Ontologies at the Center of AI: Why Semantics Will Define Value in 2026
Over the past few years, the AI conversation has been dominated by scale: bigger models, more GPUs, faster training, and increasingly sophisticated algorithms. But as we move through 2025, that narrative is starting to shift.
In a recent discussion, Alex Wang made a point that resonates strongly with what many organizations are experiencing in practice: the real competitive advantage in AI is no longer just compute or algorithms, it is access to data (watch the clip). Models are becoming more capable and increasingly interchangeable, but the data they are trained on and reason over remains highly differentiated.
That insight is directionally correct, but it only tells part of the story. To understand where AI value will really come from in 2026, we need to look at how data, meaning, and structure come together inside the enterprise.
The Three Pillars of AI: Compute, Algorithms, and Data
At its foundation, AI rests on three core components:
- Compute – the infrastructure that powers training and inference
- Algorithms – the models, architectures, and reasoning techniques
- Data – the information used to train, ground, and guide those models
Over the last decade, progress has been driven largely by compute and algorithms. Model performance has improved rapidly, access has expanded, and innovation cycles have accelerated. In many cases, these two pillars are becoming increasingly commoditized.
Data, however, is different.
Data is unique to each organization. It reflects how work gets done, how decisions are made, and how value is created. And increasingly, data-not models is becoming the true bottleneck.
But even this framing is incomplete. The real challenge is not whether organizations have data. Most do. The challenge is whether that data can be understood, connected, and used effectively by AI.
A Common Enterprise Challenge, Especially Visible in Oil & Gas
In oil and gas, the enterprise data challenge is especially visible because of the scale, complexity, and longevity of operational systems.
Across oil and gas organizations, a consistent pattern emerges.
These companies are extraordinarily data-rich. They manage:
- Subsurface and geological data
- Well, production, and reservoir data
- Maintenance, reliability, and equipment data
- Supply chain and procurement data
- Financial, AFE, and cost data
- Regulatory, HSE, and ESG data
Yet this data is almost always deeply siloed.
Different functions own different systems. Vendors model the same concepts in different ways. A single asset or well may have multiple identifiers depending on whether you look at it from a production, finance, or operations perspective.
The result is familiar:
- Teams spend enormous time reconciling definitions
- Data scientists struggle to join datasets reliably
- AI projects stall or remain stuck in pilots
- Insights fail to travel across organizational boundaries
Ironically, the most valuable data, structured operational data, is also the hardest for AI to use.
This data is deeply embedded in transactional systems, fragmented across domains, governed by inconsistent schemas, and rarely expressed in a way that captures business meaning. This is because it is deeply embedded in transactional systems, fragmented across domains, governed by inconsistent schemas, and rarely expressed in a way that captures business meaning. Without a semantic layer, AI can access rows and columns but cannot understand how entities relate, why they matter, or how they should be used together. This severely limits its ability to reason or act with confidence.
This challenge becomes even more pronounced with agentic AI. Agents need more than access to data; they need clarity, consistency, and meaning. Without that, agents may retrieve information, but they cannot reason over it safely or accurately.
The Enterprise Data Paradox
This leads to a paradox many organizations are now facing:
We have more data than ever, more powerful models than ever , and yet limited, inconsistent AI-driven value.
Research from MIT and others highlights this gap. Despite growing investment in AI, many organizations struggle to realize measurable returns. The limiting factor is rarely model capability. Instead, it is organizational and data readiness.
AI systems struggle when:
- Definitions differ across systems
- Relationships are implicit rather than explicit
- Business logic lives in code or tribal knowledge
- Context must be reconstructed manually
- Structured data cannot be easily connected
Access alone is not enough. AI needs understanding.
Meaning Is the Missing Layer
This is where the real opportunity and competitive advantage begins. The gap between raw data and usable intelligence is not compute or algorithms, but meaning.
For AI to reason effectively, it must understand what things are, how they relate, and what rules govern them. This is where semantic models and ontologies become essential.
What Is an Ontology?
At a simple level, an ontology defines the nouns and verbs of the enterprise the building blocks of meaning:
- Nouns: the core entities, such as wells, assets, equipment, contracts, invoices, vendors, and reservoirs
- Verbs: the relationships and actions, such as produces, owns, depends on, supplies, approves, and maintains
Together, these define a shared vocabulary and logic for how the business works, allowing systems and people to speak the same language.
Ontologies do not replace data models. They sit above them. They capture meaning rather than storage structure, allowing different systems to align around common concepts even when their schemas differ.
This distinction becomes critical in complex, federated environments like oil and gas.
Semantic Models: Turning Meaning Into Something AI Can Use
Alongside ontologies, the market is increasingly investing in semantic models as a foundational layer for analytics and AI. This shift reflects a broader recognition that AI systems need consistent meaning, not just access to tables or files.
We see this clearly in recent platform directions, including initiatives such as Snowflake’s Open Semantic Interface (OSI), which aim to standardize how metrics, entities, and relationships are defined and shared across tools.
Semantic models act as a bridge by translating raw data into shared, governed meaning:
- Between raw structured data and analytics
- Between databases and AI systems
- Between domain experts and technical implementations
They provide governed definitions, reusable metrics, and consistent relationships that AI systems can rely on.
When semantic models are combined with ontologies and represented as knowledge graphs, they create a powerful foundation for enterprise AI:
- Structured data becomes discoverable
- Relationships become explicit
- Meaning becomes machine-readable
- Reasoning becomes possible
Why Structured Enterprise Data Is the Key to Unlocking Value
Unstructured data often gets the spotlight, but structured enterprise data is where the highest-value decisions live:
- Costs and profitability
- Production performance
- Operational efficiency
- Risk and compliance
- Supply chain optimization
The challenge is that this data is locked behind schemas, systems, and inconsistent definitions.
Semantic models and ontologies help unlock this value by:
- Harmonizing definitions across systems
- Connecting entities across domains
- Making relationships explicit
- Enabling AI to query and reason safely
- Supporting governance, lineage, and explainability
Instead of brittle integrations and one-off pipelines, organizations gain a durable semantic foundation.
From Data to Knowledge to Intelligence
A useful way to think about the journey ahead is:
Data → Meaning → Knowledge → Intelligence
- Data is raw and fragmented
- Meaning comes from semantics and definitions
- Knowledge emerges when relationships are connected
- Intelligence arises when AI can reason over that knowledge
Ontologies define meaning. Knowledge graphs connect it. Semantic models operationalize it.
Together, they turn enterprise data into something AI can truly understand.
Looking Ahead to 2026: Why Ontologies Become a Strategic Advantage
As we move into 2026, the differentiator will not be who has the largest models or the most compute. Those capabilities are increasingly accessible to everyone.
The real advantage will belong to organizations that invest in understanding their business at a semantic level.
Companies that treat ontologies and semantic models as foundational infrastructure, not academic exercises, will be able to deploy AI systems that reason, adapt, and operate with context and trust.
In a world where models and compute are becoming commodities, knowledge becomes the moat.
And knowledge starts by defining the nouns and verbs of your enterprise.
This is ultimately where the discussion about data, structure, and meaning converges. If access to data is becoming the competitive advantage, then the next step is turning that access into shared understanding. In 2026, the organizations that pull ahead will be those that invest in turning data into shared knowledge, using ontologies and knowledge graphs to make enterprise AI and agents reliable at scale.