Scale AI-driven improvements across your organisation: A Q&A with Michael Wade

From ensuring AI delivers on sustainability, to managing change fatigue within organisations, IMD Business School Professor and tech author Michael Wade discusses all things digital transformation

Michael Wade  is Professor of Strategy and Digital at the IMD Business School in Lausanne, Switzerland where he directs the TONOMUS Global Center for Digital and AI Transformation. He is co-author, with Konstantinos Trantopoulos, of Twin Transformation, a business novel exploring the integration of AI and sustainability strategies. Their research is based on extensive interviews with executives leading digital and environmental transformation initiatives across multiple industries.

Business 4.0 caught up with Michael to learn more about how organisations can overcome the challenges of AI and therefore seize upon the myriad opportunities it presents. We also get the inside track on his new book and its unique fictional approach.

There are concerns about an AI energy paradox – whereby the technology offers significant opportunities in how organisations deliver efficiency, yet consumes vast amounts of energy in itself. How can business leaders navigate this sustainability contradiction?

Michael Wade: While AI’s own energy consumption has drawn attention, with data centres accounting for about 1.5% of global electricity consumption in 2024, forward-thinking organisations are discovering that AI enables them to dramatically reduce harmful environmental impacts across their operations. The key lies in AI’s ability to help companies see better, act better, and scale better when it comes to environmental stewardship.

Most companies operate with significant blind spots in their environmental impact. AI can transform this by providing real-time visibility into previously hidden sources of waste and inefficiency. Here are some examples based on our research at IMD of how AI transparency can help organisations see better:

Supply chain monitoring: AI-powered tracking systems can monitor suppliers’ environmental performance in real-time. For example, food companies are using satellite imagery and AI to detect deforestation in their palm oil supply chains within days rather than months. Textile manufacturers deploy AI to track water usage and chemical discharge across their supplier networks, identifying high-risk facilities before regulatory violations occur.
Internal operations intelligence: IoT sensors can be connected to AI platforms throughout facilities to track energy consumption, water usage, and waste generation at granular levels. Manufacturing companies are discovering that AI can identify energy waste in specific production lines, showing exactly which processes consume unnecessary power during off-peak hours or idle periods.
Predictive impact assessment: AI can be used to model the environmental consequences of business decisions before implementation. Logistics companies analyze route optimisation algorithms that simultaneously reduce fuel consumption and delivery times. Retail chains predict the environmental impact of new store locations by analysing local energy grids, transportation patterns, and waste management infrastructure.

AI-driven environmental impact reduction can not only help you see better, but act better.Because once you can see your environmental impact clearly, AI enables targeted interventions that deliver measurable results, including:

Smart resource optimisation: AI systems can automatically adjust energy consumption, heating, cooling, and lighting based on real-time occupancy and weather data. Some companies report 20-30% reductions in energy costs within the first year of deployment. Manufacturing firms can use AI to optimise machine settings for minimum material waste while maintaining quality standards.
Predictive maintenance programmes: AI algorithms can predict equipment failures before they occur, reducing emergency repairs, extending equipment lifespan, and preventing environmental damage from malfunctioning systems. This approach can reduce maintenance costs while preventing the environmental cost of premature equipment replacement.
Waste stream intelligence: AI can be used to analyse waste patterns and identify reduction opportunities. Food service companies are employing AI to predict demand more accurately, reducing food waste. Manufacturing companies are using AI to optimize cutting patterns and material usage, minimizing offcuts and byproducts.

The true power emerges when you scale these AI-driven improvements across your entire organisation and ecosystem. Significant efficiencies can be achieved by implementing the following:

Cross-facility optimisation: AI platforms can share learnings and best practices across locations. When one facility discovers an energy-saving optimisation, AI systems can automatically test and deploy similar solutions across the entire network.
Supplier network transformation: AI environmental monitoring can be extended to key suppliers and provide them with insights to improve their operations. This creates a multiplier effect where environmental improvements can cascade through a supply chain. Companies that share AI-driven efficiency insights with suppliers typically see greater environmental impact than focusing on internal operations alone.
Influencing customer behaviour: AI can be used to design products and services that encourage more sustainable customer behaviour. E-commerce companies deploy AI to suggest lower-carbon shipping options and bundle orders to reduce packaging. Software companies use AI to optimise their applications for lower energy consumption on user devices.

Companies implementing these AI-driven approaches report significant environmental improvements. Taking some simple steps can make a significant dent in environmental impact – potentially shaving 10% to 20% off energy consumption, according to recent research from MIT Sloan Management Review.  

“AI enables targeted interventions that deliver measurable results”

But every business is different: how can leaders make this work for their organisation?

MW: Start with one high-impact area where you have good data availability. Deploy AI monitoring and optimisation tools in that area first, measure results carefully, and then expand to other parts of your operations. By collocating AI capabilities at or near your main operational centers, you can reduce implementation costs and energy losses while enhancing overall efficiency and sustainability.

The organisations that master this approach transform from reactive compliance to proactive environmental leadership, using AI to see hidden impacts, act on specific opportunities, and scale improvements across their entire value chain.

And what about ‘change fatigue’ within organisations: how can senior management lead through internal resistance to digital transformation?

MW: Change fatigue is normally not about stamina – it’s about value. No one tires of winning. If individuals in an organisation is exhibiting change fatigue, it is often due to a misbalance between effort and perceived rewards. According to KMPG, 54% of employees feel unprepared to handle changes brought by new technologies. And, according to research from Backlinko, only 35% of businesses are accomplishing their objectives related to digital transformation. Addressing change fatigue has therefore become mission-critical.

Digital transformation fatigue is a phenomenon that can occur when businesses and organisations undergo continuous and rapid technological changes without adequate time for adaptation or realisation of full benefits from previous transformations. This fatigue can manifest as resistance to change, decreased engagement, and burnout among employees, leading to diminished productivity and innovation. Often, it manifests in the workplace as employee apathy, frustration, confusion, and stress. Most detrimental to transformation, however, is passive resignation because it can fracture teams and erode trust between employees and management.

These are the strategic leadership approaches for 2025 that we believe at IMD are at the heart of navigating the modern change landscape:

Value-centric communication: Change can’t be a distraction from routines – change has to become the routine. To achieve this, employees need to see the value of change and need to be rewarded accordingly to prevent fatigue.
Incremental wins strategy: Target smaller, more frequent wins. Big-bang, waterfall initiatives, with their long lead time between effort and value, are particularly prone to creating change fatigue. Roadmaps that deliver tangible, widely shared successes based on small steps and fast iterations are the best antidote to change fatigue.
Human-in-the-loop design: Rather than positioning AI as replacement technology, successful organisations design systems that augment human capabilities. Instead of a hierarchical structure, strive to empower teams at all levels to contribute to the innovation process. Treat your employees like adults and trust that because you hired them, they have the skills to rise to the challenge and meet expectations when it comes to change.
Transparent governance: Project leaders should strive to establish and control lines of communication. Employees need to know enough to do their jobs properly, but they don’t need detailed information on everything going on. Too much information can be a major cause of fatigue.

The fundamental insight is that change fatigue isn’t about the pace of change; it’s about people feeling powerless within that change. Give them agency, clear value propositions, and structured support, and resistance transforms into ownership.

There is a school of thought that argues that economics needs a radical reset to be truly sustainable. What are your thoughts on this? 

MW: The debate over whether economics needs a radical reset for true sustainability has intensified, particularly following Covid-19’s disruption of traditional business models. “We need a ‘Great Reset’ of capitalism” argued Klaus Schwab of the WEF in 2020. I am not convinced.

Rather than dismantling economic systems entirely, evidence suggests that sustainability can become economically superior through intelligent evolution. The changes we have already seen in response to Covid-19 prove that a reset of our economic model is possible, but this doesn’t necessarily require complete systemic overhaul.

Evidence points to integration, not evolution. Private financial institutions are increasingly aligning with sustainability goals, channeling funds into low-impact or even nature-positive investments. Examples include sustainable financial assets, such as green bonds or carbon credits, as well as projects that aim to protect or enhance natural capital. Some of them are even doing it in secret today – the opposite of green washing.Therefore the alignment of market forces is gathering real momentum.

There is also technology-enabled efficiency: modern AI and automation increasingly make sustainable practices more cost-effective than wasteful alternatives. When efficiency becomes profitable, market mechanisms naturally guide toward sustainability without requiring fundamental system restructuring.

The hybrid model in practice

Sustainable capitalism challenges the linear framework that underpins the capitalist economy’s common model. In the concept of sustainable capitalism, it appears to be necessary to shift to a more circular economy where the end product would be reinvested not only to the capitalist business or financial institution but into the basis for raw materials, and people who support this business. This might include:

• Decoupling economic growth from environmental degradation, in accordance with the 10-year framework of programmes on sustainable consumption and production, as laid out in Goal 8 of UN’s SDGs.
• Carbon pricing that internalises environmental costs.
• Circular economy models that eliminate waste through design.
• AI-optimised resource allocation that maximises both efficiency and equity.

The emerging consensus suggests that we don’t need to abandon market principles entirely; we need to expand them to include costs and benefits that were previously invisible. The question isn’t whether capitalism needs transformation, but whether that transformation can be evolutionary rather than revolutionary, leveraging technology and changing stakeholder expectations to drive sustainable outcomes.

In your recent book Twin Transformation, you and your co-author chose to tackle modern organisational challenges through a fictitious company. What were the advantages of choosing this format? Were you able to explore certain ideas or scenarios in greater depth, and why?  

MW: “Why a novel?” This question dogged us throughout the writing of Twin Transformation.

When we set out to write Twin Transformation, we were driven by a simple conviction: most business books fail to capture the human drama at the heart of real organisational change. In their quest to be rigorous, factual, and serious, many end up feeling distant, dry, and detached from the lived experiences of those who drive and endure transformation. We decided to build the book around fictional characters that were composites of the thousands of mangers we worked with over the years. Elena Navarro’s late-night doubts, Vikram Mehta’s breakthrough moments, Thomas Greaves’ boardroom politics – these emotional realities disappear in sanitised case studies and linear frameworks.

So, by making it fictitious we sought to overcome the limitations of traditional business writing, maximising the advantages of the fictional approach, namely:

Emotional resonance: Fiction allows readers to experience transformation from the inside. When readers watch Hector Ramirez evolve from AI skeptic to champion, they understand resistance and conversion in ways that survey data cannot convey. Transformation is not just about spreadsheets, profit margins, and quarterly results. It is a deeply human story, woven from ambition, innovation, fear, resistance, hope, and the relentless pursuit of a better future.
Complex scenarios: Real transformation involves multiple stakeholders with conflicting motivations. The fictional format gave us the freedom to portray this reality, rather than the sanitized, linear versions you find in case studies or white papers. The novel format allowed exploration of how CFO Katarina Svensson’s financial concerns clashed with Elena Navarro’s environmental goals, creating realistic tension that business cases typically simplify away.
Experimentation without consequences: Fiction provides a safe space to explore “what if” scenarios. What happens when AI recommendations conflict with human expertise? How do companies navigate regulatory changes while maintaining profitability? By choosing the business novel as our format, we aimed to bring these dynamics to life. Our hope is that this story not only informs but resonates, challenges, and inspires.
Accessibility and pattern recognition: We’ve written numerous articles and books filled with data and frameworks, but stories have a unique ability to connect, persuade, and inspire. A factory manager might never read a Harvard Business Review article on digital transformation, but they’ll relate to Hector Ramirez’s journey of AI adoption.
Psychological truth: While the characters are fictional, they are inspired by the real people, challenges, and transformations we have encountered through our teaching, consulting, and research. Change fatigue, resistance to automation, fear of job displacement – these human elements drive transformation success or failure more than technical specifications.
The human side of business: Through the journey of ThermaDynamics, we wanted to show how these forces collide, clash, and ultimately converge into a new model of industry leadership. But we also wanted to show the human side of transformation; the resistance from those who fear losing control, the breakthroughs that come from unexpected collaborations, and the courage it takes to bet on a vision when the odds seem stacked against you.

As we discovered, business transformation mostly isn’t about strategy and technology – it’s about humans under pressure, making difficult decisions with incomplete information. Fiction captures that reality in ways that frameworks cannot.

The novel format also allowed exploration of nuanced themes: How does AI implementation affect company culture? What psychological factors drive sustainability adoption? How do personal relationships influence business outcomes? These questions resist simple answers but drive practical results.

“By making Twin Transformation fictitious, we sought to overcome the limitations of traditional business writing”