Category: Feature

Featured on the front page

The Case of the Declining Newsletter Engagement

Background: A B2B SaaS provider relied heavily on email marketing to nurture leads and retain customers. Their weekly newsletter shared case studies, feature updates, and opinion pieces. Over a six-month period, email open rates dropped by half and click-throughs declined even more. Feedback was minimal, and marketing ROI fell off a cliff.

Workaround

The marketing team tried increasing frequency—sending multiple campaigns per week, experimenting with send times, and tweaking subject lines. They also ran one-off promotions to re-engage users. However, these efforts only resulted in increased unsubscribe rates and flagged emails.

Symptom: Open and click-through rates were steadily declining. Engagement was down and complaints were up.
Workaround applied: More frequent, more aggressive emailing.

Deeper Analysis

A review of email data showed no segmentation in the audience list. Long-time customers, new sign-ups, and trial users all received the same generic newsletter. Many emails were flagged by spam filters due to lack of authentication protocols and poor domain reputation. Feedback surveys revealed the content wasn’t relevant or timely.

Cause: Poor audience segmentation and deliverability issues.

Root Cause

An outdated CRM with basic mailing list functionality and no clear owner of email performance KPIs. Content planning was done reactively, based on internal priorities, not audience needs.

Root Cause: No clear owner of KPI measures, no audience need research t drive content planning and an out of date CRM with poor functionality – a perfect storm!

Solution

They moved to a modern marketing automation platform with smart segmentation and behavioural triggers. The team reworked their content calendar around user journeys and implemented proper email authentication (SPF, DKIM, DMARC).

Solution: New content calendar based on user journeys and a new modern marketing automation platform with segmentation.

Outcome

Within two months, open rates rose 45%, and user feedback became positive. New leads received tailored onboarding content, while long-term customers were offered relevant product tips and advanced usage guides. Engagement and pipeline value rebounded.

Feature Problem examples

The Case of the Inaccurate Inventory

Background: A regional online retailer experienced frequent disruptions due to incorrect stock records. Products listed as available would turn out to be out of stock when pickers arrived at the shelves, leading to delayed shipments, cancelled orders, and dissatisfied customers. The issue worsened during promotional periods and seasonal peaks.

Workaround

To prevent order errors, warehouse supervisors instituted daily manual counts for top-selling items. Staff used spreadsheets to log counts and reconcile discrepancies each morning before operations began. This temporary fix created new problems: increased workload, delays in order processing, and stress among floor staff.

Symptom: Frequent fulfilment errors and a spike in customer service complaints.
Workaround applied: Manual daily recounting of popular SKUs.

Deeper Analysis

The root of the issue wasn’t human error, but system design. The inventory management software updated stock levels in batches overnight, and it wasn’t connected to the ecommerce platform in real time. As a result, stock shown online didn’t match physical inventory. Discrepancies compounded daily.

Cause: A disconnect between sales and inventory systems, with no live syncing.

Root Cause

Technology and process had not kept pace with changing workplace behaviours. The company had no visibility over how spaces were actually used, nor any consequences for hoarding or misusing shared resources.

Root Cause: The company had grown quickly and patched its operations with separate tools. Integration and automation were sacrificed for speed. Inventory logic had not been reviewed since the company was much smaller.

Solution

They adopted a cloud-based ERP solution that integrated sales, inventory, and warehouse management. The system updated stock levels in real time and included handheld scanner integration for immediate adjustments during picking. Inventory accuracy was audited weekly to improve discipline.

Solution: the organisation adopted a cloud-based ERP solution that updated stock levels in real-time.

Outcome

Order accuracy improved dramatically. Customer complaints reduced by over 60%, and warehouse efficiency improved as manual tasks were phased out. Seasonal peaks were handled without overtime or errors.

Feature Problem examples

The Case of the Overbooked Meeting Rooms

Background: In a fast-growing professional services firm, meeting rooms had become a precious commodity. With hybrid working patterns, need for video-calls, desk-sharing, and an increase in collaborative project work, the demand for quiet, private meeting spaces surged. However, despite having enough rooms on paper, teams constantly found themselves wandering the building looking for an available space. Double-bookings and phantom reservations (where a room appeared booked but was unused) became a daily nuisance.

Workaround

To combat this, staff began reserving rooms informally. Some left their personal belongings and laptops to stake a claim; others created back-to-back recurring bookings “just in case.” A few teams even adopted unofficial room ownership, treating certain spaces as their default.

Symptom: constant scheduling clashes, missed meetings, and frustration about the lack of transparency.
Workaround applied: Employees relied on physical indicators (laptops, jackets) and informal norms to book and hold rooms.

Deeper Analysis

The company’s room booking software was outdated. It allowed for bookings without check-ins, didn’t automatically cancel no-shows, and wasn’t integrated with employees’ calendars. As a result, the system showed rooms as occupied when they weren’t and couldn’t identify patterns of misuse. Additionally, no policies were in place to guide booking etiquette.

Cause: An ineffective booking system and lack of governance around shared space usage.

Root Cause

Root Cause: Booking technology and policies not moving more quickly than workplace behaviours.

Solution

The organisation deployed a smart meeting room system with real-time availability, automatic check-ins using occupancy sensors, and integration with MS Teams and Google Calendar. They also introduced booking guidelines and monthly reports highlighting no-show rates and underutilised bookings. Training helped embed the new approach.

Solution: the organisation introduced a smart meeting room system with automation, reporting, and training to improve usage.

Outcome

Meeting availability increased by 40%. Teams reported higher satisfaction with the booking system. Rooms were no longer claimed with jackets but scheduled fairly and visibly, and missed meetings due to room confusion dropped significantly.

Feature

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.

Feature Problem examples

Slow Software Deployment

Background: A tech company experiences slow software deployment, causing frequent delays in launching updates. This has led to customer dissatisfaction and a decline in product reliability perception.

Workaround:

The development team decides to increase manual testing and patching before each release to catch and fix issues quickly. This helps minimise the delays and ensures the software works as expected, but it’s not a perfect solution. It still consumes a lot of time and resources, adding to costs.

Symptom: Slow software deployment and frequent delays.
Workaround Applied: Manually patching and increasing testing time to catch last-minute issues.

Deeper Analysis:

Upon investigation, it is found that the cause of frequent delays is frequent bugs and integration issues appearing late in the development cycle. The manual patching helps to catch some of these issues, but it doesn’t address why they happen in the first place.

Cause: Frequent bugs and integration issues late in the development process.

Root Cause:

Looking further, the root cause was discovered to be a lack of proper code review and integration testing throughout the development process. Developers worked in silos, leading to a buildup of conflicts that were only noticed during final integration.

Root Cause: Lack of continuous integration and code reviews during development.

Solution:

The company decides to implement a Continuous Integration/Continuous Deployment (CI/CD) pipeline with automated testing and regular code reviews. This allows bugs to be detected earlier and fixed immediately, avoiding the last-minute rush to patch things up. Additionally, it encourages collaboration among developers, ensuring that code conflicts are resolved quickly and cleanly.

Solution: Implementing a CI/CD pipeline with automated testing and regular code reviews.

Outcome: With the new solution in place, the team can deploy software more reliably and quickly. The need for manual patches is reduced, and customers are happier with the timely, high-quality updates.

Summary:

Workaround: Manual patching and extended testing time.
Symptom Addressed: Slow software deployment.
Cause: Bugs and integration issues detected late.
Root Cause: Lack of continuous integration and code reviews.
Solution: Implementing a CI/CD pipeline with automated testing.

This scenario illustrates how a workaround can temporarily relieve symptoms but doesn’t solve the underlying issue. Giving developers greater access to testing tools and avoiding manual steps is a winner.