AI, once a differentiation strategy, is increasingly becoming infrastructure. Across the B2B space, companies are using AI to infuse analytics, consumer interactions, risk management, logistics, and business operations. However, due to subsequent spending on AI, companies are facing disjointed results along with diminishing returns.
The problem is neither the quality of the models nor the complexity of the algorithms. The problem lies in integration
The future of AI integration is about the freedom to move intelligence in a safe and contextual way. All of this is facilitated by modular approaches to building AI, the development of the Model Context Protocol (MCP) standard, the ability to run the AI agent autonomously across operational platforms, and DePIN as an infrastructure model.
The paradigm shift in AI adoption and the rise of AI engineering
The new determinism of point solutions
Enterprise adoption of AI in the early years was aimed at solving isolated problems. Although it works in an isolated environment, it becomes ineffective when there are interconnected activities in companies that AI has to handle. For example, there may be no link between forecasting solutions and procurement systems, or risk drivers may operate independently of compliance processes.
As a result, AI not only needs to improve at the mission level, it needs to become a common intelligence layer in a more mature enterprise. This means having systems that not only understand data, but context and process and policy dependencies.
Therefore, current systems must be able to:
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Share understanding in context between functions and departments
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To run continuously rather than in isolated execution cycles
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Deal with real-time operational and market dynamics
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Compliance with governance, audit and compliance frameworks
This marks the transition from building AI models to building the AI architecture itself, where intelligence will not be added on top of the previous setup.
Modular AI: Designing for scalability, flexibility, and sustainability
The commercial value of composability
Modular AI dissects intelligence into loosely coupled elements, models, tools and agents, which can be externally coordinated according to business requirements. In fact, this approach directly mirrors the architecture of microservices, and as such, it makes AI scalable without extensive rewriting.
In the B2B context, the need for this model is even more important since regulatory requirements for businesses are unlikely to remain the same for a long period of time. In other words, business units are always expanding, and newer information sources are being created all the time.
Important advantages that can be derived from modular AI are:
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Faster experience with limited operational risk
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Independent scaling of compute- and latency-intensive components
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Simple integration with existing systems, including third-party software
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Reducing dependence on a single supplier/customer in one’s business
Instead of having to rebuild their AI solutions every time a set of requirements changes, companies are now able to reconfigure and scale them. This is especially the case in today’s complex and ever-changing markets.
Standardized protocols as a foundation for building AI interoperability
Context segmentation problem
AI models rely heavily on data context, permissions, past behavior, and operating rules. Without a standardized protocol, all of these factors are often codified directly within applications, leading to variations between systems and difficulty maintaining them.
Context fragmentation poses many difficulties for:
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The behavior of the AI system is internally inconsistent
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The challenge of applying common governance guidelines
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Increased security risks caused by duplicate access logic
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Lack of ability to collaborate and share between AI systems
Standard protocols cover these challenges by defining:
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How artificial intelligence systems request and receive information
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What data is available in context and under what permissions
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How responses are structured and validated
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How to log in and use for auditing purposes
This approach to protocols allows for AI interoperability while maintaining predictability and control.
Model Context Protocol (MCP): The new standard for integration
Why MCP is changing how organizations connect AI with data
the Model Context Protocol (MCP) Provides a clear separation between AI intelligence and enterprise context. Unlike embedding business logic into models, permissions, or data access rules, MCP allows AI systems to request structured context from approved and governed sources.
This architecture has many benefits for organizations:
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Centralized control over data access and permissions ends
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Consistent AI behaviors across tools, agents, and departments
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Simplified data protection and audit compliance
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Reduce the attack surface by limiting direct data exposure
By treating context as a managed service layer, MCP enables organizations to responsibly scale AI while maintaining security, governance, and operational visibility.
Implementing an MCP: Integrating AI and enterprise systems
MCP servers as control points for enterprises
MCP servers act as a trusted intermediary between AI entities and business systems in actual implementation. They handle authentication, authorization, data scope, and response format issues to ensure that interactions are policy compliant.
An important use case here is the integration of AI agents with local databases via MCP servers. Instead of providing AI agents direct access to databases, MCP servers provide only the required context, which can be a structured query, aggregated reports, or approved results, according to criteria defined in enterprise-wide security policies.
This design supports:
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Decision making using artificial intelligence in real time, without revealing actual data
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The attack surface is reduced with minimal direct access to the system
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Audit trails for compliance and risk management
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Extend the use of multi-agents with fixed rules
With context accessed centrally by MCP serversAI governance is becoming simpler, facilitating enterprise-level deployment of AI.
Artificial Intelligence Agents: Operational Intelligence in Motion
Reactive tools for proactive systems
AI agents are a paradigm shift in enterprise automation. Compared to previous AI applications, which only responded to stimuli and computed on demand, agents are constantly working and responding to changing conditions.
B2B systems are increasingly seeing AI-based agents being used as operational collaborators rather than just passive tools. Customers now have the ability to interpret signals coming from different systems.
Examples of common organizations include:
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Monitor key performance indicators and implement corrective or preventive actions
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Collaborate on finance, operations and supply chain process flows
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Assist with compliance checks and automated reporting
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Dynamically optimize resource allocation and scheduling
As agent ecosystems expand, the need for standardized protocols, such as MCP, increases so that agents can align with companies’ rules and goals.
The role of infrastructure in enabling smart systems
Limitations of mainframe computing models
Traditional cloud infrastructure supported the needs of traditional cluster computing and cloud-based applications, but did not support the needs of always-on, autonomous AI agent operations. Latency, cost concentration, and single points of failure are just a few of the challenges to scaling AI workloads that are starting to come to the fore.
A future-ready integrated AI infrastructure must have:
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Able to handle distributed and variable workload patterns
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Transparent and usage-compliant cost
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Operable in the event of outages or local disturbances
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Supports edge-level intelligence
Such needs have stimulated interest in alternative infrastructure models that have the potential to support permanent, decentralized AI computing.
DePIN: Decentralized Physical Infrastructure for Artificial Intelligence
Why DePIN is important in enterprise AI
the Decentralized physical infrastructure networksDePIN, or DePIN, offers an innovative approach to provisioning computing, storage, and networking resources via incentivized networks and via a decentralized network approach. Furthermore, the initial applications that gave birth to the DePIN concepts came from the Web3 environment.
For B2B AI integration, DePIN provides:
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Geo-distributed execution of latency-sensitive AI agents
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Reliance on infrastructure providers is reduced
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Enhance redundancy and fault-tolerance capabilities for core applications
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It is scaled with the use of infrastructure demand
When combined with the use of modular architecture in AI and protocol implementation, DePIN provides a flexible platform for supporting intelligent systems in a multi-entity environment.
The rise of the complex artificial intelligence enterprise
Integrating intelligence across layers
Sophisticated business organizations are moving towards a composable architecture for AI, where the architecture is broken down into layers that can easily communicate with each other so that the company can get the most out of the technology.
The future enterprise AI stack will typically consist of the following:
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Modular AI agents Support specialized processes such as analytics, operations, compliance and customer engagement
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MCP-based protocols To handle secure, policy-controlled context transfers between AI systems and enterprise data sources
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Hybrid infrastructure structures Which integrates centralized cloud infrastructure with DePIN resource availability for distributed computing capabilities
This approach helps companies adopt new AI solutions without impacting the existing process. The modules are replaceable or expandable without affecting the existing process and thus help in reducing technical debt.
By dividing information, context, and infrastructure into coordinated layers, companies can achieve better visibility, governance, and speed. AI systems can work together in better ways, seamlessly respond and adapt to changes in their operation, and maintain consistency with business and regulatory constraints. Thus, composability is one of the key principles for achieving AI.
