From Theorem to Executable System: A Continuously Adaptive Enterprise OS Powered by Neurosymbolic AI

1. Introduction

Modern businesses are defined by complexity, requiring distributed operations with various levels of autonomy appropriate to individual tasks, while maintaining a unified organization working towards a common mission. Legacy systems, disparate data and siloed applications have created a landscape of fragmentation that tends to work against the needs of a competitive enterprise, causing inefficiencies, delayed insights, and an inability to scale operations effectively.

In recent years, the combination of platform consolidation, regionalization, trade wars, emergence of AI and other trends have exponentially increased complexity and risk, contributing to what many CEOs report is the highest levels of uncertainty in their career. Forty percent of CEOs now believe their company will no longer be viable within ten years if it continues on the current path.¹ In order to thrive and in some cases survive, businesses must evolve from a collection of disparate tools into a unified, continuously adaptive, intelligent organism. Doing so requires purpose-built AI systems with end-to-end data management systems optimized specifically for AI technologies and tailored to the needs of each organization and worker.

To provide the level of accuracy needed for the organization, comply with government regulations, enforce corporate policies, and work effectively within the confines of physics and economics, rules-based data structure is necessary. Large language models (LLMs) and other self-generating algorithms are valuable but insufficient for enterprise AI — they need to be executed within a purpose-built enterprise AI OS like the KOS to be competitive. Functionality in enterprise AI necessitates combining creativity and generalization through neural networks with the precision accuracy and governance of symbolic AI.

The KOS is more than just a software platform; it is a business OS with strategic architecture that employs enterprise-wide data management and centralized governance over a distributed network to connect data streams, business processes, and interactive human workflow into a single, cohesive system. The system design enables proactive insights and a level of operational efficiency that was previously impossible.

2. Precision Data Management: The Foundation for the Modern Business OS

At the heart of the KOS is a commitment to precision, high-quality data. The system design moves beyond mere quantity to emphasize quality, context, and reliability for semi-automated and automated decision-making. Precision data management is the end-to-end lifecycle of data. Pre-designed semantic structure (e.g., RDF, OWL), carefully managed data collection, validation, and strong governance ensures accuracy, integrity, and appropriate accessibility. The end-to-end data structure also allows auditability.

The KOS was designed over time to realize the underlying theorem developed in 1997, which is to optimize knowledge yield in the digital workplace. The theorem emerged while operating GWIN (Global Web Interactive Network), a learning network for thought leaders. The purpose for managing the knowledge yield curve (KYield) is not simply for the sake of obtaining knowledge, but rather to plan and execute evidence-based intelligence, and continuously adapt in near real-time.

Proprietary Neurosymbolic Architecture

The KYield theorem is executable in an efficient system due to our proprietary neurosymbolic AI architecture that acts as the core engine in the KOS, enabling specific functions.² The data management system (DMS) core mitigates the weakness and combines the strengths of neural networks with symbolic AI to provide a rules-based system that has the capacity to meet the needs of any type of organization, from highly regulated to unregulated. By fusing these two AI technologies, the KOS can make accurate predictions and provide a transparent, explainable chain of reasoning.

According to McKinsey, the same number of companies reporting use of Gen AI also report no material impact on earnings (80%). This GenAI paradox is due to an imbalance between "horizontal" and "vertical" use cases. One-off projects targeting use cases "seldom make it out of the pilot phase because of technical, organizational, data, and cultural barriers".³ To remain competitive moving forward, most businesses will need deep domain expertise, enterprise-wide precision data, vertical data integration, and purpose-built infrastructure powered by an efficient EAI OS with the specific capabilities of the KOS.

Descriptive Data: Ontologies, Schemas and Taxonomies

Similar to the popular idiom "that which gets measured gets managed," we can say "that which is described with precision in the digital workplace gets communicated accurately" between humans and machines. Otherwise it doesn't — hence the accuracy problem with LLMs and big data vs. high quality data, which is why symbolic AI is essential in the enterprise.

Rules-based AI systems have the capacity to provide accuracy and security due to descriptive data known as ontologies, which define classes (or types), attributes (or properties), individuals (or specific members of a class), and relationships among class members. Schemas provide the plan, organization and rules for databases and knowledge bases. A taxonomy is essentially the specific naming system — for industry-specific versions of the KOS it defines language commonly used within the industry for accurate machine translation.

Survival of the Fittest

Even more important than short-term ROI is mid-term survival. It's impossible for one-off projects to be competitive with efficient system design. Native digital platform companies like Amazon and Google set a new bar for competitiveness in the network economy with systems engineering. They have been displacing traditional businesses ever since. Competitors of all sizes and types need to build or adopt top-tier systems that include knowledge networks throughout their ecosystems in order to remain competitive and relevant in the network economy.

3. Multi-Modal AI Functions: Augmented Intelligence

The KOS doesn't rely on a single AI algorithm but rather a sophisticated, integrated suite of functions executed by software applications for each function. The technologies employed within the functions and across the KOS include predictive analytics, natural language processing (NLP), machine learning, deep learning, and language models. In addition, the four layers of security within the KOS include multifactor authentication, data-centric security, behavioral security, and encryption.

The power of these technologies is unlocked by the precision data they consume and are trained on. Their effectiveness is directly tied to the quality and context provided by the proprietary neurosymbolic architecture, tradecraft gained from three decades of R&D, and the patented core of the KOS.

The data layer and AI functions are not separate components; they are interwoven to create a single, cohesive system. An important benefit of the end-to-end data structure with natural language administration is the ability for management and authorized employees to target an unlimited number and type of use cases, rather than one-off projects that have failed to produce an ROI for most companies.⁸ Just as a computer OS manages hardware and software to run applications, the KOS manages data and algorithms to augment the human workforce so they can optimize the business.

The efficiency in an end-to-end EAI like the KOS is necessary to unlock productivity, achieve an attractive ROI in enterprise AI, and avoid the 95% failure rate in GenAI investments recently reported by MIT.⁹ The same survey found that three times as many investments succeeded when acquired from vendor specialists than when built by internal teams, confirming that the not-invented-here (NIH) syndrome is alive and well, and still costing enterprises billions of dollars per year, in addition to increasing existential risk for their organizations.

4. Self-Tailoring: A Continuously Adaptive System

The old approach of rigid, costly to change enterprise technology is a relic of the past. The KOS is designed to be continuously adaptive, with the ability to self-tailor to the unique needs of each organization and individual worker with natural language. The system can observe and learn from user interactions, automatically customizing dashboards, display important alerts that require action, and semi-automatically adapt as directed by individual settings.

For an engineer, the KOS might prioritize supply chain data and technical specifications; for a salesperson, it might surface customer sentiment analysis and lead-scoring models. This personalized experience ensures that every employee is equipped with the precise information and tools they need to perform their job effectively, enhancing productivity, value to the organization and job satisfaction. The KOS evolves with the organization, dynamically reconfiguring itself based on human workflow to meet new challenges and opportunities as they emerge.

Human-Centricity with DANA

The KOS is not a replacement for human intellect; it is a force multiplier. Although the enterprise can do more with less, the design is fundamentally human-centric, empowering employees rather than replacing them. DANA is the digital assistant in the KOS. It is automatically tailored to each individual within the governance parameters set by management and each person. DANA automates data-intensive tasks, freeing up workers to focus on learning, skills training and high-value activities.

Research and experience have convincingly demonstrated that DANA should be provided to every employee. It is priced accordingly to incentivize enterprise-wide adoption. DANA can also be extended to consumers. Intuitive and simple to use, DANA provides a unified view of relevant information without overwhelming the user. By augmenting human capabilities, enterprises can create a synergistic relationship between human and artificial intelligence, leading to better outcomes and a more engaged workforce. The end goal is a continuously adaptive learning organization (CALO) that executes on evidence-based learning to achieve the mission.

5. Discussion: Horizontal and Vertical AI Systems

One of the more consistent areas of confusion in EAI relates to the question of vertical vs horizontal EAI systems. The short answer is the data doesn't care, as evidenced by consumer LLMs that attempt to generalize knowledge. The longer answer is that people are still the source of the majority of valuable data. Even in small teams employing AI to accelerate discovery in science — especially then — human talent is the great differentiator, hence the focus of the KOS on human centricity and augmentation.

By focusing on high quality and enabling self-tailoring, any team focused on any topic can benefit from the horizontal (universal) KOS. The primary missing piece for creating an industry-specific or a business-specific KOS is integration of business-specific (vertical) data. The reason is that while seamless integration with vertical data is optimal, notes from the CFO on interpreting the financial statement are typically more succinct, accurate and valuable than vertical data. The same is true for a sales manager in a high value customer relationship, or an engineer's solution to an expensive product flaw.

6. Conclusion: The Long Voyage to an Intelligent Enterprise

The three-decade odyssey since developing the KYield theorem has involved many unexpected twists and turns. Performance and price finally came together at about the same time as the Covid pandemic. The system is ready and available to scale at very attractive pricing down to mid-market companies in terms of size.

There is a great deal to understand about enterprise AI, and an EAI OS specifically. Our research transcends several disciplines beyond computer science and AI to organizational psychology, physics, behavioral economics, biology, and neuroscience — which is why Mark Montgomery spent several years at the Santa Fe Institute (SFI) while maturing the R&D. The technology, economics and market have all finally come together, and the time is right for the KOS.

It remains to be seen which customers will adopt from the authentic inventor and benefit from decades of hard-earned knowledge capital. We have no control over the behavior of others, but we do protect our IP, and we intend to continue to maintain high levels of integrity in all that we do. We look forward to exploring flexible options in licensing and installing the KOS with management teams who share our principles.