As artificial intelligence permeates the corporate landscape, a critical question emerges: how can businesses move beyond the unpredictable nature of general-purpose chatbots? Salesforce has stepped into this conversation with a new proposal: Enterprise General Intelligence (EGI). Rather than chasing the elusive 'holy grail' of Artificial General Intelligence (AGI)—which aims to replicate human-level cognition across all domains—Salesforce argues that the business world requires a more disciplined, reliable evolution of the technology. EGI is not about creating a single 'brain' for the company. Instead, it defines a framework for deploying specialized, high-performing agents that prioritize consistent, domain-specific decision-making over broad, conversational versatility.

The core of this proposal lies in what the team calls the 'Capability-Consistency Matrix.' This tool acts as a yardstick for evaluating AI systems, plotting them based on their ability to solve complex problems and their reliability over time. Most early-stage systems fall into categories like the 'Prodigy'—impressive in one instance but dangerously unpredictable the next—or the 'Workhorse,' which is reliable but fundamentally limited in scope. The target state for any serious enterprise deployment is the 'Champion.' A Champion-level AI is defined by its ability to perform high-level reasoning while maintaining a steady, trustworthy output that business leaders can rely on to make material decisions.

Achieving this level of maturity is not a simple 'plug-and-play' endeavor. The EGI framework maps out a rigorous, three-phase evolution for any organization. It starts with base-level pre-training, progresses through industry-specific fine-tuning, and culminates in 'Ultra-fine tuning.' This final, critical stage injects the model with deep, organizational-specific context. By tailoring the AI to the unique data, culture, and workflows of a specific company, businesses can move away from generic answers and toward automated systems that actually understand the proprietary context in which they operate.

Beyond the models themselves, the EGI Readiness Framework highlights three infrastructure pillars that businesses must cultivate to succeed. First, they need a robust technical architecture that includes memory systems, Retrieval-Augmented Generation (RAG), and integrated data pipelines to serve as the system's 'memory.' Second, they must implement strict risk governance, specifically a 'human-at-the-helm' model that explicitly defines when the AI has the authority to act and when it must pause for human review. Finally, and perhaps most overlooked, is the human element. The framework demands a dedicated strategy for skill development, ensuring that the workforce is trained not just to use these tools, but to effectively manage and collaborate with them as colleagues.

Ultimately, the move toward Enterprise General Intelligence represents a maturation of the AI industry. We are shifting away from the 'wow factor' of general consumer-facing chatbots and toward a future of specialized, agentic ecosystems. In this vision, a single company won't rely on one massive, monolithic model. Instead, it will leverage a fleet of interconnected, domain-specific agents—each operating at a 'Champion' level within its own department. This is how the promise of AI transitions from a speculative experiment into an operational imperative.

As artificial intelligence permeates the corporate landscape, a critical question emerges: how can businesses move beyond the unpredictable nature of general-purpose chatbots? Salesforce has stepped into this conversation with a new proposal: Enterprise General Intelligence (EGI). Rather than chasing the elusive 'holy grail' of Artificial General Intelligence (AGI)—which aims to replicate human-level cognition across all domains—Salesforce argues that the business world requires a more disciplined, reliable evolution of the technology. EGI is not about creating a single 'brain' for the company. Instead, it defines a framework for deploying specialized, high-performing agents that prioritize consistent, domain-specific decision-making over broad, conversational versatility.

The core of this proposal lies in what the team calls the 'Capability-Consistency Matrix.' This tool acts as a yardstick for evaluating AI systems, plotting them based on their ability to solve complex problems and their reliability over time. Most early-stage systems fall into categories like the 'Prodigy'—impressive in one instance but dangerously unpredictable the next—or the 'Workhorse,' which is reliable but fundamentally limited in scope. The target state for any serious enterprise deployment is the 'Champion.' A Champion-level AI is defined by its ability to perform high-level reasoning while maintaining a steady, trustworthy output that business leaders can rely on to make material decisions.

Achieving this level of maturity is not a simple 'plug-and-play' endeavor. The EGI framework maps out a rigorous, three-phase evolution for any organization. It starts with base-level pre-training, progresses through industry-specific fine-tuning, and culminates in 'Ultra-fine tuning.' This final, critical stage injects the model with deep, organizational-specific context. By tailoring the AI to the unique data, culture, and workflows of a specific company, businesses can move away from generic answers and toward automated systems that actually understand the proprietary context in which they operate.

Beyond the models themselves, the EGI Readiness Framework highlights three infrastructure pillars that businesses must cultivate to succeed. First, they need a robust technical architecture that includes memory systems, Retrieval-Augmented Generation (RAG), and integrated data pipelines to serve as the system's 'memory.' Second, they must implement strict risk governance, specifically a 'human-at-the-helm' model that explicitly defines when the AI has the authority to act and when it must pause for human review. Finally, and perhaps most overlooked, is the human element. The framework demands a dedicated strategy for skill development, ensuring that the workforce is trained not just to use these tools, but to effectively manage and collaborate with them as colleagues.

Ultimately, the move toward Enterprise General Intelligence represents a maturation of the AI industry. We are shifting away from the 'wow factor' of general consumer-facing chatbots and toward a future of specialized, agentic ecosystems. In this vision, a single company won't rely on one massive, monolithic model. Instead, it will leverage a fleet of interconnected, domain-specific agents—each operating at a 'Champion' level within its own department. This is how the promise of AI transitions from a speculative experiment into an operational imperative.