The challenge now is to turn adoption into value
The service industry is among the most mature in Grant Thornton's AI Impact 2026 survey, standing out for its clear intention to invest in AI and for advancing the expansion of the technology to multiple areas of the organization.
In theory, this level of maturity should result in more significant gains in efficiency and economic performance. However, a more in-depth analysis reveals a different scenario: one of the industries that has made the most progress in AI adoption still presents lower-than-expected results in several return metrics.
Indicators related to cost reduction, quality improvement and benefits for decision-making remain below the average observed in the market. While many organizations are investing to preserve their competitiveness and avoid falling behind, the current profile of the benefits realized suggests an increase in capabilities without a commensurate expansion of margins.
The service industry encompasses segments with very different business models, including law firms, consultancies, engineering companies, process outsourcing organizations, and corporate service providers. For this reason, the way AI generates value, and the points where it can threaten existing revenues, varies significantly between organizations.
The companies that are already achieving the most consistent results are those that have first clearly defined their future operating model and, from that vision, identified where AI could contribute to drive value.
Adoption without economic impact
The lag between adoption and financial results has a structural cause. Traditionally, service companies base their revenue on charging for hours worked. When AI reduces the time it takes to perform an activity, it doesn't automatically translate into increased margin.
In addition, many organizations are implementing AI while maintaining their current team structures, which continue to be pressured by utilization and billing targets. In this context, there is little room left to redesign the way services are priced and delivered. Operational capacity evolves, but the business model remains unchanged.
Without a clear definition of AI's role in the delivery model, adoption tends to follow a predictable pattern: broad expansion rather than targeted implementation. More pilots, more tools, and more areas using AI will not result in margin gains without a commercial and operational architecture prepared to sustain them.
The data reflect this reality. 48% of respondents from the service industry point to accelerated innovation as a benefit of AI, a percentage 17 points above the general average. On the other hand, efficiency gains, quality improvement, and cost reduction remain below average. Innovation advances, but financial results still do not keep up.
Talent drives value with AI, but models don't keep up
Service industry executives identify talent as the primary driver of return on AI investments at more than twice as high as in the survey as a whole. No other industry comes close to this result. This reflects a fundamental characteristic of the sector: value is generated by people.
Many organizations are wondering if their professionals are prepared to work with AI. However, few are evaluating whether the work itself has been redesigned so that technology improves deliveries and increases the amount that can be charged for them.
Workforce readiness in the service industry is relatively high. 62% say that their professionals are mostly or fully prepared to use AI, a rate significantly higher than the survey average. The challenge lies in whether delivery models have been adapted to turn AI into a true productivity and value multiplier.
If AI changes the way work is performed but does not change the way it is structured, reviewed, and controlled, the economic benefits do not reach the organization's bottom line. Earnings remain restricted to the execution of tasks.
AI training and governance remain the areas with the lowest level of investment in the industry, cited by 44% and 48% of respondents, respectively, both indexes above the overall average. Leaders recognize what needs to be strengthened, but investment decisions do not yet reflect this understanding. This gap ultimately fuels governance risks that arise as AI adoption advances.
Governance is now a matter of customer relations
Regulatory pressure is now the main external force influencing the adoption of AI in the service industry, outpacing factors such as competitive movements and customer expectations. But the governance challenge goes further: many organizations still don't know exactly who is using AI, what information is being shared with these tools, or whether customer data is being sent to environments not approved by the company.
Clauses related to the use of AI are already starting to appear in contracts and hiring letters across the industry. Customers are asking questions about the topic, and boards of directors participate in AI enablement initiatives above the average observed in the survey. Even so, the formalization of governance policies remains more than nine percentage points below the general average.
In the service industry, reputational risk makes this scenario especially relevant. A single AI-generated error in a job delivered to the client can directly affect the perception of the organization's quality and credibility.
The protection mechanisms used by companies to mitigate this risk, such as human review of content and deliveries produced with the support of AI, help explain part of the delay in capturing economic benefits. The challenge becomes to incorporate this supervision into workflows in a structured and sustainable way, also reflecting it in the delivery and pricing models of services.
Organizations that have already formalized how AI-backed jobs are planned, reviewed, and validated are turning a potential vulnerability into a competitive differentiator.
Three actions to turn implementation into results
Companies in the service industry have already reached a relevant level of maturity in AI. The challenge now is to build the business and governance infrastructure needed to turn that adoption into financial return. Organizations that are closing the AI proving gap make more accurate decisions about where the technology should be applied and develop the framework needed to sustain those results.
- Define what problem AI is solving
Many service companies use AI in different areas of the organization without a clear answer about the initiative's primary goal: improving deliverables to customers, reducing execution costs, or achieving both.
Each objective requires a different implementation approach, success metrics, and governance mechanisms. Organizations that have clearly defined this priority are the ones that can turn adoption into measurable results. The others are accumulating activities without knowing exactly if they are generating value. - Redesign the delivery model and business
model Workforce readiness in the service industry is above average. The challenge is to redesign the work so that AI improves deliveries and increases the amount that can be charged for them.
If AI changes how work is performed but does not change how it is managed, reviewed, and priced, the economic benefits remain constrained to the operation and do not reach the organization's bottom line. - Formalize governance of AI-backed work before customers ask
Clauses related to the use of AI are already present in service agreements across the industry. Companies that fail to explain how AI-backed work is produced, supervised, and validated take reputational risks on every delivery made to the customer. Developing this governance now transforms a structural vulnerability into a competitive differential.
The AI proof gap is not a technology problem. It is a problem of commercial architecture, which is amplified as models of delivery, governance, and investments in people remain disconnected from a clear commercial objective.
The organizations that are overcoming this gap are not necessarily the ones that use the most AI. They are the ones that precisely define where technology should generate value and build the necessary infrastructure to turn these decisions into results.
Grant Thornton supports companies in the service industry in solving these challenges by helping them define the role of AI in their business models, redesign workflows to increase the value of deliveries, and build governance structures capable of meeting the expectations of customers and regulators.
The difference between organizations that already have this clarity and those that continue to scale AI without a defined direction is already measurable. And this distance will not decrease on its own.
Methodology
Between February 23 and March 18, 2026, Grant Thornton surveyed 950 business leaders, including CFOs, CIOs/CITOs, COOs, vice presidents, department leaders, and directors reporting directly to senior management.
The specific group of the service industry was composed of 100 respondents.