Revolutionizing The Enterprise Part I: The Beginner’s Guide to Understanding AI-Driven Business Transformation

Introduction

In the dynamic realm of enterprise management and market competition, the advent of specialized, Automated Reasoning heralds a revolutionary shift in value creation. This article explores how this technology, with its focus on real-time optimization and swift decision-making, is fundamentally reshaping the dynamics of value creation in established markets. It is guided by a Pareto-based conceptual framework that defines the world we compete in today.

Automated Reasoning Changes How You Think About and Participate in Enterprises

Automated Reasoning represents a fundamental change in the approach to artificial intelligence. It employs Knowledge AI and Data AI for problem-solving and enterprise decision-making, enabling a Special-Purpose Intelligence for full end to end optimization to achieve maximum potential benefits. This intelligence significantly enhances operations and strategies with its speed, accuracy, and consistency, far surpassing the human capabilities assembled for that purpose in the organizations we use today.

Paradigm Shift to The Collective intelligence of All an Enterprise’s Cumulative Knowledge Making Decisions in Real Time

The shift from human-driven to machine decision-making in enterprises signifies a major paradigm change. Automated systems can analyze complexity and make rapid decisions, a critical capability in today’s fast-paced markets.

Understanding Why Decision Speed Matters: The Formulaic Approach

The formula V(t)=A?(1?e?D?P?t) quantifies the impact of decision speed on value improvement. Here, V(t) represents value improvement over time, D the decision-making speed, P the Pareto factor, A the maximum potential value improvement, and t time. This formula encompasses the tangible benefits of optimized decision-making, the upper limit of improvement achievable, and the significantly higher speed in automated reasoning.

This Changes Everything to First-In, Last-Out Scenario in Established Market Dynamics

Early adopters of automated reasoning technologies gain significant competitive advantages, establishing a 'first-in, last-out' competitive scenario. In contrast, late adopters face challenges and are likely never to catch up, as we shall see in a chart below.

Navigating the Future of Enterprise Management: A Comparative Analysis of AI-Driven Value Creation

The integration of advanced cognitive and causality theories in AI, complemented by the efficiency of Large Language Models (LLMs) and Knowledge Graphs, is ushering in a transformative era in enterprise management. This shift, fueled by automated reasoning, facilitates rapid and informed decision-making across various domains, thereby revolutionizing value creation and the competitive dynamics in the market.

To exemplify the range of possibilities in value creation driven by AI, let's explore two hypothetical scenarios using the formula V(t)=A?(1?e?D?P?t). This formula sheds light on how the value improvement V(t) over time t is impacted by the maximum potential value improvement A, the speed of decision-making D, and the Pareto factor P. Through these scenarios, we can grasp the significant influence of AI in reshaping enterprise strategies and outcomes.

Scenario 1: High-Speed Decision-Making with Automated Reasoning

In this scenario, we consider an organization that has fully integrated automated reasoning, leading to high-speed decision-making. Let's assign high values to D and P to reflect this:

• A =100 (maximum potential value improvement)
• D =10 (high speed of decision-making)
• P = 0.8 (high Pareto factor) Enhanced effectiveness in the use of cumulative knowledge across the enterprise to achieve the organization's objectives (80% of the work is done in automatic)
• t varies over time

This scenario reflects a situation where an organization can rapidly respond to market changes and operational challenges, optimizing processes efficiently.

Scenario 2: Low-Speed Decision-Making in Traditional Settings

Here, we consider an organization that relies on traditional decision-making processes, characterized by lower speed and efficiency. We assign lower values to D and P:

• A = 100 (maximum potential value improvement, same as in Scenario 1 for comparison)
• D = 1 (low speed of decision-making)
• P = 0.2 (low Pareto factor) Lower effectiveness in the use of cumulative knowledge across the enterprise to achieve the organization's objectives (20% of the people get 80% of the work done)
• t varies over time

This scenario represents a more traditional and slower approach to decision-making, often resulting in delayed responses to changes and opportunities.

Comparative Analysis Over Time Reveals That the Potential Advantage is So Great, It's Likely Over as Soon as It Starts

Let's calculate the value improvement over time for both scenarios to illustrate the difference in outcomes due to the varying speeds of decision-making and Pareto factors. We will plot the value improvement V(t) over a period of time for both scenarios.

The plotted graph visually demonstrates the comparative analysis of value improvement over time for the two scenarios:

1. High-Speed Decision-Making with Automated Reasoning Enabling Special Purpose Intelligence (SPI) (Scenario 1): This curve shows a rapid increase in value improvement over time, indicating that the organization quickly reaches its maximum potential value improvement. The steepness of the curve reflects the high speed of decision-making and the significant impact of the Pareto factor.
2. Low-Speed Decision-Making in Traditional Settings of Today’s ERP Enabled Organizations (Scenario 2): In contrast, this curve rises much more slowly, illustrating that the organization takes a longer time to approach its maximum potential value improvement. The gradual slope of this curve is indicative of the slower decision-making process and the lower impact of the Pareto factor in traditional settings.

The stark difference between the two curves highlights the transformative impact of integrating Automated Reasoning into enterprise operations and optimizing it all through SPI. The ability to make faster and more efficient decisions significantly accelerates value creation, giving organizations a competitive edge in rapidly changing market environments.

The plotted graph demonstrates a comparative analysis of value improvement over time for two scenarios: High-Speed Decision-Making with Automated Reasoning and Low-Speed Decision-Making in Traditional Settings. The former shows a rapid increase in value improvement over time, indicative of efficient, Automated Reasoning processes. The latter, in contrast, rises more slowly, reflecting the slower, more traditional decision-making methods employed by today’s organizations.

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Real-Time Optimization: A Game Changer Only for Those Who Do IT First.

Automated Reasoning enables businesses to instantly adjust to market and operational changes, fostering a rapid continuous improvement cycle. This capability allows the enterprise to utilize optimization knowledge in real-time, assessing production as represented in the data at the asset level, and evaluating performance relative to needs, thereby minimizing the necessity for retrospective adjustments.

Incorporating Advanced AI Theories Changes Everything About How you Think about AI

Integrating Daniel Kahneman cognitive theories and Judea Pearl causality models into AI not only enhances its capabilities but also fosters an understanding of causal pathways in human thought and speech. Knowledge Graphs are crucial in informed decision-making and in discerning causal relationships. See link for more insights: (2) Revolutionizing Artificial Intelligence: Harnessing the Giants – Kahneman Insights and Pearl's Causality in the Dawn of The Automated Scientist | LinkedIn

Value Capture in Established Organizational Structures

Traditional organizational structures can hinder decision-making due to their inherent complexity, which arises from attempting to manage through the necessary discipline of institutional rigor between functions and processes across organizational silos. Decisions in areas like supply chain management, which might take days or weeks, can now be accomplished in seconds, guided by the reference KPIs of the SPI

Transition to Special Purpose Intelligence (SPI) via Automated Reasoning

Adopting SPI through automated reasoning transforms the business landscape. Decisions are made in seconds, affecting everything from supply chain to customer interactions and including instant analysis in supply chain optimization, dynamic pricing strategies, and personalized customer relationship management.

Real-World Application Builders Prove This is Not About Waiting on Technology

Organizations like Parabole.AI demonstrate the practical application of these advanced AI techniques, transforming theoretical concepts into impactful solutions. They are bringing Agency of Automated Reasoning to the forefront of conversation demonstrating the future is already here. We business leaders just haven't caught up yet.

Arthur Kordon’s AI Axioms and Their Integration

Integrating Arthur Kordon’s AI axioms, we recognize AI's ability to learn, predict outcomes, and make decisions based on specific reasoning. This approach leverages AI to learn domain knowledge from experts and develop multi-dimensional graph databases capturing variable relationships and causality, differing from traditional AI by integrating domain knowledge for reasoned data selection.

Automating Causality and the Automated Scientist is The Problem Solvers Approach to AI

The goal is to develop AI systems capable of autonomously understanding and establishing causal relationships. Utilizing Kahneman's insights and Kordon's principles, these systems will generate and test hypotheses against data, with human intervention as a vital guide. Such systems promise to discern cause and effect with unprecedented precision.

Comparisons with Traditional AI Guide Us Back to Knowledge-First Approaches, and Away from the Reliance on Vast Amounts of Data, The Easter Egg Hunts They Create and the Quest to Find Something That Fits a Curve

This next-generation AI, integrating cumulative domain knowledge, offers a more comprehensive, knowledge-based problem-solving approach compared to traditional AI, which primarily relies on statistical analysis. This historical approach has led us to remain principally anchored to a singular perspective, merely one degree away from the symptoms. Consequently, we tend to attach correlation more readily, quickly gaining comfort in our understanding of the issues at hand. We then apply our discoveries in a manner like how the media uses headlines to capture attention, only to wonder why our solutions do not resolve the problems in the long term. This approach can serve as a distraction to the organization when it's not integrated into the work process, leading to a decline in performance. For more on this, see Paul Boris and my article. (2) Unlocking Productivity Paradoxes: Lessons from Lockdowns and the Evolution of Work Practices | LinkedIn

Demonstration Makes Use-Cases Disappear

Why do use cases really exist? Let's be honest. It's our lack of maturity in understanding and applying the principles that drives our need for use cases. As Marc Laplante recently noted, the 'Immaturity Complex' has likely never been more real than it is today, nor the danger more evident. Don’t believe it? Just look at the graph. Matthew Littlefield put it best: 'If you don't do this (Transform), your assets won't go away; they’ll just wear someone else's name.' I’ll add to that if you don’t act first, the same is now likely true. Because AI with Automated Reasoning has shown remarkable efficiency in complex areas like order management and supply chain optimization, adaptable across domains, orchestrating decisions for optimal benefit. This proficiency explains why specific use cases diminish after AI proves capable of complex decision-making in enterprises. Initially, businesses view AI as a solution for well-defined problems. Early use cases, designed to tackle certain challenges or inefficiencies, act as proof of AI's effectiveness. However, once AI masters these scenarios, perceptions and uses within organizations shift. The focus moves from predefined use cases to integrating AI as a key component in decision-making. This leads to AI being used more holistically and dynamically. It's no longer just for isolated cases but becomes part of the enterprise's core operations. As a result, original use cases become less distinct. AI evolves into a vital part of the broader strategic and operational framework, blurring the lines between specific applications and continuous, adaptive problem-solving.

Where It Matters and Considering What You Already Have

Looking forward, this novel application of AI promises to revolutionize how we engage in nearly everything, particularly in complex areas. Contrary to what one might think, it does not disrupt existing ecosystems. The concept of agency nearly ensures minimal disruption to current investments, such as ERPs and MESs. This sets the stage for a revitalization of existing infrastructure investments in ways we can barely imagine. It creates a landscape of developing application agents that form the basis of an orchestrated SPI. This enhances end-to-end supply chain and procurement processes, streamlines supplier collaboration, supports revenue growth and margin expansion, extracts value from the cumulative knowledge captive in human capital, and facilitates a shift toward knowledge-centric operational models.

Conclusion – For Whom will the Bell Toll?

The stark contrast between traditional and AI-enhanced scenarios highlights the transformative impact of integrating automated reasoning into enterprise operations. The ability to make faster and more efficient decisions provides organizations with a competitive edge. The integration of advanced cognitive and causality theories in AI, along with the efficiency of LLMs and Knowledge Graphs and Arthur Kordon AI axioms, marks a new era in enterprise management, revolutionizing value creation and competitive dynamics in the market.

Read Part II here: Revolutionizing the Enterprise Part II: The Beginner's Guide to Building Cumulative Advantage that Initiates Chain-Reaction Value Creation | LinkedIn

Shelley Nandkeolyar Ron Norris Subrata Sen Harirajan Padmanabhan Arthur Kordon Rajib Saha John B. Vicente Jr. PhD Sarath Chandershaker parabole.ai

Herb Dowdy Dr. Joshua Thomason, CPA, MBA

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