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Belgium 08-09-2026 Invitation Only Virtual english

What an AI Architect Actually Does in Practice; Skills, responsibilities, interfaces, and career moves behind one of the most misunderstood emerging roles.

The AI architect role is becoming more visible, and the scope varies across organisations. The challenge is defining what the role owns, where it sits, and how it works with existing architecture, data, security, risk, and business teams. Three pressure points need clarity. - Role definition matters because the position can span solution architecture, data architecture, governance, integration, security, vendor selection, and business process design. - Interfaces matter because the role must connect teams while respecting existing responsibilities. - Skills matter because technical depth needs to be combined with judgement around controls, delivery choices, and operational boundaries. The working question is simple: how do we define the AI architect role so it becomes useful, credible, and connected to delivery? If this role is emerging in your organisation, let’s compare how others are defining it and where they are placing it.

Belgium 10-09-2026 Squad Only Physical english

Building the Infrastructure AI Needs: From computer and data access to scalability, cost control, and operational readiness.

AI is moving from pilots into operational use, and the infrastructure question is becoming more concrete. The challenge is deciding what needs to be built, expanded, controlled, or delayed as usage grows. Three pressure points usually appear first. - Compute capacity requires careful planning: teams need enough GPU, cloud, or specialised processing power, while keeping utilisation and cost under control. - Data movement becomes more demanding: AI needs access to data across systems, with latency, security, integration, and quality managed from the start. - Operational control becomes essential: monitoring, access rights, resilience, cost visibility, and ownership need to scale with usage. The working question is simple: how do we support AI at scale while keeping infrastructure efficient, governed, and financially sustainable? If you are working through these choices, let’s compare approaches with others facing similar constraints.

Belgium 15-09-2026 Squad Only Virtual english

Turning AI Pilots into Daily Workflows: How to move from experimentation to adoption by redesigning work, ownership, controls, and habits.

AI pilots are easier to launch than to embed into daily operations. The challenge is changing how work happens once AI becomes part of decisions, tasks, handovers, and controls. Three pressure points tend to decide whether adoption holds. - Workflow fit matters: AI has to enter the flow of work as part of the process. - Ownership needs to be clear: someone must own the process, the controls, the exceptions, and the outcome. - User trust has to be built: people need to know when to rely on AI, when to challenge it, and when to escalate. The working question is simple: how do we move AI from promising pilots into daily work while keeping clarity, accountability, and risk control? If this is part of your current reality, let’s compare choices, constraints, and lessons learned.

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Legal & General use AWS AI to transform Customer Experience, Speed, and Productivity

Using AWS, we now have a much more robust, elastic, and event driven architecture.” - Tariq Surty, IT Director, Retail Retirements Division, Legal & General

“Using AWS, we now have a much more robust, elastic, and event driven architecture.” - Tariq Surty, IT Director, Retail Retirements Division, Legal & General

Imagine you have over 5 million customers who send in paper forms amounting to 5,000 pieces daily. To best serve your customers—and to meet regulatory requirements—you need a way to process all these documents quickly and accurately. To meet this challenge, financial services firm Legal & General (L&G) modernized the daily ingestion of thousands of pension documents for its UK retail retirements division (RRD) using Amazon Web Services (AWS). L&G’s UK RRD migrated from its legacy scanning solution to AWS, using artificial intelligence (AI)–powered services. As a result, the division improved document processing speed, compliance with its regulatory service level agreements (SLAs), and staff productivity.

Modernizing Document Management Using AWS

L&G’s UK RRD has 5.2 million workplace pension members who send in more than 1 million paper documents per year, requesting, for example, a change of address or beneficiary. The division must process and respond to each one within a set period. It wanted to make the process more efficient, scalable, and cost-effective. “Using AWS, we now have a much more robust, elastic, and event-driven architecture,” says Tariq Surty, IT director for the RRD at L&G.

The UK RRD, alongside its local AWS team, created an internal proof of concept to ratify use of Amazon Textract, a machine learning (ML) service that automatically extracts text, handwriting, layout elements, and data from scanned documents. The division then engaged AWS Professional Services—a global team of experts that helps companies use AWS—to complete its project quickly and reliably, from designing the proof of concept to scaling into full production.

Improving Processing Speed and Staff Productivity with ML

L&G’s cloud-focused solution for document management is Docusort. It processes documents with ML using Amazon Textract and Amazon SageMaker, a fully managed service that brings together a broad set of tools for high-performance, low-cost ML. Docusort classifies documents, extracts the relevant datasets, and pushes that data to backend systems to update customer information.

Using AWS, L&G can now process 1,000 documents concurrently in 30 minutes, and with the serverless architecture, its solution can scale up and down to meet any workload. Before, processing was limited by the number of people handling the documents. With these time savings, the UK RRD meets SLA requirements more accurately and consistently. As a result, the division has been able to redeploy five employees, improving business productivity.

Moving Forward with Generative AI

L&G plans to use its Docusort solution as a template for other use cases where ML might be useful. In particular, the company is exploring how to apply generative AI to processes such as managing complaints, supporting the underwriting process, and analyzing customer feedback.

“We’ve learned how to manage, train, and maintain ML models, and we’re in a great place to use generative AI for new use cases now that we have a proven modular, elastic, and serverless architecture, which is really exciting,” says Surty.

Technologies

Amazon SageMaker Amazon Textract

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