1) Cognitive Enterprise Definition
The cognition of an otherwise legal entity is created by unifying Human Intelligence with Artificial Intelligence to a new intelligent structure, the cognitive enterprise. While parts may be replaced, updated, or changed, it requires both mutually exclusive types of intelligences to exist as a cognitive enterprise.
The fusion of Human Intelligence and Artificial Intelligence unfolds a new level of intelligence that shapes the cognition of ‘The Cognitive Enterprise”.
This is an elevated perspective of the cognitive enterprise defined by IBM.
Over the last five years of research and work on Human Intelligence Augmentation, and an intensive four-year study of neuroscience, we have deepened our understanding of applied AI and its utilization. Now, we dared to question the idea of an AI singularity and the notion that AI’s power over human intelligence would grow infinitely.
1) The Cognitive Enterprise
Starting from scratch: In general, an enterprise is a legal entity. A structure that is created but does not physically exist. It is represented by people, buildings, and all types of equipment.
Now this is changing, because that very structure is changing. It’s no longer only people who intelligently perform tasks like creating products, shaping models, developing ways to market those products, selling and delivering them, servicing them, and more. Suddenly, there is another form of intelligent existence: The AI.
THE COGNITION OF AN ENTERPRISE
“The cognition of an otherwise legal entity is created by unifying Human Intelligence with Artificial Intelligence to a new intelligent structure, the cognitive enterprise. While parts may be replaced, updated, or changed, it requires both mutually exclusive types of intelligences to exist as a cognitive enterprise.”
The artificial part is a fixed asset of the company, while the human part is a contributed and leased asset. In both cases, Intelligence is not a thing. The human brain and its neurons give rise to intelligence. In the case of AI, neural networks and models bring forth their intelligence. Now we join both to one. The opportunities are mind-bending.
COGNITIVE ENTERPRISE TECHNOLOGY
At BlueCallom, we created the first Native AI Enterprise Platform, the first Enterprise Workflow Intelligence architecture, and the first PROacting AI design. Together, these technologies form the technological foundation for what we call ‘Neuro-AI-Fusion’. They allow:
- Adaptive, intelligent workflows ready for unpredictable situations. Instead of deterministic, software-coded processes, it understands the dynamics of a flow and can adapt to optimized behavior.
- PROacting engagements from both sides, human and AI. It triggers users based on assessments, external information, and alerts, and helps navigate workflows of almost any complexity.
- Environmentally aware AI to understand the context in which it works, its workflow’s objectives, and undertake optimal measures based on workflow-specific constraints.
- Those and more situations are optionally observed and managed by a Human in the Loop of the workflow.
And we are just at the beginning. Learning, self-improving, reasoning, and decision-making at that level of complexity is another frontier for The Cognitive Enterprise we are working on.
It’s the first Native-AI Cognitive Enterprise Technology, turning the idea and concept into a real product that companies can buy and build on.
2) Cognitive Enterprise Benefits
ACCELERATING WORKFLOWS
Probably the biggest advantage is Workflow Acceleration. By using intelligent, adaptive, and situation-responsive workflows, friction can be significantly reduced, user guidance can be enhanced, and situational awareness can be amplified. The result is faster response times, shorter go-to-market cycles, more accurate and more intense marketing, faster delivery, faster service, and, altogether, a hard-to-compete-with competitive advantage.
INCREASING PRODUCTIVITY
When the Cognitive Enterprise has reached its full potential, it is twice as productive as a competitor with the same number of people, skills, and intelligence level.
REDUCED HUMAN DEPENDENCY
The Cognitive enterprise is less dependent on individuals because the Workflow Intelligence can help new employees to step in almost immediately. Illness, vacation, accidents, maternity leave, or leaving the company is far less dramatic. It is certainly a cut but doesn’t risk the operation.
INCREASED EMPLOYEE SATISFACTION
Employees who are interested in those types of jobs are far more motivated and satisfied than those who just want a job to make money with the least effort. Cognitive Enterprises will need far more employees than conventional companies with no AI, due to the massive increase in AI utilization. AI outperforms humans in many ways, so enterprises will need to attract more people willing to work.
REDUCING IT COST
Cognitive Enterprises are built on “NativeAgentic IT” not software. That reduces technology, development, customization, and service costs by roughly 50%.
3) Cognitive Enterprise Origin
The term “Cognitive Enterprise” was popularized and coined by IBM as a strategic business framework in the mid-2010s. Original concepts gravitated around machine learning and automation. Technology-wise, it was a heavy algorithmic AI structure.
IBM began transitioning the core of its business and client offerings toward “cognitive computing” (powered by IBM Watson). By late 2017, they were actively defining the “Cognitive Enterprise” as a holistic model connecting cloud infrastructure, data, and intelligent workflows. Ref: IBM
While the concept is said to be evolving, the underlying architecture is still conventional algorithmic and code-based agentic computing.
4) The AI Paradox
Today, with rapid advances in artificial intelligence, systems have become so powerful that we have experienced a phenomenon known as part of the AI Paradox: “the more we use AI, the more people we need.” But in this case, the AI contributes a large amount of intelligence that humans can’t provide, and at the same time, such an artificial intelligence level requires humans to operate in dimensions that were previously unimaginable.
5) Enterprise History
GLOBAL ENTERPRISE GROWTH
Over the past 250 years, enterprises have grown from small groups to literally millions of people. And since people have a wide range of intelligence attributes, company owners, executives, managers, and employees bring a wide range of intelligence to every step of a business process. Yet, the bigger the companies grew, in particular those with complex products, services, and operational requirements, the harder it was to manage those organizations. Many organizations have reached a point where scalability is no longer meaningful and is extremely hard to manage. The economics of many global enterprises are driven largely by value maintenance and very little by profitability, even with no perspective on progress. They are in survival mode until they die, without knowing when.
ENTERPRISE INFORMATION TECHNOLOGY
For more than five decades, enterprise software has been built around predefined business processes. While these systems successfully automated repetitive work, they assumed that the optimal sequence of activities could be designed in advance. Whenever markets changed, customers behaved differently, supply chains were disrupted, or priorities shifted, people—not software—had to adapt. Just the cost for updates has grown into the millions. Let alone purchasing or licensing the software and fees for service contracts.
Yet THE BIGGEST PROBLEM could not be eliminated: Software-based solutions are dumb. No intelligence whatsoever. If I made a spelling mistake by adding a company name that was already in the database, a second account was created without warning. And that is only the most trivial problem. Without noticing, we all agreed:
People type in data that is stored in a database, and others or the user themselves can retrieve it in different ways.
Thats all
No matter how many agents and tools you glue onto such a system, it will not become intelligent. Even worse. Almost all early agents are written in Python. So they are created with code as their own backbone. Only when intelligence is needed do they call an LLM.
So now we have two code layers, one on top of the other, and an orchestrator is needed to make it all work – just to be able to say “AI”.
Today’s AI enterprises –> Lipstick on a pig.