Recent studies show that CEOs and CFOs are shifting sustainability from a reporting and communications topic to a core question for strategy and business development. It is increasingly seen as a source of competitive advantage rather than a compliance obligation.
According to Deloitte’s 2025 CxO Sustainability Report, 83% of executives say they have increased sustainability investments in the past year, and 81% are already using AI to support those efforts. The PwC Global CEO Survey 2024 found that more than half of CEOs are actively adjusting business models to address climate and social risks. McKinsey’s State of Organizations 2023 highlights that boards and CFOs are increasingly linking sustainability metrics with long-term financial performance, signalling a deeper transformation in how value is defined.
Across industries, the strategic conversation is shifting from compliance to capability and from reporting to readiness.
The problem is that much of today’s sustainability work still looks backward. It measures what has happened, not what could happen next. As global systems become more volatile, this retrospective approach is losing relevance. The next phase of sustainability will require foresight, and artificial intelligence can help us get there.
AI and advanced analytics have the potential to turn the noise of data into meaningful insight, revealing early signals of change and guiding strategy before disruption strikes. The question is not whether organizations can use AI for sustainability reporting, but whether they can use it for strategic readiness.
AI and the End of Retrospective Sustainability
Traditional sustainability reporting focuses on tracking performance indicators such as emissions, diversity, or energy use. While important, these metrics rarely help leaders understand where risks and opportunities will emerge next.
AI changes this by enabling real-time sensing and predictive analysis. Natural language models can scan thousands of research papers, policy proposals, and news sources to detect early signals of change. Machine learning algorithms can model correlations between supply chain dependencies, market volatility, and environmental constraints.
The shift is profound: from describing the past to anticipating the future. Instead of static sustainability data, AI offers dynamic foresight. It enables organizations to run “what if” simulations, from raw material scarcity and regulation shifts to social sentiment and climate events and translate insights into proactive strategies.
From Digital Efficiency to Strategic Intelligence
Early in my career, I worked in technology strategy, where digital transformation was largely about optimization. The focus was on process efficiency, integration, and cost reduction. Digital systems were powerful, but they were built to manage the known, not explore the unknown.
Today, the landscape is different. AI allows us to connect diverse data sources, from IoT sensors to ESG disclosures, and uncover patterns that humans alone would never see. This transforms technology from a tool of efficiency into a tool of foresight.
In manufacturing, for example, companies now use AI to assess the environmental footprint of products during design rather than after production. By integrating lifecycle data, suppliers’ emissions, and logistics patterns, design teams can identify low-impact alternatives early in development. This changes sustainability from a compliance task into a creative, forward-looking design principle.
Making Sustainability Strategic
During my time building Taival, I saw many organizations struggling to connect sustainability with strategy. They often had strong data and bold goals, but weak alignment between daily decisions and long-term direction.
We introduced foresight methods such as horizon scanning, weak signal detection, and scenario modelling to help leadership teams explore plausible futures. When AI entered that process, the impact multiplied. Algorithms could process vast quantities of data to uncover early indicators of change, from shifting consumer expectations to resource bottlenecks and regulatory momentum.
In manufacturing, for example, AI-driven market analysis can be combined with sustainability foresight to anticipate how policy changes might affect material availability. Such analysis helps companies redesign products with recyclable components and alternative materials, creating a first-mover advantage.
In this sense, AI did not replace strategic thinking. It expanded it. It gave decision-makers the evidence to act before the window of opportunity closed.
Energy and Infrastructure: Anticipating Transition Risks
In the energy and infrastructure sectors, the challenge is not knowing whether change is coming, but how fast. The direction toward decarbonization is clear, yet the path is fragmented across regions and policies.
AI helps by integrating data across physical assets, climate models, and regulatory environments. Some utilities now use predictive analytics to simulate how energy transition scenarios could affect grid stability, investment portfolios, or regional demand.
In some cases, operators have applied AI-based forecasting to evaluate how future carbon pricing scenarios might alter long-term project economics. Such systems can continuously scan policy developments and commodity markets to update assumptions in real time. This kind of adaptive planning turns sustainability from a reporting cycle into an ongoing strategy process.
Technology Sector: Responsible AI and Sustainable Innovation
In the technology sector itself, the relationship between AI and sustainability is both an opportunity and a responsibility. AI can drive efficiency, accelerate innovation, and open new business models, but it can also increase energy demand and amplify bias.
Responsible innovation requires foresight. AI can be used to simulate the potential environmental and social impacts of new technologies before they are deployed. It can model how different data governance choices affect trust, privacy, and fairness.
At the same time, regulatory frameworks such as the EU AI Act and the OECD AI Principles provide essential guardrails for ensuring that AI systems align with human and environmental well-being. The most forward-looking tech firms treat ethical AI not as a compliance burden but as a core design principle and market differentiator.
Insights from Research and Practice
At VTT, the Technical Research Centre of Finland, I led the Future of Sustainability Study, which outlined the key trends and megatrends likely to shape sustainability over the coming decade. The study emphasized how technological innovation, geopolitical shifts, and societal expectations are converging to redefine what sustainable business means.
These trends underscored a common reality: while awareness is rising, the systems that support sustainability decision-making often lag behind. Across manufacturing, energy, and technology sectors, data remains fragmented, and foresight capabilities are still maturing. Most organizations collect ESG and operational data in silos, limiting their ability to anticipate systemic risks and opportunities.
This is where AI can play a transformative role. Intelligent systems can bridge these data gaps by connecting internal and external information sources into unified models that support cross-functional strategy.
In the energy sector, I have seen organizations use AI to assess how different climate adaptation policies might affect economic and resilience outcomes. These approaches improve investment prioritization and help shift the definition of “sustainability success” toward long-term viability.
Designing the AI-Foresight Loop
In my advisory work today at iksait Ventures, I often help organizations think about how to embed foresight into daily strategy. AI plays a critical role in that process.
I use what I call the AI-Foresight Loop, which connects continuous learning with strategic decision-making:
- Scan – AI monitors global data ecosystems: policy developments, research, markets, and public sentiment.
- Sense – Analytics identify material patterns, separating weak signals from noise.
- Simulate – Scenario models test how different sustainability drivers could interact and evolve.
- Strategize – Insights feed back into decisions about investments, innovation, and governance.
Organizations that implement this loop begin to see sustainability differently. Instead of reacting to reports, they manage change as it happens, and in some cases, shape it.
Leadership and Governance in the Age of Predictive Sustainability
Bringing AI and foresight into sustainability strategy is not only a technical endeavour. It demands new leadership habits and governance structures.
Executives must create space for long-term thinking in an environment dominated by short-term pressures. Boards must ensure that AI systems are transparent, explainable, and ethically sound. Cross-functional collaboration between data scientists, strategists, and sustainability experts must become the norm.
I increasingly see organizations appointing dedicated foresight roles or embedding AI-driven scanning into strategic planning cycles. These are encouraging signs that sustainability is evolving from an isolated function into a shared responsibility across the enterprise.
A Strategic Call to Action
Across sectors, a clear pattern is emerging: the organizations that thrive in the sustainability transition are those that combine technological capability with strategic foresight.
In manufacturing, this means designing circular business models that anticipate material and energy constraints. In energy and infrastructure, it means using predictive insights to guide long-term investment and risk management. In technology, it means embedding ethical foresight directly into product design and governance.
AI provides the scale and speed, and foresight provides direction. Together, they form a powerful system for sustainable transformation.
Conclusion – Seeing the Future First
AI is not a crystal ball, but it can help us see the outlines of the future more clearly. When combined with foresight and purpose, it becomes a strategic instrument for navigating complexity.
Sustainability is no longer about reporting what has happened. It is about preparing for what will happen next. Those who learn to use AI as a lens for foresight, rather than as a dashboard for compliance, will lead the next chapter of sustainable business.
