The AGI debate is misguided. The bigger potential is Enterprise General Intelligence – News

Silicon Valley’s debates over the viability of human-like artificial general intelligence overlook the likelihood that true superintelligence won’t emerge in our phones but in enterprise systems. The race for AGI is heading in the wrong direction.

Today’s foundation models, impressive as they are, remain fundamentally reactive. They are sophisticated autocomplete systems that respond to prompts but cannot autonomously run true business functions. Enterprise environments are the perfect proving ground for genuine machine superintelligence, which some are now calling Enterprise General Intelligence.

EGI is a superior proving ground for advanced AI. Unlike the chaotic real world, enterprise systems operate within human-designed parameters where cause-and-effect relationships are engineered, not inferred. From healthcare information systems to supply chain management and cloud orchestration, these environments offer structured complexity that current AI can actually master.

Agentic edge

The critical leap to EGI isn’t better language models but agentic AI that can set goals, plan actions and execute them across enterprise systems without constant human oversight. For example, an EGI wouldn’t just analyze a company’s supply chain disruption; it would automatically reroute orders, update inventory forecasts, renegotiate supplier contracts and notify stakeholders while documenting its reasoning at every step.

It would build, enhance and modify its elements over time based on user feedback. For example, an EGI system that recognizes that it lacks a specific reasoning capability that necessitates an application programming interface would build the API itself.

EGI requires five core capabilities: autonomous goal decomposition and planning; persistent awareness of system states across heterogeneous platforms; multi-system action selection and verification; an AI-ready enterprise ecosystem, including data and APIs; and compositional reasoning that builds unified understanding across sometimes conflicting enterprise terminologies. A sixth underlying capability is continuous reinforcement of learning over time.

The technical challenges are substantial but more tractable than general-purpose AGI. EGI requires shifting from static prompts to real-time system integration, from text generation to multi-system orchestration and from human validation to autonomous error correction based on predefined, human-generated rules and guardrails.

Humans are essential

Early EGI capabilities are already emerging. Autonomous agents and copilots are being rolled out to support enterprise workforces, hinting at cross-application intelligence and providing glimpses of autonomous customer relationship management. But these remain narrow applications of what could become a comprehensive enterprise superintelligence.

In a fully formed EGI of the future, humans will collaborate with EGI as its creative force, arbiter of judgment, auditor and guarantor of risk mitigation. EGI will give people more creative capacity to engage with customers and stakeholders and conceive of new experiences for EGI to tackle.

In an enterprise environment with continuous learning and development capabilities, people in every type of role and at every level of professional experience will have customized learning pathways. They will continually learn new skills, uncover new opportunities and keep pace with managing and symbiotically collaborating with EGI systems and new forms of intelligent automation.

Commercial impact

The implications for enterprise software are profound. Companies that achieve EGI-level capabilities won’t just build better software; they’ll create autonomous business intelligence that knows more than any single human and can act on that knowledge in real-time. Traditional enterprise software based on reactive tools will face the same disruption that has made some legacy industry leaders redundant since the dawn of the internet.

The real signal isn’t simply consumer AI use cases or adoption metrics. It’s enterprise AI that demonstrates genuine autonomy in business operations. For business leaders, the question isn’t whether to adopt AI, but whether your organization will be supercharged by truly intelligent systems or merely assisted by sophisticated tools.

The path forward

We’re approaching an inflection point where enterprise systems become genuinely intelligent agents rather than passive databases and workflows. The companies that recognize this transition early will define the next era of business.

The superintelligence revolution won’t arrive through philosophical debates about consciousness or viral consumer applications. It will emerge quietly in corporate back offices, making millions of autonomous decisions that collectively demonstrate intelligence far exceeding human capability alone.

That transformation is closer than most realize, and its impact will be far more immediate than any theoretical AGI breakthrough. The question isn’t whether machines will become super-intelligent but whether the enterprise will be among the first to harness that intelligence in a well-managed and strategic way, or among the last to recognize it has already arrived.

Prem Natarajan is executive vice president, chief scientist and head of enterprise AI at Capital One Financial Corp. He wrote this article for News.

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