Key takeaways:
- The AI adoption metrics management teams touted in earnings calls over the past few years is increasingly being replaced by discussion of financial metrics related to AI.
- Companies are providing concrete examples of how AI is impacting margins, revenue growth, and product development, with some management teams gaining sufficient confidence to issue forward guidance on AI-driven financial metrics.
- While tech, Internet, and industrials companies have enjoyed a headstart on AI deployment, consumer, financial, materials, and healthcare firms are also leaning into the power of advanced models.
Artificial Intelligence remains the dominant force in global equity markets. Investors are captured by the growing capabilities of the most advanced frontier AI models. Meanwhile, the unprecedented scale – and price tag – of the AI infrastructure buildout continues to be a flashpoint, dividing AI optimists and those concerned about whether future returns will ultimately justify this level of investment.
Neither of these headline grabbers can be looked upon in isolation. Instead, investors must understand that these developments share a common objective: to fundamentally transform the way work is conducted across the global economy.
More than words: Judging companies on their results
Early in the AI era, exactly how this revolutionary technology would be deployed and the degree to which it would enhance productivity were largely matters of conjecture. That is no longer the case. The recently concluded earnings season provides quantifiable evidence that AI is delivering on its ambitious promise.
Importantly – and in contrast to recent quarters – management commentary has shifted from AI adoption metrics to AI financial results. Areas in which executive teams highlight AI’s financial impact include operating margins, productivity gains, revenue generation, and even physical workflows. And while tech companies, with their emphasis on coding, and industrial concerns, given their headstart in automation, are at the forefront of AI adoption, healthcare, financial, consumer, and materials firms are also reporting how integrating this technology contributes to their bottom line.
This dispersion of AI efficiencies is no surprise to us. We’ve spoken previously of AI enablers, enhancers, and end users. Given the backlog of AI infrastructure initiatives, the enablers – led by hyperscalers – are still a major part of the story.
But the other two categories have now entered the conversation. This, in our view, has major investment implications, as 1) not all companies will have the same level of success in adopting AI and 2) as was illustrated during the digital revolution a quarter century ago, the greatest share of economic gains was generated by novel technologies’ end users. We believe we are on the cusp of a similar transition.
Watching margins
To gauge AI’s financial impact on the corporate sector, we deployed – not by coincidence – an AI model to scour the recent earnings reports of 109 listed companies. Of those, 69% disclosed some degree of margin improvement due to productivity gains and AI-associated cost reductions.
While the rate of margin improvements did not surprise us, in many instances the scale did. IBM, for example, stated it realized $4.5 billion in AI-related savings in 2025. As is the case with other companies, IBM has grown more confident in referencing AI in company guidance: the company is targeting these savings to reach $5.5 billion in 2026. Within financials, PayPal is guiding the street toward an expectation of $1.5 billion in run-rate savings, with most driven by AI.
Margin improvement is coming in the form of both operating leverage (Alphabet reports roughly 50% of its internal software code is written by AI agents) and declining product development expense, as illustrated by MercadoLibre reporting that line item has declined by 120 basis points (bps), year over year. These examples highlight the trend that much of the cost savings is occurring within the tech (coding) and customer service functions. MercadoLibre also announced a 90% resolution rate in AI-based customer service interactions.
Building on its foundation of machine learning, AI is now reaching the factory floor and mining operations. In one of its facilities, Hitachi reduced inventory held by 50% and lead times by 77%. Copper miner Freeport McMoRan reported increasing mill throughput by 10% while reducing unit costs by up to 15%. Within the energy space, Shell announced $400 million in annual savings and reduced unplanned downtime by 45%.
The top line gets involved
It’s not just margins that are providing an AI-powered lift to earnings. Within our data set, 26% of companies discussed AI’s impact on revenue growth, with most operating digital platforms in the tech and consumer space.
One development highlighted by several companies is digital assistants materially contributing to sales growth. Amazon’s assistant, for example, was credited with $12 billion in incremental annual retail sales, and Walmart reported that orders utilizing its shopping assistant were 35% larger than those that did not.
Platforms in which advertising comprises a significant contribution to revenue also indicated that AI improved results. In a telling example, Alphabet’s AI Max tool was stated to have increased conversion volume by 15%. On the buyer side, Proctor & Gamble’s Auto-Bidder system adjusts ad-bids every 15 minutes in response to real-time retail data. The upshot, according to the company, is a four-fold higher return on its branded sales compared to legacy static bidding.
Working smarter
Many of the workflows likely affected by AI tend to have a large human element. While productivity may be defined as doing more with less, thus far AI is delivering more with the same.
Several companies in our data set reported growth while keeping headcount flat. Commentary reveals that AI has been most effective when being applied to repetitive or high-volume tasks. This is freeing up companies to reallocate labor to more value-added work.
Many companies have slowed incremental hiring, and there have been layoffs, but this is evidence of the emerging trend of growth decoupling from headcount. An example is JPMorgan expecting to reduce consumer banking headcount 10% by 2030 while growing the business 25%. Across sectors, companies reported a shift from limited pilot programs to increasing licenses across their workforce.
While these initiatives are showing up in company financials today, other AI applications are aimed at delivering economic value in the future. Pfizer, for example, stated it expects AI integrated into drug discovery, clinical trials, and manufacturing to increase enterprise value by up to $4 billion over the mid term.
Acting on the evidence
We view these examples as just the beginning. Other AI enhancers and end users will invariably apply the technology in innovative and unique ways. The cadence and breadth of the rollout, in our view, have significant investment implications. Early movers have the potential to gain an advantage over slower-acting peers. Some management teams will underestimate AI’s potential for disruption and be left behind. We expect future earnings seasons to shed additional light on this dispersion.
For the first time, investors have the opportunity to analyze real data to gauge AI’s financial impact. Given our ethos that equities follow earnings, we believe investors should seek to identify the companies that can translate AI deployment to top-line growth, margin expansion, and ultimately higher earnings.
And it’s not only equities markets where AI is a driving – and differentiating – force. Improving margins should be on bond investors’ radar as well, as they may improve the coverage ratios of AI-forward companies.
Conversely – and given the magnitude of the AI investment cycle – the hyperscalers that deliver the best value proposition to their customers are likely better positioned to generate the cash flows to pay down debt, while those employing less effective strategies may be left with overleveraged balance sheets.
IMPORTANT INFORMATION
Artificial intelligence (“AI”) focused companies, including those that develop or utilize AI technologies, may face rapid product obsolescence, intense competition, and increased regulatory scrutiny. These companies often rely heavily on intellectual property, invest significantly in research and development, and depend on maintaining and growing consumer demand. Their securities may be more volatile than those of companies offering more established technologies and may be affected by risks tied to the use of AI in business operations, including legal liability or reputational harm.
Equity securities are subject to risks including market risk. Returns will fluctuate in response to issuer, political and economic developments.
Technology industries can be significantly affected by obsolescence of existing technology, short product cycles, falling prices and profits, competition from new market entrants, and general economic conditions. A concentrated investment in a single industry could be more volatile than the performance of less concentrated investments and the market as a whole.
These are the views of the author at the time of publication and may differ from the views of other individuals/teams at Janus Henderson Investors. References made to individual securities do not constitute a recommendation to buy, sell or hold any security, investment strategy or market sector, and should not be assumed to be profitable. Janus Henderson Investors, its affiliated advisor, or its employees, may have a position in the securities mentioned.
Past performance does not predict future returns. The value of an investment and the income from it can fall as well as rise and you may not get back the amount originally invested.
The information in this article does not qualify as an investment recommendation.
There is no guarantee that past trends will continue, or forecasts will be realised.
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