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Enabling Sustainable AI Adoption with AWS Infrastructure

Event artwork for Enabling Sustainable AI Adoption with AWS Infrastructure

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About this event

What to expect

A practical Dublin session for founders who want to take AI from prototype to production without overspending or accumulating technical debt.

Build AI that can scale sustainably

Enabling Sustainable AI Adoption with AWS Infrastructure takes place on Tuesday 29 September 2026, from 10:00am to 1:00pm, at The Guinness Enterprise Centre (GEC) in Dublin 8. Hosted by Kimber, this founder-focused event explores how startups and scale-ups can adopt AI confidently while keeping infrastructure costs and long-term technical complexity under control.

The session addresses a critical challenge for growing companies: an AI prototype may demonstrate value quickly, but turning it into a reliable, production-ready feature requires more deliberate decisions. Attendees will examine how to move beyond early experimentation without being forced to re-platform later, overspend on infrastructure, or create technical debt that slows future development.

What to expect

The three-hour programme combines practical guidance, tactical insights and real founder stories. Contributions from AWS, Genese, Kimber and MCS Recruitment will help connect infrastructure choices with the realities faced by new and scaling businesses.

The event will focus on:

  • Moving AI ideas from an initial prototype towards production-ready features
  • Building an infrastructure foundation that can support future growth
  • Avoiding unnecessary expenditure as AI products and workloads scale
  • Reducing the risk of re-platforming and preventable technical debt
  • Learning from founder experiences rather than relying on theory alone
  • Approaching AI adoption with greater confidence and commercial discipline

This is designed for Ireland’s startup and scale-up community, particularly founders considering how AI should fit into their product or company. It will be useful whether you are validating an early AI concept or working out how to support a feature that now needs to operate reliably in production. The emphasis is on actionable thinking and hands-on guidance rather than broad predictions about AI.

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