From alignment to execution, the priorities shaping AI-ready networks.
As AI moves from experimentation toward real-world deployment, the focus is no longer on what’s possible, but on what it takes to operationalize across infrastructure, domains, and ecosystems.
Industry research from firms including Analysys Mason, STL Partners, and Gartner points to a common challenge. While investment in automation, APIs, and AI-enabled infrastructure continues, progress is increasingly constrained by fragmentation, coordination, and the challenge of translating capability into scalable, repeatable implementation. In Europe in particular, market structure and investment pressures are adding urgency to this shift.
These are the challenges being actively addressed through member collaboration across Mplify programs.
The work reflects a clear set of priorities. How networks evolve to support AI workloads, from training to inference, and what that means for transport, performance, and network design. How automation is applied in practice, building on LSO APIs and emerging interaction models such as MCP (Model Context Protocol) to enable more intelligent, context-aware orchestration across domains. And how federation, interconnection models, and commercial frameworks evolve to support an AI-driven digital economy.
There is also a growing focus on execution. How agent-driven automation is implemented in real environments. How verification, trust, and reliability are maintained as systems become more dynamic. And how standardized services and multi-provider delivery models come together to support NaaS at scale.
These themes will shape discussion and working sessions throughout the Mplify Member Summit - EMEA and co-located Member Workdays, taking place 8-11 June in Lisbon.
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