Cognizant and Palantir Team Up to Accelerate AI-Driven Modernization in Healthcare and Enterprise Operations
Cognizant is partnering with Palantir to bring AI into the core of healthcare and enterprise operations. The plan: combine Palantir Foundry and AIP with Cognizant's TriZetto healthcare platforms and business process operations to deliver secure, scalable AI across real workflows.
For operations leaders, this isn't about pilots that never leave the lab. It's about moving data, decisions, and people into one system so work gets done faster, with fewer errors, and clear audit trails.
What's in the stack
- Palantir Foundry: Data integration, modeling, and operational analytics across complex systems. Learn more
- Palantir AIP: AI agents and LLMs grounded in enterprise data with controls, lineage, and monitoring.
- Cognizant TriZetto: Healthcare payer platforms for claims, benefits, and member services. Product overview
- Business Process Operations: Cognizant's managed services to execute and scale the work.
Why operations teams should care
- Shorter cycle times and higher straight-through processing (STP) on rules-heavy workflows.
- Fewer handoffs and rework through shared data models and governed AI agents.
- Better forecasting, capacity planning, and SLA management with real-time insights.
- End-to-end traceability for audits, regulatory checks, and model oversight.
Healthcare use cases that can move the needle fast
- Claims adjudication and edits: AI-assisted validations, duplicate detection, and payment integrity.
- Prior authorization: Triage, clinical criteria checks, and auto-approvals where rules allow.
- Care management: Risk stratification and next-best actions surfaced inside existing tools.
- Revenue integrity: Overpayment detection, coordination of benefits, and subrogation flags.
- Provider data ops: Directory accuracy, contracting updates, and credentialing workflows.
Beyond healthcare: enterprise ops plays
- Demand and supply planning: Scenario planning, constraints, and service-level tradeoffs.
- Field and workforce scheduling: Shift optimization and skills-based routing.
- Procurement and vendor risk: Terms analysis, spend insights, and compliance checks.
- Finance operations: Close automation support, variance explanations, and anomaly detection.
- Contact center: AI-assisted responses, QA summaries, and intent routing.
Data governance and risk management
Healthcare brings PHI, consent, and state-by-state rules. AIP and Foundry offer role-based access, policy enforcement, and full lineage so every prediction, prompt, and decision is traceable. Pair that with TriZetto's domain workflows and you get speed without losing control.
Keep humans in the loop where regulation or risk demands it. Automate the rest with clear thresholds, overrides, and alerts.
Practical integration path
- Map the process: Identify the system-of-record, decision points, SLAs, and failure modes.
- Pick one high-volume workflow: Claims edits, prior auth triage, or member servicing are good starters.
- Stand up a thin slice: 8-12 weeks to integrate data, define policies, and pilot with a small team.
- Prove control: Add quality gates, sampling, and bias/variance monitoring from day one.
- Scale with playbooks: Reuse data models, prompts, and decision templates across adjacent processes.
Metrics that matter
- STP rate and average handle time (AHT)
- First-pass yield and rework percentage
- Service levels and backlog days on hand
- Exception rate per 1,000 transactions
- Audit findings, model drift, and override frequency
What this means for operations leaders
Treat this as an operating model change, not a software rollout. Data contracts, decision rights, and QA need to be built into day-to-day work. Start small, show results, then scale through shared templates and governance.
If your teams already run on TriZetto, this partnership reduces integration lift and speeds up time to value. If not, the playbook still applies: standardize data, wrap decisions in policy, and plug AI where it trims time and errors.
Next steps
- Identify one process with high volume and clear rules to pilot.
- Define success criteria and guardrails with compliance and clinical leaders early.
- Co-design with front-line operators; they know where the friction is.
- Publish a one-page runbook: inputs, prompts/policies, outputs, and escalation paths.
- Review results weekly; scale only after control limits are met for two consecutive cycles.
Upskill your team for AI in operations
Building internal capability is the multiplier. For role-specific learning and certifications, see Courses by Job and the AI Automation Certification.
Your membership also unlocks:
