Teradata Highlights New Enterprise AI Customer Engagements Across Healthcare & Life Sciences
Enterprise AI customer engagements show how healthcare and life sciences organizations are activating intelligence from their data and institutional knowledge at scale
SAN DIEGO, Oct. 7, 2026 /PRNewswire/ -- Teradata (NYSE: TDC) showcased three customer engagements that demonstrate how leading healthcare and life sciences organizations are closing the gap between institutional knowledge and real-world outcomes, advancing disease prediction, improving patient engagement at scale, and navigating high-stakes regulatory initiatives with governed, in-database intelligence. Each engagement demonstrates how AI can move from concept to measurable outcome without compromising the governance, security, and trust that these environments demand.
The urgency is real, and in healthcare the data foundation challenge is more acute than in other industries. According to a Wakefield Research survey of 1,000 senior technology and data leaders commissioned by Teradata, a staggering 90% of healthcare leaders report that 20% or less of their data is sufficiently described and contextualized for AI agents to act on reliably. Healthcare organizations lag noticeably behind every other vertical in agentic AI maturity, with 40% still in the earliest stage, experimenting with activating their intelligence but not leveraging it for advantage. Yet some organizations are pulling ahead, and these engagements illustrate what a purpose-built data foundation for healthcare AI can deliver.
AI case studies —Healthcare & Life Sciences
- AI-powered disease prediction that could transform how health systems plan via a leading European research institution
Challenge: Healthcare systems face mounting pressure to plan services more accurately as populations age and costs rise. Traditional demand forecasting cannot predict which services different patient cohorts will need, and when. At the same time, large language model inference costs have historically made enterprise-scale healthcare AI prohibitively expensive for most organizations.
AI solution: Researchers at a leading European molecular biology and oncology institution developed a compact, 2-million-parameter model that repurposes transformer architecture to predict disease onset across patient populations up to 10 years in advance. Rather than processing text, the model encodes patient age timelines in place of positional sequences and is fed longitudinal patient data to forecast future disease onset at cohort scale. Teradata's forward-deployed engineering team used Bring Your Own Model (BYOM) technology to deploy the model directly in-database, and nPath to structure complex patient journey sequence data, all of which was operational within three days.
Outcome: The model delivers 70% accuracy in disease onset prediction a decade ahead, sufficient for aggregate, population-level healthcare demand planning. Running in-database, inference costs are a fraction of what large language model alternatives would require. What began as a research proof-of-concept became a live demonstration of platform agility and a reusable foundation for healthcare AI that health systems can act on today. - AI-powered patient engagement and digital care at a large U.S. integrated health system
Challenge: A health system serving more than one million patients across predominantly rural communities has unique challenges. Disengaged patients forgo care, chronic conditions go unmanaged, and underserved populations, including expectant mothers in areas with limited access to obstetric specialists, can fall through the gaps entirely.
AI solution: The Teradata platform provided the data harmonization and machine learning foundation the organization needed to improve care for its dispersed patient population. With a decade of patient data unified in a single governed environment, the health system built a portfolio of digital care programs powered by Python-based ML risk-stratification models running directly in-database, drawing on nearly 150 patient features, to identify, target, and proactively connect with the right patients. The models provide clinicians with real-time decision support as they round on patients and enable an obstetrics remote monitoring program that extends 24/7 digital care to expectant and postpartum mothers in underserved communities.
Outcome: The results were transformative: a 520% increase in patient engagement across digital programs, with patients aligning to their care protocols at dramatically higher rates –- thereby improving patient health. The model also enables dynamic tiering. As patients' chronic conditions improve under remote monitoring, care can be appropriately scaled. - Governed in-database AI for a high-priority regulatory initiative at a major U.S. healthcare payer
Challenge: In healthcare, regulation often moves faster than the systems built to meet it. New transparency requirements, audit mandates, and compliance initiatives can arrive as executive-level priorities with timelines measured in weeks, not quarters. For a major U.S. healthcare payer facing exactly this scenario, the question was not whether the organization had the data. The question was whether it could reason on it accurately, securely, and at scale.
AI solution: Teradata applied advanced AI directly within the Teradata platform to support the payer's high-priority regulatory initiative. The solution combined large language models, embeddings, and in-database similarity matching to deliver intelligent, governed insights where the data already lives. There was no data movement, no external pipelines, and no compromise on the security model or governance controls the organization required. The approach is designed to scale for enterprise healthcare workloads.
Outcome: What began as a compliance initiative has the potential to become a repeatable blueprint for how healthcare payers tackle regulatory and compliance challenges across the broader market. The engagement demonstrated that governed, in-database AI is not a workaround for high-stakes scenarios, but instead is the architecture purpose-built for them. The Wakefield Research survey found that 43% of healthcare organizations cite governance, security, or access restrictions as a primary barrier to deploying AI agents on enterprise data; this engagement shows what it looks like when that barrier is resolved by building governance into the foundation from day one, rather than attempting to bolt it on after the fact.
Executive Perspective
"Healthcare and life sciences are industries rich in institutional knowledge such as clinical records, claims data, utilization patterns, and decades of patient history. The problem has never been the data. It has been the gap between what these organizations know and what they do with that knowledge. These engagements show what it looks like to close that gap: disease prediction that reshapes how health systems plan for the future, digital care programs that reach patients who would otherwise fall through the cracks, and compliance intelligence that gives payers the confidence to act in days rather than months."
- Mike Hutchinson, Chief Operating Officer, Teradata
Teradata AI Services are purpose-built to turn that knowledge into action by combining expert methodology with the Teradata platform in a sprint-based delivery model. For healthcare enterprises, that means a structured, accelerated path from proof of concept to production, without compromising the governance, auditability, and trust their regulators, clinicians, and patients expect.
About Teradata
Teradata empowers enterprises to turn intelligence into autonomous action, grounding AI agents in deep business context and trusted data. As AI agents multiply, Teradata is the context foundation, governance layer, and performance backbone that companies need now. The Teradata Autonomous Knowledge Platform puts AI into production across cloud, on-premises, and hybrid environments.
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MEDIA CONTACT
January Machold
january.machold@teradata.com
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SOURCE Teradata Corporation
