The performance of AI models is no longer an issue. Their inability to interact with your data, yes. The challenge now is that of common language.
After two years of widespread experimentation, the observation that I share with my CAIO peers can be summed up in one sentence: model performance is no longer the limiting factor. Integration with business data has become so. Generative AI has entered a more complex phase, that of connecting these new tools and existing systems, without recreating an additional layer of fragmentation.
AI only truly creates value when it can work with business data. Certainly, an agent capable of writing a text or summarizing a document is useful, but an agent capable of analyzing a budget gap and reconciling information from an ERP, a CRM and a reporting tool becomes strategic.
This is also where the difficulties begin.
To answer a seemingly simple question, an AI agent must often query several systems including financial management, invoicing, but also customer relations, documentary bases, as well as decision-making tools. However, each of these environments is based on its own formats, its own access rules, its own authentication mechanisms and its own integration constraints. This multiplication of technical bridges generates complexity which slows down innovation and dilutes the expected benefits of AI.
The criticality of standardization
Faced with this fragmentation, common language becomes a necessity. Businesses need a standardized framework that allows AI applications to interact with business systems in a consistent, secure, and auditable way. The emergence of standardized protocols such as the Model Context Protocol, introduced by Anthropic in November 2024, illustrates this collective awareness. This type of standard functions as a “USB-C port for AI systems,” replacing the jungle of proprietary connectors with a single framework that allows any AI application to communicate with any enterprise system.
The benefits of such an approach are tangible. They include a reduction in development time, a centralization of security policies, easier scalability and the guarantee of interoperability. Above all, an open protocol frees businesses from dependence on a single proprietary ecosystem. Organizations that embrace standardization today gain a major competitive advantage by accelerating their AI deployment.
Without clear rules and a shared language, our AIs risk creating more problems than they solve by interacting with sensitive data. Imagine an intelligent assistant responsible for orchestrating financial processes, but unable to uniformly understand data from the ERP, accounting platform and analytics tools. Each interaction becomes an area of risk such as errors of interpretation, security breaches, or even inconsistencies in the processing of sensitive information.
This challenge is not just about technology, it goes to the heart of how we can trust AI. When each AI system imposes its own data access rules, its own authentication mechanisms and its own exchange formats, governance becomes a nightmare. This lack of common language creates an opacity that fuels skepticism and hinders large-scale adoption.
Towards simple AI for everyone, secure for the business
This standardization must natively integrate the security principles of robust authentication and traceability of data access. It must enable centralized enforcement of governance policies, ensuring that every interaction between AI and business data follows the rules established by the organization. Let’s stay clear, an open protocol does not solve everything. The security of the MCP chain, the control of exposed servers, the prevention of injection attacks via connected tools are all still active projects. But the course is set, and the trajectory is the right one.
This movement does not only concern large groups. All companies, whatever their size, need open standards to benefit from AI without complicating their digital environment. This is precisely what makes this subject strategic beyond the circle of CIOs because it conditions the ability of each organization to transform the promise of AI into concrete value.
Including the requirement for open protocols in each specification, and asking its publishers for a clear roadmap on these standards must be the requirement of AI managers. It has become a selection criterion in the same way as safety or performance.
The future of AI in business will now be built on our collective capacity to build the language that allows AI to dialogue with the business. It is on this condition that AI will keep its promises.
