The Next AI Investment Winners May Be Companies That Still Need Human Workers

PwC’s June 2026 AI Jobs Barometer found that industries most exposed to artificial intelligence recorded 40% higher productivity growth than those least exposed. Employment and wages also grew faster in those industries.

An April 2026 Greater London Authority report, drawing on International Labour Organization research, identified carpenters and care workers among occupations less exposed to generative AI. Their work involves tasks that remain difficult to perform using software alone.

Construction companies are using AI to prepare estimates and manage projects, while healthcare providers are testing its use in clinical documentation. These applications can reduce administrative work without replacing employees who deliver the services.

The commercial value of these changes remains uncertain. Construction companies are beginning to report how much time AI saves on administrative work, although evidence of higher revenue and profits is still limited.

Construction Firms Are Cutting Administrative Work With AI

A September 2026 Houzz survey found that 52% of construction businesses used artificial intelligence for everyday tasks, up from 32% a year earlier. Among AI users, 80% reported using the technology daily.

Respondents reported saving an average of 4.7 hours a week. Houzz also said estimates prepared with its AI tools took 60% less time than manually produced quotations.

The shorter preparation time could allow contractors to submit more bids without adding administrative staff. Winning additional projects would still require sufficient skilled workers to complete them, making labour availability an important factor in any expansion.

The survey did not measure changes in contract awards, revenue or operating margins. Those figures would establish whether faster administrative work is delivering financial gains for construction businesses.

Healthcare Providers Test AI to Reduce Nurses’ Paperwork

Sciensus tested AI speech-capture technology during 100 home visits involving 10 nurses over 16 weeks. The system recorded clinical conversations to prepare patient records and achieved approximately 90% transcription accuracy.

The company plans to reduce duplicate documentation as it develops the technology further. It has not disclosed how much nursing time the trial saved.

Home-care providers rely on nurses to complete scheduled visits while maintaining patient records.

Automating part of that documentation could give clinical teams more time for appointments, allowing providers to serve additional patients without a corresponding increase in staffing.

Sciensus has not reported higher patient volumes or lower operating costs from the trial. The potential financial benefit remains dependent on whether reduced paperwork translates into more billable care.

Accenture’s Revenue Per Worker Rises Alongside AI Investment

Accenture reported $74.2 billion in revenue for fiscal 2026, compared with $69.7 billion a year earlier. Its workforce increased from 779,000 to 814,000 employees, raising annual revenue per worker from approximately $89,000 to $91,000.

The company spent about $1 billion on employee learning and development during the year. Its adjusted operating margin increased to 15.8% from 15.6%, indicating that revenue growth was accompanied by a small improvement in profitability.

Accenture has not quantified AI’s contribution to those results. The company provides an example of rising revenue productivity alongside continued hiring and investment in employee skills, although the relationship between its AI spending and financial performance remains unmeasured.

The results also leave a broader question for companies introducing AI into labour-intensive operations: whether additional revenue can outpace employee compensation and technology expenses sufficiently to raise operating margins.

AI Investment Returns Extend Beyond Workforce Reductions

The Organisation for Economic Co-operation and Development (OECD) estimates that AI could add 0.5 to 1 percentage point to annual labour-productivity growth across G7 economies over the next decade under its central scenario. The estimate covers the wider economy, including industries where employees remain essential to delivering services.

For labour-intensive businesses, the potential gains extend to decisions about expanding capacity and hiring additional staff. Companies may increase output by reducing the time employees spend on routine tasks, while demand for specialised skills continues to influence workforce requirements.

The OECD forecast does not establish how those gains will be distributed among companies. Future hiring, capital spending and earnings disclosures will provide evidence of whether businesses are using AI to expand services while maintaining the skilled workforces on which their operations depend.