Beyond the buildout: How AI adoption could reshape business productivity

An insurance claim arrives with pages of records to review. Before an employee can resolve it, someone must organize the information, identify what is missing and determine what needs closer attention. AI tools could shorten that preparation, leaving more time for judgment and service.

For investors, this is where the AI story reaches the workplace. Chips, models and data centers supply the capacity. Businesses must decide how to put it to work. At Laffer Tengler Investments, we examine whether those applications can help companies accomplish more with their resources and turn improvements in daily operations into lasting economic value.

Productivity begins with the work itself

Some of the clearest applications begin with routine work that consumes substantial employee time.

In August 2024, Walmart reported using generative AI to create or improve more than 850 million pieces of product catalog data. Management estimated that completing the work in the same time without AI would have required nearly 100 times the staffing. That estimate applies to the catalog project, rather than Walmart’s overall productivity.

Morgan Stanley’s Debrief tool addresses another routine task: meeting documentation. With client consent, it generates notes, identifies action items and prepares follow-up emails for advisers to review.

These applications illustrate productivity’s practical promise: accomplishing more with available resources. The broader test is whether improvements in individual tasks translate into better service, greater capacity or lower costs across the business.

Adoption takes different forms across industries

On a factory floor, productivity can depend on keeping a production line running. Steelmaker BlueScope uses Siemens’ Senseye technology to analyze equipment data and identify signs of deterioration before machinery fails. In September 2025, Siemens reported that BlueScope had avoided approximately 2,000 hours of unplanned downtime over three years. The initiative began as a pilot in 2022 before expanding to additional facilities.

For UPS, the challenge includes the distance between delivery stops. The company reported in May 2025 that AI and machine learning helped optimize routes, saving 10 to 14 miles per driver each day. Those gains concern physical operations, extending AI’s applications beyond generating text or summarizing documents.

At Bank of America, generative AI assists software developers with writing and refining code. In April 2025, the bank reported efficiency gains exceeding 20% from that assistance. The reported gains apply to software development, not the bank’s overall productivity.

The measures differ because the work differs: hours of production preserved, miles avoided and coding efficiency improved. These company-reported results offer concrete evidence of adoption. Establishing their contribution to sustained profitability requires further analysis.

The economic evidence is still taking shape. U.S. labor productivity rose over the year through the second quarter of 2026, and Federal Reserve research has found an association between greater AI adoption and faster productivity growth within industries. Isolating AI’s contribution remains difficult, however. Its economywide impact will depend on how widely businesses adopt the tools and how effectively they use them. For now, company results provide a closer view of what is changing.

Efficiency must survive the economics

A shorter delivery route saves miles. Keeping a production line running preserves capacity. Whether those improvements strengthen profitability depends on what the business spends to achieve them and how much of the benefit it retains.

AI software carries a price, and implementation can require changes to data systems, employee training and ongoing review. A tool that produces work quickly may still need substantial human oversight. The relevant comparison is the full cost of completing the task reliably.

Competition also shapes the outcome. If rivals adopt similar tools, customers may capture part of the savings through lower prices or better service. An early improvement can become an industry expectation.

For investors, the distinction matters. We examine whether AI helps a company build an advantage it can sustain through its data, expertise or execution. Greater efficiency creates possibilities. Stronger margins and lasting competitive advantages must be demonstrated over time.

What we watch as adoption broadens

As AI moves into more business functions, we look for evidence that its usefulness extends beyond a successful trial. Are employees incorporating the tools into their work? Do improvements hold as adoption expands? Are the benefits meaningful after implementation costs and ongoing oversight?

We also examine what the company retains. Better service can strengthen customer relationships. Greater capacity can support growth. Lower costs can improve profitability. Each outcome needs evidence, and none follows automatically from adopting the technology.

At Laffer Tengler Investments, those questions guide our assessment of AI across industries. The claim still needs to be resolved. The delivery still needs to arrive. The production line still needs to run.

AI’s next chapter will take shape in the work businesses accomplish with it.

Sources and references

Bank of America. (2025, April 8). AI adoption by BofA’s global workforce improves productivity, client service [Press release]. Read the release.

Çakır Melek, N., & Miller, S. (2026, February 11). A new U.S. productivity chapter? What industry data say about AI. Federal Reserve Bank of Kansas City. Read the analysis.

Morgan Stanley. (2024, June 26). Morgan Stanley Wealth Management announces latest game-changing addition to suite of GenAI tools [Press release]. Read the release.

Siemens. (2025, September 4). BlueScope saves ~2,000 hours of downtime with predictive maintenance technology [Press release]. Read the release.

UPS. (2025, May 12). How UPS is helping customers with sustainable logistics. Read the article.

U.S. Bureau of Labor Statistics. (2026, September 3). Productivity and costs: Second quarter 2026, revised [News release]. U.S. Department of Labor. Read the release.

Walmart Inc. (2024, August 15). Q2 2025 earnings call [Corrected transcript]. FactSet CallStreet. Read the transcript.