Observed Signal · Aug 17, 2026 · Research Note · Source: CNBC Investing · Impact: 3/5 · Sentiment: Neutral
Goldman: AI productivity gains not here yet
Goldman Sachs strategists say widespread enterprise-level productivity gains from AI have not yet materialized because the market is still focused on infrastructure spending rather than application-level automation. Goldman developed a methodology combining the share of a company's wage bill exposed to AI automation with labor costs as a percentage of sales to identify Russell 1000 companies most likely to benefit when productivity gains arrive. Goldman's list includes CoStar Group, Dollar Tree and eBay. Sectors highlighted as having high labor intensity and AI sensitivity include software, professional services, finance and biotech. The article cites isolated examples of early productivity surprises (e.g., Doximity) and references prior productivity forecasts from Goldman Sachs economists, McKinsey and MIT researchers.
Goldman Sachs research provides a replicable investor methodology to identify companies likely to benefit from future AI-driven productivity gains and highlights sectors to watch; notable for investor and corporate strategy but not an immediate industry-wide policy or platform change.
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Key Takeaways & Evidence Grounding
- Goldman Sachs strategists created a metric combining a company's wage-bill exposure to AI with labor costs as a percentage of sales to identify potential AI productivity beneficiaries.
- Goldman’s list of potential AI productivity beneficiaries within the Russell 1000 includes CoStar Group, Dollar Tree and eBay.
- Goldman identified software, professional services, finance and biotech as commercial sectors with the highest labor intensity and AI sensitivity.
- The article cites Doximity as an example where CEO Jeffrey Tangney said the company’s AI search product generates revenues-per-search about 10x its running cost.
- Prior estimates cited: Goldman Sachs economists (2023) forecasted a 1.5 percentage-point productivity lift over a decade; McKinsey estimated up to 3.4 percentage points through 2040; MIT researchers estimated 0.53% through 2034.
Connected Companies & Entities
7 Entities mapped“but strategists at Goldman Sachs have come up with a way to identify companies that stand to benefit the most from AI productivity increases...”
“Goldman’s list includes real estate data company CoStar Group , discount consumer retailer Dollar Tree , and e-commerce website eBay ....”
“Goldman’s list includes real estate data company CoStar Group , discount consumer retailer Dollar Tree , and e-commerce website eBay ....”
“For example, shares of medical platform Doximity exploded earlier this month when CEO Jeffrey Tangney said that the company’s AI search prod...”
“Consultancy McKinsey put those gains as high as 3.4 percentage points through 2040, while researchers at MIT have more modest estimates......”
“while researchers at MIT have more modest estimates, with productivity increases of 0.53% through 2034....”
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Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
AI Productivity Paradox: Leaders Doubt ROI, May Cut Budgets
A G‑P (Globalization Partners) AI-at-Work-Report 2026 survey of 2,850 executives across six markets (including the USA, France and Germany) finds rising scepticism about enterprise AI's productivity benefits. 82% of surveyed leaders say AI has reduced the value they place on human employees. Many respondents report no measurable productivity or employment impact from AI in recent years, and a large share fear employees may use AI to feign productivity. Nearly 70% say they are willing to cut AI budgets if 2026 goals are not met. The report also notes increased employee time spent supervising, evaluating or reworking AI outputs, which may erase expected efficiency gains.
Why AI Isn't Boosting Firm-Level Productivity
This essay argues that while AI and LLM tools (e.g., Claude Code, ChatGPT) have clearly increased individual and team-level productivity, those gains have not yet translated into proportional firm-level ROI. The author maps the phenomenon to a three-stage adoption ladder inspired by electrification: Stage 1 (lightbulb) improves individual tasks, Stage 2 (group drive) speeds workflows without changing organisational decision logic, and Stage 3 (unit drive) requires reorganising firms around faster decision-making and autonomous AI agents. Empirical signals cited include widespread internal use of Claude Code, fast-growing Anthropic enterprise spend, and surveys showing only 27% of executives report AI meeting ROI expectations. The piece concludes firms must redesign decision pipelines and allow AI agents to take certain decisions to realize full productivity gains.
AI Cost Savings Fall Short for Many Firms
A manager-magazin article (Harvard Business manager) reports on a Bain & Company study showing that many companies' expectations for cost savings from AI have not been met. The survey of 951 global firms found that while 59% targeted cost reductions greater than 10%, only 43% actually reached that threshold, typically achieving 11–20% savings. Data access and integration (41%) are cited as the largest barrier. Bain also reports that only 7% of companies run fully autonomous AI agents in production; more common are models requiring human approval or escalation. The piece warns that the problem is strategic (governance, process redesign, senior management ownership), not purely technological, and notes many companies are increasing AI budgets again despite underwhelming returns.
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