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.
