Before regulation hampers AI’s positive potential due to a lack of corporate responsibility, businesses must prove the technology’s positive outcomes—for themselves, the environment, and society. That means individuals, teams, and organizations going beyond what the current system mandates to illustrate a potentially new, sustainable system where AI is a net positive.
The AI conversation still hasn’t clarified its positive and negative spheres of impact. That has to change quickly. AI can do more good than harm to the environment and to social and economic sustainability. This can only be realized through genuine collaborations among policymakers, business, and society, focused on sustainable outcomes.
This piece aims to clarify AI’s positive and negative spheres of influence and impact (see Exhibit 1). The following examples highlight how various industries see progress in intertwining sustainability and AI. There are so many more out there, including: data, analytics, and AI in combination for optimizing physical, financial, and business processes; waste reduction through AI designing new products and supply chains; resilient business models leading to cost avoidance; and direct datacenter and app modernization achieved through AI development.
Exhibit 1: Focus on material spheres of influence to maximize AI’s positive impact and mitigate the negatives
Source: HFS Research, 2025 | adapted from the original HFS spheres of influence
AI uses a lot of energy, water, and metal, but…
Have you seen the footprint of the energy sector that isn’t powering digital or of manufacturing, agriculture, transport, and buildings? AI, emission-wise, consumes a fraction of the 2–3% portion of global emissions that the tech sector contributes (see Exhibit 2 and our original assessment of AI emissions here). It can help mitigate so much more. The World Economic Forum sees a 10% positive global potential by 2030 and maybe 20% or more in energy, materials, and mobility.
Exhibit 2: Breaking down the global context drives a focus on addressing the most material—positive and negative—sustainability spheres of influence
Source: HFS Research, World Data Lab, and World Economic Forum, 2025
AI can find a positive role throughout the global sustainability context if we break it down and interlink it correctly (see Exhibit 3 and our outline here). The following sections look at some of the negative and positive spheres of impact that businesses can mitigate and amplify, respectively.
Exhibit 3: Breaking down the global context to align you, your team, and your organization’s spheres of influence
Source: HFS Research, 2025
Decarbonization and environment: Industrial efficiency, climate modeling, and sustainable finance represent a much larger positive sphere of influence than the AI footprint
The negatives to mitigate:
- Hardware, software, compute, and other tech services firms must address the infrastructure supporting AI, which consumes electricity, water, and critical natural resources.
- Resource supply chains, for example, require transparency. Ensuring human rights, living wages, and environmentally sustainable sourcing is essential in the new networks that underpin AI.
The positives to amplify:
- AI can optimize digital grids, supply chains, manufacturing, and more. Hyperscalers, such as AWS, Google, and Microsoft, are using AI to optimize datacenter operations, including moving workloads based on clean energy availability.
- Startups in nuclear fusion, such as Helion Energy, are incorporating AI in their R&D efforts to find new breakthroughs in innovation.
- Climate modeling also benefits from AI through enhanced precision and scenario planning. Storytelling and education platforms, such as ClimateAi, are demonstrating how data-driven narratives empower broader societal action.
- Financial services giants, such as BlackRock, use AI to manage climate risk in portfolios to push systemic change (we cover the financial sector’s need to help its clients transition here).
- HSBC and Allianz promote their use of AI for sustainable finance strategies, such as green underwriting models and ESG risk analysis.
- Startups such as Climate X are helping banks with AI-assisted climate risk models to make lending and insurance decisions.
- AI helps detect methane leaks in oil and gas operations. Companies such as Shell are investing in satellite AI monitoring systems. The energy sector, to be clear, is nowhere near an acceptable transition plan (we also cover the recent mindset shift for AI in energy here).
- General Electric’s digital twin technology is improving plant performance metrics.
- Holcim uses AI to optimize cement production’s energy profile.
- Transport firms such as UPS apply AI to optimize delivery routes, reducing emissions by 5–10%.
- Engineering firms such as Honeywell show similar savings in buildings management systems.
Social, governance, and economics: Secure work, health innovation, democracy, and cost of living can overcome AI’s risks of unemployment, disconnection, and inequality
The negatives to mitigate:
- AI risks diminishing human connection, influencing societal behaviors, and widening privacy and equality gaps.
- Workforce displacement looms if the transition is managed poorly. Transition planning (as we call for here) is essential from a corporate and policy perspective.
- Opaque ‘black box’ AI decision-making remains a concern and requires business transparency with the public and regulators.
The positives to amplify:
- AI done right can free up time and energy from undesirable jobs.
- Automation in mining (for example, Rio Tinto’s autonomous trucks) showcases how robotics is improving safety at work.
- Health startups such as Tempus are innovating with AI for precision medicine.
- Estonia’s e-government initiatives exemplify how digital services can improve citizen engagement and reduce bureaucratic burdens.
- Demand management platforms such as OhmConnect can help households save on energy bills with AI-based usage insights.
- The surge in AI startups tackling sustainability, healthcare, and finance creates new economic dynamism.
Talk about a time when responsibility and finding a positive sphere of impact are differentiators for businesses
Responsible business practices, including the ethical use of AI (see Exhibit 4), support risk management and differentiation, making it the right course of action. Our tangential report expands on the excerpt below.
The next generation of leaders must realize that there’s still time to get ahead and help create a system that works for the business, environment, and society. Building this system before tipping points are reached is vital; history shows that change often arrives suddenly, from civil rights to an ecosystem collapse. Those who lead now will set the benchmarks for policy, consumers, and industry to emulate later.
Exhibit 4: Getting the ethics of AI right goes hand-in-hand with realizing a positive sphere of influence
Source: HFS Research, 2025
The Bottom Line: If we assume regulation will always trail technology, then it’s up to business leaders to show the best of AI and interlinked digital technology.
Businesses must prove the positive outcomes of AI. They should mitigate the negative without stifling innovation through overbearing governance structures (we cover this here in the energy sector). This will give politicians and officials the confidence to build frameworks that best facilitate those positive outcomes: for the environment, people, and their economies.
To read more, check out our separate call to the public sector to rebuild its long-outsourced capability for the sake of positive outcomes.
