AI and tech stocks continue on a volatile trajectory. Goldman Sachs reported that hedge funds are selling U.S. tech stocks at a record pace, while Bloomberg said big tech needs to justify AI spending. AI tech budgets are under scrutiny and facing an ROI test.
Small, medium and large business decision-makers, who are modernizing their tech stacks and operations, are making moves and adjusting. In this report, I speak to experts from Fujitsu North America, Citizens, Nisum, Gorilla Logic and other firms to answer the hard questions. How should leaders react? Is a full tech budget revision necessary and what stays and what goes?
Should Decision-Makers React to AI Stock Market Volatility and Adjust Tech Budgets?
On July 23, Bloomberg reported big tech companies lost $797 billion, driven by AI skeptics that continue to dump tech stocks. Volatility picked up pace in late June, driven by concern over AI spending and reports that failed to meet investors’ expectations.
Spilling beyond Wall Street, the market’s shifts caught the attention of business executives from all industries. The big question: How should SMB and large firms react?
“Decision-makers should not panic-cut; rather, they should transition their operational mindset from reactive post-billing alerts to proactive, autonomous control,” Dippu Kumar Singh, senior director and solution architecture at Fujitsu North America, told me.
“The subsidy-era of artificially cheap AI is ending, and we are entering the era of AI-based Token Economics, where the cost of intelligence will become a primary budget driver,” said Singh.
At the heart of AI spending, we find tokens. Unlike cloud computing, which runs on a SaaS subscription model, the AI token economy is structured as a usage-based model. This model, when left unchecked, can incur high costs. Business owners are learning this the hard way.
“AI-based Token Economics goes far beyond tracking standard cloud compute; it requires standardizing the measurement of prompt and completion tokens and embedding token-awareness directly into engineering cultures so developers understand the financial weight of their code,” said Singh.
Large enterprises must adopt a ‘crawl, walk, run’ model to mature their agentic cost management, recognizing that AI consumption scales exponentially compared to traditional cloud workloads, Singh added.
On the other hand, Mark Valentino, Head of Business Banking at Citizens, told me that owners are still trying to answer two basic questions: “How do I grow faster, and how do I run more efficiently?”
The best leaders in periods like this treat AI investment the way they treat any other spend, said Valentino. “Can it improve revenue, margins, customer experience or productivity? If not, the timing isn’t right to implement it,” Valentino explained.
Is A Full Tech Budget Revision Called For?
The 2027 budget planning report from Forrester Research recently found that tech leaders face constant pressure to deliver AI value, modernize and meet governance and compliance with speed, all amid “persistent volatility and budget scrutiny.”
Despite this tightrope, the same report also found that more than 80% of business and technology leaders expect budget increases for 2027. Strategic areas of investment for 2027 are already being drawn up. Is a full tech budget revision mandatory?
“A full tech budget revision isn’t necessary, but spend should be focused in areas where governance exists, processes are defined and success criteria are clear,” Drew Naukam, CEO of Gorilla Logic, a company building digital platforms and products driven by AI, agreed.
“There’s no value in eliminating AI initiatives; chasing AI isn’t the value,” Paxon Brown, Strategic Growth Partner at Nisum, a global consulting partner specialized in digital commerce and evolution, told me.
“The value remains in the outcomes we can create — every investment has to tie to an accountable business result,” Brown said.
“A full budget reset may not be needed, but companies should redirect spending toward the data, security, integration and training required to make AI work at scale,” Cesar Donofrio, CEO and cofounder of MakingSense, a digital transformation company that develops software solutions for mid-market businesses and private equity portfolios, also told me.
Executives should eliminate ownerless pilots, overlapping tools, unused licenses, disconnected data projects and custom development that replicates existing solutions, Donofrio added.
“They should keep investing in cybersecurity, data quality, integration, modernization, customer-facing digital experiences that differentiate the business and AI that creates value in daily work,” said Donofrio.
“Real value shows up when you run AI like you run any other part of the business,” Brandon Tobman, CEO at Get Covered, a company developing AI software solutions for the property insurance sector, told me. “Someone owns it, there’s a defined outcome you’re measuring against and it actually lives inside your workflow instead of sitting off to the side as a science project.”
“That doesn’t necessarily require a complete overhaul of technology budgets, but it does call for reallocating resources toward initiatives that can demonstrate operational impact,” said Tobman.
Cleaning House: What Goes And What Stays?
In its 2027 Budget Planning Guide, Forrester highlights agent-driven enterprise context, AI brand visibility and agents as areas to invest. Budgets for AI pilots that lack governance, clear ownership and success criteria should be cut, the Forrester report said.
I asked experts if they agree, and what stays and what goes.
David Renta, senior managing director and global head of hedging at Convera, told me that AI “science projects” without business value, multiple tools solving the same problem and pilots spinning in a pre-production loop should all go.
Renta listed human accountability with AI governance, enterprise data and analytics platforms, cybersecurity and risk management, automation of core business processes and human expertise augmented by AI, as keepers.
Singh from Fujitsu North America told me that unmonitored token consumption, manual anomaly investigations via disconnected spreadsheets, shadow AI deployments and pilot projects that lack direct business alignment should go.
“The tolerance for opaque pricing abstractions like undocumented monthly credit burns or unpredictable usage spikes is over,” said Singh.
Agentic AI systems, rigorous context engineering and autonomous rate optimization should stay, Singh said. “Practices that build transparent unit economics, enforce robust AI ethics and leverage standard frameworks like FOCUS 1.4 (a standardized, vendor-neutral schema for ingesting and normalizing multi-cloud and AI billing data) are here to stay,” he added.
Tech Budgets and AI Literacy: Understanding the Business Elements
As Singh said, understanding the ethical and financial implications of AI is a core component of modern executive literacy that directly impacts an organization’s bottom line and operational resilience.
“AI literacy for modern leadership is no longer about knowing how to code; it’s about context,” said Singh.
AI is going through an ROI test; at the market level, it is being repriced, while inside executives’ corridors, the AI talk has already shifted to strategy. Experts agree that overhauling tech budgets is not necessary. However, a better understanding of the business elements that interact with AI is fundamental.
