In the current economic landscape, sustainable, margin-focused expansion is key to any long term business growth. While "Digital Transformation" is a term that is often overused, its true value is found when data directly protects the bottom line and aids that expansion. However, many organisations still remain in a reactive cycle, identifying revenue loss only after it has impacted their quarterly earnings report.
At The Information Lab, in partnership with Alteryx, we have developed repeatable AI Operating Model designed to move organisations from retrospective reporting to proactive intervention. This AI agent-led approach focuses on the "leaky bucket" of customer churn, ensuring that your most valuable assets, your existing customers, remain on the books. However, the underlying Predict, Explain, Act engine is a blueprint for solving any enterprise classification or optimisation problem.
Deploying any AI operating model at the enterprise level requires a risk-averse approach that prioritises data integrity. Our solution never exposes Personally Identifiable Information (PII) to the AI tools, and removes hallucinations by using deterministic predictive models. This protection removes risk and helps answer any compliance questions.
The Challenge: The Cost of Information Lag
In a volatile market, hindsight is an expensive luxury and traditional analytical models fail to provide the immediacy required for effective revenue protection today. Decision-makers are frequently faced with issues in three areas:
Businesses faced with this problem are often stuck reacting to issues, and faced with urgent issues, waste investment in AI projects that don’t lead to action.
Our Approach: Predict, Explain, Act
We have condensed the complexity of churn management into a high-impact, three-stage workflow that integrates seamlessly into existing enterprise tech stacks.
1. Predict: Proactive Risk Identification
The process begins with an automated Alteryx workflow. By leveraging historical and operational datasets, stored securely in Cloud Data Warehouses like BigQuery, the model identifies high-risk churn patterns on a weekly basis. This stage moves the organisation from guesswork to a data-backed probability of future revenue loss.
2. Explain: Democratising Data via Natural Language
Raw predictions require context. We ingest this risk data into an Alteryx Auto Insights (AAI) “Mission”, creating a centralised environment for discovery. Employees can interact with a dedicated ChatBot to extract insights using natural language, removing the technical barrier to entry. With this approach stakeholders can immediately see the "why" behind the risk, identifying if the leakage is driven by service issues, pricing, or contract renewals.
3. Act: Automated Intervention Recommendations
The final stage is the transition from insight to execution. We utilise an AI Agent, integrated with Vertex AI and OpenAI, to generate bespoke "battle-cards" and intervention recommendations for the sales team. These automated emails provide account managers with personalised risk assessments, customised for each client to give specific churn probabilities. Through this it provides the user with strategic talking points and tailored recommendations to mitigate the risk and retain the account.
In Detail: AI without the Risk
The video below shows our approach, and walks through the workflow and outputs, built as by The Information Lab team as part of a Hackathon with Alteryx and Google.
Deploying AI at the enterprise level requires more than just clever prompts; it requires a defensible architecture that prioritises data integrity. As you see in the video, we have built this solution with a "safety-first" mindset to ensure that your customer data remains secure and your insights remain reliable.
Strict PII Separation: Personally Identifiable Information (PII) is kept entirely separate from all Large Language Models (LLMs).
Zero Exposure during Processing: PII is never exposed to AI tools; it is only reintroduced at the final delivery stage to personalise the Account Manager emails.
Deterministic Accuracy: We use a deterministic predictive model, rather than relying on generative AI for the initial classification, to ensure that customer risk scores are based on hard data and cannot be "hallucinated".
Fact-Based Classification: By using an XG boosted predictive model, the system classifies each customer’s risk based on specific, contributing factors, ensuring the "why" is always grounded in your actual operational data.
By keeping the “science” deterministic and the “language” generative, we provide a solution that is both highly advanced and fully compliant with enterprise security protocols.
Quantifiable Business Impact
While this framework is a strategic blueprint, its components are built on a foundation of massive, measurable ROI delivered across the UK. By leveraging the same Alteryx-driven automation and intelligence:
We identified over $120M in cost-optimisation opportunities and reduced manual maintenance from 120 hours to just 2 hours per year.
For NHS procurement, we reduced benchmark analysis time from 10 working days to just 1 hour.
From UK train operators boosting revenue efficiency to global retailers saving 170 FTE hours per month, we have a proven history of turning data burdens into strategic assets.
Strategic Next Steps
The Information Lab and Alteryx are currently prioritising a focused GTM play for a select group of UK-based Alteryx customers. We are shortlisting accounts where this use case can be most rapidly deployed to drive immediate margin optimisation.
If you’re ready to stop guessing and start protecting your revenue, let’s talk about how we can plug the holes in your bucket.