative testing, enabling innovative problem-solving.

4. Business Model Canvas

  • A visual tool to map out key components of a business model, helping startups and SMEs refine their business strategies.

5. SWOT Analysis

  • Identifies internal Strengths and Weaknesses and external Opportunities and Threats, helping to develop strategies.

6. Five Whys

  • Simple yet effective technique for drilling down to the root cause of a problem by asking “why” repeatedly.

7. OKRs (Objectives and Key Results)

  • Goal-setting framework that helps small organizations focus on what matters most and track progress.

8. Rapid Prototyping

  • Building quick prototypes to test and validate concepts, enabling faster feedback and iteration.

9. Customer Journey Mapping

  • Visual representation of the customer experience, highlighting pain points and opportunities for improvement.

10. Growth Hacking

  • Combines marketing, data analysis, and product development to find low-cost and creative ways to grow the business quickly.

11. Kanban

  • Visual workflow management tool to improve task tracking, productivity, and efficiency.

12. Bootstrapping

  • Focuses on building and scaling a business with minimal external funding, emphasizing efficiency and resourcefulness.

13. Pareto Analysis (80/20 Rule)

  • Helps prioritize actions by identifying the small percentage of causes that contribute to most problems or benefits.

14. Lean Canvas

  • A streamlined version of the Business Model Canvas, specifically tailored for startups to validate their ideas and assumptions quickly.

15. Mind Mapping

  • Visual brainstorming tool that helps organize thoughts, ideas, and solutions around a central concept.

16. A/B Testing

  • Experimentation method to test different versions of a product, service, or marketing approach to determine what works best.

17. Bootstrapped Marketing Techniques

  • Cost-effective strategies such as content marketing, social media, and SEO, essential for startups with limited budgets.

18. SCRUM

  • A subset of Agile methodology focused on small, self-organizing teams that deliver working increments of a product in short cycles.

19. Value Proposition Canvas

  • Complements the Business Model Canvas by focusing on aligning product offerings with customer needs and desires.

20. Risk Management Matrix

  • A tool to identify, assess, and prioritize risks, enabling SMEs to mitigate potential threats and seize opportunities.

These methods and tools enable startups and SMEs to address problems efficiently, adapt quickly to changes, and scale their operations with limited resources.

Categories
Resources Feature Problem solving

The National Programme for IT (NPfIT)

Why did NPfIT fail?

The National Programme for IT (NPfIT) in the UK’s National Health Service (NHS) was one of the most ambitious and costly IT initiatives ever undertaken in the healthcare sector. However, it faced numerous challenges and was ultimately considered a failure. Here are the primary reasons for its failure:

1.  Scale and Complexity: The NPfIT was an extraordinarily large and complex project. It aimed to revolutionise IT across the entire NHS in England, including the creation of electronic health records for every patient and a national broadband network for the NHS. The sheer size and complexity made it difficult to manage.
2.  Top-Down Approach: The program was largely driven from the top down, without adequate consultation and engagement with the end-users, mainly the clinicians and NHS staff. This led to a mismatch between the system’s design and the users’ actual needs.
3.  Lack of Flexibility: The NPfIT was criticised for its one-size-fits-all approach. Healthcare settings are diverse, and a rigid system failed to accommodate the varying needs of different hospitals and clinics.
4.  Technical Challenges: There were significant technical hurdles, including issues with software design and interoperability between different systems. Integrating new systems with existing legacy systems was also a major challenge.
5.  Cost Overruns and Delays: The project faced significant cost overruns and delays. Originally estimated to cost around £6.2 billion, the costs reportedly ballooned to over £12 billion. Delays in delivery and deployment further eroded confidence in the project.
6.  Vendor Issues: The project relied heavily on a few large IT vendors, some of which were unable to deliver as promised. This reliance on external contractors also led to issues with accountability and quality control.
7.  Change in Political and NHS Leadership: The NPfIT also suffered from changes in political and NHS leadership, which affected the continuity, focus, and direction of the program.
8.  Privacy Concerns: There were significant concerns regarding patient privacy and the security of electronic health records, which led to resistance from both healthcare professionals and patients.

In summary, the failure of the NPfIT can be attributed to its over-ambitious scope, lack of user engagement, inflexibility, technical challenges, cost overruns, vendor issues, leadership changes, and privacy concerns. These factors combined to make the program untenable, leading to its eventual dismantling.

Avoid the NPfIT and US Medical Records project failures
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Strategies to avoid NPfIT-type failures

To avoid the pitfalls experienced by the NPfIT and ensure the success of a future large-scale IT program, especially in a complex and sensitive sector like healthcare, several key strategies should be implemented:

1.  Stakeholder Engagement: Actively involve end-users, such as healthcare professionals, in the planning and implementation stages. Understanding their needs and workflows is crucial for designing a system that is user-friendly and adds real value to their work.
2.  Incremental Approach: Rather than a big bang approach, adopt an incremental and agile methodology. This allows for regular feedback and adjustments, reducing the risk of large-scale failures and enabling more manageable project scopes.
3.  Flexibility and Customisation: Recognise the diversity within the healthcare system and allow for a degree of customisation in different settings. A flexible system that can adapt to various environments is more likely to be successfully integrated.
4.  Robust Project Management: Implement strong project management practices, including clear governance structures, regular progress reviews, risk management, and contingency planning.
5.  Transparent and Realistic Budgeting: Set realistic budgets and timelines, and maintain transparency about costs and schedules. Regularly review and adjust budgets and plans as needed.
6.  Vendor Management and Diversity: Diversify the range of vendors and avoid over-reliance on a few large suppliers. This can reduce risk and improve innovation. Rigorous selection criteria and performance monitoring should be employed.
7.  Technical Excellence and Interoperability: Focus on high-quality software development practices. Ensure systems are interoperable, scalable, and compliant with standards to facilitate integration with existing and future systems.
8.  Data Security and Privacy: Prioritise patient data security and privacy. Build robust security protocols and involve data protection experts. Transparent communication with patients about how their data will be used is essential.
9.  Change Management: Recognise that introducing a new IT system is a major change. Provide adequate training and support to users, and manage the transition carefully to minimise disruption.
10. Continuous Learning and Adaptation: Establish mechanisms for continuous learning and improvement. Use data analytics and feedback to refine the system and adapt to changing needs.
11. Political and Leadership Support: Ensure consistent support from political and health system leaders. Leadership should be stable, committed, and aligned with the project’s goals.

By addressing these areas, a future program can mitigate the risks associated with large-scale IT projects and increase the likelihood of a successful implementation.

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Resources

BIG – Business Integrated Governance

The “Business Integrated Governance” (BIG) methodology within the Praxis framework is a comprehensive approach designed to ensure that the management and governance of projects, programmes, and portfolios are seamlessly integrated with the governance structures of the parent organisation. This methodology is a crucial aspect of the Praxis framework, which is a free, community-driven framework that blends guidance on projects, programmes, and portfolio management into a single integrated guide.

Components of BIG

Key components of the Business Integrated Governance methodology in the Praxis framework include:

  1. Alignment with Organisational Objectives: It emphasises aligning project, programme, and portfolio management with the strategic objectives of the organisation. This ensures that all initiatives contribute effectively to the overall business goals.
  2. Stakeholder Engagement: Central to this approach is the engagement of stakeholders at all levels of the organisation. This includes not only the project team and management but also executive-level stakeholders, ensuring that decisions are made with a comprehensive understanding of their impact across the organisation.
  3. Governance Structures: The methodology advocates for clear and effective governance structures. These structures should facilitate decision-making processes that are both agile and robust, allowing for rapid response to change while maintaining control and alignment with strategic objectives.
  4. Integrated Processes: It calls for the integration of processes across different levels of project, programme, and portfolio management. This integration ensures that practices and procedures are consistent and that information flows smoothly across different levels of management.
  5. Performance Management: A strong emphasis is placed on performance management, including the use of key performance indicators (KPIs) to monitor and measure the success of projects, programmes, and portfolios in alignment with business objectives.
  6. Risk Management: The methodology incorporates comprehensive risk management strategies to identify, assess, and mitigate risks at all levels, ensuring that they are managed effectively within the broader context of organisational governance.
  7. Resource Optimisation: It emphasises the efficient and effective use of resources across projects, programmes, and portfolios, ensuring that they are allocated in a way that maximises value and supports organisational priorities.
  8. Continuous Improvement: The Business Integrated Governance approach promotes a culture of continuous improvement, encouraging regular reviews and adaptations of governance practices to ensure they remain effective and aligned with the evolving needs of the organisation.

In summary

In summary, the Business Integrated Governance (BIG) methodology is about ensuring a harmonious and effective relationship between project-level management and the broader organisational governance.

It strives to align projects, programmes, and portfolios with the strategic goals of the organisation, ensuring effective use of resources, stakeholder engagement, and risk management, all within a framework of continuous improvement and adaptive governance structures.

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Resources

IPA Annual Report 2020-21

IPA annual report 2020-21

The IPA annual report of the Infrastructure and Projects Authority is here.

This year the report references:

  • Managing the GMPP
  • Capacity & Capability
  • Infrastructure Delivery
  • Net Zero
  • COVID-19
  • EU Exit
  • Commercial Advice
  • PFI Centre of Excellence and Contract Management
  • International work

Case Studies

  • HS2 Phase 1
  • DEFRA’s Future Farming and Countryside programme
  • Dreadnought
  • The Technology Sourcing Programme (TSP)
  • Army Basing Programme
  • Wellingborough New Build Prison (Five Wells)
  • Leeds Phase 2 Flood Scheme
  • Social Housing Decarbonisation Fund
  • Vaccines Task Force
  • Space-based PNT Programme