The Cognitive Supply Chain: The Benefits of AI
Originally published in Forbes Book Author Post, February 2026(opens in new tab)
Artificial Intelligence (AI) technology went mainstream more than two years ago, and we continue to marvel at the innovative disruptions it brings, both realized and anticipated benefits, as well as its potential liabilities. The cycles of innovation we have witnessed in the past have involved transformative technologies that have changed the business and consumer landscape, creating new opportunities, especially for early adopters and potential disruptors of incumbents. Like all major technological shifts, there will be big winners and losers, and the race is to see who can move with agility and speed in capturing realizable Return on Investment (ROI). Additionally, there will be several significant risks to contend with, including maintaining data security, eliminating bias, and ensuring transparency. For example, companies are moving quickly and investing heavily to reap the benefits, which feels like less traditional oversight of security practices. With the added complexity of AI, security threats and challenges have increased, as security often lags, and bad actors recognize the opportunity to exploit.
There are several descriptions for the innovation cycle we are witnessing today, and my preference is the term Cognitive Cycle. The primary characteristic of this cycle will be systems that learn, and these systems will change what people do. This means that, ultimately, AI will lead to significant improvements in productivity through faster innovation, better decision-making, and greater efficiency.
Most disruptive innovations follow a technology life cycle that can last between about 50 and 100 years and can be described in four distinct stages. The first stage is the Innovation stage, characterized by the research and development of a new concept with limited availability. The Growth stage then follows, ignited by increased investments and rapid adoption. The third stage is the Maturity stage, in which the concept becomes stable and widely used. The final stage is the Decline stage, in which the concept becomes obsolete as newer, better alternatives emerge.1
The concept of AI began in the 1950s, and we have seen evidence of deep learning systems since then. One of those is Deep Blue, the IBM system that defeated chess grandmaster Garry Kasparov in 1997. In addition, there are advanced question-and-answer (QA) systems such as Watson, the IBM system that defeated Jeopardy! Champions Ken Jennings and Brad Rutter in 2011. Since 2022, we have been experiencing a family of large language models (LLMs), such as ChatGPT, designed to engage in natural-language conversational dialogue in response to user prompts. We witnessed an “AI gold rush” of building solutions using LLMs, SLMs (Small Language Models), DSMs (Domain Specific Models), and numerous homegrown AI language models. The AI technology cycle is likely to extend well into the late 21st century, encompassing various AI waves, including machine learning, deep learning, generative AI, autonomous agentic AI, and future paradigms such as Quantum AI.
Inflection Point
I like to think about the central inflection point that started in the 1990s, driven by the adoption of the Internet. It opened access to information for everyone, changing how people and companies interact with their employees, customers, partners, and supply chains. The ongoing, unprecedented growth in data and information was further fueled by technologies and applications such as cloud computing, mobile devices, social applications, and the Internet of Things (IoT). Now, given the rapid deployment of AI, we are seeing the rise of more systems and applications that can understand and reason by extracting information and insights from both structured and increasingly unstructured data. This enables more automation and redefines the future of work, shaping what businesses will look like in the years to come.
As a result, AI is moving deeper into the strategy, operations, and infrastructure of every company, and significant investments are being made to capture the efficiency gains it promises. Our curiosity should lead us to the big questions businesses must keep in mind: “Why?” “How?” and “What if?” For example:
1) Why would the market benefit from using it, and how would customers, partners, and employees be advantaged?
2) How are the benefits being measured for reducing the cost of operations, and how are the talent strategies required being kept current for continuous execution?
3) What if guardrails and policies fall short, creating increased enterprise risk, and how rapidly can remedies be deployed?
AI will take the global supply chain to the next level of efficiency, and we need to be able to trust it first, just as we need to trust each other, according to Joe Hudicka.2 In the world of distribution and logistics, the global supply chain of the past was fragmented, reactive, and reliant on human decision-making with manual processes and limited visibility.
Today, it is digitally connected, more predictive, and data-driven, with operations using GenAI and emerging Agentic AI for deeper insights. Therefore, consider an AI platform for an integrated supply chain that can transform the operations using various language models, thereby improving performance and efficiency. It is crucial to keep in mind that the best outcome depends on the choice of language models – whether large, small, or domain-specific – to optimize performance for a particular task or workflow. In the future, it will be a self-optimizing, autonomous, and quantum-AI supply network that anticipates, resolves, and coordinates globally. Just imagine a day in the future where companies could instantly optimize their entire supply chains on a global scale. This will be possible when systems combine the computational power of quantum computing with the learning and pattern recognition capabilities of AI, enabling them to solve complex problems that exceed the capabilities of today’s classical systems.
In the meantime, we will need to reap the benefits of applying AI to solve challenges and deliver a transformative experience and service across Product Information and Inventory Management (PIIM), Supplier Information Management (SIM), and Customer Information Management (CIM). In addition, leveraging AI in global logistics reduces supply chain disruptions and enables real-time management of supply, demand, inventory, and pricing.
In Practice
I serve on the boards of directors of a few public companies, all of which, like most enterprises today, are actively investing in building solutions across their supply chains and logistics networks.
Avnet is looking to evolve its supply chain with AI, transitioning from a reactive to a more predictive, adaptive approach across electronic components, embedded systems, and logistics. The company is exploring ways to leverage AI to improve its information management tools, demand forecasting, inventory optimization & positioning, logistics planning & route optimization, warehouse operations, and risk prediction. The potential benefits for suppliers include better demand visibility, reduced inventory risk, and improved order accuracy, among other things. The potential benefits for customers include higher product availability, faster fulfillment, and reduced costs.
Grainger has leveraged AI across its supply chain to optimize product availability, enhance operational efficiency, and improve the customer experience for Maintenance, Repair, and Operations (MRO) supplies. Machine learning models are being used for demand forecasting and planning. Computer vision is used in the KeepStock program to simplify inventory management, and advanced AI models are employed in warehouse and logistics systems to optimize fulfillment. The benefits for suppliers include improved demand signals, effective restocking, and painless customer installations. The benefits for customers include improved product availability, reduced manual errors, faster stocking, and reliable fulfillment.
UPS leverages AI across its supply chain and integrated network to drive efficiency and reliability in demand forecasting and capacity planning, global logistics and transportation, predictive maintenance, and package delivery. An AI-powered system, ORION, is a dynamic routing and last-mile delivery platform that enables greater efficiency and improved fuel efficiency. The use of AI for smart facilities and RFID-enabled packages helps streamline fulfillment and increase throughput. The benefits for the supplier include more reliable and optimized shipping options, improved network efficiency, and scalable global logistics support. The benefits for customers include faster deliveries with real-time visibility and insights, lower logistics costs, and proactive issue management.
The power of AI has enabled us to continuously reimagine the entire supply chain, providing enhanced capabilities end-to-end from planning and sourcing to manufacturing, distribution, logistics, fulfillment, and returns. Studies suggest that AI can unlock $1.3 trillion to $2 trillion in annual value across global supply chains through efficiency gains, risk reduction, and improved decision-making.3
- Succurri, “What Is Technology Lifecycle Management?,” Succurri, last modified August 22, 2022, https://www.succurri.com/blog/what-is-technology-lifecycle-management/(opens in new tab)
- Hudicka, Joe. The AI Ecosystems Revolution: Transforming the Global Supply Chain through Real-Time Collaboration. Forbes Books, 2025.
- McKinsey & Company. Most of AI’s Business Uses Will Be in Two Areas. McKinsey & Company, 2018. Accessed September 28, 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/most-of-ais-business-uses-will-be-in-two-areas(opens in new tab)