Is there any logic to outsource (our/ your) reason to AI?
Logic and reason are both critical cognitive tools, but they are different and operate differently in strategic thinking and decision-making.
Logic is a systematic, structured approach to thinking that follows strict rules of inference and validity. It's about drawing conclusions based on predetermined premises using mathematical or computational principles. In a business context, logic is like a precise algorithm – it moves from A to B to C with clear, sequential steps. It's about consistency, mathematical soundness, and the ability to derive conclusions that necessarily follow from established statements. The problem is, which we will explore later, this is falling apart because logic depends on stable rules.
Reason, by contrast, is a broader, more flexible cognitive process. It encompasses logic but embraces intuition, experience, empirical evidence, and contextual understanding. Reason allows for nuanced interpretation, considers probabilistic outcomes, and can incorporate subjective insights that pure logic will miss. Reason is the ability to make sound judgments by weighing multiple factors, understanding complex systems, and adapting to changing circumstances.
To illustrate:
In leadership, we need both. Logic provides the structural framework for sound decision-making (even in uncertain times), ensuring your strategies are internally consistent and mathematically sound (our model). Reason provides the adaptive intelligence to apply that logic creatively, ethically, and strategically in a complex, often ambiguous, volatile and uncertain business landscape.
Think of logic as the blueprint and reason as the individuals who know how to interpret and apply that blueprint to create something truly exceptional. Both logic and plans often fail.
However, logic can fail.
Logic fails in several critical domains. These limitations reveal “logic’s” limitations as a purely algorithmic (think LLM) reasoning tool:
Logic is a powerful tool, but it is fundamentally a simplified model of reasoning that breaks down when confronted with the rich, messy complexity of human experience.
Unfortunately, reason also fails.
Reason fails in equally profound and fascinating ways that complement the limitations of logic:
A profound example is how brilliant scientists and world-class business leaders can become completely irrational when their core beliefs or professional reputations are challenged. When reason becomes a defensive mechanism rather than a tool for discovery - it fails.
We know that logic fails due to structural limitations, and reason fails due to human psychological and perceptual complexity. Reason is a more flexible but equally fallible approach to understanding reality.
Wisdom (which is only a label for data) lies not in choosing between logic and reason but in understanding the strengths and limitations of each and cultivating the humility to recognise when our thinking and the thinking being imposed on us by those using LLMs to write might be fundamentally flawed. Further - as a business, you need to comprehend the risk of using an LLM as its logic and reason fail.
Outsourcing “logic”
While economics, business models, and finance preferences logic, marketing tends to side with reason, but what is happening is that our “communication rules”, which are crafted for sales and marketing and create our income and margin from customers, are being rewritten. The rigid categories we've long used to define our world — gender, nationality, diversity are crumbling before our eyes. This isn't just an academic exercise; it's a fundamental shift that could transform how we analyse data, make decisions, understand people, and interact with emerging technologies.
For centuries, we have relied on a straightforward approach to understanding words. They mean exactly what they label (point to/ infer). "A dog is a dog," we'd say, confident in our clear-cut definitions. As the world and words have become more complex (nuanced), single words carry multiple meanings with contextual interpretations and layers.
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By example. Take the word "Stud." In a traditional understanding, it means a specific category of male horse. But in reality, the meaning shifts depending on who's speaking, who's listening, and the context of the conversation - stud becomes a particular type of person.
For the most part, this did not matter, but then entered (AI) Artificial Intelligence and (LLM) Large Language Models. The point here isn't just linguistic gymnastics; this is about how we communicate in an increasingly nuanced world - where logic starts to fail. Traditional logic treated words like fixed objects in space — immovable, unchanging. But modern communication is more like a dynamic network (reason), where meaning emerges through relationships and inference, not through rigid categorisation.
AI and LLMs aren't just fancy calculators; they're learning systems that guess the next word based on patterns and context. They "hallucinate" because they're trying to navigate a world where meaning isn't fixed but fluid and ambiguous. The Stud is called Dave, who may be a horse or a person, the new question is does the person or horse identify as male or not?
For business leaders, this matters profoundly. In an era of global communication, diverse teams, and rapid technological change, our old models of understanding what was said and being said are breaking down - think marketing messages/ brand identity/ brand voice but also the use of LLM deep in operations (communication). We need a more flexible approach — one that recognises that meaning comes from connection, not just definition. Think of it like a complex business ecosystem. A successful strategy isn't about rigidly defining every component but understanding how different elements interact, influence, and transform each other. The same is true for language, for technology, for human understanding.
We're moving from a world of "this is true (logic)" to a world of "this is one possibility (reason)." And in that possibility lies an incredible opportunity for some because of uncertainty - but also a new risk at a scale where your brand can be lost in a day.
Is there any danger in outsourcing logic and reason to AI?
This is a provocative and nuanced question that cuts to the heart of emerging technological and leadership challenges. We know that “big systems” make terrible decisions because of incentives, hurdles and compromises; therefore, why are we even considering outsourcing logic and reason to AI, as logic and reason drive decision-making?
How do you know if anyone in your organisation is utilising any LLM to summarise facts and data to help with what should be presented to you? Why do they do this? Because they are too busy as we already reduced headcount, the issues are complex, or there are other priorities that directly affect their KPI and bonus!
Based on the text so far, it is easy to think, let’s stop, but when the world is rapidly adopting AI technology, not being at the table is not an option. Part of your efficiency gains last year may have already come from outsourcing logic to a machine. This dilemma is one we all face, and the argument for and against outsourcing logic and reason to AI has several compelling dimensions:
Computational Advantages
Potential Limitations
Philosophical Considerations
Critical Risk Assessment
The most responsible approach isn't wholesale outsourcing but collaborative reasoning—using AI as a powerful analytical tool while maintaining human ethical oversight, contextual understanding, and ultimate decision-making responsibility. However, this approach is inefficient and ineffective. While AI can calculate probabilities and identify patterns, humans decide what those patterns mean and what actions to take. However, humans can see patterns and connections that no AI can fathom, and machines are less influenced by incentives and politics to take the best action.
Reason isn't just about processing information—it's about understanding purpose, meaning, and ethical implications. These domains remain under threat but also offer the biggest opportunity.
The takeaway
Embrace complexity, challenge your assumptions, and recognise that the most powerful insights often emerge from the spaces between our traditional categories—not from the categories themselves.
Would you like to explore how this new way of thinking might revolutionise your approach to communication, technology, or strategy? Then, start with questions.
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1wYes - 'challenge your assumptions, and recognise that the most powerful insights often emerge from the spaces between our traditional categories—not from the categories themselves' a dialogue not dependencies!
🌍 Pᴏʟʏᴍᴀᴛʜ Mɪɴᴅsᴇᴛ Sᴛʀᴀᴛᴇɢɪsᴛ | Author | Sᴘᴇᴀᴋᴇʀ | Fᴜᴛᴜʀɪsᴛ | Board Advisor
1wBrilliant insights! I particually love this: "We’re moving from a world of “this is true (logic)” to a world of “this is one possibility (reason).” And in that possibility lies an incredible opportunity for some because of uncertainty — but also a new risk at a scale where your brand can be lost in a day."
𝗕𝗼𝗮𝗿𝗱 𝗔𝗱𝘃𝗶𝘀𝗼𝗿, 𝗣𝗶𝗼𝗻𝗲𝗲𝗿, 🅼🅰🆅🅴🆁🅸🅲🅺, 𝗣𝗼𝗹𝘆𝗺𝗮𝘁𝗵
1wRobbie Stamp Ajit Jaokar Bruce McTagueRoberta Profeta Amy Lewin Ben Cattaneo Nathan Furr Susannah Furr Joan Leung Paul Clarke Bill Murray Caitlin E McDonald, PhD Patricia Shaw Huma Shah Luukas Ilves Lord Holmes Yael Rozencwajg Dr. Peter Crow Jerry Fishenden Dr Bharat Vagadia Nicola Breyer Wan Wei, Soh Tudor Finneran Ammar Younas Jesús Martín González Claudia Heimer Aksinya Staar Darragh Power Ian Wright
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1wThank you for these insights Tony Fish, recommended reading for attendees of the #DataJam