Rearchitecting life sciences and pharmaceutical customer experience for the MCP generation
AI is transforming how life sciences and pharmaceutical organizations engage patients, support healthcare professionals, empower employees, and operate across increasingly complex technology environments. But one question stands above the rest: Who controls the future of your patient, provider, and employee experiences, your organization or your technology vendors?
At Avaya, we believe the answer should be you. That is why we are embracing the open future of AI, building the Avaya Infinity® platform to support Model Context Protocol (MCP).
It is the right move for pharmaceutical, biotechnology, medical technology, and other life sciences organizations that need to innovate without abandoning trusted systems, sensitive data, established workflows, or the security, privacy, and governance requirements that protect patients, healthcare professionals, and the enterprise.
What is MCP?
MCP stands for Model Context Protocol, an emerging open standard that enables AI models such as GPT, Claude, or Gemini to securely and reliably interact with external tools, data sources, APIs, and user context in a structured way.
For life sciences and pharmaceutical organizations, this can give AI access to the context needed to understand a patient, healthcare professional, product, therapy, service request, case, interaction, or business process across multiple systems. Instead of operating in isolation, AI can work with relevant enterprise context, established workflows, permissions, and operational intent.
The result is AI that can become significantly more useful across patient support, medical information, healthcare professional engagement, product support, clinical operations, adverse event workflows, market access, employee support, and other life sciences environments where accuracy, privacy, continuity, and trust matter.
"This is not a 'wait-and-see' moment. Avaya believes the time to be intentional about building the definitive open orchestration engine for the modern enterprise is now."
David Funck, Chief Technology Officer, Avaya
How does MCP work?
MCP is built on a client → model → server architecture:
- The Client gathers context such as patient, healthcare professional, or employee information, role, current task, permissions, interaction history, product information, or service environment.
- The Model such as Claude or GPT receives the context and a list of available tools or actions.
- The Server hosts approved tools, APIs, databases, and enterprise systems and executes authorized actions requested by the model.
All communication follows structured schemas so interactions can be interpretable, traceable, and governed, an essential requirement when AI is interacting with sensitive health-related information, regulated processes, product information, and business-critical workflows.
Avaya Infinity with MCP: Achieving AI-powered life sciences communications intelligence
Life sciences organizations can move beyond basic dashboards to analyze experience data, uncover trends, and generate real-time insights. Patient services, medical info, contact center, commercial, and ops leaders can securely explore enterprise data within required access controls.
Removing friction from life sciences AI with MCP
For too long, bringing AI into life sciences customer and patient experience has meant choosing between two difficult options:
- Lock in to a single provider's closed ecosystem, limiting flexibility as AI models, regulatory requirements, therapies, patient expectations, and business strategies evolve.
- Create costly, one-off integrations across CRM, patient services, medical information, pharmacovigilance, clinical systems, knowledge platforms, case management, identity, data environments, and other applications.
This false choice slows innovation and places additional pressure on already complex life sciences technology environments. It can also fragment experiences when information is distributed across patient programs, medical teams, commercial functions, clinical operations, service providers, and legacy and modern platforms.
Enter MCP, an open, vendor-agnostic standard designed to help AI models interact with tools, data, and operational logic securely and at scale.
Think of MCP as a universal connection layer for AI. Instead of building a different connection between every model and every life sciences system, organizations can create a standardized way for approved AI capabilities to access the context and tools they need.
As the AI ecosystem evolves, this approach can give life sciences and pharmaceutical organizations greater freedom to innovate while maintaining control over the systems, data, workflows, products, and relationships that make their organization unique.
MCP enables AI systems to operate with dynamic awareness of patient, healthcare professional, employee, session, product, and workflow context.
For Avaya Infinity, this means AI models can deliver experiences informed by relevant information across complex life sciences environments. A patient support or medical information interaction could draw upon approved CRM, product, program, case, knowledge, and interaction data while respecting the permissions governing each source.
That level of contextual intelligence can be especially valuable for organizations managing thousands of employees, therapies, products, patient programs, healthcare professional relationships, workflows, integrations, and interactions across regions and channels.
Shaping the future of open AI for life sciences customer experience
Avaya is not just talking about open AI. We are actively helping shape open standards while bringing native MCP support to Avaya Infinity for secure, model-agnostic, enterprise-ready orchestration.
For life sciences and pharmaceutical organizations, that creates three important advantages:
- Control: Gain more control over AI models and tools so you can select capabilities based on the requirements of each patient, medical, commercial, or operational use case instead of the limitations of a single vendor.
- Governance: Strengthen interoperability and oversight across customer experience systems, enterprise data, regulated workflows, applications, and AI so capabilities can be introduced responsibly.
- Future-readiness: Build an AI architecture designed for complex life sciences environments where privacy, security, traceability, interoperability, and long-term flexibility are essential.
Collaboration with Databricks
As part of our commitment to secure, scalable, and open AI, Avaya is collaborating with Databricks to bring enterprise-grade governance and data privacy capabilities to the MCP implementation within the Avaya Infinity platform.
For life sciences and pharmaceutical organizations, this approach can help establish controlled access between AI and the structured and unstructured data distributed across patient services, medical, clinical, commercial, operational, and enterprise systems.
With fine-grained access controls, audit logging, and governed integration with enterprise data sources, life sciences organizations can explore AI-powered patient, healthcare professional, and employee experiences while maintaining greater visibility into how information is accessed and used.
"Generative AI offers tremendous potential to transform customer experiences, and we are thrilled to collaborate with Avaya to help organizations quickly unify their data, simplify data and AI governance and security, and ultimately deliver AI that understands their business."
Heather Akuiyibo, VP of GTM Integration, Databricks
From fragmented life sciences systems to freedom with MCP
Before MCP, connecting every system to each AI model required point-to-point integrations, increasing development effort, maintenance, and complexity. MCP introduces a standardized intermediary layer, shifting life sciences organizations from an N x M integration challenge to a scalable N + M model. Approved enterprise tools expose capabilities through MCP servers, while AI models and applications connect via MCP clients.
This standardized connection offers a flexible foundation for linking AI with existing and future systems, significantly reducing redundant work and preserving current technology investments. Because life sciences interactions span multiple applications—such as CRM, patient services, medical info, case management, and analytics—Avaya Infinity with MCP connects these tools and workflows, allowing organizations to modernize without replacing their entire tech stack.
The potential ROI of MCP for life sciences and pharmaceuticals
With MCP, Avaya Infinity users can unlock deeper business and operational value by giving AI access to more relevant context and approved enterprise capabilities.
For life sciences organizations, that can mean faster patient support, more informed healthcare professional interactions, more efficient employee workflows, better continuity across service programs, greater workforce productivity, and more consistent engagement across channels.
MCP creates the foundation for generative and agentic AI that is not simply intelligent, but situationally aware of the patient, healthcare professional, product, therapy, case, interaction history, and permissions surrounding an interaction.
| Before MCP | After MCP | |
| Integration | Custom connectors between individual AI models and life sciences systems | A standardized MCP interface can work across models and approved enterprise tools |
| Deployment time | Long, expensive due to bespoke engineering | 50–70% faster, lower costs via reuse (quiq.com, Palma AI, Humanloop, aibase.com) |
| Data access | AI operates with limited visibility into patient, healthcare professional, product, and workflow context | AI can access approved real-time context across CRM, patient services, medical information, knowledge, case management, and other systems |
| Risk management | Manual lookup, delayed fraud detection | Instant cross-system checks reduce fraud by 30% |
The future of life sciences AI is open
Life sciences and pharmaceutical organizations shouldn't have to sacrifice decades of investment in therapies, research, data, and systems to adopt AI. Avaya shares the industry vision for open, contextual AI. By integrating MCP, Avaya enables life sciences firms to connect AI to trusted systems, choose optimal models, retain architectural control, and innovate continuously. The future of healthcare experience should be driven by intelligent connections across people, knowledge, systems, and science—not closed tech stacks.
Learn how Model Context Protocol reinforces Avaya’s open AI strategy
Glossary
Full glossary of terms below.
| Term | Definition | Why It Matters for Life Sciences and Pharmaceuticals |
| MCP (Model Context Protocol) | An open protocol that standardizes how AI models interact with tools, APIs, and memory via structured context objects. | Creates a scalable way to connect AI with CRM, patient services, medical information, case management, clinical systems, knowledge platforms, and other life sciences technologies. |
| Context | A bundle of structured information passed to a model—such as user ID, session history, roles, time, or tool availability. | Helps AI understand the patient, healthcare professional, employee, product, therapy, case, and situation surrounding an interaction. |
| Model | The generative AI (e.g., Claude, GPT-4, Gemini) that receives the MCP context and generates responses. | Gives life sciences organizations flexibility to select different AI models based on security, performance, governance, cost, or use-case requirements. |
| Client | The application or interface (e.g., chat window, agent framework, customer portal) that gathers and sends context to the model. | Can include patient support applications, employee desktops, HCP portals, virtual assistants, contact center interfaces, and other digital experiences. |
| Server | The logic layer that hosts tools, APIs, functions, and data sources the model can use via MCP. | Creates a governed way for AI to interact with life sciences systems without giving models unrestricted access to sensitive or regulated resources. |
| Tools | Discrete functions or APIs (e.g., “search knowledge base,” “check ticket status,” “update CRM”) exposed to the model via MCP. | Allows AI to move beyond conversation and assist patients, healthcare professionals, and employees with real life sciences service processes. |
| Schemas | The structured definitions (in JSON or YAML) for how tools and context should be represented and passed to models. | Creates consistency between AI models and life sciences systems, making integrations easier to understand, govern, audit, scale, and troubleshoot. |
| Memory | Persistent context retained across sessions (user history, preferences, past actions) provided to the model. | Can enable more continuous experiences while allowing organizations to apply appropriate policies for how sensitive context is retained and used. |
| Ephemeral Context | Short-lived, session-specific information (e.g., current task, temporary variables). | Helps AI remain focused on the immediate patient, HCP, or employee need without unnecessarily retaining temporary information. |
| Agent | An AI system that can take actions, invoke tools, and make decisions based on MCP context — often used in autonomous or semi-autonomous workflows. | Provides the foundation for controlled agentic AI that can assist with multi-step patient support, medical information, employee, and operational workflows. |
| Orchestration | Coordinating multiple steps, tools, and model outputs to achieve a higher-order business goal. | Helps life sciences organizations connect interactions with the many systems, teams, and processes required to deliver complex support journeys. |
| Observability | The ability to inspect what context was passed, what decisions the model made, and how tools were used. | Supports transparency, governance, auditability, troubleshooting, security, and oversight when AI interacts with sensitive or regulated information. |
| Interoperability | The ability for different AI models and platforms to work with a shared structure of tools and context. | Gives life sciences organizations greater flexibility to evolve their AI strategies without replacing established systems or becoming dependent on a single AI provider. |