AI, Automation, and Productivity: Long-Term Themes That May Drive Returns for Investors
Artificial intelligence is no longer just a technology story. It is becoming a capital spending story, an earnings story, and potentially a long-term productivity story that could reshape how companies compete and how investors identify durable return opportunities. For investors, the key question is not whether AI will matter, but where the economic value is likely to accumulate over time and which parts of the market may benefit as adoption broadens beyond early enthusiasm.
Why productivity matters to investors
Over extended periods, productivity growth is one of the most powerful drivers of corporate earnings, wage growth, and rising living standards. When businesses can produce more output with the same or fewer resources, they can improve margins, reinvest in expansion, and/or return capital to shareholders. That is why AI and automation deserve attention from investors: they are not simply tools for convenience, but technologies with the potential to influence profit growth across large parts of the economy.
McKinsey estimates that generative AI could add $2.6 trillion to $4.4 trillion annually to global corporate profits across studied use cases, with meaningful impact in banking, high technology, and life sciences. The same research suggests generative AI could increase labor productivity by 0.1 percent to 0.6 percent annually through 2040, and potentially more when combined with other automation technologies.
For investors, this matters because productivity-led returns tend to be more durable than returns driven purely by valuation expansion or speculation. If AI helps businesses lower costs, improve service, shorten development cycles, and allocate labor more efficiently, then the beneficiaries may include not only headline technology firms but also companies in sectors that successfully apply AI to improve economics at scale.
Where value may accrue
The market’s first phase of AI enthusiasm centered on the companies building the infrastructure: semiconductors, cloud platforms, data-center operators, and software vendors enabling model development and deployment. That phase still matters because AI requires enormous computing power, data architecture, and energy support. But over time, investors may find the larger opportunity in the downstream adopters that use AI to improve pricing, customer acquisition, workflow automation, and decision-making across traditional industries.
Stanford’s 2025 AI Index shows that organizational AI adoption rose from 55 percent in 2023 to 78 percent in 2024, while use of generative AI in at least one business function rose from 33 percent to 71 percent. Those figures suggest that the investment story is moving from proof of concept toward broader implementation. Even so, Stanford also notes that most companies reporting financial benefits from AI still describe them as modest, with common cost savings below 10 percent and revenue gains below 5 percent in many functions, an important reminder that transformative technologies often take longer to affect earnings than markets assume.
For investment portfolio construction, it may be helpful to think in layers rather than headlines alone:
- Enablers: chipmakers, cloud providers, networking firms, cybersecurity companies, and data-center real estate tied to the infrastructure needed to run AI systems.
- Adopters: businesses in banking, health care, industrials, logistics, and professional services that can use AI to remove friction, improve throughput, and expand margins.
- Second-order beneficiaries: utilities, energy infrastructure, and specialized software firms that support the broader buildout and operating demands created by AI adoption.
This layered view can help investors avoid treating AI as a narrow trade. Over the long term, the more durable gains may come from owning businesses that translate technology spending into sustained free-cash-flow growth rather than simply participating in early narrative momentum.
Implications for investment portfolios
For investors, AI is relevant in two ways: as an investment theme and as a force that may change the earnings power of existing holdings. A portfolio may already have significant AI exposure through large-cap U.S. equities, technology funds, private market strategies, or companies in adjacent sectors that benefit from automation spending. The issue is often less about whether there is exposure and more about whether that exposure is balanced, intentional, and aligned with long-term objectives.
This is especially important because thematic opportunities can create concentration risk. A small number of mega-cap technology companies have captured much of the market’s attention, and in some portfolios, much of the market’s returns. While that can be rewarding, it also raises valuation, liquidity, and correlation questions, particularly for investors who have substantial wealth tied to business ownership, executive compensation, or concentrated stock positions.
For that reason, a prudent approach may include the following considerations:
- Maintain exposure to long-term innovation but avoid allowing one theme to overwhelm the portfolio’s risk budget.
- Look beyond pure technology companies to sectors where AI can improve operating margins and asset productivity.
- Evaluate public and private market exposure separately since venture and growth investments may offer different risk-return profiles than public equities.
- Revisit sector and manager selection with an eye toward which firms are likely to be disciplined adopters rather than simply AI marketers.
The goal is usually not to chase every new development. It is to identify which trends are likely to influence earnings, capital allocation, and competitive positioning over a multiyear horizon, then size exposure appropriately within a broader wealth plan that also accounts for taxes, liquidity needs, charitable goals, and estate planning considerations.
The risks behind the opportunity
Long-term themes can generate powerful returns, but they can also produce periods of overexcitement. AI faces familiar risks: inflated expectations, uneven adoption, regulatory scrutiny, cybersecurity concerns, data governance failures, and the possibility that early winners may not be the same companies that capture the largest long-run economic profit pools. History shows that transformative technologies can be enormously important without every related stock becoming a sound investment.
There is also execution risk. McKinsey emphasizes that capturing AI’s value will require companies to redesign workflows and shift employees toward higher-value work, not simply add a new software layer. In other words, the return on AI investment may depend less on buying access to a model and more on management quality, data readiness, and organizational discipline. For investors, security selection and manager due diligence are especially important.
The larger point is that AI and automation should be viewed as long-duration drivers of economic change, not short-term certainties. A balanced investment framework recognizes both the upside and the noise: AI may support long-run earnings growth and productivity, but markets will alternate between enthusiasm and disappointment as real adoption catches up with expectations.
A practical investor lens
A useful way to think about AI, automation, and productivity is to treat them as a secular theme that intersects with several others, including energy demand, digital infrastructure, labor scarcity, health care innovation, and the modernization of financial services. For investors, that perspective can be more productive than trying to guess which quarterly earnings report will best capture the next burst of market enthusiasm.
Disclosure
This material is provided by Gryphon Financial Partners, LLC (“Gryphon”) for informational purposes only. It is not intended as a substitute for personalized investment advice or as a recommendation or solicitation of any particular security, strategy, or investment product. Facts presented have been obtained from sources believed to be reliable, though Gryphon cannot guarantee their accuracy or completeness. Gryphon does not provide tax, accounting, or legal advice. Individuals should seek such guidance from qualified professionals based on their specific circumstances.