Cognitive Computing: An Antidote to the Data-Intensive Age

The AI revolution isn’t happening in GPUs or model architectures. It’s happening quietly in the data pipelines that feed them.

The future of AI in the enterprise is recognizing that intelligence, whether human or artificial, must be governed by clear, written code.

Graph technology expert Dominik Tomicevic on why Anthropic’s model context protocol offers a much stronger way for LLMs to connect with the outside world than previous approaches, but it needs the helping hand of graph technology and GraphRAG.

Compute is no longer a background resource that scales automatically with demand. It is a constraint that shapes how systems are designed, how quickly they can be deployed, and how much control organizations retain over their own operations.

Modern enterprise applications, especially those driven by real-time data, AI inference, and latency-sensitive user experiences, are exposing the limitations of a one-size-fits-all cloud strategy, forcing organizations to rethink workload placement across hybrid and distributed environments. The shift is no longer about “cloud vs. on-prem,” but about architecting for performance, cost predictability, and data gravity in a world where systems must operate continuously and adapt dynamically.

The AI revolution isn’t happening in GPUs or model architectures. It’s happening quietly in the data pipelines that feed them.

The future of AI in the enterprise is recognizing that intelligence, whether human or artificial, must be governed by clear, written code.

Graph technology expert Dominik Tomicevic on why Anthropic’s model context protocol offers a much stronger way for LLMs to connect with the outside world than previous approaches, but it needs the helping hand of graph technology and GraphRAG.