## Overview and Context This case study presents findings from a large-scale ROI survey conducted by Super AI (specifically referenced as "super intelligent" in the transcript, an AI planning platform) examining real-world enterprise AI and agent adoption across industries. The CEO of Super AI, who also hosts the AI Daily Brief podcast, initiated this study in late October 2025 in response to growing concerns about an "AI bubble" narrative and the lack of systematic data about actual ROI from production AI deployments. The study represents one of the first comprehensive attempts to gather self-reported ROI data directly from practitioners implementing LLMs and AI agents in production environments. The motivation for this research stemmed from a critical gap in the LLMOps landscape: while enterprises are rapidly increasing AI spend (from $88 million in Q4 2024 to an expected $130 million over the next 12 months according to KPMG data), traditional impact metrics and measurement approaches are failing to adequately capture the value being generated. As noted in the study, 78% of organizations reported that traditional impact metrics were "having a very hard time keeping up with the new reality" of AI deployments. This measurement challenge creates significant problems for organizations trying to justify continued investment and scale their AI initiatives beyond pilot phases. ## Study Methodology and Scale The survey was distributed to listeners of the AI Daily Brief podcast beginning at the end of October 2025, with the presented analysis covering approximately 2,500 use cases from over 1,000 individual organizations submitting around 3,500 total use cases. The study design asked respondents to categorize their AI implementations into one of eight primary ROI impact categories: time savings, increased output, improvement in quality, new capabilities, improved decision-making, cost savings, increased revenue, and risk reduction. Importantly, respondents were required to select only a single primary benefit category to force prioritization and clearer signal about what the most significant impact was for each use case. The study's limitations are explicitly acknowledged: this is a self-selected, highly engaged audience of daily AI podcast listeners who voluntarily chose to share their experiences. This likely introduces positive selection bias toward organizations and individuals who are more invested in AI success. However, the scale of responses (over 1,000 organizations across multiple industries and company sizes) provides valuable directional insights about production LLM deployments that have been largely absent from the public discourse, which has been dominated by vendor claims and consulting firm surveys with less granular use case data. ## Current State of Enterprise AI Adoption The study situates its findings within the broader context of enterprise AI adoption in 2025. While 2025 was anticipated to be "the year of agents" with mass automation across enterprise functions, the reality has been more nuanced. According to KPMG's quarterly pulse survey of companies over $1 billion in revenue, the percentage with actual production agents (not pilots or experiments) jumped dramatically from 11% in Q1 2025 to 42% in Q3 2025. This represents significant progress in moving AI from experimental to production deployment, though it fell short of some expectations for wholesale automation. The McKinsey State of AI study referenced in the presentation shows that most enterprises remain stuck in pilot and experimental phases, with only 7% claiming to be "fully at scale" with AI and agents, while 62% are still experimenting or piloting. This creates a bifurcation in the market between leaders and laggards, with a key distinguishing factor being that leading organizations think more comprehensively and systematically about AI adoption rather than pursuing isolated experiments. Leaders are also focusing not just on first-tier time savings and productivity use cases, but thinking strategically about revenue growth, new capabilities, and new product lines. Interestingly, the data shows that larger organizations are generally ahead of smaller ones in scaling AI, contrary to expectations that smaller, more nimble companies would adopt faster. The study also notes that IT operations has emerged as a leading function in AI adoption, breaking out ahead of other departments in what had previously been relatively uniform adoption rates across functions. ## ROI Findings and Impact Distribution The headline finding from the Super AI survey is overwhelmingly positive: 44.3% of respondents reported modest ROI from their AI implementations, while 37.6% reported high ROI (combining "significant" and "transformational" categories). Only approximately 5% reported negative ROI, and critically, even among those with current negative ROI, 53% expected to see high growth in ROI over the next year. Overall, 67% of all respondents expected increased and high growth in ROI over the coming year, indicating extremely optimistic expectations from practitioners actually implementing these systems. Time savings emerged as the dominant category, representing approximately 35% of all use cases. The distribution of time savings clustered heavily between 1-10 hours per week, with particular concentration around 5 hours saved per week. While this may seem modest compared to transformational visions of AI, the study emphasizes the significance of these gains: saving 5-10 hours per week translates to reclaiming 7-10 full work weeks per year, which represents substantial productivity gains when multiplied across an organization. Beyond time savings, the next most common categories were increased output and quality improvement—all falling within what the study characterizes as "productivity" benefits that represent the starting point for most organizations' AI journeys. However, the study reveals that the story extends well beyond simple time savings, with meaningful variation across organization types, roles, and implementation approaches. ## Variation by Organization Size and Role One of the study's most interesting findings involves how ROI patterns differ based on organization size. Organizations with 200-1,000 employees showed a notably higher concentration of use cases focused on "increasing output" compared to other size categories. The presenter speculates that this may reflect these mid-sized organizations having reached a certain scale but still striving for growth, leading them to focus more on use cases that expand capabilities rather than simply making existing work more efficient. The smallest organizations (1-50 employees) showed a higher proportion achieving transformational impact early in their AI adoption journey. The presenter notes that this category likely contains significant internal variation—a three-person startup may have vastly different use cases and needs compared to a 40-person company—and expresses interest in future research disaggregating this category further. Role-based analysis revealed significant differences in focus and outcomes. C-suite executives and leaders generally reported being more optimistic and seeing more transformational impact than those in junior positions, with 17% of use cases submitted by leadership already showing transformational ROI impact. Leaders showed less focus on time savings use cases and more emphasis on increased output and new capabilities. This pattern may reflect selection bias in terms of what types of initiatives leaders focus on (inherently more strategic and potentially transformational) or may indicate that leadership has better visibility into organization-wide impacts that junior employees might not perceive. ## Industry and Functional Patterns While the survey had heavy concentration in technology industries and professional services (reflecting the podcast's audience), it achieved sufficient sample sizes in other sectors to identify some interesting patterns. Healthcare and manufacturing organizations reported meaningfully higher impact on average compared to the cross-industry average, though the study doesn't speculate extensively on why these sectors are seeing outsized benefits. This finding merits further investigation to understand what characteristics of these industries make them particularly well-suited to current AI capabilities. As expected, coding and software-related use cases showed higher ROI than average and lower negative ROI than average, consistent with the broader industry narrative about 2025 seeing a "major inflection" in the adoption of coding assistance tools. The study notes this wasn't limited to software engineering organizations—other parts of enterprises also began thinking about how they could communicate with code and build things with code, expanding the impact of these tools beyond traditional engineering functions. ## Risk Reduction and High-Impact Use Cases One of the most striking findings involves risk reduction use cases. Despite representing only 3.4% of all submitted use cases (the smallest category), risk reduction use cases were by far the most likely to be rated as having transformational impact—25% of risk reduction use cases achieved this highest rating. The presenter discusses this finding with colleagues who work in back office, compliance, and risk functions, who confirmed that these domains often involve challenges of sheer volume and quantity that AI can address particularly effectively. This finding has important implications for LLMOps strategy: while risk reduction may not be where organizations start their AI journey (hence the low percentage of total use cases), it may represent one of the highest-value opportunities once organizations move beyond initial productivity improvements. From a production deployment perspective, risk reduction applications may also face higher requirements for reliability, explainability, and auditability, potentially requiring more sophisticated LLMOps practices around monitoring, evaluation, and governance. ## Automation and Agentic Workflows A critical finding for understanding the future direction of production LLM deployments is that use cases involving automation or agents "wildly outperform in terms of self-reported ROI." The study notes this applies both to automation generally and to agentic workflows specifically, representing what the presenter characterizes as "the next layer of more advanced use cases" beyond the first tier of productivity improvements. This finding aligns with and provides ground-truth validation for the KPMG data showing rapid growth in production agent deployment from 11% to 42% of large enterprises in just two quarters. From an LLMOps perspective, this shift toward automation and agents introduces significantly more complexity compared to simpler assistive use cases. Agents that take autonomous actions require more sophisticated approaches to reliability engineering, safety constraints, monitoring, human oversight mechanisms, and failure recovery. The fact that organizations are nonetheless seeing high ROI from these more complex deployments suggests that the LLMOps tooling and practices have matured sufficiently to make production agent deployment viable, at least for the 42% of large enterprises that have reached this stage. The study explicitly notes that there has been "a shift in the emphasis around the human side of agents and how humans are going to interact with agents," involving new approaches to upskilling and enablement work. This highlights an often-overlooked aspect of LLMOps: successful production deployment isn't purely a technical challenge but requires organizational change management, training, and the development of new workflows that effectively combine human and AI capabilities. Organizations are experimenting with "sandboxes where people can interact with agents," suggesting a pattern of providing safe environments for users to develop familiarity and skills before full deployment. ## Systematic and Multi-Use Case Approaches One of the study's most significant findings for LLMOps strategy is the clear correlation between the number of use cases an organization pursues and the ROI they achieve. Organizations and individuals that submitted more use cases generally reported better ROI outcomes. While the study acknowledges multiple possible explanations for this correlation (it could reflect that successful organizations naturally pursue more use cases, or that organizations further along in their journey both have more use cases and have learned how to achieve better ROI), it aligns with the McKinsey data showing that "high performers" in AI adoption are distinguished by thinking comprehensively and systematically rather than pursuing isolated experiments. From an LLMOps perspective, this finding has important implications for platform strategy and infrastructure investment. If ROI increases with the number of deployed use cases, then investments in reusable LLMOps infrastructure—shared model deployment platforms, common evaluation frameworks, centralized monitoring and observability tools, standardized prompt management systems, and shared agent orchestration capabilities—become increasingly valuable. Organizations that treat each AI use case as a bespoke implementation may struggle to achieve the scale advantages that come from systematic approaches. This also suggests that LLMOps maturity models should emphasize the progression from single experimental deployments to platforms that can support multiple concurrent production use cases efficiently. The ability to rapidly deploy, monitor, evaluate, and iterate on multiple AI applications simultaneously becomes a competitive advantage, as organizations that can do this effectively achieve better overall ROI from their AI investments. ## Measurement Challenges and Evolution A recurring theme throughout the study is the inadequacy of traditional impact measurement approaches for AI deployments. The finding that 78% of organizations struggle to apply traditional metrics to AI represents a fundamental challenge for LLMOps practice. Effective LLMOps requires not just deploying and maintaining production systems, but also demonstrating their value in ways that justify continued investment and guide improvement efforts. The study itself represents an attempt to address this measurement gap through self-reported data collection, but the presenter acknowledges the limitations of this approach. More sophisticated LLMOps practices will need to develop better frameworks for capturing and quantifying AI impact, particularly for benefits that don't fit neatly into traditional productivity metrics. The study's eight-category framework (time savings, increased output, quality improvement, new capabilities, improved decision-making, cost savings, increased revenue, and risk reduction) provides one possible taxonomy, though the challenges of measuring categories like "quality improvement" or "improved decision-making" objectively remain significant. The shift in CEO expectations about ROI realization timeframes—from 63% expecting 3-5 years in 2024 to 67% expecting 1-3 years in 2025, with 19% now expecting ROI within 6 months to 1 year—creates additional pressure for LLMOps practices to demonstrate value quickly. This accelerated timeline may push organizations toward the "first tier" productivity use cases that are easier to measure and demonstrate, potentially at the expense of more transformational but harder-to-quantify applications. ## Resistance and Organizational Dynamics The study notes that there has been "a decrease in the sort of resistance to agents as people start to actually dig in with them," suggesting that hands-on experience with AI capabilities helps overcome initial skepticism or concerns. This has implications for LLMOps rollout strategies—providing safe environments for experimentation and building familiarity may be as important as the technical deployment itself. The finding that despite negative narratives in media about an "AI bubble," enterprise spending intentions continue to increase dramatically (to $130 million over the next 12 months) suggests that practitioners working directly with these technologies have confidence based on their direct experience that contradicts broader market skepticism. This disconnect between media narratives and practitioner experience creates an interesting dynamic where organizations with successful production deployments may have a clearer path forward than those still in early exploration phases who may be influenced by external doubts. ## Critical Assessment and Limitations While the survey provides valuable insights into production AI deployment, several important limitations must be considered. The self-selected nature of the respondent pool—listeners of a daily AI podcast who voluntarily shared their experiences—almost certainly introduces positive selection bias. Organizations and individuals experiencing difficulties or failures are less likely to participate, potentially overstating the ease of achieving positive ROI. The self-reported nature of ROI measurements also raises questions about consistency and accuracy. Different organizations may calculate ROI differently, and individuals may have varying levels of rigor in their assessments. The study doesn't appear to include detailed methodological guidelines for how respondents should calculate or estimate ROI, potentially leading to inconsistent measurement approaches across submissions. The requirement to select a single primary impact category, while helping to generate clearer signals, also obscures the reality that most production AI deployments likely generate multiple types of value simultaneously. A coding assistant might save time while also improving code quality and enabling new capabilities—forcing respondents to choose one category may miss the full picture of impact. The study's finding that expectations for future ROI growth are extremely high (67% expecting increased high growth) should be viewed with some skepticism. While optimism may reflect genuine confidence based on early results, it may also reflect hype and unrealistic expectations that will be moderated as implementations mature. The disconnect between the 42% of large enterprises with production agents and the 7% claiming to be "fully at scale" suggests that many organizations still face significant challenges in moving from initial deployments to comprehensive transformation. ## Implications for LLMOps Practice Despite these limitations, the study provides several important insights for LLMOps practitioners. First, the rapid movement from pilots to production agents (11% to 42% in two quarters) demonstrates that production deployment is increasingly viable, though it requires significant infrastructure and process maturity. Second, the correlation between systematic multi-use case approaches and higher ROI suggests that investment in shared LLMOps platforms and reusable infrastructure provides real value. Third, the finding that automation and agentic workflows significantly outperform assistive use cases in ROI indicates that organizations should be preparing for the operational complexity these more advanced deployments entail. The study also highlights the continuing importance of measurement and evaluation in LLMOps practice. The struggle to apply traditional metrics to AI deployments creates opportunities for developing better frameworks and tools for capturing AI impact, particularly for dimensions like quality improvement, new capabilities, and risk reduction that are harder to quantify than simple time savings. Finally, the study reinforces that successful LLMOps extends beyond pure technical concerns to include organizational change management, user enablement, and systematic strategic thinking about how AI fits into broader business objectives. The distinction between leaders and laggards appears to be less about technical sophistication and more about comprehensive, strategic approaches that think beyond isolated experiments toward systematic transformation.