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“Solving a problem simply means representing it so as to make the solution transparent.”
This quote captures one of Simon’s core insights, developed through his work in cognitive psychology, AI, and problem-solving—especially in his influential book The Sciences of the Artificial (1969). He argued that the way a problem is represented largely determines how easily it can be solved. This idea became foundational in both human cognition and artificial intelligence. In AI, it directly connects to the concept of knowledge representation—how information is structured so that machines (or humans) can reason with it effectively.
The work of Simon
In AI, it directly connects to the concept of knowledge representation—how information is structured so that machines (or humans) can reason with it effectively.
Simon believed that the challenge in problem-solving often lies not in the complexity of the solution, but in how the problem is framed. His work shifted the conversation from raw computation to the structure of thought itself.
Trained in political science and economics, Simon reshaped how we understand a fundamental human skill: the ability to make decisions. With a formidable intellect, infectious curiosity, and an insatiable appetite for learning, he spent 65 years conducting research that spanned—and transformed—political science, economics, psychology, and computer science.
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Herbert Simon made several groundbreaking contributions to the field of artificial intelligence, establishing himself as one of its founding fathers. His work laid the foundation for many aspects of modern AI research and development.
As early as 1956, together with Allen Newell, Simon developed what is often considered the first AI program—the Logic Theorist. It was capable of proving mathematical theorems, demonstrating for the first time that machines could engage in logical reasoning.
In the 1960s, Simon famously predicted:
“Machines will be capable, within twenty years, of doing any work a man can do.”
While optimistic on the timeline, the trajectory he envisioned has proven remarkably accurate.
Why Simon
Herbert Simon was one of the first to argue that intelligence isn’t about raw computational power—it’s about how systems operate within limits. He introduced the concept of bounded rationality, showing that decision-makers—whether human or machine—never have perfect information. They always work within constraints: limited time, limited resources, and incomplete data.
Even today, many people still assume that AI is about brute-force intelligence. But Simon reminds us that the most effective intelligence is goal-driven and constraint-aware.
Modern AI systems aren’t trying to replicate human reasoning in full. Instead, they’re optimized for narrow, well-defined problem spaces—often in ways humans cannot match.
Simon’s work gives us a more grounded way to talk about AI with executives, so instead of asking how smart AI is, Simon teaches us that we bettter ask:
What problem space is this AI solving for?
What constraints is it operating under?
How can we structure our business problems so AI can be most effective?
Conclusion
The best applications of AI in business aren’t the ones chasing general intelligence—they’re the ones solving real problems under real-world constraints. Whether it’s in supply chain, forecasting, or decision-making, the value of AI lies in how well we define the context it operates in.
Simon’s work reminds us of a simple truth: Intelligence—whether human or artificial—is shaped by limitations. What distinguishes smart systems isn’t unlimited power, but focused purpose.
The companies that recognize this—and design AI solutions around clearly framed problems, realistic constraints, and the right context—won’t just use AI.
They’ll outthink their competitors with it
Citations:
[2] https://www.sigmaxi.org/programs/prizes-awards/william-procter/award-winner/herbert-a.-simon
[3] https://en.wikipedia.org/wiki/Herbert_A._Simon
[5] https://history.computer.org/pioneers/simon.html
[6] https://www.linkedin.com/pulse/decoding-ais-evolution-herbert-simons-legacy-rise-generative-ai
[7] https://www.ubs.com/microsites/nobel-perspectives/en/laureates/herbert-simon.html
[8] https://www.investopedia.com/terms/h/herbert-a-simon.asp
Digital Planning and the Importance of Human Expertise
As supply chain technologies rapidly advance, it’s tempting to think AI and automation will soon take over. However, while Digital Planning and AI will play an increasingly important role in supply chain management, human expertise remains indispensable. Companies that combine human and machine intelligence will thrive in this changing landscape. Human intervention will be more crucial than ever to fully leverage the power of AI.
Complexity and Unpredictability of the Supply Chain
Global supply chains are more complex than ever, influenced by volatile market conditions, geopolitical tensions, and unpredictable natural disasters. AI can analyse data and predict patterns, but it lacks the nuance to account for unforeseen factors such as cultural differences, political decisions, or sudden shifts in customer behaviour. Human intuition and creativity will continue to be essential in adapting to these unpredictable challenges. In this context, fully automated systems without human involvement are not yet equipped to offer the flexibility and responsiveness required.
Decision-Making Under Uncertainty
AI depends on data for decision-making, but what happens when the data is incomplete, especially in a crisis? Human decision-making becomes critical, particularly when working under pressure with limited information. Over the next decade, Digital Planning will advance, but systems dependent on historical data will struggle to adapt to novel situations. Human expertise remains vital to assess risks, generate alternatives, and make informed decisions in these scenarios.
Context and Emotional Intelligence
Supply chain decisions often require a deep understanding of a company’s broader context, including relationships with suppliers, customer expectations, and ethical considerations. While AI can provide data, it cannot replicate the emotional intelligence and interpersonal skills needed for negotiations, trust-building, and relationship management. Humans provide the nuance and context that technology cannot, making them essential in maintaining strong supply chain networks.
Innovation and Adaptability
AI can streamline operations and optimize processes, but true innovation often comes from people. Technology may analyze trends, but human creativity sees new opportunities and improvements beyond the algorithm’s scope. Over the next decade, companies that successfully integrate human innovation with AI tools will distinguish themselves. People will remain at the heart of new ideas and advancements that push AI further.
Conclusion
While Digital Planning and AI will undeniably transform supply chain management in the years ahead, human expertise will be essential to fully harness its potential. The evolving balance between AI autonomy and human intervention, as illustrated below, demonstrates that while AI will handle more tasks independently, humans will remain critical in navigating uncertainty, providing context, and driving innovation.
Create a safe environment | the sandbox
We’ve seen firsthand that creating an environment where small, scrappy teams can innovate without being slowed down by endless reviews is key to unlocking AI’s potential.
How to deploy In-Company AI
Building a Company-Specific Vertical AI: A Practical Guide for C-Level and Supply Chain Leaders. Why Companies Are Exploring Vertical AI.
What we Learned building our Vertical
Going from an abstract vision of “AI that works for supply chain” to a functional, compliant, production-ready AI stack is harder than it sounds
When numbers start talking
Our founder Bas Groothedde meets our In-Company AI Agent AVA. Great stuff.
Giving your AI context in the supply chain
Context-prompting, an important tool to give an llm context. There is strong theoretical and empirical evidence that providing AI with better context improves its performance.
The Evolution of AI Agents
The landscape of AI agents has advanced from basic chatbots to sophisticated entities managing complex tasks across industries.
This is World interview featuring Professor Yann LeCun
LeCun brings clarity to the current discourse on AI with a number of thoughtful, remarks and putting it into perpective. A must-watch for anyone navigating the future of artificial intelligence.
AI is moving so fast, which is why your strategy should be long-term.
Danny Hillis, a pioneer in AI He understood something decades ago that many executives are only now coming to terms with. Technology does not evolve in stable, predictable increments.
The future of AI is not just language models. It’s causal reasoning.
Judea Pearl, is another true pioneering in artificial intelligence. But he is a little bit different. And not on the bandwagon if LLMs. Teach machines to understand the question why.
Intelligence is not what we think it is
In our series about AI, we came across the work of Professor Herbert “Herb” A. Simon, one of the founding figures in artificial intelligence.
Why the human factor remains essential in the Digital Era
No matter how advanced technology becomes, human insight remains essential in managing real-world complexity.
Network optimization
While many fields are buzzing with discussions about the possibilities of AI and other technological innovations, the realm of network design in supply chains remains conspicuously silent. The major breakthroughs and innovations celebrated in other domains seem absent here. Yet, the potential for transformation in supply chain network design is immense, though the changes required are subtler and more complex than we’re used to. So where are the big new developments? And I’m not talking about AI by the way. Big fan, but that is not where I think we should focus on.
Let me take a step back first. In 1736, Leonard Euler laid the groundwork for graph theory and network topology to solve the famous Königsberg Bridge problem, proving that the structure of connections could be analysed mathematically. Centuries later, this foundation continues to revolutionize fields ranging from artificial intelligence—earning a Nobel Prize in physics just this year—to the internet’s very existence.
Yet in the field of supply chain management, network topology remains a glaring blind spot. Despite its critical role in shaping speed, reliability, costs, flexibility, and resilience, we often reduce networks to a mere cost or logistics optimization exercise. In my latest column, I wrote about this oversight, emphasizing that we need to do better. As someone deeply invested in advancing this field, I see it as a personal obligation to educate and inspire others to embrace the power of network structure.
1. The Information Theory Perspective
Claude Shannon’s Information Theory (1948) explains that the more structured and relevant information we provide, the less uncertainty there is in an output. AI systems—especially Large Language Models (LLMs) like GPT—operate on this principle: when an AI model is given vague or insufficient input, it must “guess” more based on probabilistic inference, leading to lower accuracy or more generic, hallucinated, or misaligned responses. When we provide an AI with well-structured, high-quality context, it can reduce uncertainty and generate more precise, relevant, and insightful outputs. In practical terms, this is why prompt engineering and context injection are essential when interacting with AI models: they reduce ambiguity and allow the model to focus on relevant information.
The Lessons of Network Topology: From Euler to Baran
The story of Paul Baran at RAND Corporation illustrates the profound implications of network topology. Tasked with designing a communication system resilient to nuclear attacks, Baran analysed three network structures: centralized, decentralized, and distributed. His recommendation for a distributed structure became the blueprint for the internet—an interconnected web of nodes capable of rerouting data even when parts of the system are disrupted.
Imagine if supply chains adopted a similar mindset, prioritizing flexibility and redundancy over sheer efficiency. Concepts like Network Redundancy Ratio, Path Redundancy Index, and Closeness Centrality could redefine how we evaluate supply chain performance, expanding our focus beyond short-term cost savings to long-term strategic resilience.
Network Topology as a Strategic Asset
To build true resilience, companies must start treating network topology – the structural design of their supply chains – as a strategic element. Traditional metrics like cost and lead time need to be augmented with network resilience indicators such as the Network Redundancy Ratio, Closeness Centrality, and Path Redundancy Index [2]. These metrics reveal the robustness, scalability, and flexibility of a network, which are critical for navigating disruptions.
The analogy to neural networks in AI is instructive here. Just as robust neural network topologies have unlocked breakthroughs in AI, strategically designed supply chain networks can empower organizations to not just survive but thrive in a world of constant uncertainty.
From Blind Spot to Breakthrough
We are living in a time of unprecedented technological advancement. AI and big data are revolutionizing how we approach supply chains, but their potential will remain underutilized if we fail to consider the underlying network structure. A strategically designed network can be a game-changer, enabling companies to adapt, recover, and innovate in the face of disruption.
The lesson from Euler, Baran, and the internet’s distributed architecture is clear: the structure of a network matters. It is not a secondary consideration but a strategic choice. As supply chain leaders, we must embrace this perspective, leveraging network topology as both a science and an art.
In a world where disruption is the norm, the organizations that view their supply chains through the lens of network topology will not just survive—they will thrive. Now is the time to rethink, redesign, and rebuild. Euler showed us the way; it’s up to us to take the next step.
[1] BCI. (2024). Latest BCI report reveals escalating supply chain disruptions drive increased tier mapping and insurance uptake*. Retrieved from https://www.thebci.org/news/supply-chain-disruptions-drive-increased-tier-mapping-and-insurance-uptake.html
SCMR. (2024). The 10 Top Disruptions in 2024 (So Far). Retrieved from https://www.scmr.com/article/the-10-top-disruptions-in-2024
Intuendi. (n.d.). Supply Chain Disruption: Causes, Effects, and Management. Retrieved from https://intuendi.com/resource-center/supply-chain-disruption/
Resilinc. (2024). Life Sciences and Healthcare Supply Chain Recap Q1 2024. Retrieved from https://www.resilinc.com/blog/life-sciences-q1-2024-supply-chain-recap/
[2] Network Redundancy Ratio: Measures the availability of alternative paths, ensuring continuity during disruptions.
Closeness Centrality: Assesses how efficiently goods flow through the network, minimizing delays and bottlenecks.
Flow Entropy: Quantifies the diversity and robustness of supply routes, balancing risk and efficiency.
Path Redundancy Index: Captures the ability to reroute goods, enhancing reliability and resilience.
In-company RAG
Start building you In-company RAG system and learn how to train your model. Learn the basic setup and common pitfalls when it comes to in-company AI.
Create a safe environment | the sandbox
We’ve seen firsthand that creating an environment where small, scrappy teams can innovate without being slowed down by endless reviews is key to unlocking AI’s potential.
How to deploy In-Company AI
Building a Company-Specific Vertical AI: A Practical Guide for C-Level and Supply Chain Leaders. Why Companies Are Exploring Vertical AI.
What we Learned building our Vertical
Going from an abstract vision of “AI that works for supply chain” to a functional, compliant, production-ready AI stack is harder than it sounds
When numbers start talking
Our founder Bas Groothedde meets our In-Company AI Agent AVA. Great stuff.
Giving your AI context in the supply chain
Context-prompting, an important tool to give an llm context. There is strong theoretical and empirical evidence that providing AI with better context improves its performance.
The Evolution of AI Agents
The landscape of AI agents has advanced from basic chatbots to sophisticated entities managing complex tasks across industries.
This is World interview featuring Professor Yann LeCun
LeCun brings clarity to the current discourse on AI with a number of thoughtful, remarks and putting it into perpective. A must-watch for anyone navigating the future of artificial intelligence.
AI is moving so fast, which is why your strategy should be long-term.
Danny Hillis, a pioneer in AI He understood something decades ago that many executives are only now coming to terms with. Technology does not evolve in stable, predictable increments.
The future of AI is not just language models. It’s causal reasoning.
Judea Pearl, is another true pioneering in artificial intelligence. But he is a little bit different. And not on the bandwagon if LLMs. Teach machines to understand the question why.
Intelligence is not what we think it is
In our series about AI, we came across the work of Professor Herbert “Herb” A. Simon, one of the founding figures in artificial intelligence.
Why the human factor remains essential in the Digital Era
No matter how advanced technology becomes, human insight remains essential in managing real-world complexity.
