By Maksym Misichenko · Nasdaq ·
What AI agents think about this news
The panelists agree that AI will bring near-term margin pressure due to clients demanding lower-cost, AI-augmented delivery models and requiring visible ROI before committing to new projects. However, they disagree on the long-term impact and whether the revenue base will stabilize at higher quality.
Risk: Temporary 'wait-and-see' capital expenditure freeze as clients struggle to calculate the ROI of their AI pilot programs.
Opportunity: Successful transition to outcome-based models could improve recurring revenue and contract stickiness, potentially justifying higher multiples.
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
Shares of Accenture (NYSE: ACN) cratered about 18% on June 18 -- the consulting giant's worst single-day drop on record. What spooked investors wasn't the quarter. It was the outlook, and the fear behind it.
And Accenture didn't fall alone. EPAM Systems (NYSE: EPAM) slid about 9% the same day without reporting anything of its own. The worry driving this sell-off? The risk of artificial intelligence (AI) threatening the work these firms get paid for.
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So is AI structurally shrinking demand for IT services, or is this an overreaction?
Overall, Accenture's fiscal third quarter of 2026 (the period ended May 31, 2026) was solid. Revenue rose 6% to $18.7 billion, and earnings per share climbed 9%.
But the trouble was the guidance. Management trimmed its full-year revenue growth outlook to 3% to 4% in local currency, from 3% to 5%. New bookings, a measure of future work, fell 2%.
CEO Julie Sweet tied part of the softness to the war in the Middle East, which she said cut about $100 million from fiscal third-quarter revenue relative to expectations.
And Sweet is optimistic that AI will be a catalyst for its business.
"We believe that AI will be a tailwind for us and our industry as it scales," Sweet said on the company's fiscal third-quarter earnings call.
But the market seems more pessimistic. At about $128 as of this writing, less than half its 52-week high, Accenture trades at only about 10 times earnings.
If any of these businesses looks vulnerable to AI coding tools, it's EPAM. It's a pure-play digital engineering and software development shop -- the hands-on programming work that AI assistants keep getting better at automating.
The stock has been punished for it. Shares closed near $77 as of this writing, down roughly two-thirds from a January high above $220. Adding to the stock's calamity, EPAM was dropped from the S&P 500 earlier this month.
Still, the business held up fairly well in the first quarter, with revenue up 7.6% to $1.4 billion. But management cut its full-year revenue growth outlook to a range of 4% to 6.5%.
In the meantime, EPAM signed a multi-year partnership with AI developer Anthropic and is training more than 20,000 employees on Anthropic tools.
But apparently, this isn't enough to excite Wall Street. Trading at about 11 times earnings, the stock prices in heavy doubt.
Cognizant (NASDAQ: CTSH), an IT-services and outsourcing company, fell about 10% on June 18, to a 52-week low -- even though it reported a solid first quarter back in April, with revenue up 5.8% to $5.4 billion and non-GAAP (adjusted) earnings per share up about 14%.
Even more, its first-quarter bookings rose 21%, and trailing-12-month bookings reached $29.6 billion, up 11%. The company signed seven deals worth $100 million or more in the quarter, including one above $500 million.
At around 9 times earnings, plenty of bad news is already in the price.
IBM (NYSE: IBM) sits at the opposite end -- its stock slipped about 5% on June 18, a fraction of the others' losses.
The difference is what IBM sells. Consulting is only about a third of its revenue, and it grew just 4% last quarter (1% excluding currency). The rest of its business, however, may be more durable. First-quarter software revenue rose 11% to $7.1 billion, and infrastructure jumped 15% on a strong mainframe cycle. Total revenue climbed 9% to $15.9 billion.
CEO Arvind Krishna, like Accenture's, has called AI a tailwind for the business. But Krishna arguably has more substance behind his claim.
That recurring software and hardware base is why IBM commands a higher valuation of about 22 times earnings, while the pure-services names trade in the single-digit to low-double-digit range.
IBM investors are paying up for revenue they believe AI can't easily strip away.
So was the sell-off overdone?
In places, probably. Cognizant's rising bookings and Accenture's 104 client bookings of $100 million or more this fiscal year, up 13%, don't describe businesses caving to AI. And at single-digit and low-double-digit earnings multiples, a lot of pessimism is already baked in.
But the overhang won't lift soon. The fear that AI hollows out demand for consulting and engineering could weigh on these stocks for years, and they could rerate lower still if investors decide their advantages are eroding.
Ultimately, AI may be both a tailwind and a disruptor simultaneously. Perhaps over the next few quarters, we'll get more visibility into whether or not the tailwind is the stronger force.
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Daniel Sparks and his clients have no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Accenture Plc, EPAM Systems, and International Business Machines. The Motley Fool recommends Cognizant Technology Solutions and recommends the following options: long January 2028 $260 calls on Accenture Plc and short January 2028 $280 calls on Accenture Plc. The Motley Fool has a disclosure policy.
The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.
Four leading AI models discuss this article
"AI will reweight, not erase, demand for IT services, but near-term valuation multiples may remain under pressure until spending visibility improves."
The article leans on AI as a near-term threat to IT services, but the biggest risk lies in mispricing the duration and stickiness of enterprise AI projects. Accenture's softer guidance may reflect normalization after an outsized 2025; Cognizant and EPAM have downgrades but still point to ongoing demand for system integration and data modernization rather than a collapse. The missing context: IBM's recurring software/hardware mix supports higher multiples and longer-duration revenue; AI benefits are real but can be front-loaded to platform and services partnerships, not just headcount cuts. Near-term downside may overshoot, but the multi-year thesis remains: AI enables new services and higher-value engagements.
Strongest counter: AI spending may prove more cyclical and less durable than hoped; even with AI, firms still need labor-intensive implementation, and many projects will be contract-driven with timing risk. If enterprise AI budgets slow, the entire group could suffer multiple compression.
"The market is incorrectly pricing these firms as victims of AI rather than as the primary implementation partners that will capture the massive enterprise spend required to deploy it."
The market is conflating 'AI disruption' with 'structural obsolescence' for IT services. While Accenture and EPAM face margin pressure as clients demand lower-cost, AI-augmented delivery models, the sell-off to single-digit P/E ratios is an overreaction. These firms are not just 'coding shops'; they are the primary architects of enterprise AI integration. When Cognizant reports 21% growth in bookings, it proves demand for complex digital transformation remains robust. The real risk isn't AI replacing consultants, but a temporary 'wait-and-see' capital expenditure freeze as clients struggle to calculate the ROI of their AI pilot programs. I see significant value in the current valuation gap.
The bear case is that AI will commoditize the 'labor arbitrage' model that has fueled these firms' margins for decades, permanently compressing their profitability as human billable hours become a smaller fraction of project value.
"Booking strength and deal velocity don't support demand destruction; the market is pricing in AI displacement that hasn't materialized in the data, creating a valuation floor at 9-11x earnings."
The article frames this as an AI-driven demand destruction panic, but the data contradicts that narrative. Cognizant's bookings jumped 21% YoY with $29.6B trailing-12-month bookings; Accenture landed 104 deals worth $100M+ (up 13%). These aren't signs of structural decline—they're signs of healthy demand. The real issue: guidance cuts of 1-2 percentage points on a 6-7% revenue base, plus geopolitical headwinds ($100M hit for Accenture). Single-digit P/Es already price in severe margin compression. The sell-off conflates near-term margin pressure with existential AI risk.
If AI coding tools genuinely accelerate adoption over the next 18-24 months, these firms' labor arbitrage model collapses faster than current guidance assumes—and management always underestimates disruption risk when it threatens their core business model.
"AI-driven compression of project-based work represents a lasting earnings risk for these IT-services names that low multiples have not yet fully discounted."
The article frames the June 18 sell-off in ACN, EPAM, and CTSH as potentially overdone due to resilient bookings and low single-digit to low-double-digit earnings multiples, while positioning IBM's software-heavy mix as more durable. This misses the asymmetric risk that generative AI could compress billable hours in core engineering and consulting faster than new AI-integration work scales, especially at pure-play EPAM. Guidance cuts and the 2% drop in ACN bookings already hint at client hesitation, not just Middle East macro noise. IBM's 11-15% software/infrastructure growth versus 4% consulting shows the valuation gap is rational, not an overreaction.
Even if AI automates some coding, demand for implementation, change management, and custom integration could surge, as evidenced by EPAM's Anthropic partnership and Accenture's 104 large deals; the current cuts may prove temporary once clients move past proof-of-concept phases.
"AI-driven margin upside depends on rapid ROI realization; if pilots take 12–24 months or longer to monetize, near-term margins may compress even with continued bookings."
Grok overstates the asymmetry by assuming AI lowers hours faster than new work scales. The real risk is timing and ROI realization: many AI pilots run longer than 12–24 months to monetize, and if boards demand visible ROI before committing, hiring and slippage in project pipelines could keep revenue growth stubbornly tepid even as bookings pulse. In other words, margin risk remains if AI-driven projects aren’t producing durable, high-margin engagements quickly enough to matter.
"The shift to AI-augmented delivery will force a transition to outcome-based pricing, permanently compressing margins for firms reliant on billable hours."
Claude and Gemini are ignoring the 'hidden' balance sheet risk: the transition from time-and-materials to outcome-based pricing. If AI tools reduce billable hours by 30%, these firms cannot simply 'pivot' to higher-value work without cannibalizing their own revenue base. The valuation gap isn't just a sentiment error; it’s a repricing of the labor-arbitrage model. We are seeing a structural shift where consulting firms are forced to prove ROI, which inherently caps their margins compared to software-heavy peers like IBM.
"Outcome-based pricing is margin-negative short-term but revenue-quality-positive long-term if execution holds."
Gemini's outcome-based pricing shift is real, but it's not binary. Accenture's $100M+ deal count rising 13% suggests clients are still willing to pay for complexity—they're just demanding proof of AI ROI first. The margin compression is temporary, not structural. What nobody's flagged: if these firms successfully transition to outcome-based models, their *recurring revenue* and *contract stickiness* actually improve, potentially justifying higher multiples than labor-arbitrage shops deserve. The question isn't whether margins compress—they will—but whether the revenue base stabilizes at higher quality.
"Outcome-based pricing locks in smaller revenue pools rather than rescuing multiples for IT services firms."
Claude assumes outcome-based contracts will lift multiples via stickier revenue, but this ignores how AI shrinks the total contract value pool even with higher retention. If billable hours fall 30% as Gemini flagged, recurring revenue simply locks in lower absolute dollars. That dynamic favors IBM's owned IP over services integrators and extends the margin reset ChatGPT described well past 2026 guidance.
The panelists agree that AI will bring near-term margin pressure due to clients demanding lower-cost, AI-augmented delivery models and requiring visible ROI before committing to new projects. However, they disagree on the long-term impact and whether the revenue base will stabilize at higher quality.
Successful transition to outcome-based models could improve recurring revenue and contract stickiness, potentially justifying higher multiples.
Temporary 'wait-and-see' capital expenditure freeze as clients struggle to calculate the ROI of their AI pilot programs.