How AI can support in paving the way for more resilient, regenerative, and responsible Value Chains
Hypercompetitive business environments in a world of uncertainty, with the aftermath of COVID-19 and the war in Ukraine — are some of the challenges today’s companies face. This makes planning and decision-making complex. At the same time there is an explosion of all types of data — structured and unstructured — coming from first, second and third parties whose value is just waiting to be unlocked.
Agility via data-driven decision-making enables companies to change direction. The idea that becoming data-driven is crucial for success in the future is a truly widespread one. But is there really some truth to it? How can AI help in paving the way and in what way can it make companies more resilient, regenerative, and responsible? These are some of the things that I would like to explore in this article. I will do that by looking at 1) Why a paradigm shift is necessary, 2) Specific use cases 3) What should be in focus to realize the value from a change management perspective.
Data-driven with AI has a potential
According to Cap Gemini (Cap Gemini, 2023) research shows, that there are many real benefits for organizations that strive to become data driven. Here are some examples:
· Higher topline growth (above 70%)
· Efficiency in their business
· Significantly higher productivity (17% higher profit per employee)
· Higher customer satisfaction and loyalty
· Talent retention (a measured increase in job satisfaction rate from 67% to 80%)
· Return on investment
I hope that we can agree on that data is a wonderful thing. It contains so much unused potential to help organizations become better in almost every aspect. Even more resilient, regenerative, and responsible. The challenge lies in taking advantage of this potential. To many companies’ data is still a sleeping giant. “Data is the new oil” is a catchphrase that you may have heard being bandied about. It refers to the fact, that data and information is now an even more valuable asset than oil. However, the idea that “data is more like sunshine than oil”, as stated by Googles CFO at the World Economic Forum in 2021, might be more accurate. The underlying meaning is that we can keep using it and it will keep regenerating insights.
When talking about regenerating insights I think everyone by now understands that AI, as a tool or technology, has the possibility to play a significant role in the way we operate, make use of data and make decisions in our businesses and our Value Chains in the future. The challenge for many leaders most often lies in understanding what use cases it could be applied for and how value is generated in doing so.
In short, AI offers powerful tools and capabilities to drive innovation within Value Chains, helping companies reduce their environmental footprint, improve efficiency, and create long-term value for both businesses and society. AI is estimated to have a global impact of 26% increase in global GDP by 2030 (Brodie, 2023).
It is about protecting the long-term value of the business.
The health and survival of every Value Chain and every individual depend on the health of the surrounding world. We have created a society and a way of living that will most certainly not continue to provide the opportunity for people and other life to flourish on earth forever. We degrade and disrupt the earth’s natural processes and deplete the resources on which humanity and all other life depend (Deloitte, 2024). Social inequality is rising, and the basic needs of millions of people are not being met.
We witness immense pressure and stress not just in our societies, economies, and ecosystems. Our minds and bodies are “out of order,” too. We have never been as stressed out as we are today. Stress has been labelled the “global health epidemic of the 21st century” by the World Health Organization (WHO) (Hutchins & Storm, 2019). Addressing issues like climate change, biodiversity loss, refugees, food waste, and widespread degradation can be difficult when constantly stressed. We have moved from a Holocene state — that is, a state in which the earth was able to regenerate the resources that we humans use, to an Anthropocene state, i.e., a state in which we humans use more resources than the earth can manage to regenerate (Robertson, 2021).
We need a new ambition for sustainability & businesses
Neutrality, or “do no further harm” is increasingly seen as an insufficient ambition considering the natural capital losses and climate tipping points, which threaten the health, security, and livelihoods of billions. Customers and employees, especially Gen Z, are weary of corporate greenwashing and “green wishing” and demand demonstrable impact. Calls for environmental justice and a just transition, underscore the reality that “E” (environment), “S” (social) and “G” (governance) can no longer be treated as silos.
Indeed, we have seen the rise of positive approaches in recent years — climate-positive, nature-positive, and net-positive — to address our interlinked natural and social challenges. At the same time, there is a growing recognition that efforts to restrain climate change and halt biodiversity loss will not be successful unless we address them together as part of a broader holistic approach.
Regeneration is a concept that addresses challenges comprehensively and provides Business and Value Chain leaders with a framework for creating and protecting long-term value, one that aligns the organization to new value drivers, encourages innovation, and builds the resilience and responsibility of the company (Sindbjerg Hinrichsen & Haas de, 2023).
Beyond neutrality, regenerative approaches aim for complete systems changes that address the root causes of global challenges. The goal is to create the conditions for all life to thrive, generating self-sustaining positive outcomes for nature, people, and the economy. Regeneration offers a bold vision of the future we must achieve rather than the climate catastrophe we must avoid.
We need Value Chain’s which are more Resilient, Responsible and Regenerative (3 R’s) if we are to ensure the long-term survival of companies and Value Chains. Let’s look at how innovation can be created by looking at some of the potential use cases.
It takes innovation to embrace the paradigm shift
Digital technologies i.e. AI, can transform the company’s business model and accelerate regenerative performance. Innovation is more than just developing and implementing technology; it is about combining the right technologies at the right time and place. Technology is a lever to solve complex problems. Having a data-driven innovation approach puts you in a position to utilize new technological breakthroughs at the right time. When useful data is available to the many in an organization, it is much more likely that employees will use the information to innovate in their daily work. A great data-driven culture will also drive many small innovations every day.
To kick-start your imagination — Let’s have a look at the potential use cases of the application of AI as a driver to more regenerative, responsible, and resilient Value Chains:
Responsibility
AI can optimize Value Chains by analysing vast amounts of data for example to identify ethical sourcing practices. It can also enable transparency and traceability, allowing consumers to make informed choices. AI-powered blockchain systems can enhance transparency and traceability within Value Chains by providing real-time visibility into the movement of goods and raw materials. This transparency enables companies to track the origin of products and verify sustainability claims.
It can encourage sustainable behaviours by providing personalized recommendations and feedback to individuals. AI can also assist educational initiatives by providing interactive learning experiences on sustainability topics. Amazon is a frontrunner in application of AI in operations, they have developed AI algorithms to screen new products for information about environmental impact to be able to estimate the carbon footprint of a product. Amazon estimates having changed the time needed for analysis and registration from month to hours (Dominguez, 2024).
Resilience
Risk detection: AI can process data from sensors, satellites, and other sources to monitor and analyse environmental conditions. It can help detect and predict natural disasters, monitor air, and water quality, track deforestation, and identify patterns related to climate change.
Supplier Selection and risk Management: AI can assess the sustainability performance of suppliers by analysing various factors such as environmental impact, labour practices, and social responsibility.
By selecting suppliers with strong sustainability practices, companies can reduce the environmental and social risks within their Value Chains. AI can be used to assess the sustainability of partners and suppliers, while identifying potential areas for improvement. By analysing available data on supplier performance — including carbon emissions — businesses will be empowered to make informed decisions about their Value Chain partners.
Regeneration
Biodiversity and bio-solutions: AI can aid wildlife conservation efforts by analysing camera traps, satellite imagery, and acoustic sensors data. It can help identify endangered species, track their movements, detect poaching activities, and support habitat preservation initiatives. It can lead to preserving biodiversity.
Energy Management: AI can analyse energy consumption patterns and optimize energy usage in buildings, factories, and transportation systems. Machine learning algorithms can identify opportunities for energy conservation and suggest strategies for reducing energy waste. AI can be used to develop intelligent grids that monitor and manage electricity generation, distribution, and consumption. By analysing data from various sources, AI algorithms can optimize power generation and distribution, minimize grid inefficiencies, and integrate renewable energy sources effectively.
Waste management and recycling: AI can improve waste management systems by analysing data to optimize waste collection routes, reduce landfill usage, and identify recycling opportunities. Image recognition algorithms can assist in automating waste sorting processes, enhancing recycling efficiency. By optimizing manufacturing processes and identifying ways to reuse materials, companies can minimize waste generation and promote a circular economy within their Supply Chains.
Another example from Amazon is how they use AI to optimize the packaging needed for different products, including size and shape of a product and quality information from prior deliveries. This AI model is estimated to have saved more than 2 mill.t of packaging material (Dominguez, 2024).
Transportation: AI can optimize traffic flow and improve transportation efficiency. AI-powered algorithms can analyse real-time traffic data, predict demand patterns, and suggest optimal routes or new Supply Chain designs or operating models. Optimizing transportation have the potential to give significant cost savings. In US the cost of flight delays is estimated to 39bn USD. (Naveen, 2019)
Product Lifecycle Assessment: AI can conduct lifecycle assessments of products to evaluate their environmental impact from raw material extraction to disposal. By understanding the environmental footprint of products, companies can identify opportunities for improvement and develop more sustainable alternatives.
Demand Forecasting and Inventory Management: AI algorithms can analyse historical data, market trends, and external factors to accurately forecast demand. By predicting demand more accurately, companies can optimize inventory levels, reducing excess stock and minimizing waste.
Amazon uses AI as part of their fulfilment processes, there they detect damaged goods before they are sent to a customer. This way they are saving the efforts shipping, the customer returning the defect product and the need to resend a new product. (Dominguez, 2024)
AI is all about decision making
When working with AI it is important to understand and embrace the fact, that the technology itself plays a minor role. The place where AI will have the greatest impact and require the greatest changes in approach lie hence not in technology but within the organization. The impact of AI is not on the steps within processes but within the decisions that the organization takes.
AI, therefore, will be much more about a new way of viewing the organization than it will be about Process or Technology. AI have the potential to eliminate traditional roles in the organization and create new roles and providing new dynamic platforms for training and learning in the organization, giving people new opportunities to move across traditional organizational boundaries (Brodie, 2023).
The organization needs to decide where the change needs to happen. AI should be interlinked with the roles and responsibility of the organization. Which also would mean a fundamental change in the business culture, it’s organization and the responsibilities of that organization. To succeed with AI, it is key to succeed with the people transformation.
The obvious place many people put AI is into the technology bucket. It is certainly true that AI’s leverage technologies. AI aims to take ownership of decisions rather than just execute instructions. Decisions that today are probably taken by people, decisions that require authority and approval.
An example: The concept of ordering stock is a business process, the system that does it a technology, but the right to do it and to set the volumes that are ordered is a business decision. Controlling AI is much more about authority and governance (the overlap of people and processes) than it is simply about steps and order.
Today many businesses are run by people filling in the gaps in processes, using ad-hoc data (or guesswork) to make a lot of their decisions and relying on their historical and contextual knowledge to get things right. These processes are described as everything from “desktop processes” to “cultural knowledge” and it’s all about how people work. A great example of cultural knowledge is fear, you don’t avoid doing a task because you fear that you will get fired if you do the task — you avoid doing a task because you fear the effect of being fired. Many organizations build on this fear of doing wrong to guide doing the tasks they want. Applying AI in decision making based on data insights, will provide better foundation for decision making.
So, when we move AI into the organization it isn’t the same problem that we’ve historically had. An AI has no conscience, it has no fear, it has only the cultural knowledge that you explicitly tell it about, and only respects boundaries that you enforce. You need to be able to build the specific digital context required by the AI to correctly deliver its outcome at that specific point of time. This means we have an entire new layer of data that is required, a layer of data that enables AI to understand the business, the current business, and the context within which it needs to make a business decision.
The organization becomes an Organism, and I think that is a smart way to think about this new challenge. Instead of a clear separation between the people side (the roles and responsibilities), the processes (execution steps) and the technology (the how), we instead have a new organism reacting in a much more dynamic way as a result.
Managing change in this sort of world is remarkably different, because unlike today where we have clear separations between technology and business teams in most organizations, in the AI driven organism we end up with viewing AI skills as part of the organization, as fundamental to the success of the role. It requires us to look at change not in terms of deploying an AI as a technology, but in terms of how it alters the roles and responsibilities of the organization.
It takes data mastery
Is your company a data master? A company which masters data is a company which creates, processes and leverages data proactively to fulfil their purpose, achieve their business objectives and drive innovation. Data mastery is driven by two complementary dimensions: data foundations and data behaviours.
Data foundations are the necessary tools and technologies with which an organization can use and leverage data, while data behaviours are part of the DNA of the organization and relates to people, processes, skills, and culture. Data literacy is a commonly used term when discussing how to understand data, what you can do with it and what your role is in creating or using it (Agarwai, et.al., 2023)(Cap Gemini, 2023).
Companies often have too much data or poor data infrastructure that most people in the organization don’t understand the value of or spend more time on “fixing” rather than using it. There is also a lack of trust in the data.
Let’s have a look at the nine characteristics of data mastery:
· Features: Data masters are using advanced reporting and analytics in all business areas
· Data discovery: Data masters have very well managed master data and data catalogues implemented and in use.
· Relationship between digital and business users: Enabling the business and IT to work jointly with data towards business objectives.
· People: Self-service features are offered, and users are empowered to build their own dashboards
· Training: Support continuous learning and upskilling of people on data
· Processes: Robust frameworks covering all aspects of data access, privacy, and ethics
· Technology for data activation: Have built unified data platforms leveraging cloud and self-service tools.
· Data advantage: Actively working with external players to collect various sorts of data.
· Data monetization: Data is an integral part of decision-making.
On each of the above points — where is your company?
It takes trust, quality and democratization
No one wants to make decisions based on data they do not trust. Data masters work constantly to engage in data that is relevant, timely and trustworthy. Building trust in data is an indispensable and exceedingly critical step to achieving data mastery. Data trust is key to organizational agility and collaboration and then finally leads to generating business value for organizations. How can you create trust in data? Quality, trusted AI and democratization:
Quality:
When talking about data quality there are many facets. Accessible, accurate, coherent, complete, consistent, defined, relevant, reliable, timely. Data quality is ensured by strong data management and capabilities.
Trusted AI:
Data masters ensure that they can trust their AI solutions. This could be defining an AI Charter, defining AI ethics, setting up AI design and development processes.
Data democratization
Democratization of data is the ability to provide the required data to business users at the required speed and in the appropriate form and granularity, thereby allowing them to explore data and derive actionable insights. For most of the last 5 decades, data was “owned” by IT departments and used by business analysts and executives to drive business decisions. As organizations became inundated with data and bottlenecks increased due to volume, it became apparent that more business users needed access to the data to explore it independently without IT being a gatekeeper.
In addition to the voluminous amount of data being created today, what else contributed to the adoption of democratizing data? Let us look at the barriers to data democratization.
Barriers to data democratization
There are several reasons why more organizations are open to democratizing their data today, but barriers have certainly been eliminated or significantly reduced. Here are just a few of them.
· Data silos: Although there has been improvement in breaking down data silos in organizations they still exist. Data used to be only accessible to executives who required it to manage the business. Data specialists were expected to gather and analyse the data and then report back to management. If you plan to take full advantage of data, it must be accessible to all. If it is locked away and only one business unit can access it, it will potentially block opportunities for your organization.
· Fear: There was and still is real fear about maintaining the integrity of the data when it’s accessible to more people. Security concerns exist when you allow a bigger group access to the data. In addition, fear about how people would use and interpret the data was prevalent and blocked earlier adoption.
· Analysis tools: Another barrier to data democratization was the availability of appropriate tools to help analyse the data. These tools are needed to allow those without a data analysis background to easily extract meaning from the data.
Considerations for organizations when democratizing data
As with any evolution in an organization, data democratization requires policies and training to ensure everyone understands expectations.
· Data governance: Data must be carefully managed. IT experts must work with management to ensure policies are in place to protect the data.
· Culture: For your team to be engaged to extract meaning from the data they will need to be inquisitive, persistent, and armed with an open mentality to succeed.
· Training: To allay the fears that people will misinterpret the data, any organization that endeavours to democratize their data needs to train employees to use it to achieve its goals. Ongoing education via seminars, self-study guides and allowing new learners easy access to the experts is crucial for success.
· Data democratization will be a game-changer for organizations that implement it properly with the right training and tools to allow their employees to extract powerful business meaning quickly and easily from the data.
3 take aways from this article
1) There is a potential to use AI to drive data-driven decisions in a volatile world: There is a potential in using AI to generate insights in a data-driven way. At the same time there is a need to create companies which are more resilient, responsible and regenerative as a response to the uncertain world. There is a need to rethink the way we operate our businesses and our Value Chains, so we can protect the long-term value of the business. It requires new ambitions and metal models for sustainability and businesses.
2) It takes innovation to embrace the paradigm shift, the use cases are many to create companies that are more resilient, responsible, and regenerative: AI can transform the company’s business model and accelerate regenerative performance. Innovation is more than just developing and implementing technology; it is about combining the right technologies at the right time and place. Technology is a lever to solve complex problems.
3) AI alters the roles and responsibilities of the organization and should be treated so: To utilize AI effectively for decision making it takes data mastery, as well as quality, trusted AI and democratization of data. As with any evolution in an organization, data democratization requires policies and training to ensure that everyone understands expectations. When implementing AI to drive decision-making requires companies to look at change not in terms of deploying AI as a technology, but in terms of how it alters the roles and responsibilities of the organization.
Would you like to learn more about how you can transform your Supply Chain based on the Regenerative principles? then you can learn more — or purchase our book: “Reimagining the Value Chain — a regenerative approach” right here.
References:
Brodie, P., (2023) The Impact of AI On Organization Design, Forbes, (feb.2024) https://www.forbes.com/sites/forbescoachescouncil/2023/08/08/the-impact-of-ai-on-organization-design/?sh=36bfab552832
Deloitte, 2024 The Circularity Gap Report 2024, https://www.circularity-gap.world/2024 (download, feb.2024)
Sindbjerg Hinrichsen, Alis. & Haas, H. de., (2023). Reimagining the value chain : a regenerative approach (1. udgave.). Forlaget Praksiz.
Hutchins, G., Storm, L., (2019) Regenerative Leadership: The DNA of life-affirming 21st century organizations, Wordzword
Naveen J. (2019) How AI Can Transform The Transportation Industry, Forbes, (feb.2024) https://www.forbes.com/sites/cognitiveworld/2019/07/26/how-ai-can-transform-the-transportation-industry/?sh=7b617a854964
Robertson, Margaret. (2021). Sustainability principles and practice. (3. edition.). Routledge.
Dominguez, L., (2024) How Amazon Taps AI to Achieve Sustainability Goals: Packaging, Returns, Waste Prevention, Download feb.24, https://risnews.com/how-amazon-taps-ai-achieve-sustainability-goals-packaging-returns-waste-prevention
Agarwai, A., et.al. (2023) The Future of data, how Nordic companies scale and transform with data and AI, Kunskapshuset Förlag, Stockholm The Future of data, how Nordic companies scale and transform with data and AI, Cap Gemini, www.kunskapshusetforlag.se, 2023, Anil Agarwai, Ivar Aune, Raghava Rao Mukkamala, Rickard Sandberg
About the author:
Alis Sindbjerg Hinrichsen is a Supply Chain and operations practitioner, thought leader, book author and inspiring keynote speaker on the transformation agenda. In our modern world, businesses often focus on short-term gains and profits, but we must not forget that our very existence is intricately tied to the health and sustainability of the natural world.
All opinions in this article are solely the opinion of the author.