How AI Is Advancing Sustainability: Insights from BCG's Miklós Veszprémi

Artificial intelligence is rapidly transforming how we approach sustainability challenges. From regenerative agriculture to smart grid management, AI is providing the tools needed to measure, monitor, and optimise our environmental impact at unprecedented scale. In this episode of Chatting GPT, host Maryrose Lyons speaks with Miklós Veszprémi, Sustainability Strategy Consultant at Boston Consulting Group (BCG), World Economic Forum member, and former concert pianist, about the cutting-edge applications of AI in sustainability.

The Three Levels of Sustainability Action

Veszprémi introduces a compelling framework for understanding sustainability action across three distinct levels. Level one encompasses individual behavioural changes—recycling, reducing personal consumption, making more sustainable lifestyle choices. While these actions matter, Veszprémi argues that their impact is ultimately negligible when compared to the systemic changes possible at higher levels.

Level two involves organisational and community action—businesses implementing sustainability programmes, local governments creating green initiatives. This is where meaningful change begins to accumulate. However, it is level three—enterprise and system-level transformation—where the greatest leverage exists.

"Individual behavioural changes have negligible impact compared to enterprise-level action," Veszprémi explains. This insight is crucial for professionals considering where to focus their sustainability efforts. The highest-leverage role available to any individual is often within an organisation positioned to drive systemic change.

AI in Regenerative Agriculture

One of the most promising applications of AI in sustainability is in regenerative agriculture. Traditional carbon measurement in farming has been expensive, time-consuming, and difficult to scale. AI-powered remote sensing is changing this equation dramatically.

Using satellite imagery and machine learning algorithms, AI can now measure soil organic carbon and monitor carbon sequestration across vast agricultural landscapes. This technology makes carbon measurement cheaper, faster, and scalable enough to underpin policy frameworks and farmer incentive schemes.

The implications are significant. For the first time, farmers can be accurately compensated for carbon sequestration practices. Carbon markets can operate with confidence in the underlying data. And policymakers can track the effectiveness of agricultural sustainability programmes in real time.

Smart Grid Optimisation

Energy waste represents one of the largest untapped opportunities in sustainability. AI is addressing this through smart grid optimisation—intelligent energy management systems that reduce waste at both national infrastructure and individual company levels.

Traditional energy grids operate on relatively simple principles: generate power to meet predicted demand, with limited ability to respond to real-time fluctuations. AI transforms this model by creating an intelligent energy management layer that optimises distribution, predicts demand with greater accuracy, and integrates renewable sources more effectively.

For individual companies, AI-powered energy management systems can analyse consumption patterns, identify inefficiencies, and automatically adjust building systems to reduce waste. The energy transition becomes not just an environmental imperative but a cost-saving opportunity.

Automating CO₂ Accounting

Perhaps the most immediately applicable AI sustainability tool is in CO₂ accounting and life cycle analysis. Traditional carbon accounting has been a manual, slow process—companies would measure emissions annually, often with significant delays and limited granularity.

AI is automating this process, enabling real-time carbon dashboards that track emissions across entire supply chains. Machine learning algorithms can analyse procurement data, shipping records, and operational metrics to calculate carbon footprints continuously rather than annually.

This real-time visibility transforms how companies approach emissions reduction. Rather than discovering problems months after they occur, sustainability teams can identify issues as they emerge and take immediate corrective action.

The Future of AI and Sustainability

As Veszprémi notes, sustainability is currently facing something of a global backlash. The costs of the energy transition are concentrated at the beginning, while the benefits accrue over time. This temporal mismatch creates political and economic challenges.

However, AI offers a path forward. By making sustainability measurement more accurate, optimisation more effective, and accountability more transparent, AI tools can accelerate the transition while reducing its costs. The technology provides the data foundation needed for credible carbon markets, effective policy frameworks, and genuine corporate accountability.

For organisations in Ireland and the UK, understanding these AI applications is becoming essential. Whether in engineering, architecture, construction, or professional services, the ability to leverage AI for sustainability will increasingly differentiate market leaders from laggards.

Want the full conversation? Watch the Chatting GPT episode on YouTube here: https://www.youtube.com/watch?v=wyeOPDD1I_0