AI Buyer Paralysis: "Value Maxing" and Elongated Sales Cycles Drive Historic Software Pullbacks

AI Buyer Paralysis: "Value Maxing" and Elongated Sales Cycles Drive Historic Software Pullbacks

The thesis of AI-induced buyer paralysis and sales cycle elongation was dramatically validated in July 2026 by Q2 2026 financial results and earnings commentary from Pegasystems (NYSE: PEGA), which reported a sharp slowdown in total Annual Contract Value (ACV) growth and an 18.6% miss on adjusted EPS. Pega’s results illustrate how enterprise buyers are freezing technology investments in a "max confusion moment," driven by the anxiety of opaque, variable AI token costs and the challenge of proving ROI on early AI experiments.

The "Token-Maxing" Whiplash and Opaque Reasoning Costs

In Pega’s Q2 2026 earnings call on July 22, 2026, CEO Alan Trefler delivered a blunt assessment of the enterprise AI landscape, comparing frontier model providers to "drug dealers" who initially offered AI for free or at low flat rates before shifting to variable token pricing. This shift has introduced severe cost uncertainty for enterprise buyers, particularly due to hidden "reasoning tokens" consumed during complex multi-step operations.

Trefler explained:

"Across the industry, organizations are rethinking how software is designed, built, operated, and evolved. For the time, AI providers acted a little bit like drug dealers, offering their products for free or charging $20 a month for what felt like unlimited usage... Now they’re going to need to seek a return on that investment... what was once available for free or for all you can eat licensing is priced now by token use with the attendant anxiety and ambiguity."

He added that this opacity has led to a complete freeze in buyer decision-making:

"The challenge for enterprises is token consumption is opaque until the bill arrives. Many of the tokens these models consume are reasoning tokens. They don’t show up in input or output, but are used by the model itself as it loops through increasingly complex logic. These reasoning costs can become surprisingly and prohibitively expensive. This cost uncertainty is leading many organizations to sort of freeze and try to figure out what’s going on and take a more deliberate approach to technology investments as they assess the economic environment."

Stalling ACV Growth and a Cautious Buying Cycle

This "frozen" buyer behavior had a direct, material impact on Pega’s financial performance. While Pega Cloud ACV grew 22% year-over-year to $926.3 million (representing 57% of total ACV), Pega’s total ACV growth slowed to just 7% reported (8% in constant currency), missing management expectations. The company missed Q2 earnings estimates by $0.08, reporting an adjusted EPS of $0.35 against the $0.43 consensus, prompting a 19.2% premarket plunge in its stock price to $25.

CFO Ken acknowledged that both market conditions and go-to-market change management contributed to the slower-than-expected first half:

"Unprecedented change in the software market created significant buyer uncertainty. Organizations wrestle with fundamental questions about how AI would reshape software development and whether they should build more capabilities themselves. The market entered a token-maxing mindset where organizations encouraged, even celebrated token consumption, then whiplash to the opposite extreme, where companies sought to tightly monitor and control token usage. The resulting uncertainty made customers more cautious and contributed to a more confused and longer buying cycle.¹"

Ken noted that while Pega originally modeled its full-year Net New ACV additions to be split one-third in the first half and two-thirds in the second half, the company "significantly underachieved on the one-third in the first half," making a full recovery in the back half highly challenging.

The Architectural Defense: Design-Time vs. Runtime AI

To combat this buyer paralysis, Pegasystems is pitching a highly differentiated architectural approach that separates "design-time" AI from "runtime" AI. Through Pega Blueprint and the newly released Infinity Studio (launched in July 2026), Pega uses AI aggressively at design time to help clients visually map out, design, and structure their workflows. Once designed, the workflows run repeatedly at scale in a deterministic, case-based execution model, using AI only selectively at runtime (e.g., for document summarization) to avoid runaway token costs.

This architecture enables Pega to offer its AI capabilities as a flat uplift to its case-based pricing model, completely bypassing variable per-token fees. As Trefler noted:

"Our approach is fundamentally different. Use AI extensively at design time through Blueprint AI, design the workflows and get them right and really make them excellent. Once you get them right, run them repeatedly at scale, thousands or millions of times, only using the AI selectively in the runtime steps where it makes sense... This also means we’re not using up massive amounts of costly reasoning tokens at runtime. It’s how we are able to offer our agentic AI as an uplift to our case-based price with no variable or token cost."

Whether this predictable cost model can successfully break through buyer paralysis and accelerate Pega's sales cycles in the second half of 2026 remains the key metric to watch.