Introduction: A new type of user has arrived
Digital transformation continues to evolve, but we’re now seeing the next frontier emerge, one that changes the very definition of a “user.”
Where organisations once designed experiences for people, they must now design for machines.
Artificial intelligence agents, automation platforms, and connected systems are rapidly becoming active participants in the digital economy. These systems interpret, decide, and act - often autonomously. As a result, they must now be treated as users in their own right.
This marks the rise of Machine Experience (MX) - a new discipline focused on designing, governing, and optimising the experiences that machines have when interacting with your business.
At the heart of this transformation is a new open standard known as the Model Context Protocol (MCP) - a framework that enables interoperability and consistency in how AI models connect to systems, data, and tools. In short, MCP is the API for AI.
Understanding MCP: The API for the Machine Era
The Model Context Protocol (MCP) is an emerging open standard designed to help AI systems communicate with other digital systems securely and contextually. It defines how AI models can access resources and tools to complete context-specific tasks.
Originally introduced by Anthropic in late 2024, MCP is now rapidly becoming the default standard for enabling tools, data sources, and resources to connect into AI agents - including through OpenAI’s new Apps SDK and similar frameworks being adopted across the AI ecosystem.
In practice, MCP acts as a universal API layer for AI, allowing different models, tools, and data sources to work together in a structured and predictable way.
MCP enables organisations to:
- Standardise AI integration: Define a consistent way for AI models to describe what they can do, what data they need, and how they interact with systems.
- Add business context: MCP doesn’t just connect endpoints - it carries context about purpose, permissions, and boundaries, allowing AI systems to make more accurate and compliant decisions.
- Accelerate interoperability: Bridge the gap between enterprise systems and AI ecosystems, enabling machine-to-machine (M2M) collaboration across vendors, environments, and clouds.
- Build confidence and control: Standardised interfaces give businesses greater visibility, traceability, and governance over how AI systems operate within their environment.
In essence, MCP provides the plumbing for the intelligent enterprise - the structure through which machines can interact safely, efficiently, and meaningfully.
Why Machine Experience Matters
1. Machines are now active participants in your business
AI systems are no longer passive tools. They analyse data, make recommendations, and trigger workflows autonomously.
For example:
- A digital agent might use MCP to query a CRM, retrieve a customer record, and draft a support response.
- A supply chain model might interact with IoT sensors and logistics APIs to automatically reorder stock.
- A compliance bot might use MCP to assess documentation across multiple repositories in real time.
Each of these interactions represents a machine experience - a digital touchpoint that can be designed, measured, and optimised just like human ones.
2. Machine users have different needs
Human experiences rely on clarity, empathy, and usability. Machine experiences rely on structure, context, and consistency.
To engage effectively with machines, businesses must ensure:
- Data is structured, labelled, and discoverable.
- APIs are context-rich and predictable.
- Business processes are exposed in machine-readable ways.
Where humans need intuitive design, machines need semantic clarity.
3. Early adopters will define the next competitive advantage
Just as “mobile-first” design reshaped industries a decade ago, “machine-first” design will define leaders in the decade ahead.
Organisations that prepare now - by building MCP-ready interfaces and embedding MX principles - will unlock new levels of automation, intelligence, and speed.
Businesses that ignore this shift risk becoming invisible to the AI systems that are increasingly deciding where and how digital interactions occur.
Real-world example:
A logistics company using MCP to expose its services
Imagine a national logistics company that provides parcel tracking, route optimisation, and delivery scheduling. Traditionally, these services were accessed through human-facing portals and APIs that required custom integrations for every business client. By developing an MCP (Model Context Protocol) layer, the company exposes these same services in a way that’s machine-readable and AI-accessible. Now, AI agents from retailers, e-commerce platforms, or even customer service bots can directly query the logistics company’s MCP endpoints to check delivery statuses, reschedule shipments, or optimise routes — all without human intervention.
This move doesn’t just improve efficiency; it opens a new channel for revenue and integration. The company effectively becomes “AI-ready,” allowing machine users (AI agents, assistants, or autonomous business systems) to interact seamlessly with its logistics network. As AI adoption accelerates, the MCP layer becomes a key differentiator — positioning the business as a preferred integration partner for next-generation digital ecosystems.
Four Steps to Prepare for the Machine Experience Era
1. Establish AI Governance and Strategy
Machine Experience begins with clarity of purpose. Organisations must set the parameters for how AI systems will participate in operations and decision-making.
Build a governance framework that defines:
- The roles and responsibilities of AI systems.
- Accountability for outcomes and decisions.
- Data access and control policies.
- Ethical and operational safeguards to ensure trust and transparency.
Strong governance ensures that AI interactions remain aligned with business strategy and values.
2. Develop and Implement MCP Standards
Next, define your organisation’s approach to MCP implementation - the layer through which your AI systems and machine users will interact.
This means:
- Designing context protocols that describe how data is accessed, validated, and interpreted.
- Creating MCP endpoints that represent your key business processes and data sources.
- Ensuring consistency across systems so that every machine interaction follows a standard pattern.
Think of this as building your AI operating interface - a coherent way for machine users to “understand” and engage with your business ecosystem.
By adopting MCP early, organisations position themselves to integrate seamlessly into the broader AI ecosystem as it matures.
3. Modernise Infrastructure and Data Foundations
Machine experiences are only as good as the data and systems they depend on.
Invest in:
- Event-driven architectures that support real-time decision-making.
- Scalable cloud infrastructure capable of handling high volumes of machine-to-machine traffic.
- Robust identity and security controls to authenticate and authorise machine users.
As the number of AI agents and automated workflows grows, your infrastructure must be built to handle constant, intelligent interactions.
4. Foster a Culture of Innovation and Machine Literacy
Finally, organisations must evolve culturally as well as technically.
Teams should understand how AI systems think, how they consume data, and how to design for them. Encourage collaboration between designers, engineers, data scientists, and business leaders to create experiences that work for both humans and machines.
This can be achieved through:
- Ongoing training in AI literacy and ethics.
- Innovation programs focused on automation and intelligent systems.
- Cross-functional experimentation to identify new machine-driven opportunities.
A workforce that understands and embraces Machine Experience will be better equipped to innovate responsibly and effectively.
Conclusion: The Future Is Machine-Inclusive
The rise of Machine Experience represents a fundamental shift in how businesses design, operate, and compete.
Just as the web connected people to information, and APIs connected systems to data, MCP will connect AI to enterprise - unlocking a new era of autonomous collaboration and intelligent interaction.
Machines are becoming users. They are evaluating, selecting, and integrating services - just as customers once did. Designing for them means thinking differently about data, structure, and experience.
The organisations that master Machine Experience today will shape the standards of tomorrow - building systems that are not only human-centred but machine-inclusive.
Our View
At Exco Partners, we see Machine Experience as the next logical evolution in digital design - the point where technology strategy meets intelligent automation.
Businesses that prepare for machine users today will not just improve efficiency - they’ll future-proof how they operate, integrate, and scale in an AI-first world.
Our view is clear: organisations should start treating AI systems as part of their customer base. By adopting MCP standards, strengthening governance, and reimagining their data architecture, businesses can turn machine interactions into a competitive advantage.
Machine Experience isn’t science fiction - it’s the new foundation for digital business.