From Intuition to Algorithms: The Evolution of Decision Intelligence

A New Era of Intelligence - AI as an Amplifier, Not a Replacement

The rapid evolution of Artificial Intelligence (AI) in decision-making has sparked intense debates, such as: Will AI replace human decision-makers? Will machines become more intelligent than humans? Will our intuition, experience, and ethical reasoning become obsolete?

These fears stem from a fundamental misunderstanding of intelligence, both human and artificial. Consider this - AI is not replacing us. It’s augmenting us.

By understanding the key differences between human cognitive decision-making and AI-driven computation, we can develop a new model. A new model that fuses human adaptability with AI precision to unlock a new era of high-performance decision-making.

This article introduces a new formula for the new model called The Shears’ AI Hybrid Decision Intelligence Equation (SDIE). In this article, I also present OODA 2.0, an AI-enhanced adaptation of the Observe, Orient, Decide, Act (OODA) loop, which is designed to bridge the gap between human cognition and AI’s computational intelligence so that we are not competing, but adapting as complementary forces.

Human Intelligence - A Foundation for High-Performance Decision Making

Human intelligence is a multifaceted, adaptive system that enables us to process information, draw insights, and make decisions in both predictable and highly uncertain environments. It is not purely a logical or computational process, but it’s a intricate fusion of memory, perception, emotions, and executive function, all driven by biological neural networks that continuously refine and adapt.

Unlike artificial intelligence, which processes structured data through statistical learning models, human intelligence thrives on contextual understanding, abstraction, and meta-cognition (thinking about thinking). Our cognitive framework relies on five key pillars, each rooted in decades of scientific research in neuroscience, cognitive psychology, and behavioral science.

Pillar #1 Situational Awareness – Perception, Context, & Emotional Intelligence : Understanding context, emotions, and unstructured data

Situational awareness is the ability to perceive, interpret, and anticipate changes in our environment—a skill critical to survival, leadership, and decision-making.

From a neuroscientific perspective, situational awareness is driven by the prefrontal cortex, which integrates sensory input, emotional cues, and contextual information from the hippocampus (memory processing) and amygdala (emotional regulation).

Scientific Basis:

• The dual-process theory of cognition (Daniel Kahneman) explains that humans process information in two systems:

  • System 1: Fast, intuitive, and emotionally driven.

  • System 2: Slow, deliberate, and analytical.

• Emotional intelligence (EQ) (Goleman, 1995) plays a key role in reading social cues, empathy, and subconscious biases, impacting high-stakes decision-making.

• Cognitive load theory (Sweller, 1988) suggests that an individual’s ability to process multiple data points at once is limited, which is why filtering out irrelevant information is crucial.

Note : Why this is Important - Situational awareness allows humans to make sense of incomplete or ambiguous data, an ability AI struggles with because it requires abstract reasoning and emotional perception.

Pillar # 2 - Pattern Recognition & Intuition – The Brain’s Predictive Model : Making rapid decisions based on past experiences

Humans have a natural ability to recognize patterns, forming the basis of intuition—a cognitive shortcut that enables fast, unconscious decision-making.

The brain functions as a predictive engine, constantly matching current inputs with past experiences stored in long-term memory. This is primarily governed by the hippocampus and neocortex, which encode patterns, associations, and probabilistic models from past learning.

Scientific Basis:

• Hebbian Learning ("Neurons that fire together, wire together") – The more frequently neurons fire in response to a stimulus, the stronger the association becomes, reinforcing pattern recognition.

• Predictive Processing (Friston, 2005) – The brain minimizes uncertainty by constantly generating and updating hypotheses about the world.

• Chunking Theory (Miller, 1956) – Humans group information into meaningful chunks, allowing faster recall and pattern recognition.

Note: Why This is Important - While AI identifies statistical correlations in massive datasets, human intuition recognizes non-obvious connections between concepts, seeing the “bigger picture” where data is incomplete.

Pillar # 3 Adaptability & Learning – The Neuroplastic Brain : Adjusting to new environments, learning from mistakes

Unlike AI, which must be retrained on new datasets, human intelligence is capable of real-time learning and adaptation. This ability is linked to neuroplasticity, the brain’s ability to reorganize itself by forming new neural connections.

Scientific Basis:

• Neuroplasticity (Merzenich, 1984) – The human brain rewires itself in response to experiences, strengthening or weakening connections based on feedback.

• Error-Driven Learning (Rescorla-Wagner Model, 1972) – Humans improve decision-making by detecting mistakes, adjusting behavior, and re-evaluating models of the world.

• Metacognition (Flavell, 1979) – The ability to reflect on one’s own thinking, enabling deliberate self-improvement.

Note: Why this is Important - AI struggles with adaptive reasoning. Humans can learn from single experiences, understand abstract concepts, and pivot strategies dynamically without massive data retraining.

Pillar # 4 Ethical & Emotional Judgment – The Moral Compass of Decision-Making : Factoring in long-term consequences and morality

Unlike AI, human decision-making is not solely driven by efficiency—it integrates ethics, emotions, and cultural considerations. This is largely due to the limbic system, which governs empathy, fear, trust, and reward systems.

Scientific Basis:

• Moral Cognition (Greene, 2001) – Humans evaluate ethical dilemmas using both rational analysis (prefrontal cortex) and emotional intuition (amygdala).

• The Somatic Marker Hypothesis (Damasio, 1994) – Emotional signals act as shortcuts in decision-making, helping individuals prioritize long-term consequences over short-term gains.

• Prosocial Behavior (Batson, 1991) – Humans factor in collective well-being rather than optimizing for individual gain, something AI struggles to emulate.

Note : Why this is Important - AI is fundamentally amoral—it optimizes for objectives without inherent ethical understanding. Humans assess fairness, justice, and unintended consequences, ensuring ethical oversight.

Pillar # 5 - Strategic Thinking & Leadership – Long-Term Vision Under Uncertainty : Making long-term, vision-driven decisions under uncertainty

Human intelligence enables strategic foresight, the ability to plan for long-term consequences despite unknowns. This requires abstract reasoning, imagination, and risk assessment—capabilities AI currently lacks.

Scientific Basis:

• Executive Function (Miyake, 2000) – Higher-order cognitive skills like planning, goal-setting, and risk analysis originate in the prefrontal cortex.

• Game Theory (Von Neumann, 1944) – Humans excel at adapting to multi-agent environments, anticipating how others will respond to decisions.

• Scenario Planning (Schoemaker, 1995) – Unlike AI, humans can create narratives about the future, evaluating uncertainty and "what-if" scenarios.

Note : Why It’s Important - AI optimizes for short-term efficiency, but humans navigate complexity, balancing trade-offs and managing uncertainty in ways that AI cannot replicate.

The Science of AI Cognitive Decision-Making: The Mechanics Behind Machine Intelligence

Unlike human intelligence, which evolves through experience, emotion, and adaptability, artificial intelligence (AI) operates purely on mathematical optimization, probabilistic models, and pattern recognition. AI does not "think" in the way humans do; rather, it processes, analyzes, and optimizes within defined parameters.

Where human intelligence excels in creativity, emotional judgment, and adaptability, AI dominates in speed, scalability, and precision, particularly in environments where structured, repetitive decision-making is required.

This section explores the core components of AI-based decision-making, drawing from computer science, cognitive modeling, and machine learning theory, while also identifying its inherent strengths and limitations.

Pillar #1 Data Absorption at Scale – The Foundation of AI Cognition : AI can process trillions of data points in seconds

The primary advantage of AI is its ability to process vast amounts of information at speeds impossible for humans. While the human brain can store approximately 2.5 petabytes of data (Bartol et al., 2015), AI systems can access, structure, and analyze information from millions of data points instantly, making it particularly effective for pattern recognition and decision optimization.

In AI, data absorption is handled through:

• Big Data Processing – AI uses cloud computing and distributed computing networks to ingest and structure massive datasets.

• Neural Networks & Deep Learning – Inspired by biological neurons, deep learning models extract features from raw data, refining understanding over time.

• Natural Language Processing (NLP) – AI interprets human language by analyzing word associations, sentiment, and context in text or speech.

Scientific Basis:

• Shannon’s Information Theory (1948) - The foundation of AI processing, defining how data is encoded, transmitted, and interpreted by machines.

• Big Data Analytics (Chen et al., 2014) - AI’s ability to extract patterns from unstructured data (text, video, speech) at unprecedented scale.

• Neural Networks (McCulloch & Pitts, 1943) - The conceptual model for modern AI, replicating how biological neurons process information.

Note : Why It’s Important - AI’s power lies in scalability—it can process trillions of transactions, interactions, and sensory inputs in milliseconds, making it indispensable for real-time analytics and predictive decision-making.

Pillar # 2 Pattern Detection & Prediction – AI’s Equivalent of Intuition : Machine learning models predict outcomes based on data

AI simulates intuition by recognizing patterns, correlations, and anomalies in massive datasets. Unlike humans, who rely on experience-driven intuition, AI quantifies patterns, using statistical models to predict likely outcomes with increasing accuracy over time.

This is achieved through:

• Supervised Learning – AI is trained on labeled data (e.g., email spam filters, medical diagnosis models).

• Unsupervised Learning – AI discovers patterns without predefined labels (e.g., clustering customer behavior).

• Reinforcement Learning – AI learns through trial and error (e.g., self-learning chess engines, robotics).

Scientific Basis:

• Bayesian Inference (Bayes, 1763): AI updates probability estimates based on new data, improving predictive accuracy.

• Deep Learning (Hinton et al., 2006): Neural networks with multiple layers recognize hierarchical features in images, speech, and text.

• Generative AI (Goodfellow et al., 2014): AI creates new data (e.g., deepfake images, GPT language models) by predicting the next best sequence.

Note: Why It’s Important - AI’s ability to process more patterns than humans makes it highly effective in fraud detection, supply chain forecasting, and medical diagnostics, where spotting hidden correlations is critical.

Pillar # 3 - Bias Reduction (but Not Elimination) – AI’s Objectivity Challenge : AI eliminates human emotional bias but can introduce algorithmic bias

While AI is often praised for removing human bias from decision-making, it is not inherently neutral—AI models inherit biases from the data they are trained on.

AI reduces cognitive biases such as:

• Confirmation Bias – AI evaluates all possible data, rather than reinforcing preexisting beliefs.

• Recency Bias – AI assigns equal weight to historical and recent data, avoiding emotional overreactions.

• Affect Heuristic – AI makes decisions without emotional attachment, purely on mathematical probability.

However, AI can introduce algorithmic biases, including:

• Data Bias – If trained on imbalanced datasets, AI reinforces historical inequalities (e.g., biased hiring algorithms).

• Model Bias – The structure of AI models can unintentionally prioritize specific factors over others.

• Proxy Bias – AI may infer race, gender, or socioeconomic status even when those attributes are excluded.

Scientific Basis:

• Algorithmic Fairness (Binns, 2018): Studies AI’s ability to eliminate vs. reinforce social biases.

• AI Ethics (Floridi & Cowls, 2019): Examines the unintended consequences of biased machine learning models.

• Adversarial AI (Biggio & Roli, 2018): Demonstrates how AI models can be manipulated if biases are not controlled.

Note: Why It’s Important - AI reduces emotional decision errors, but humans must oversee ethical safeguards to prevent bias amplification.

Pillar #4 - Automation of High-Frequency Decisions – AI’s Role in Reducing Cognitive Load : AI removes decision fatigue by handling repetitive, structured tasks

Human decision-making declines in quality as cognitive load increases—a phenomenon known as decision fatigue (Baumeister, 2011). AI mitigates this by automating repetitive, structured decisions, freeing human intelligence for higher-order strategic thinking.

AI excels at:

• High-frequency stock trading – AI algorithms process thousands of trades per second.

• Supply chain optimization – AI predicts demand fluctuations and adjusts inventory.

• Cybersecurity threat detection – AI monitors network traffic in real time.

Scientific Basis:

• Automation Theory (Parasuraman et al., 2000) - Defines AI’s role in reducing human cognitive workload.

• Decision Fatigue Research (Baumeister, 2011) - Demonstrates how repeated decision-making depletes cognitive resources.

Note: Why It’s Important - By handling high-volume, low-risk decisions, AI allows humans to focus on creative, strategic, and ethical decision-making.

Pillar # 5 Speed & Consistency – AI’s Competitive Advantage : AI does not hesitate or overthink—it operates at machine speed

Unlike humans, who experience hesitation, doubt, and overthinking, AI makes instantaneous, repeatable decisions without cognitive limitations.

AI outperforms humans in:

• Medical imaging analysis – AI detects cancers with higher accuracy and speed than radiologists.

• Speech recognition & translation – AI translates languages instantly, surpassing human speed.

• Real-time fraud detection – AI detects suspicious transactions before they are completed.

Scientific Basis:

• Computational Complexity Theory (Cook, 1971) - Defines how AI solves problems in logarithmic time.

• Parallel Processing (LeCun et al., 2015) - Explains AI’s ability to compute millions of operations simultaneously.

Note : Why It’s Important - AI’s unmatched processing speed makes it indispensable for real-time decision environments, where milliseconds matter.

The Difference Between Human and AI Intelligence in Decision-Making

Comparison of Cognitive Abilities: Human Intelligence vs. AI Intelligence

So how do we merge these two into a single, high-performance decision model?

Introducing OODA 2.0: A New Era of High-Performance Decision Making

The OODA Loop (Observe, Orient, Decide, Act), developed by military strategist John Boyd, revolutionized the way fighter pilots process information under extreme pressure, enabling them to react faster and more effectively than their adversaries. Boyd’s framework was built for the human mind; a system designed to synthesize sensory data, contextual understanding, and experience-driven intuition to navigate uncertainty.

But today, decision-making is no longer solely a human endeavor. The rise of artificial intelligence (AI) has introduced a new dimension to high-stakes decision-making. One where algorithms process vast amounts of data at machine speed, identify hidden patterns, and automate structured tasks far beyond human capabilities. However, AI lacks the contextual awareness, ethical judgment, and creative problem-solving that humans inherently possess.

As AI becomes an integrated force in decision-making, the original OODA Loop needs to evolve. To truly assimilate the strengths of both human cognition and AI intelligence, we need to reassess decision-making for the modern era, where AI doesn’t replace human thinking but enhances it, creating a new model for high-performance, AI-augmented decision intelligence.

OODA 2.0: The Human-AI Decision Framework

Observe → Optimize → Decide → Automate

The original OODA Loop was designed for human cognition, leveraging a pilot’s situational awareness, intuition, and rapid pattern recognition to make high-stakes decisions in real-time. But in an era where AI can process vast amounts of information at machine speed, decision-making is no longer just about human capability—it’s about human-AI synergy.

Enter OODA 2.0, a modernized Human-AI Decision Framework designed to combine the best of human intelligence with AI-driven insights. This model redefines decision-making by integrating AI’s computational power with human adaptability, ethical reasoning, and strategic thinking, ensuring that decisions are not only fast and data-driven but also contextually aware and ethically sound.

Step 1: Observe – AI-Enhanced Situational Awareness

In the traditional OODA Loop, the Observe phase required gathering raw data from one’s environment—whether visual, auditory, or sensory input. Today, AI has transformed how we perceive and process information, allowing decision-makers to analyze vast amounts of structured and unstructured data in real time.

How AI Enhances Observation:

• Machine Vision & Sensor Data Processing – AI-powered cameras and IoT sensors detect patterns, anomalies, and risks faster than human perception.

• Big Data Analytics – AI can process, filter, and prioritize massive datasets, providing only the most relevant insights for human decision-makers.

• Predictive Insights – AI anticipates outcomes based on historical trends, offering early warnings about potential risks.

Human-AI Synergy: While AI absorbs structured data instantly, humans provide context, emotional intelligence, and ethical oversight, interpreting subtle social cues, moral considerations, and unstructured data that AI cannot fully comprehend.

Example: In cybersecurity, AI detects suspicious network behavior in milliseconds, but a human analyst must determine the intent behind the activity and decide on the appropriate response.

Step 2: Optimize – AI-Powered Cognitive Processing

In Boyd’s original OODA Loop, the Orient phase involved filtering information through experience, training, and prior knowledge to determine the best course of action. In OODA 2.0, this step evolves into Optimize, where AI assists in rapidly analyzing scenarios, modeling potential outcomes, and optimizing choices for the best result.

How AI Enhances Optimization:

• Cognitive Load Reduction – AI processes thousands of potential scenarios instantly, reducing decision fatigue.

• Algorithmic Risk Assessment – AI evaluates probabilities and trade-offs, optimizing decisions for efficiency and effectiveness.

• Bias Detection & Correction – AI flags potential cognitive biases that could cloud human judgment.

Human-AI Synergy: AI provides data-backed recommendations, but humans must evaluate risks, apply ethical considerations, and think strategically about long-term consequences.

Example: In business strategy, AI might suggest pricing adjustments based on market trends, but executives consider customer loyalty, brand perception, and ethical concerns before finalizing the decision.

Step 3: Decide – Human-AI Synergy in Judgment

Once AI has optimized potential outcomes, it’s time to make the final decision. While AI can suggest statistically optimal choices, humans provide the leadership, creativity, and ethical oversight needed to make complex decisions.

How AI Enhances Decision-Making:

• Confidence Scoring – AI assigns probability ratings to different decision paths, helping humans weigh risks more accurately

• Multi-Scenario Simulations – AI can run decision trees in real time, simulating potential consequences of various choices

• Automated Decision Support – AI eliminates low-impact, routine decisions, allowing humans to focus on high-stakes choices

Human-AI Synergy - AI provides instant, data-driven insights, but humans bring contextual awareness, creativity, and strategic vision that machines lack.

Example: In healthcare, AI may analyze a patient’s symptoms and suggest a treatment plan, but a doctor must consider ethical concerns, patient history, and human factors before making the final call.

Step 4: Automate – AI-Driven Execution & Continuous Learning

The final stage of OODA 2.0 is where AI automates routine tasks, freeing up human cognitive resources for high-level strategy and complex decision-making. Unlike the traditional Act phase, where execution relied entirely on human reflexes and instincts, AI can now handle many execution steps automatically, while humans provide oversight and learning.

How AI Enhances Execution:

• Process Automation – AI handles repetitive tasks, eliminating human inefficiencies.

• Real-Time Adjustments – AI continuously refines execution strategies based on feedback, improving future decisions.

• Self-Learning Systems – AI models update dynamically, improving their recommendations with every iteration.

Human-AI Synergy - While AI excels at speed and efficiency, humans oversee long-term strategy, ethical boundaries, and complex problem-solving.

Example: In autonomous vehicles, AI automatically adjusts speed and navigation, but a human driver can intervene in unpredictable or ethically complex scenarios.

The Future of AI-Augmented Decision-Making

OODA 2.0 represents a paradigm shift in decision intelligence, where human cognition and AI computation work in tandem to create a high-performance, adaptive decision-making system.

Where AI Excels:

• Processing vast amounts of structured data

• Detecting hidden patterns and anomalies

• Reducing decision fatigue and automating routine choices

• Optimizing decision pathways using probability models

Where Humans Excel:

• Contextualizing abstract, unstructured information

• Applying ethical and emotional intelligence to decisions

• Making creative, strategic, and vision-driven choices

• Adapting quickly in unpredictable, novel situations

The Future of AI-Human Decision Intelligence

As we step into an era where AI and human intelligence must work in synergy, the ability to determine when to rely on human cognition, when to leverage AI, and when to integrate both is crucial for achieving optimal decision-making outcomes; an AI Hybrid Decision Intelligence Factor (or formula) could provide a structured, quantitative approach to guide this kind of decision-making process:

Where:

• H = Human Adaptability (intuition, ethical reasoning, strategic foresight)

• C = Complexity of Context (ambiguity, unstructured data, need for ethical judgment)

• A = AI Computational Power (speed, pattern recognition, automation potential)

• S = Scale of Data (amount and complexity of structured data AI can process)

• B_h = Human Cognitive Bias (emotional interference, decision fatigue)

• B_a = AI Algorithmic Bias (training data errors, model bias, lack of ethical reasoning)

To ensure consistency and reliability, each input factor is rated on a scale from 1 to 10, where 1 represents the lowest influence (worst condition) and 10 represents the highest influence (best condition). Once calculated, the score will help to determine the optimal balance of AI-human decision-making:

Scoring Framework

Note: While this kind framework could provide structured guidance; its accuracy is subject to a margin of error of ~ ±24.80 within a 95% confidence interval; meaning real-world applications will introduce variability. Several factors influence the precision and effectiveness of this kind of evaluation equation like :

• Data Accuracy & AI Bias – AI’s effectiveness is only as good as its training data; if models are trained on biased or incomplete datasets, the formula may overestimate AI’s reliability.

• Human Variability – Cognitive biases, emotional states, and decision fatigue fluctuate unpredictably, introducing greater variance in the formula predictions.

• Task-Specific Adjustments – Some industries (e.g., military strategy, ethics-driven fields) may require customized weight adjustments for more accurate scoring.

• Real-Time Context Shifts – Crisis situations, warfare, and dynamic operational environments can disrupt previously predictable variables, impacting the reliability of the scores in rapidly evolving scenarios.

Therefore this kind of formula should only be used as a decision framework, not an absolute rule. Its effectiveness improves when bias is minimized, data sources are validated, and decision-makers fully understand contextual complexity.

“Decisions are only as good as the information that you have to make the decision; so; the future of decision-making is not about AI vs. humans; it’s about AI + humans working together for better, faster, and more responsible decision intelligence.”……. Jay Shears