Life Sciences Firms Scale Trusted AI Decision Systems

Information Services Group (Nasdaq: III) published its 2026 report examining specialty analytics and AI services for life sciences companies.

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Organizations adopt AI and analytics to integrate scientific data, shorten development cycles and improve decisions, ISG Provider Lens^® report says

STAMFORD, Conn.--(BUSINESS WIRE)-- Life sciences companies are seeking validated capabilities that transform data into actionable insights for daily research, clinical and commercial work, according to a new research report published today by Information Services Group (ISG) (Nasdaq: III), a global AI-centered technology research and advisory firm.

The 2026 ISG Provider Lens^® global Specialty Analytics and AI Services — Life Sciences report finds that companies need to make faster, better-informed decisions as they face challenges including rising development costs, clinical attrition and patent expirations. Many are turning to AI tools to address the increasing complexity of decision-making based on genomic, imaging, clinical and real-world data sources. However, legacy platforms, fragmented data and inconsistent semantics can limit the scale of AI implementations and make outputs less reliable.

“The next test is whether companies can turn the growing volume and variety of scientific data into decisions people will trust,” said Jenn Stein, partner and industry leader, ISG Life and Health Sciences. “In life sciences, an AI output has little value if its evidence and limitations cannot be inspected by researchers, clinicians and regulators.”

Companies need to unify structured and unstructured information across the life sciences value chain. They expect ontologies, metadata, data lineage and semantic layers to be aligned to provide usable context. These foundations must support analytics and context-rich AI while preserving privacy, data quality and access controls.

To shorten clinical timelines, enterprises are adopting AI for target and biomarker identification, molecular design and protocol digitization. At the clinical trial stage, they are adopting AI tools for patient stratification, site selection, recruitment and trial monitoring. Demand is strongest for capabilities embedded in scientific workflows with transparent evidence and expert validation. Companies favor tools that coordinate retrieval, reasoning, document generation and validation within regulated processes.

Safety, regulatory and quality teams are using AI assistance in tasks including intake, extraction, triage and narrative generation. Organizations are also applying these tools to anonymization, submission preparation and compliance assessment. AI auditability, source attribution and confidence scoring remain essential requirements. Life sciences companies adopting AI also need to monitor model drift, inappropriate outputs and the escalation of ambiguous findings to qualified experts, ISG says.

“The market will take shape around bounded AI workflows that remove friction while preserving expert accountability,” said Manav Deep Sachdeva, ISG principal analyst and lead author of the report. “Leading providers will be able to redesign workflows and roles and sustain hybrid human-AI operations as buyers move from demonstrations to measurable production results.”

The report also explores other AI and analytics trends in affecting life sciences enterprises, including the expansion of managed, transaction-based and outcome-linked sourcing arrangements and the adoption of conversational analytics in commercial and patient services.

For more insights into the analytics challenges faced by life sciences enterprises, along with ISG’s advice for addressing them, see the ISG Provider Lens Focal Points briefing here.

The report evaluates the capabilities of 22 providers in one quadrant: Specialty Analytics and AI Services — Life Sciences.

The report names Agilisium, Axtria, C5i, CitiusTech, Fractal Analytics, Quantiphi, Straive, Tiger Analytics and ZS as Leaders in the quadrant.

In addition, Infocepts is named as a Rising Star — a company with a “promising portfolio” and “high future potential” by ISG’s definition — in the quadrant.

In the area of customer experience, HCLTech is named the global ISG CX Star Performer for 2026 among Specialty Analytics and AI Services Lifesciences providers. HCLTech earned the highest customer satisfaction scores in ISG's Voice of the Customer survey, part of the ISG Star of Excellence™ program, the premier quality recognition for the technology and business services industry.

The 2026 ISG Provider Lens global Specialty Analytics and AI Services — Life Sciences report is available to subscribers or for one-time purchase on this webpage.

About ISG

ISG (Nasdaq: III) is a global AI-centered technology research and advisory firm. A trusted partner to more than 900 clients, including 75 of the world’s top 100 enterprises, ISG is a long-time leader in technology and business services that is now at the forefront of leveraging AI to help organizations achieve operational excellence and faster growth. The firm, founded in 2006, is known for its proprietary market data and research, in-depth knowledge and governance of provider ecosystems, and the expertise of its 1,500 professionals worldwide working together to help clients maximize the value of their technology investments.

View source version on businesswire.com: https://www.businesswire.com/news/home/20261022972832/en/

Press Contacts:

Laura Hupprich, ISG
+1 203-517-3132
laura.hupprich@isg-one.com

Erik Arvidson, Matter Communications for ISG
+1 978-518-4542
isg@matternow.com

Source: Information Services Group, Inc.

Key Terms

data lineage technical

Data lineage is a map that shows where a piece of data came from, how it moved and changed, and where it is used — like a package tracking history for information. For investors, clear lineage helps verify that financial metrics, risk models, or product analytics are built on reliable inputs, supports regulatory audits, and reduces the chance that decisions are based on corrupted or misunderstood data.

biomarker medical

A biomarker is a measurable indicator found in the body, such as in blood or tissues, that provides information about health, disease, or how the body responds to treatment. For investors, biomarkers can signal the potential success or risk of medical products or therapies, influencing the value of related companies and industry trends. They act like signals or clues that help assess the progress of medical advancements and their market impact.

patient stratification medical

Patient stratification is the practice of grouping patients into subgroups based on shared characteristics — such as symptoms, test results, or likely response to treatment — so therapies and trials can be targeted more precisely. For investors, it matters because better matching of treatments to the right patients can raise the chances of clinical success, reduce time and costs in development, and increase the likelihood of regulatory approval and profitable market adoption, much like tailoring a product to the right customer segment.

model drift technical

Model drift is the gradual loss of accuracy in a statistical or machine-learning model because the real-world patterns it learned from have changed, like a weather map that becomes less useful as the climate shifts. For investors, drift matters because models that power trading signals, risk scores, pricing or fraud detection can start giving misleading outputs, increasing the chance of bad trades, mispriced risk or regulatory problems unless the models are monitored and updated.