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Examining Turbocharged AI Adoption Through a Sustainability Lens

03 October 2025

5 min read

Companies’ rapid AI uptake brings environmental and social benefits—along with risks.

Companies are accelerating their adoption of artificial intelligence (AI) to boost productivity and rein in costs—an urgent priority in today’s environment of elevated inflation and sluggish growth. This speed makes it important for investors to pay close attention. In our view, the environmental and social implications of AI—energy intensity, workforce impact and data governance—are financially material to most companies around the world.

Indeed, artificial intelligence is the most consequential technological innovation since the internet, poised to permeate every industry and reshape economies and societies in the years ahead. Business adoption of AI has intensified since late 2022, when generative AI* tools like ChatGPT burst onto the scene. Functions such as IT, finance, and supply chain and manufacturing have been early leaders, while areas from marketing and sales to product development and human resources are poised for broad adoption in 2025 (Display).

AI Is Already a Critical Part of Finance, IT and Manufacturing Functions

AI adoption by function (IT, HR, Sales, etc.) from not using to limited to widespread to critical role.

Current analysis does not guarantee future results.
As of March 31, 2025
Source: International Energy Agency (IEA), MIT, Morgan Stanley and AllianceBernstein (AB)

As AI adoption spreads across more business functions, we think it helps to take a balanced, critical look from multiple angles. One way is to map environmental and social risks and opportunities—along with large-scale effects, business and investment implications, and impacts on everyday life—into an AI impact matrix (Display).

AI Impact Matrix: Framing the Environmental and Social Risks and Opportunities

List of risk and opportunities from data-center energy consumption growth and water security to re-skilling for AI.

For illustrative purposes only
The above is not a comprehensive list.
As of June 30, 2025
Source: AB

Under the environmental dimension of the matrix, energy emerges as the dominant theme, making energy the natural place to start.

AI Ramps Up Energy Demands—but Efficiencies Too

Perhaps the biggest environmental risk posed by AI comes from its energy requirements as hyperscalers—companies that provide massive, scalable and on-demand computing resources—undertake a large-scale build-out of data centers. Demand is likely to continue climbing for the foreseeable future (Display).

Hyperscaler Energy Demand Is AI’s Biggest Risk to the Environment

Terrawatt hours of electricity consumption by data centers for US is 2nd only to China. Consumption is growing dramatically.

Current analysis and forecasts do not guarantee future results.
TWh: terawatt-hour
As of April 30, 2025
Source: IEA

Data-center share of global energy consumption is set to increase significantly. In 2022, data centers made up about 2% of global electricity demand, and this is projected to reach as much as 7% by 2030. There are already six states in the US where data centers consume more than 10% of electricity supply, with Virginia leading at 25%.

This energy demand is translating into opportunity in two ways. It’s facilitating greater adoption of clean energy, and hyperscalers are making their data centers more energy efficient—driving changes across their supply chains that may translate into opportunities for investors. The risk, however, is that many new AI data centers are being powered by natural gas—a non-clean energy source—which could put hyperscalers’ long-term zero-carbon commitments at risk.

For bond investors, AI’s energy and capital intensity are a double-edged sword. Issuers slow to adapt may face credit pressures, reduced access to capital and higher funding costs. But many companies that are building or financing AI-intensive data centers, together with utilities that are modernizing their grids and sourcing renewable energy, are issuing green bonds to address AI’s growing energy footprint.

From an actively managed perspective, the key is to identify issuers with credible transition strategies, AI-related competitive advantages, disciplined capital allocation and diversified funding access. These are the companies most likely to enhance credit quality over time and deliver attractive opportunities.

Growth in power demand is also putting pressure on infrastructure more broadly. Companies likely to benefit from upgrades include suppliers of high- and medium-voltage cables, providers of energy-efficient climate control systems, manufacturers of gas turbines (particularly with AI-driven control systems) and makers of fuel cells that can generate electricity on-site at data centers.

Water security is another large-scale environmental risk. Failure to manage it may inflict material damage on business and investment outcomes, while companies that are strategically focused on the issue could gain competitive advantages.

In other environment-related areas, AI can help improve the monitoring and measurement of CO₂ emissions—for example, capturing and interpreting satellite data to assess emissions from power plants or vehicles. It can also refine estimates of wildfire emissions, strengthen the monitoring of carbon sequestration efforts and improve the integrity of carbon markets.

Risk to Employment Depends on Successful Transition

Within the social dimension of our AI impact matrix, three themes stand out: employment risk, the spread of misinformation and bias.

One of the most significant social risks is mass job displacement. According to the World Economic Forum’s Future of Jobs Report 2025, businesses expect AI and information technology to have the most transformative effect on their operations by 2030. Yet the overall impact on employment may be benign (Display).

AI’s Potential Impact on Businesses and Jobs

AI is the top tech trend driving business transformation, yet its net expected impact on jobs is slightly positive.

Current analysis and forecasts do not guarantee future results.
As of January 2025
Source: World Economic Forum and AB

As previous technological revolutions have shown, disruption involves not only the number of jobs lost and gained but also changes in the composition of the workforce and the nature of work. As AI displaces clerical and administrative roles, we expect new jobs to emerge in AI-related fields. Whether this results in a net gain in employment will depend on how well businesses and individuals adapt and reskill.

While transition poses risks for many companies, it also creates opportunities for others—for example, providers of AI-related education and training. Investors, in our view, should monitor developments in this area.

Misinformation and disinformation—already endemic to social media and now amplified by AI—pose large-scale risks to public trust in business, government and other institutions. A loss of confidence on this scale could carry significant social and economic costs.

Bias in AI models is also a risk. Consumers may be disadvantaged by human biases inadvertently built into training data—for example, facial recognition systems that perform less accurately on darker skin tones. Where such biases occur, companies may face reputational damage or legal action, with consequences that could extend to investors.

Stay Alert as AI Evolves

In our analysis, governance is the next stage in assessing AI’s environmental and social impacts. To address this, we’ve identified 10 core principles for the responsible use of AI, along with a set of questions investment teams can use when engaging† corporate management.

While asset managers exercise corporate oversight, investors also need to remain vigilant, as differentiating between AI winners and losers is more important than ever. AI is reshaping companies rapidly, and in our view, investors must stay alert to both the risks and the opportunities that emerge as the technology matures.

^*Generative AI is a subset of AI. While AI is a broad field in which systems can perform intellectual tasks, generative AI can create new content and ideas, such as images and videos, and reuse what it knows to solve new problems.

^†AB engages issuers where it believes the engagement is in the best interest of its clients.

The views expressed herein do not constitute research, investment advice or trade recommendations, do not necessarily represent the views of all AB portfolio-management teams and are subject to change over time.

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