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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Machine learning can help businesses find new revenue, redesign workflows and make better decisions—but adoption alone does not prove growth. The 2026 evidence points to financial value concentrated among a minority of organizations, while surveys show that scaling AI across a business remains difficult. Most available findings concern AI broadly, not machine learning in isolation, so they indicate where opportunity may lie rather than guarantee a particular company’s results.
Where machine learning can support business growth
Machine learning is one part of the broader AI category used in most current business surveys. Its growth potential is not limited to automating existing tasks: it can also help a company develop new offerings, change how it serves customers or make an established process work differently. The useful question is not simply whether a business uses AI, but whether an application advances a clear business objective.
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New revenue and business-model opportunities
PwC describes leading organizations as using AI to pursue new revenue opportunities and business reinvention, rather than treating it only as a cost-cutting tool. In practice, that means evaluating whether a machine-learning capability could enable a new service, improve an existing offer or change how value reaches customers. The survey findings do not establish that any one application will generate revenue; the opportunity must be tested in the company’s own market and operations. PwC’s 2026 AI Performance Study frames growth and reinvention as strategies used by leading organizations.
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Workflows designed around useful applications
Another route is redesigning a workflow, rather than placing a new tool on top of an unchanged process. PwC identifies workflow redesign, alongside data, governance and trust foundations, as characteristics of organizations capturing more value. That points to a practical distinction: a promising use case should improve a real business process, with responsibilities and handoffs considered, not just demonstrate that a model can perform a task.
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- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
Better-informed decisions
Machine-learning applications may also support decisions by using patterns in business data. Whether that creates growth depends on the decision being improved, the quality and availability of the data, and whether employees can act on the result. A prediction or recommendation is not itself a financial outcome; the company needs to track what changed after it was used.
Why reported AI returns are concentrated
PwC’s April 2026 release reports that 74% of AI’s economic value was captured by 20% of organizations in a study of 1,217 senior executives, primarily at large publicly listed companies across 25 sectors. This is a finding from a survey study, not a forecast for an individual firm or a causal estimate showing what a particular machine-learning project will earn. It does, however, caution against assuming that widespread interest or investment translates into broadly shared returns. PwC’s study announcement describes the findings and the growth-oriented practices associated with leaders.
PwC Global Chief AI Officer Joe Atkinson summarized the distinction: “Many companies are busy rolling out AI pilots, but only a minority are converting that activity into measurable financial returns. The leaders stand out because they point AI at growth, not just cost reduction, and back that ambition with the foundations that make AI scalable and reliable.” The statement appeared in PwC’s April 13, 2026 release summarizing the study. Read the release.
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Adoption is growing, but scaling is still a hurdle
U.S. Census Bureau Center for Economic Studies working-paper figures show that 18% of firms used AI in a business function during November 2025–January 2026. The employment-weighted figure was 32%, meaning the measure that gives greater weight to firms with more employees was higher. Adoption was more common among very large firms and in selected knowledge-intensive sectors. These figures describe U.S. firms during that reference period; they are not machine-learning-only adoption rates. The Census Bureau working paper provides the scope and estimates.
Adoption does not necessarily mean an organization can deploy successfully across its operations. Gartner’s survey, conducted from January through April 2026, found that 22% of 1,303 respondents said their organizations had successfully scaled AI across multiple business units or adopted an AI-first approach. The sample covered organizations with at least $50 million in fiscal-2025 enterprise-wide revenue. This is a survey result for those larger organizations, not a rate for all businesses. Gartner’s survey announcement reports the finding.
How to evaluate a machine-learning opportunity
The following criteria are a practical way to compare projects, not a standardized scoring framework published by the cited organizations.
Start with the business objective
State what the project is meant to change: revenue, productivity or cost, risk, customer experience, or innovation. A clear objective helps distinguish a growth opportunity from a technical demonstration.
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Check the workflow fit
Identify the process the application would affect and what would change in that process. Consider whether the output can be used within existing responsibilities and handoffs, or whether the workflow itself needs redesign.
Assess data, governance and trust
Determine whether the organization has usable data and can oversee the system reliably. PwC’s findings emphasize data, governance and trust foundations as part of the approach associated with stronger AI performance; a model without those foundations may be difficult to deploy responsibly or consistently.
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Measure outcomes against a starting point
Before deployment, define the baseline and the outcomes that would count as success. Track costs as well as benefits, and separate measured results from projected or intended impact. This makes it possible to decide whether a pilot is worth extending.
Test whether it can scale
Consider whether a successful use case can work across teams or business units, not just in its original setting. Gartner’s survey result underscores that successful pilots and broad organizational adoption are different milestones.
What spending forecasts do—and do not—show
Gartner forecasts worldwide end-user spending on AI models and platforms of $64 billion in 2026, up from $39 billion in 2025, a 63.4% year-over-year increase. Within that market, AI platforms for data science and machine learning are forecast to grow 36.3% in 2026. These are market-spending forecasts, not evidence that a buyer will achieve a return or that spending growth directly causes business growth. Gartner’s forecast announcement gives the figures.
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Why intentions are not the same as proven growth
The Bureau of Economic Analysis’ July 2026 analysis finds links between firms’ stated AI motivations, changes to production processes and research-and-development intensity. It also notes that the connection between intended motivations and observed outcomes remains unclear. That distinction matters: a company may adopt AI to pursue growth or change production, but neither intention nor process change alone proves that the deployment caused firm-level growth. BEA’s analysis discusses these relationships and their limits.
Treat machine learning as a potential means to a business result, not as a result in itself. The evidence supports looking for growth-oriented uses, redesigning workflows where needed, and building the foundations to deploy reliably; it does not establish a universal causal effect on business growth.
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