GPU dependency, energy inflation, and output-verification overhead are costs AI pilots never modeled. Quarrio says its deterministic architecture removes those pressures, bypassing GenAI data-center cost structure entirely.
, /PRNewswire/ — Enterprise AI is running into a problem that is no longer theoretical: GenAI is becoming too expensive to scale. As AI moves from pilots into production, the hidden costs of GPU dependency, data processing, inference, and output verification are surfacing in real budgets. According to Quarrio, that pressure is exposing a widening gap between AI that can generate an answer and AI that can deliver one accurately enough to run the business. That is where Deterministic AI comes into the picture.
As boards, CFOs, and operating leaders are increasingly held accountable for measurable returns, they are being forced to confront the question: What does it cost to get from a user query to an accurate, auditable answer the business can act on? This is where the economics of GenAI begin to fail.
The first phase of the AI market was built on the idea that if demand showed up, the infrastructure would follow. “That idea no longer holds. What enterprises are discovering in 2026 is that the real cost of GenAI was never fully in the model; GPU dependency, energy inflation, and the overhead of verifying outputs at scale were all deferred,” said KG Charles-Harris, CEO of Quarrio. “They are not deferred anymore. Quarrio was built for the reality that is showing up now.”
Costs Are Rising Faster Than Outcomes
The numbers tell the story. An MIT-affiliated report says that, despite $30 billion to $40 billion in enterprise GenAI investment, 95% of organizations are seeing no quantifiable return. BCG reports that only 5% of companies are getting substantial economic value from AI, while 66% cite model accuracy and reliability as a roadblock. Gartner states that more than 40% of agentic AI projects will be scrapped by 2027. BCG also finds that 75% of executives rank AI or GenAI among their top three strategic priorities, but only 25% report significant operational or financial value.
Together, those figures point to a defining tension in the market: demand for AI remains high, but the cost of scaling current GenAI approaches into dependable enterprise execution is getting much harder to justify. For Quarrio, this is not a distant issue. It is a 2026 issue that is already showing up in budgets, procurement cycles, and board-level ROI scrutiny.
From AI Output to Business Execution
“The question is no longer whether AI can generate an answer,” Charles-Harris added. “The question is whether that answer is trustworthy enough for decision-grade intelligence or workflow execution, and whether the infrastructure required to get there still makes economic sense.”
Scalability now depends as much on trust and operating economics as it does on raw model capability. That distinction is especially important in environments where the impact of getting it wrong can include delayed decisions, inaccurate actions, margin leakage, audit risk, or regulatory exposure.
Cost-Effective Path to Decision-Grade AI
What is changing now is not just AI technology, but the economic test it has to pass. Salesforce has publicly argued that probabilistic agents need a “deterministic backbone” of data, business logic, workflows, and guardrails to remain trustworthy in enterprise settings. Quarrio’s position is that the market must move beyond wrapping controls around probabilistic systems. The more urgent issue now is how much compute, infrastructure, and verification it takes to get from a query to an answer that supports sound business decisions.
According to PwC’s 29th Global CEO Survey, 56% of CEOs report no significant financial benefit from AI, seeing neither higher revenue nor lower costs, while only 12% say AI has delivered both. What gets funded and scaled today is AI that works reliably, earns organizational trust, and clears the bar of production deployment.
Quarrio’s deterministic platform was built on the premise that enterprise AI must be accurate, auditable, and economically viable. Designed to run on CPUs bypassing the expense of compute that scaling GenAI-heavy deployments typically require, it delivers repeatable, auditable outcomes on infrastructure enterprises already own. “The next phase of enterprise AI will reward platforms that can deliver real outcomes without assuming abundant GPU capacity, and that shift is already playing out in budget decisions, not five years from now,” said Charles-Harris.
For additional perspective on the true costs of scaling GenAI in the enterprise, read Quarrio’s recent blog on GenAI economics.
About Quarrio
Quarrio is a deterministic enterprise AI platform for mission‑critical decision‑making. It delivers 100% accurate, auditable insights without costly transformation projects and runs efficiently on CPUs and GPUs, optimizing AI infrastructure spend to drive measurable, positive ROI. By cutting information latency from weeks to seconds, it provides instantly available operational intelligence, enabling faster execution and superior competitive outcomes. Led by pioneers behind IBM Watson, Symantec, Machine Intelligence, and major financial platforms, Quarrio is a capital-efficient, high-growth AI company with strong momentum, positioned for disciplined scale in the enterprise market. For more information, visit www.quarrio.com
References
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- Challapally, A., Pease, C., Raskar, R., & Chari, P. (2025, July). The GenAI divide: State of AI in business 2025. MIT NANDA / Project NANDA. mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
- Gartner, Inc. (2025, June 25). Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027. gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
- PwC. (2026, January 19). PwC 2026 Global CEO Survey. pwc.com/gx/en/news-room/press-releases/2026/pwc-2026-global-ceo-survey.html
- Quarrio. (2026, April 7). The deterministic AI illusion: Why most claims don’t hold up under the hood. quarrio.com/news/the-deterministic-ai-illusion-why-most-claims-don-t-hold-up-under-the-hood
- Salesforce. (2025, October 13). How much free will should your AI agents have? Salesforce News & Insights. salesforce.com/news/stories/ai-agents-free-will-determinism/
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