From Modernization to Agentic AI: Building the Agent-Ready ISV

The rapid evolution of agentic AI is changing the question ISVs need to ask. It is becoming less about whether AI can perform a task and more about whether the organisation's technology, data and operating model are ready for AI to perform that task safely and reliably at scale.

Modernisation is increasingly the entry point. Legacy architectures, fragmented data, limited APIs, weak observability and inconsistent security controls can all become significant constraints when software agents begin interacting with enterprise systems at machine speed. The organisations best positioned for this shift are those treating modernisation not as a completed migration programme, but as an ongoing capability that continuously improves their readiness for new AI workloads.

At the same time, the technology stack itself is changing. As foundation models become increasingly interchangeable, differentiation is moving towards the systems surrounding them: orchestration, proprietary data, workflow design, evaluation, governance, deterministic controls and integration with enterprise applications. The model may be an important component, but the wider architecture determines whether an AI system can deliver reliable business outcomes.

This roundtable will bring together ISV leaders to explore what it means to build an organisation that is genuinely ready for agentic AI. We will discuss how leaders are approaching modernisation, which architectural capabilities need to be in place, and how organisations can move from compelling demonstrations to production systems that are secure, observable, scalable and economically sustainable.

We will also explore the implications for the ISV business model. As AI agents increasingly become users of software, traditional seat-based pricing may face new pressures. Token consumption, inference costs, agent activity and outcome-based models could all influence how software is packaged and monetised.

Finally, we will consider the role of pragmatism in AI adoption. Not every problem requires an LLM, and not every AI initiative requires a large-scale data transformation. The most effective organisations may be those that identify valuable use cases, prepare the relevant data, select the right technology for the problem and surround AI with the engineering controls needed to make it dependable.

Join AWS and a select group of your peers as we explore how ISVs can move from modernisation to agentic readiness — and build the foundations, architecture and business models needed for the next generation of AI-powered software.

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from global data centers. Millions of customers, including the fastest-growing startups, largest enterprises, and leading government agencies, leverage AWS to reduce costs, enhance agility, and accelerate innovation. Since 2006, we've transformed industries and lives by working backwards from our customers' needs to provide tailored cloud infrastructure solutions.