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Each year, Gartner's D&A predictions offer a window into where enterprise thinking is heading. In this blog, we're covering the predictions for 2026 and the themes that emerge.
At this year's Gartner Data & Analytics Summit in Orlando, analysts presented a set of predictions that tell a consistent story: the organizations that will lead in AI are those that have invested in the right data foundations. For life sciences, this has a specific meaning, and it's one we at ONTOFORCE have been building toward for over a decade.
Each year, Gartner's D&A predictions offer a window into where enterprise thinking is heading. In 2025, the themes were semantic layers, knowledge graphs, and composite AI. In 2026, those themes have matured into something sharper: governance at runtime, autonomous agents operating in physical and digital environments, and the emergence of a new class of efficient, AI-native organizations.
Across the predictions, a few patterns stand out for life sciences leaders:
Semantic infrastructure is becoming non-negotiable.
Gartner now treats universal semantic layers as critical infrastructure, placing them alongside data platforms and cybersecurity. This is a significant framing shift. It means semantic layers are no longer a nice-to-have architectural choice. They are now foundational to AI reliability, cost management, and cross-system consistency. For pharma and biopharma organizations dealing with fragmented data across research, clinical, and regulatory domains, this is not a new challenge. It's a chronic one. The difference is that AI has made the cost of ignoring it much more visible.
Governance is moving into the AI stack itself.
Gartner predicts that by 2030, 50% of the organizations will use autonomous AI agents to interpret governance policies and enforce them as machine-verifiable data contracts. The prediction comes with a warning: half of AI agent deployment failures over that same period will result from insufficient governance enforcement at runtime. In a regulated industry like life sciences, where explainability and audit trails are not optional, this prediction is particularly pointed. Governance that lives in documentation rather than infrastructure will not scale with agentic AI.
Agents are generating data, not just consuming it.
By 2029, Gartner projects that AI agents operating in physical environments will generate ten times more data than all digital AI applications combined. For life sciences, where agentic AI is already finding traction in target identification, cohort analysis, and regulatory workflows, this means the semantic layer that connects and contextualizes data today will need to handle volumes and relationship complexity that dwarf current requirements.
Human relational skills will define AI leadership.
Perhaps the most counterintuitive prediction: by 2030, sixty percent of organizations achieving genuine AI differentiation will be led by executives who prioritize human relational skills, such as coalition-building, influence, strategic vision. This is Gartner's acknowledgment that the bottleneck is not technology. It's organizational alignment and the ability to translate AI potential into cross-functional action.
Gartner’s predictions map closely to the capabilities DISQOVER has been built around. The universal semantic layer Gartner describes as critical infrastructure is the architecture at the heart of DISQOVER. Through an ontology-based knowledge graph, DISQOVER connects multimodal data across disparate sources — internal and external, structured and unstructured — into a single, semantically enriched knowledge base.
This is not a layer applied after the fact. It is how DISQOVER integrates data from the start, resolving synonyms, aligning identifiers, and surfacing relationships that static databases cannot represent.
The governance at runtime challenge Gartner highlights is also one DISQOVER addresses directly. When AI agents and LLMs operate within DISQOVER, they do so against a structured, ontologically grounded knowledge base, not raw, ungoverned data. This means that outputs are traceable, relationships are explainable, and the data contracts Gartner is pointing toward are already embedded in how the platform operates.
A key enabler of this is DISQOVER's provenance model, which adds a meaningful validation layer on top of the semantic foundation. Every piece of data in DISQOVER carries metadata about where it came from, when it was ingested, and which source it originated from. This means that when an AI agent surfaces a finding, such as a compound-target association, a patient cohort characteristic, a regulatory data point, that output is not just semantically grounded, it is source-attributed. Teams can trace the reasoning back to the underlying evidence, and can configure the platform to prioritize data from higher-trust, more reputable sources over lower-confidence inputs. Critically, this traceability does not stop at a single query. As agents traverse the knowledge graph across multiple sources and reasoning steps, provenance travels with the data, meaning trust and auditability are preserved at scale, not just at the point of ingestion.
The agentic AI use cases gaining traction in life sciences — think: target identification, cohort building, clinical data reuse — all rely on the same underlying requirement: connected, semantically consistent data that any agent can traverse reliably, with full visibility into where that data originates. Because DISQOVER is built around a shared semantic foundation with provenance from the start, AI agents can connect, query, and build on the insights the platform surfaces without requiring bespoke integration work for each new use case. The trust layer is already there, regardless of which sources or workflows are involved.
Looking back at Gartner's trajectory of the past few years on the topic: from knowledge graphs entering the top ten data integration trends in 2024, to semantic technologies taking center stage in 2025, to universal semantic layers as critical infrastructure in 2026. The direction is consistent. The organizations that invested early in semantic foundations are now well-positioned for the governance, agentic, and efficiency challenges Gartner is describing.
For ONTOFORCE customers, this trajectory validates what many already recognized: connecting data with meaning, not just linking it technically, is what makes AI trustworthy and scalable in a regulated, high-stakes industry.
The question for life sciences organizations still building their data foundations is not whether to invest in semantic infrastructure. Gartner has made clear that it is becoming table stakes. The question is how quickly that foundation can be established, and whether the AI investments being made today will hold up when governance and interoperability demands increase.
About DISQOVER
At ONTOFORCE, we are at the forefront of this transformation, empowering life sciences organizations to harness the full potential of their data. With DISQOVER, our knowledge discovery platform built on knowledge graph technology, we enable the industry to confidently make data-driven decisions, ensuring that the next big breakthrough is always within reach.
Gartner has recognized ONTOFORCE and DISQOVER in several recent Hype Cycles for Life Sciences:
Learn more about the trends Gartner highlighted at this year’s D&A summit.
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