Takeaways
- By 2025, six disruptive technologies—automation, artificial intelligence, the Internet of Things, advanced computing platforms, next-generation data centers, and autonomous mobility—will redefine sustainability strategies, making balanced deployment critical.
- Consider leveraging automation and artificial intelligence to cut sustainability reporting time by nearly 50 percent through automated data analysis and customized regulatory disclosures.
- Training a single large-scale AI model can consume as much energy as powering hundreds of homes for a year, underscoring the need to manage water and electricity use in resource-scarce regions.
- Use Internet of Things sensors to capture real-time environmental key performance indicators (KPIs), enabling agile adjustments to energy and resource inefficiencies.
- Proactive management of electronic waste, critical raw materials, and supply-chain complexity is essential to unlock the net environmental benefits of extended reality and autonomous mobility initiatives.
Summary
Disruptive technologies such as automation, artificial intelligence, and autonomous mobility present a paradox for environmental sustainability. A recent Forrester report highlights that while these innovations accelerate strategic priorities, they also carry substantial environmental costs—including high computational energy demand, increased electronic waste generation, reliance on critical raw materials, complex supply-chain logistics, and significant infrastructure requirements. Conversely, when deployed strategically, they can improve resource efficiency, support climate resilience, and enable real-time tracking of environmental key performance indicators.
The analysis focuses on six technologies shaping sustainability through 2025: automation and artificial intelligence, the Internet of Things, advanced computing platforms, next-generation data centers, extended reality applications, and autonomous mobility solutions. The report examines each technology’s enabling and inhibiting effects, illustrating how artificial intelligence can automate data analysis for sustainability reporting, align disclosures with evolving regulations, forecast climate risks, optimize energy usage, reduce emissions, and reinforce supply-chain resilience.
However, the training and operation of large-scale artificial intelligence models demand significant energy and water consumption, especially problematic in resource-scarce regions. The report advances actionable guidance for corporate leaders, urging them to maximize sustainability gains while carefully managing life-cycle impacts and resource intensity. By balancing scale, maturity, and correct application of disruptive technologies, organizations can achieve resilience, regulatory compliance, and sustainable profitability.
