BY HANS BERGGREN
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We are facing a paradigm shift in supply chain management. On the one hand, the sharp increase in digitalization in recent years is paving the way for increasingly automated, efficient, and flexible supply chains. Much of the inertia and resistance that resulted from a strong focus on investment ”within the four walls” and ”silo thinking” has finally been replaced by an interest in end-to-end flow.
On the other hand, most people are a bit bewildered and wonder how, in the midst of this positive trend, we can take advantage of AI technology—which has proven to require deep insights into the company’s goals, potential process improvements, and how alternative business models can help the company achieve its goals more quickly. If we don’t understand this, we won’t succeed in creating “the data-driven company” or designing AI-driven strategies. So what do we do now?
Data-driven companies are more profitable
MIT Technology Review Insights (Jan. 2024) discusses data-driven organizations and data-driven decision-making (DDDM)—an area that is attracting increasing interest and generating ever-clearer added value, where companies that excel in this area are both more profitable and grow faster than those that are not as data-driven. McKinsey also highlights this (e.g., in the article Insights to Impact: Creating and Sustaining Data-Driven Commercial Growth, (January 18, 2022), and states that data-driven companies in B2B sales generally achieve 15–25 percent higher sales growth and EBITDA compared to the average. Just to mention a couple of examples.
Understanding ”What do we do now?”
We need to understand ”how” by drawing on insights into where there may be room for improvement across all parts of our company, based on where the company stands today. The key is to start where we identify the greatest potential for improvement and the development of more profitable business models.
”Digitalization First” – No Data, No AI
Companies like PipeChain play a crucial role in driving digitalization in supply chains by enabling more stakeholders to connect digitally (customers, suppliers, carriers, and other critical players in the supply chain) and thereby automate an increasing number of activities and processes. As a result of this increasing digitalization, invaluable data is generated, often in real time or at least near real time. It is all this data that makes it possible to harness the power of AI. Now, more and more different systems are being interconnected, and the previously silo-based system structure is being broken down. This is what opens up the possibilities for leveraging human ingenuity and creativity, combined with the computational power of AI, to handle enormous amounts of data, dimensions, and decision factors. The next step is to understand that the primary goal is not simply to present data, but to use AI to identify patterns in the data and automatically draw conclusions from it so that new, better decisions can be made with the help of AI. We should therefore shift our entire mindset from ”data-to-insight” to ”insight-to-action,” where ”action” is carried out with AI support.
Transformation and Digitalization Using AI
AI and its ultimate impact are not only a function of the technical capabilities that AI technology offers, but also of how well it adapts to human behavior and organizational needs. Some of the key points we have identified in our work are summarized below.
1. The potential of AI in various aspects of a company's processes and across different industries
- AI is transforming a wide range of fields and industries, for example by optimizing sales and manufacturing or creating opportunities for entirely new business models.
- The ability to analyze vast amounts of data and extract patterns—based on which better decisions can be recommended or even automated—is AI’s core strength, but its success depends on how these insights are used, implemented, and quality-assured.
2. Four Steps to AI Transformation
- Data Collection: The foundation of AI is high-quality, relevant datasets.
- Generating insights: AI can identify trends and correlations, but understanding their significance requires expertise and human judgment.
- Implementing actions: This is where the human aspect becomes crucial. Insights must be translated into strategies that take human behavior and organizational dynamics into account. We believe that success is largely a matter of automating the ”actions” that AI insights provide.
- Ongoing quality assurance through the AI solution’s own learning and continuous improvement, as well as through human quality assurance.
3. Understanding Human Behavior
- The effectiveness of AI depends on how well it is integrated with human decision-making processes.
- Designing successful change initiatives requires a people-centered approach that ensures alignment with real-world problem-solving and genuine opportunities for improvement. In this regard, the implementation of AI-supported solutions is no different from other major change projects.
4. The Interaction Between Technology and People
- The main goal of using AI in supply chains is not to replace people, but to enhance human capabilities or to assign the company’s staff to the right tasks.
Successful AI Transformation in Supply Chains
The path to successful AI transformation in supply chains is therefore not just a technical journey, but also a deeply human one. Understanding this duality is the key to effectively harnessing AI’s immense potential.
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Hans Berggren is the CEO of the PipeChain Group
