Artificial intelligence is rapidly becoming a fixture in large enterprises. It’s being funded, deployed and tested across nearly every function that keeps a business running. On the surface, the AI story looks unified: greater investment, more use cases and growing confidence in what the technology can deliver. Look closer, though, and a different picture emerges.
Organizations aren’t moving along a single path. They’re building entirely different use cases for AI, shaped less by a global strategy than by the discrete pressures they face every day. Some are embedding it deep into core operations. Others are using it to relieve overextended teams. Still others are focused on the customer experience. The result isn’t one AI transformation but several unfolding at once.
These differences illustrate where AI creates value, where it falls short and how quickly it can move from experimentation to genuine impact. They also reveal a more important truth: The biggest obstacles to AI are structural.
These are just some of the insights explored in the latest installment of the PYMNTS Intelligence exclusive series “The Enterprise AI Benchmark Report.” The report draws on a March 2026 survey of 60 verified senior technology executives at U.S. enterprises with at least $1 billion in annual revenue, equally divided across financial services and insurance, healthcare, and media and advertising. The survey tracked adoption across 75 specific AI-supported tasks spanning eight business functions, allowing for mapping not just of whether companies are using AI, but also where and how deeply.
The Adoption Spread
Financial services and insurance firms are going all in on AI.
Every enterprise sector has jumped into AI, but embracing something and scaling it are very different things. Financial services and insurance firms have reached high adoption (i.e., at least half of companies in this sector are actively using AI for a given task) for 27 of the 75 tasks included in the survey. Healthcare manages just 10. Media and advertising land in between at 16.
In other words, financial services and insurance firms have scaled AI across nearly three times as many tasks as healthcare firms. That gap reveals telling structural differences. The financial services sector has deeply embedded AI into revenue recognition, credit scoring and sales forecasting. Healthcare, by contrast, has concentrated its AI investments in a handful of workforce and operational areas, leaving most tasks unautomated. Media firms show breadth in audience-facing functions but haven’t matched financial services’ penetration elsewhere.
All three sectors report some AI use across every function surveyed. The divergence lies in whether AI is a tool that a few teams experiment with or something most of the organization depends on. Financial services firms cross that threshold often, while the others are still in the early stages.
What makes this finding particularly striking is that it isn’t about technological access or organizational enthusiasm. It’s about the internal stuff: messy data in financial services, siloed systems in healthcare and, in media, a lack of basics such as clear governance and leadership buy-in. Fix those, and AI can potentially scale. Leave them unaddressed, and it can stagnate as a support tool.
Financial Services Focus on the Back Office
Financial services and insurance firms use AI to protect their revenue, not to grow it.
While this sector may have the deepest deployment, there are key areas they’re choosing to ignore.
The industry’s most adopted use cases cluster in structured, auditable back-office functions: the internal operations that keep a business running but that customers never directly see. Revenue recognition (the process of recording when and how income is officially counted) leads at 65% adoption. Credit risk assessment, which determines how likely a borrower is to repay a loan, and sales forecasting, which projects future revenue based on current pipeline data, each reach 60%. These are environments where outcomes can be verified, defended to regulators and traced back through clean data pipelines. AI thrives here precisely because the rules are known. These are also, notably, tasks oriented toward protecting what a firm already has: its books, credit exposure and revenue pipeline.
Customer-focused tasks tell a different story. Churn prediction sits at just 30%, 25 percentage points behind firms in the media and advertising sector. KYC, or “Know Your Customer” identity verification—the process of confirming that a new client is who they claim to be—reaches only 20% adoption. A/B testing and experimentation lag behind at 10%, the lowest rate recorded for that task in the entire survey.
Financial services firms appear to have chosen to deploy AI when outcomes are certain and the consequences of error are manageable. The tools for customer growth (retention, acquisition, experimentation and personalization) remain comparatively underdeveloped. The result is an AI portfolio that excels at protecting what already exists while underinvesting in the tools that generate what comes next.
Healthcare Targets Pressure Points
Healthcare’s AI focus is on the customer chatbot.
Of all the places healthcare organizations have concentrated their AI investments, the leading use case is customer service chatbots, at 60% adoption. That number reveals more about the workforce than about technology. Healthcare is using AI where the pressure is most acute, and right now, that means anywhere it can take duties off the plates of overburdened staff.
Workforce planning/skills gap and model development/training each follow at 55%. Logistics routing and delivery optimization comes in at 53%. These use cases reflect an industry under operational strain, reaching for tools that can absorb demand without adding headcount.
The gaps are just as telling. Customer journey orchestration (coordinating the full sequence of interactions a patient moves through, from first contact to ongoing care) sits at just 5%, the lowest figure in the entire survey. Regulatory compliance monitoring, which tracks whether an organization is meeting its legal obligations—arguably one of the highest-stakes functions in any healthcare organization—reaches only 30%. These firms are deploying AI reactively as a response to an immediate operational challenge rather than as long-term strategic design.
Healthcare firms have abundant clinical, operational and financial data, but fragmented systems prevent its consistent use. The result is that AI in healthcare is managing symptoms rather than building infrastructure. The tools are going where the immediate operational pain is most acute, not where they would deliver the greatest long-term value.
Media and Advertising Prioritize the Audience
The media and advertising sector has built AI for their audiences, not the back office.
It’s obvious that media and advertising firms put their audiences first. Three tasks tie for the highest AI adoption rate at 60%. These are content quality assurance (reviewing and improving output before it reaches viewers or readers), board and executive briefing preparation (using AI to synthesize information for senior leadership) and returns and reverse logistics optimization (improving the journey of products from the customer back to the seller).
Audience retention targeting, meaning using AI tools to identify which customers are likely to leave and what might retain them, reaches 55%, leading all three sectors on that metric. Third-party risk assessment, which evaluates the reliability and exposure of outside vendors and partners, comes in at 53%.
These choices make sense: Audience retention is an existential concern in this sector. Streaming fragmentation, the declining value of third-party cookies and rapid shifts in consumer attention have made holding an audience harder and more expensive. AI tools that can identify churn risk, improve content quality and streamline logistics directly address that pressure.
But the AI investments haven’t been matched by the infrastructure required to sustain them. User experience personalization and adaptive interfaces—tools that deepen the audience relationship rather than simply retain it—sit at just 10%, the lowest rate in the entire survey. Compliance, risk and workforce governance is at 16%. Transaction monitoring and anomaly detection trails at 16%. Strategic planning and data ingestion are both at 25%.
The risk for media firms is that audience-facing AI without the underlying organizational and data infrastructure is fragile. Retention tools work until the systems supporting them break down. Without laying the right groundwork, these gains are difficult to sustain and harder to scale.
AI Budgets Are Increasing
Every sector is spending more on AI, but for different reasons.
Across the board, most firms are increasing their AI budgets over the next 12 months: 85% of financial services firms, 80% of media and advertising firms and 60% of healthcare firms. The demand is consistent, but the companies’ reasons for it aren’t.
Financial services and insurance firms tie their spending to productivity gains and competitive positioning, both at 65%. These are outcome-oriented justifications that require AI investments to demonstrate measurable returns. Risk reduction and compliance, another concrete and auditable rationale, follows at 55%. These are the motivations of a sector that is already seeing returns and wants more.
Healthcare is taking a different tack. Sixty percent cite pilot funding without formal return on investment (ROI) requirements as a justification, meaning they’re committing a budget to AI without requiring proof it will pay off. That isn’t recklessness so much as pragmatism. A sector under immediate operational strain, facing workforce shortages and fragmented infrastructure can’t always wait for a rigorous business case before acting. These are the motivations of an industry still in experimentation mode, deploying AI without the governance infrastructure needed to measure what’s working.
Media and advertising falls in between. Eighty percent plan to increase spending, with productivity and efficiency gains cited by 65% of firms. But financial justification—monetary ROI and financial metrics—lags at 25%, the lowest in the survey. Half of these surveyed firms cite executive-driven strategic alignment (spending is justified by the leadership’s conviction rather than financial evidence) as a primary driver. Half also cite pilot funding without a formal ROI. For media firms, AI spending is endorsed at the top but not yet anchored to hard financial outcomes. That kind of top-down momentum can move organizations quickly, but it can also paper over gaps that will eventually need to be addressed.
Barriers to AI Deployment
Each sector faces its own unique obstacles to deeper AI deployment.
Every sector surveyed is committed to AI. The budgets are growing, and deployment is already underway. What’s holding each sector back has nothing to do with willingness and everything to do with the specific operational challenges.
In financial services, three in 10 leaders point to data quality and fragmentation as the primary constraint. This is notable given how deeply the sector has already deployed AI: It suggests that firms that have scaled furthest are now bumping against the ceiling of what imperfect data allows. Scaling AI further requires inputs that are clean, consistent and reliable. The technology is ready, but the data flowing into it often isn’t.
Healthcare faces two equally binding constraints, each cited by 30% of firms: system integration challenges and data quality issues. These organizations sit on enormous volumes of data, but that information lives in disconnected systems that don’t easily communicate with one another. Until a shared language is in place, even high-quality data remains difficult to access and use at scale.
The challenges facing the media and advertising sector are more scattered, with no single barrier emerging as dominant. Organizational constraints lead the list: Internal skills gaps and data quality both reach 20%. Governance failures, leadership alignment problems and difficulties integrating with existing systems follow at 15%. Without those foundations, even well-funded AI initiatives tend to fragment and stall. Moreover, the absence of a single chokepoint means there’s no single fix. AI progress in media depends on moving multiple organizational levers simultaneously.
Conclusion
All three sectors are headed in the same direction—using AI that supports rather than replaces human judgment—but each is stuck at a different speed bump. Financial services firms need cleaner data, healthcare firms need their systems to talk to each other and media firms need to get their houses in order organizationally before any of the technology investments can really pay off.
When asked about the five-year trajectory of AI, 80% to 85% of respondents across sectors say the technology will augment, not replace, human decision-making. Fewer than 20% expect semi-autonomous systems to become the norm. None anticipate full autonomy. Even the sector with the deepest AI use, financial services, doesn’t expect machines to take over complex judgment calls. Rather, companies across sectors want to make those judgment calls better informed, faster and more consistently supported by data.
For the financial services sector, the path forward is data quality: cleaning, standardizing and making consistent the inputs that their already-mature AI systems depend on. The unlock isn’t new technology so much as cleaning up the information that feeds into their existing tools.
The healthcare industry must solve two problems at once. System integration (enabling data to flow consistently across fragmented clinical and operational infrastructure) and data quality must improve in parallel. Unlike financial services, healthcare doesn’t yet have mature AI systems waiting around for better inputs. The infrastructure itself needs building.
Media and advertising firms face the broadest set of preconditions to sort out. Governance structures, AI talent, leadership alignment and data infrastructure all need to develop in parallel. Progress for this sector requires a sustained, coordinated effort across multiple fronts, not a single transformative project.
Navigating AI readiness
The shared thread across all three sectors is that at this point, AI readiness is an operational challenge.
Data, integration, governance and talent remain key concerns. The firms and industries that address those foundations first will be the ones that move AI from a collection of useful tools into a true competitive advantage. The destination is the same. The trails each sector must navigate and the road they must take to get there aren’t.
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Methodology
This report is based on a survey of 60 verified senior technology executives at U.S.-based enterprises with at least $1 billion in annual revenue. The survey was conducted in March 2026.
Respondents were drawn equally from three industry segments: financial services and insurance (n=20, 33.3%), healthcare and medical (n=20, 33.3%) and media services and advertising (n=20, 33.3%). All respondents are primary decision-makers or the most knowledgeable individuals within their organizations regarding AI strategy, adoption, and operations.
Just over half (51.7%) of respondents represent organizations with revenues between $1 billion and $5 billion, 38.3% represent organizations between $5 billion and $25 billion, and 10.0% represent organizations with revenues above $25 billion.
The survey tracked AI adoption across 75 distinct AI-supported operational tasks spanning eight business functions: Marketing and Sales, Supply Chain, Data and Technology, Product and Customer Experience, Risk and Compliance, Corporate and Strategy, HR and Workforce, and Payments and Finance. High adoption is defined as use by at least 50% of firms within a given sector for a specific task.
