New Hyperscience Report Finds That Enterprise AI Costs Are Blowing Past Budgets by Up to 30X and 4 in 5 Companies Are Ditching the "One Big Model" Strategy to Cope

Half of enterprises underestimated their GenAI costs, yet 78% are increasing GenAI spend anyway, betting the fix is architecture

77% of Organizations Now Route Non-Critical Workloads to Smaller, Cheaper Models to Control Cost

NEW YORK--(BUSINESS WIRE)--The first wave of enterprise generative AI ran on a simple bet – pick the most powerful model available and route everything through it. That bet has now broken. A new study from Hyperscience, a market leader in enterprise AI infrastructure software, focused on Intelligent Document Processing (IDP), titled The Great AI Rebuild - How the sticker shock of GenAI is forcing enterprises to abandon the single-model bet finds that enterprises are confronting the true cost of running GenAI at scale — a phenomenon called "tokenomics" — and as a result are rapidly re-architecting their strategies in response.

The report, based on an online survey conducted by The Harris Poll of 400+ decision makers in the public and private sectors between July and August, 2026 reveals that 80% of organizations are moving away from a single-large-model strategy, while 52% say their GenAI infrastructure costs have come in higher than expected. At the same time, 86% say data quality issues are hurting the performance of their GenAI applications, underscoring a broader shift toward multi-model architectures, tighter cost controls and stronger data foundations as enterprises rethink how AI is built and deployed.

“The first phase of enterprise AI was about access to intelligence. The next phase is about how you operationalize it,” said Andrew Joiner, CEO of Hyperscience. “The data shows that enterprises are recognizing that you cannot build an AI strategy around a single model or treat inference as an unlimited resource. The economics are forcing a more intelligent architecture: use the right inference for the right task, reserve expensive compute for where it creates the most value, and make sure the data feeding those systems is trusted. That is what it will take to move AI from experimentation into the operational core of the enterprise.”

The survey finds that enterprises are moving away from the early assumption that the most capable AI model should power every workload. Instead, organizations are increasingly embracing a more deliberate approach that matches models to specific tasks, controlling inference costs, improving the quality of the data feeding AI systems, and relying on outside expertise to orchestrate increasingly complex AI environments.

Tokenomics Forces the End of the "One Model for Everything" Approach

Early enterprise GenAI strategies often centered on selecting a highly capable frontier model and applying it broadly across use cases. But as organizations have moved into production, the economics of inference and the operational complexity of managing AI at scale have made that approach increasingly difficult to sustain.

By mid-2026, industry pricing trackers put the cost gap between frontier U.S. models and their fastest-growing, low-cost rivals at roughly 30x or more on output tokens for comparable workloads. The survey findings prove that this gap is why 62% of organizations say they're optimizing model selection and routing, and why 57% are evaluating cheaper alternative models.

Additionally, the survey also found that 47% of organizations are actively reevaluating their GenAI strategy because of the cost shock, and that financial pressure is landing disproportionately on the group with the least cushion to absorb it - 51% of early-stage adopters report being forced to cut spend or rethink strategy, compared with 43% of mature organizations.

Many researchers have argued that small language models can be better suited to repetitive, specialized agentic tasks, while heterogeneous architectures reserve larger models for complex reasoning. The survey finds that 77% of organizations already route non-critical workloads to smaller, cheaper models to control cost, reserving larger, more expensive models for the tasks that actually require them. Likewise 89% organizations already use, or are actively developing, workload routing that sends tasks to different models based on their characteristics.

Rather than asking which foundation model is most capable, enterprises are increasingly asking a more practical question that centers around what is the right combination of models, data and infrastructure for each job.

Data Quality Becomes the AI Performance Bottleneck

The survey also underscores a challenge that extends beyond model selection: AI is only as useful as the data it can access and understand. With 86% of organizations reporting that data quality issues are hurting GenAI performance, enterprises face a growing need to transform the unstructured information trapped in documents, forms, correspondence and other business content into reliable, usable data before AI applications can deliver consistent results.

That challenge is particularly acute as enterprises move toward AI agents and increasingly autonomous workflows, where the cost of acting on bad data compounds quickly.

AI Investment Isn't Slowing — It's Becoming More Disciplined

Despite the cost pressures revealed in the survey, organizations are not backing away from GenAI. More than three-quarters (78%) plan to increase their GenAI investment over the next year. Instead, the findings suggest enterprise AI is entering a more pragmatic phase where organizations are putting controls around infrastructure spending, reassessing architectural decisions, and increasingly turning to outside partners with 77% now relying on vendors rather than build routing and orchestration in-house to manage the complexity of a multi-model environment.

The result is a shift from AI experimentation toward AI economics, where performance, accuracy, latency, governance and cost all become part of the same equation.

Click here to download the full "The Great AI Rebuild” report.

About the Survey

The research was conducted online in the US by The Harris Poll on behalf of Hyperscience among 409 tech decision makers whose organization is actively deploying or plans to deploy GenAI in the next 12 months. Of the 409 respondents, 206 were employed at public agencies and 203 at private companies of 1,000+ employees. The survey was conducted July 28 through August 10, 2026.

Raw data were not weighted and are therefore only representative of the individuals who completed the survey.

Respondents for this survey were selected from among those who have agreed to participate in our surveys. The sampling precision of Harris online polls is measured by using a Bayesian credible interval. For this study, the sample data is accurate to within ± 4.8 percentage points using a 95% confidence level. This credible interval will be wider among subsets of the surveyed population of interest.

All sample surveys and polls, whether or not they use probability sampling, are subject to other multiple sources of error which are most often not possible to quantify or estimate, including, but not limited to coverage error, error associated with nonresponse, error associated with question wording and response options, and post-survey weighting and adjustments (not applicable in this case).

About Hyperscience

Hyperscience is a market leader in enterprise AI infrastructure software, focused on Intelligent Document Processing (IDP). The Hyperscience Hypercell platform unlocks the value of an organization’s unstructured data through the automation of end-to-end processes, and transforms complex documents into LLM and RAG-ready data to power new enterprise GenAI experiences. This enables organizations to transform manual, siloed processes into a strategic advantage, resulting in a faster path to decisions, actions, and revenue; positive and engaging customer, public, and patient experiences; and dramatic increases in productivity.

Hyperscience delivers measurable enterprise impact through higher automation, greater accuracy, and increased operational efficiency. The IDC Business Value Study found organizations using Hyperscience achieved 615% three-year ROI, $8.6M in average annual benefits, and payback in approximately seven months.

Leading organizations across the globe rely on Hyperscience to drive their hyperautomation initiatives, including Charles Schwab, HM Revenue and Customs, Rula, Stryker, Tokio Marine, The United States Social Security Administration, The United States Department of Veterans Affairs, and U.S. Citizenship and Immigration Services. The company is funded by top tier investors including Bessemer Venture Partners, Battery, FirstMark, Stripes, and Tiger Global.