Expert.ai Recognized as a Representative Vendor in the 2026 Gartner® How to Enable Trustworthy, Explainable Agentic AI With Knowledge Graphs in Healthcare and Life Science Research
In our opinion, Expert.ai’s recognition as a Representative Vendor reflects the company’s position within this evolving landscape, where knowledge-driven AI can help organizations bridge the gap between the capabilities of generative and agentic AI and the stringent requirements of healthcare and life sciences.
Expert.ai, a leading provider of enterprise AI solutions for business value creation, has been recognized as a Representative Vendor in Gartner research How to Enable Trustworthy, Explainable Agentic AI with Knowledge Graphs in Healthcare and Life Science.
According to Gartner strategic planning assumption, “By 2027, 80% of agentic AI initiatives for sensitive use cases will fail to scale without knowledge graphs ensuring the required transparency for trust.” As current AI agents often lack the reliability, precision, transparency and contextual understanding required for meaningful clinical and scientific applications, the ability to ground AI-generated outputs in verifiable knowledge is becoming essential.
For healthcare and life sciences organizations, the combination of knowledge graphs and agentic AI represents an architectural approach to making AI systems more reliable, explainable and controllable. The need for this foundation is particularly clear in high-stakes environments, where trust and human oversight remain critical.
Knowledge Graphs as critical architecture layer for healthcare and life sciences
According to Gartner, “Knowledge graphs are a critical architectural layer for organizations advancing along the agentic AI maturity roadmap. Unlike current LLM-based AI agent 1.0 solutions, which offer limited depth and task automation, knowledge graph-enabled frameworks deliver a verifiable model of clinical truth and traceable decision-making reasoning for achieving advanced and expert levels of autonomous agency.”
Knowledge graphs provide a structured and auditable foundation for AI agents by connecting facts, relationships, provenance, confidence signals and usage rules. This enables AI systems to access domain-specific context, perform more effective multi-hop reasoning and generate outputs that can be traced back to the underlying evidence.
Turning pharma, healthcare and life sciences biggest challenges into opportunities with Expert.ai
Expert.ai’s capability of combining and offering the best of different AI technologies such as knowledge graphs and agentic AI (composite AI, neurosymbolic AI), pharma, healthcare and life sciences organizations can build on AI that is more reliable, explainable and controllable. By grounding AI agents in domain-specific, verifiable knowledge and enabling traceable evidence chains, Expert.ai provides the foundation for responsible, high-autonomy AI across clinical, scientific and operational workflows, helping organizations move from AI experimentation to trusted, production-ready applications. Learn more about Expert.ai’s solutions bridging the gap between generative AI, agentic AI and the stringent requirements of pharma, healthcare and life sciences industry.
