Optimists call it “the fourth industrial revolution.” Skeptics have called it “the end of the human race.” Regardless of where you stand, artificial intelligence is rapidly transforming how companies operate—from efficiency gains to headcount reductions to sweeping changes in capital allocation.
But transformation doesn’t guarantee payoff. As the New York Times recently reported, companies are pouring billions into AI, yet many are still waiting to see a material impact on the bottom line. The issue isn’t the technology—it’s the time horizon. AI returns accrue over years, not days or weeks, and only those with the patience and discipline to reinvest will fully realize its potential.
In the meantime, many large firms are already attributing workforce reductions to AI-driven productivity—and Wall Street is cheering the resulting financial windfall. But as any investor knows, past performance does not guarantee future results. With meaningful financial gains come significant decisions: namely, how companies choose to allocate their newfound “AI efficiency dividend.” Effective use of this additional capital can pave the way for years of long-term outperformance and even a competitive edge. At the same time, managerial myopia can squander newfound profit as quickly as it came.
On paper, generative AI offers companies a win-win: it enables them to operate at higher efficiency with a fraction of the labor. Companies can automate routine tasks and focus on areas requiring trust, expertise, and judgment.
But this only holds if companies themselves are willing to reinvest in their technology, talent, and long-term capabilities. Investors love news of an improved bottom line, but as it stands, cost reductions from AI efficiency gains only represent a short-term boost. Without a broader vision, firms that simply pocket the gains or spend indiscriminately risk falling into the perils of short-termism.
At the highest level, companies have three primary options when deciding how to allocate any resulting revenue from AI productivity:
Executive pay is often tightly linked to short-term performance metrics, creating a common misalignment. The average tenure of a CEO has been falling: from 8 years a decade ago to just 5.4 years today1 – hardly enough time to realize the returns on major strategic investments.
No CEO wants to pave a “highway to riches” for the next CEO; some leaders prefer to deliver incremental, short-term wins. Touting AI’s cost savings as an efficiency win, these reductions might have little to do with the adoption of AI and more with their desire to please investors—and perhaps, through the link to short-term performance metrics, their own compensation package as well.
This dynamic is exacerbated when boards lack either a deep understanding of the business or the motivation to support—or challenge—management’s AI strategy. Boards are not immune to short-term thinking. Nearly half of corporate executives report that their board is an unexpected source of short-term pressure—at times even an impediment to long-term strategic planning. Directors, too, acknowledge that they simply spend insufficient time on strategy. And cynics point out that there’s little personal upside to doing so: when strategy succeeds, it’s typically the CEO—not the board—who gets the credit.
Another use of excess cash is rewarding shareholders directly – either through a higher dividend or buyback. This can appear responsible—even virtuous. Wall Street may love this, and it may even be viable in the short term, but it is simply not sustainable. In the short run, firms focused primarily on their shareholders perform as well as those focused on multiple stakeholders, but the positive effects fade over time.
Overdistribution of capital to shareholders in general can also crowd out investments in innovation and people. As we see in another of our whitepapers, spending too much capital on dividends and buybacks is one of the most significant negative predictors of long-term value creation. Over time, prioritizing distributions over reinvestment can hollow out a firm’s future.
The most forward-looking decision companies can make with their savings is to reinvest their AI dividend back into the business. This includes upskilling displaced workers, reinforcing R&D pipelines, and pursuing innovation beyond cost-cutting.
These efforts may not show up in next quarter’s earnings report, but they are essential for long-term competitiveness.
When AI displaces routine work, it opens up the possibility to develop more value-added functions—ones that complement rather than compete with automation. Companies that reinvest in upskilling aren’t just doing the right thing; they’re strengthening their talent pipeline.
This could involve training frontline workers how to co-pilot AI tools or training analysts to develop judgment on top of automated outputs. Forward-looking firms recognize that talent is not a sunk cost, but a renewable asset. While the common fear is that AI will be a job killer, recent findings suggest that this is not necessarily the case.2 According to QuantumBlack, a majority of survey respondents anticipate no immediate change to the size of their workforces, and may even include an increase in the number of employees in the software engineering and product development functions.
Efficiency gains are only the first chapter in AI’s business story. The more powerful opportunity lies in creating things that weren’t possible before: personalized customer experiences, AI-native products, and entirely reimagined workflows and value chains.
But these possibilities only materialize when companies look beyond AI as a cost-cutting tool and toward it as a source of growth. They require leadership willing to take risks, experiment, and fund innovation cycles that may not deliver instant results. To do so, companies need to think not just incrementally, but transformatively.
That level of transformation also demands full engagement from the C-suite—not just the chief technology officer. For AI to cut through traditional lines and deliver real impact, it must be championed by the entire leadership team. At a minimum:
Most companies aren’t there yet.
The AI dividend, as mentioned above, is only the first stage in what’s potentially a much bigger revolution. As it currently stands, many companies and investors anchor to what they know—productivity gains and savings from employee headcount.
But beyond the world of LLMs and automating repetitive tasks lies a world of far greater possibilities and deeper gains than what’s on the surface today.3 As noted by the co-leader of QuantumBlack 4,
“It pays to think big. The organizations that are building a genuine and lasting competitive advantage from their AI efforts are the ones thinking in terms of wholesale transformative change that stands to alter their business models, cost structures, and revenue streams—rather than proceeding incrementally.”
While this might be easier said than done, the companies that win in the long run will be those who see AI not as a shortcut, but as a springboard. What we’re seeing is just the first domino to fall in a much larger transformation. Companies and investors are celebrating successes in efficiency and productivity, but these things alone do not create enduring enterprises.
Responsible capital allocation will distinguish tomorrow’s leaders from those chasing short-term gains. Ultimately, the organizations that thrive will be those that use AI savings to build capacity, deepen capabilities, and strengthen trust.
Analysis of MSCI ACWI data from FCLTGlobal via LSEG Workspace.
See, e.g. “The state of AI: How organizations are rewiring to capture value,” QuantumBlack, AI by McKinsey, 2025.
“AI and the Next Wave of Transformation,” BCG, 2024.
See, e.g. “The state of AI: How organizations are rewiring to capture value,” QuantumBlack, AI by McKinsey, 2025.
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