AI Use Cases

Discussion table

Learn from
Paul Maltby
Paul Maltby

Director of AI Transformation in Government

Co-Host

Paul is the Director of AI Transformation in Government at Faculty AI, a London-based applied data science company. He was previously Chief Digital Officer at the Department for Levelling Up, Housing and Communities where he led reform to digital services such in the planning system and across local government. Prior to that he was Director of Data in the Cabinet Office’s Government Digital Service, with responsibility for open data and data science in government. He has a background in public service innovation and reform in government. He has had spells in Leicestershire County Council, the Home Office and the Prime Minister’s Strategy Unit.
Corinne Richardson
Corinne Richardson

Deputy Director, Head of Data Engineering

Co-Host

Adam Boyse
Adam Boyse

Interim CTO

Co-Host

Experienced CTO/IT Director

I build and manage productive, high quality technology teams increasing revenue, reducing costs and mitigating risks. Want your legacy technology transformed and delivered? Speak to me. Experience across retail, public sector and healthcare.

Recent clients: HMRC, The Crown Estate, Vue Entertainment, Department of Work and Pensions, The IPO, New Look, Booker Retail, Musgrave Retail, Travis Perkins.

Specialties: Technology Operating Models and Frameworks. CTO toolkits. Successful agile delivery. Digital transformation. Architecture. Strategy & planning. IT team design and management. Cyber security. Outsourcing/Supplier management. Investor and Stakeholder relations. Project and Programme management.
About the session

AI Use Cases range from predictive analytics in policy-making to chatbots in customer service and from fraud detection in financial transactions to predictive maintenance in public infrastructure. Where can AI have the biggest positive impact in government - and what changes to our current systems and processes will we need to make to realise these gains?

  1. Policy Making: AI can analyse large amounts of data to predict future trends and help in effective policy-making - but it relies heavily on the data it's trained on. How do you ensure the data used is not only high quality, but also represents the diverse circumstances of citizens?

  2. Customer Service: AI-powered chatbots can provide 24/7 customer service, answering queries, and providing information. How would AI chatbots change the way your department interacts with citizens?

  3. Fraud Detection: AI can identify patterns and anomalies that might indicate fraudulent activity - but can also generate false positives and negatives. How can you mitigate the latter to take advantage the former?