AI in Business: The Crucial Challenge of Common Language with Data

The Evolution of AI Models in Business

Artificial intelligence models have reached a level of performance that is no longer a major barrier to their adoption in businesses. However, a persistent challenge remains: their ability to interact effectively with internal company data. After several years of experimentation, Chief AI Officers (CAIOs) are finding that integrating AI models with existing data systems has become the main obstacle to their deployment.

Generative AI, while useful for tasks such as text writing or document summarization, only reaches its full potential when it can leverage company data. For example, an AI agent that can analyze budget discrepancies by cross-referencing information from ERP, CRM, and reporting tools becomes a strategic asset. However, this deep analytical capability is hindered by integration difficulties.

The Technical Challenges of Data Integration

To answer questions that seem simple, an AI agent often needs to access a multitude of systems, such as those for financial management, billing, customer relations, as well as document databases and decision-making tools. Each of these systems has its own data formats, access rules, authentication mechanisms, and integration constraints. This technical diversity complicates innovation and reduces the potential benefits of AI.

The Crucial Importance of Standardization

In the face of this fragmentation, establishing a common language becomes imperative. Companies need a standardized framework that allows AI applications to communicate consistently, securely, and audibly with business systems. The introduction of standardized protocols, such as the Model Context Protocol by Anthropic in November 2024, illustrates this necessity. This protocol acts as a "USB-C port" for AI systems, replacing the multitude of proprietary connectors with a single framework that facilitates communication between any AI application and business systems.

The advantages of such an approach are numerous. It reduces development time, centralizes security policies, facilitates scalability, and ensures interoperability between systems. Moreover, an open protocol frees companies from dependence on a single proprietary ecosystem, providing them with a competitive edge by accelerating their AI deployment.

The Risks of Lack of Standardization

Without a shared language and clear rules, AIs risk creating more problems than they solve when interacting with sensitive data. For example, an intelligent assistant tasked with managing financial processes could misinterpret data from the ERP, accounting platform, and analytical tools, leading to misinterpretations, security breaches, and inconsistencies in handling sensitive information.

This challenge goes beyond technical considerations and touches on the trust we can place in AI. When each AI system imposes its own data access rules, authentication mechanisms, and exchange formats, governance becomes complex. This opacity fuels skepticism and hinders the large-scale adoption of AI.

Towards Accessible and Secure AI for All

Standardization must incorporate security principles from the outset, such as robust authentication and traceability of data access. It should allow for the centralized application of governance policies, ensuring that every interaction between AI and business data adheres to the rules established by the organization. Although an open protocol does not solve all problems, it paves a clear path towards smoother and more secure integration. The security of the MCP chain, control of exposed servers, and prevention of injection attacks via connected tools remain active areas of focus.

This movement towards standardization is not only relevant for large companies. All organizations, regardless of size, need open standards to benefit from AI without complicating their digital environment. This makes the topic strategic beyond the realm of IT departments, as it conditions each organization's ability to transform the promise of AI into tangible value.

AI leaders must demand the integration of open protocols in every specification and ask their vendors for a clear roadmap regarding these standards. This has become a selection criterion as important as security or performance.

The future of AI in business now rests on our collective ability to develop the language that will allow AI to effectively communicate with the organization. Only under this condition can AI fulfill its promises.