Unlocking sustainable value from agentic artificial intelligence (AI) is a strategic imperative for your business. The discipline of knowledge engineering allows organizations to capture and structure their information for machine use, so they can scale their AI efforts enterprise wide.
Knowledge engineering bridges the gap between human intuition and AI by acquiring, structuring, and encoding human expertise so machines can use it effectively. This approach provides AI agents with the semantic structure required to interpret both structured and unstructured data and ensures that AI doesn’t generate answers based only on patterns, but also on trusted, contextual knowledge.
We are at the end of the 'big data' era and at the dawn of the 'big knowledge' era. Competitive advantage won't come from owning data, but from embedding it with meaning. That is the path to unlocking the true potential of enterprise AI.
Matteo Colombo
Global leader, Digital Technologies, KPMG LLC
Today's AI agents are ready to tackle complex tasks, but they don’t interpret business data the same way a human does. Reports and dashboards built for human eyes are often not structured to present all the context that machines need to interpret them effectively, leading to stalled AI initiatives and wasted investments. Here’s what you can do about it:
By providing context and business logic to AI agents, they can interpret company-specific language nuances just as an experienced employee would. This can unlock new possibilities for intelligent decision-making and operational efficiency.
Feeding a report that includes proprietary company context to advanced AI is like handing a person a pile of binary code. By enriching raw data with meaning, organizations can empower AI to not only process information but also derive actionable business value—turning your datasets into strategic advantage.
A majority of all enterprise information is unstructured data (e.g., emails, chats, posts, reviews, sensor data). Even with data in this format, knowledge engineering can provide the consistency and defined relationships that AI systems require.
Knowledge engineering is built on three pillars that work together to make knowledge explicit for agentic systems:
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Knowledge engineering
Business must evolve from data management to knowledge engineering. In this way, they can unify all data types and pair them with data management tools like semantic layers, ontologies, and knowledge graphs to increase the quality and accuracy of agentic output. Collaborate with KPMG to help lead this critical initiative. We have the skills, technologies, and domain knowledge that can be tailored to your business to help you succeed in the agentic AI era.
It's always satisfying when the marketplace cites your contributions to client success. KPMG was recently named #1 in quality rankings for data & analytics and for Improving IT Infrastructure in the US by Source; a Horizon 3 Market Leader in the HFS Horizons AADA Quadfecta Services for the Generative Enterprise 2024; and a “Leader”, the highest designation, in The Forrester Wave™: Oracle Services, Q1 2025 report.
in Generative Enterprise Services, 2025 by HFS Research
In a recent survey of clients by Source, KPMG was ranked the #1 firm for quality of work and recognized as the leading authority in risk consulting in the US. The firm was also named one of America’s best management consulting firms by Forbes.
1: Source Perceptions of Consulting in the US, 2025
2: Forbes Media LLC, 2025
3: Source Perceptions of Risk Firms in 2024
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