Analyst(s): Dion Hinchcliffe
Publication Date: June 2, 2025
What is Covered in this Article:
- The rapid emergence of the Model Context Protocol (MCP) as a vendor-backed standard for managing context across AI models and applications. Upshot: Every organization must now have a position on MCP within its AI strategy, given its sudden importance in the industry.
- Why MCP is fast-becoming a foundational pillar of long-term AI strategy, especially for enterprises pursuing composability, model interoperability, and memory management.
- How OpenAI, Anthropic, Google DeepMind, Meta, and now Microsoft are aligning around MCP to define the future of AI data access and agentic AI ecosystems.
- Strategic implications for CIOs: Can significantly reduce vendor integration complexity, especially for agentic AI, while also requiring navigating new forms of ecosystem lock-in.
- What enterprise tech leaders must evaluate now, including major implications for data privacy, information sharing, orchestration tooling, cybersecurity, and whether their vendors will fully adopt or fork MCP.
The Event – Major Themes & Vendor Moves: The Model Context Protocol (MCP), first introduced in April 2024 by Anthropic, has rapidly transformed from a promising initiative into a de facto standard for how AI systems manage memory and context across sessions and models. Initially taken up by OpenAI, Google DeepMind, and Meta, the real inflection point came when Microsoft formally joined the initiative as of May 2025, and made it generally available this week. With Microsoft’s vast enterprise footprint – from Azure OpenAI Service, Copilot, through to Office 365 – its industry alignment with MCP signaled the protocol’s elevation to enterprise relevance and mass-scale viability.
At its core, MCP establishes a shared format and API structure for storing, transferring, and interpreting context across language and multimodal models. It introduces standardized primitives like memory, messages, threads, and runs that support persistent and portable state across different agents, applications, and tools. These capabilities are essential to unlocking agent-based workflows, conversational memory, task continuity, and seamless hand-offs between models.
Major vendors have already started integrating MCP into their enterprise offerings:
- OpenAI now supports MCP-compliant memory primitives in GPT-4o, including shared memory across apps and user interactions.
- Microsoft is expected to roll out MCP-based memory capabilities across Copilot and Azure AI Studio in H2 2025, enabling enterprise customers to build agent workflows with context persistence.
- Anthropic has committed to building native support for MCP across Claude APIs, with emphasis on privacy-respecting memory state management.
- Meta plans to open-source tooling for MCP-based memory orchestration to drive adoption in open ecosystems.
- Google DeepMind is developing cross-model context sharing through Gemini and has signaled its intent to align Gemini agent frameworks with MCP primitives.
These moves together are shaping a vendor-neutral scaffolding for how enterprise AI applications will access and manage long-term state, with major implications for architecture, interoperability, and data control. MCP will almost certainly become an anchor capability for enterprise AI and CIOs must decide on how much to bet on early incarnations, given inevitable and already occurring issues, such as early cybersecurity issues.
Model Context Protocol Is the Next Strategic Control Point in AI, And CIOs Must Treat It That Way
Analyst Take: The launch of the Model Context Protocol (MCP) marks a profound shift in the architecture of enterprise AI, and not just because of who’s backing it. While OpenAI, Anthropic, Google DeepMind, Meta, and Microsoft bring undeniable gravitational pull to MCP, the deeper impact is structural: For the first time, there is a credible, shared standard for how AI systems carry context across agents, sessions, and tools. This is not just a formatting agreement. It’s an operational consensus that changes how AI behaves in memory-aware, goal-driven, multi-agent scenarios.
From Model Race to Memory Race
The era of standalone model performance supremacy is fading. What’s rising instead is the memory race: a battle to manage agentic, persistent, and interoperable context as the backbone of scalable AI. Model Context Protocol provides the primitives such as memory objects, threads, messages, runs. This allow this state to be formalized and passed across boundaries. For enterprises building AI copilots, autonomous agents, or integrated assistant networks, this means one thing: Interoperability is no longer optional.
Enterprises that embrace MCP early gain the ability to reduce bespoke glue code, accelerate multi-agent orchestration, and ensure long-term viability across evolving model ecosystems. The same organizations that struggled with vendor fragmentation in the early days of cloud APIs or container orchestration now face a similar decision: Adopt the open scaffolding or risk being isolated in closed-loop memory systems.
The Lock-In Dilemma
But MCP’s promise comes with caution. By standardizing memory handling, vendors gain a powerful new control point, one that could either remain open or become a source of strategic lock-in. Microsoft’s entrance into the MCP ecosystem significantly raises the stakes here. CIOs must ask: Will MCP remain truly portable, or will each vendor implement its own variation, selectively supporting features that reinforce its ecosystem? Early adopters should insist on full transparency in vendor MCP roadmaps, including plans for multi-cloud portability, third-party agent integration, and auditability of memory objects.
Security and Governance: Unsolved
Equally critical is the security dimension. Persistent memory is no longer theoretical. That means sensitive threads – structured and stored across agents – must be governed like any other enterprise data. CIOs must scrutinize whether MCP implementations will offer true enterprise-grade data governance, including encryption, lifecycle control, redaction, and the ability to port or purge memory across systems. The protocol is new, and early cybersecurity gaps have already been identified. Enterprises should build in audit trails, monitor vendor security practices around context data, and anticipate regulatory requirements for explainability and consent.
Bottom Line
The Model Context Protocol is no longer a cutting-edge innovation, it is rapidly becoming a pillar of AI strategy. CIOs must urgently assess where their current AI architectures intersect with MCP and whether their vendors are leading, lagging, or forking the standard. Much like the shift from isolated compute to shared cloud APIs, MCP marks a boundary-crossing moment in AI. Those who act now can define the structure of memory-aware AI inside the enterprise. Those who wait may find themselves working backward from lock-in.
What to Watch:
- Vendor Forks and Proprietary Extensions: While MCP aims for interoperability, watch for vendors quietly introducing proprietary extensions or partial support, creating subtle forms of lock-in under the guise of compliance.
- Security Standards for Context Memory: Expect pressure to establish encryption, access control, and lifecycle governance around MCP primitives, especially in regulated industries like healthcare and finance.
- Open-Source Tooling vs. Enterprise Licensing: Meta and others plan to open-source MCP orchestration tools, but enterprise-grade solutions may still come bundled with premium vendor ecosystems. CIOs must weigh flexibility vs. support.
- Enterprise Integration Platforms: Vendors like Microsoft and Google are racing to build low-code orchestration layers around MCP. These could either democratize usage or become new proprietary choke points.
- Model-Agnostic vs. Model-Centric Architectures: As more LLMs integrate MCP, watch whether enterprises embrace a single model provider or build model-agnostic pipelines leveraging MCP to switch between models in real time.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
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Author Information
Dion Hinchcliffe is a distinguished thought leader, IT expert, and enterprise architect, celebrated for his strategic advisory with Fortune 500 and Global 2000 companies. With over 25 years of experience, Dion works with the leadership teams of top enterprises, as well as leading tech companies, in bridging the gap between business and technology, focusing on enterprise AI, IT management, cloud computing, and digital business. He is a sought-after keynote speaker, industry analyst, and author, known for his insightful and in-depth contributions to digital strategy, IT topics, and digital transformation. Dion’s influence is particularly notable in the CIO community, where he engages actively with CIO roundtables and has been ranked numerous times as one of the top global influencers of Chief Information Officers. He also serves as an executive fellow at the SDA Bocconi Center for Digital Strategies.
