Nitin Rakesh, Chief Executive Officer and Managing Director of Mphasis, said enterprises must embed intelligence within core architecture, while David Cushman, Executive Research Leader at HFS Research, highlighted the need for structured knowledge systems to guide AI decision making.
Enterprise technology systems are facing renewed scrutiny as organisations prepare for the adoption of agent driven artificial intelligence.
Mphasis and HFS Research have pointed to the limitations of legacy core systems, noting that existing architectures may not support the scale and accuracy required for agentic AI. The discussion centres on the need for a structured intelligence layer that can guide decision making within enterprise systems.
HFS Research identifies ontology based knowledge graphs as a critical component in enabling reliable AI systems. Without a defined semantic layer, agentic systems risk replicating errors embedded in legacy processes while scaling operations.
Mphasis has developed an ontology driven layer within its NeoIP platform, designed to organise enterprise data, business rules and workflows into a structured knowledge system. This layer aims to provide context for AI systems, allowing them to operate within defined organisational logic.
Nitin Rakesh, Chief Executive Officer and Managing Director of Mphasis, said, “Enterprises are entering a phase where intelligence must be built into the core architecture. Adding AI on top of fragmented systems increases complexity rather than resolving it.”
David Cushman, Executive Research Leader at HFS Research, said, “Enterprises already hold significant intelligence within their systems, but it remains fragmented. Structuring this information is essential for enabling AI systems to function effectively.”
Mphasis cited outcomes from implementations of its framework, including improvements in incident prediction accuracy and reductions in response times within enterprise environments.
The discussion reflects a broader shift in enterprise technology, where organisations are moving from incremental upgrades toward reworking core systems to support advanced AI capabilities.
As AI adoption expands, the ability to structure and manage enterprise knowledge is emerging as a key factor in ensuring reliability and scalability.
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