Executive summary
AI is reshaping how tech companies compete and requiring PE-backed businesses to move beyond treating it as a product feature. AI must be embedded into strategy, operations and market positioning. Companies that connect governance, operating models and product roadmaps to measurable business outcomes are better positioned to protect valuation and strengthen exit readiness.
Tech companies redesign AI strategy and operations to remain competitive
AI is adding a new layer of valuation pressure for PE-backed companies, influencing how buyers assess growth durability, operating leverage and exit potential — and the tech industry may be feeling it the most.
Expansion has always been core to tech companies’ growth. Some of the largest players today are hardly recognizable from their original business plan: Amazon started as an online bookstore. Salesforce’s first solution was a CRM. Google's products long ago outgrew the search engine that gave the company its name.
What's different today is that AI allows reinvention to happen more quickly — but it’s also available to everyone at once, and some are using it to move faster.
That pressure also isn’t confined to mega-players alone. Midmarket PE-backed companies are being pushed by buyers to prove how AI is creating real value, both inside the business and in their offerings, or they risk losing ground to competitors who can.
Grant Thornton’s 2026 AI Impact Survey shows tech companies are already ahead in advanced AI adoption: 57% of tech leaders said their organization is scaling agentic AI across multiple business units — 28 points higher than the cross-industry average.
But that scale isn’t always backed by strategy. When asked what's driving AI-related ROI, most industries surveyed pointed to strategy first. But most tech leaders pointed to the tech stack itself, with just 29% citing strategy as the top driver.
As tech companies seek to expand their offerings beyond their initial positioning, their AI strategy needs to expand, too. For sponsors, that strategy increasingly shapes how buyers assess competitive positioning, growth potential and exit valuation.
“AI isn't just an internal efficiency play — it's becoming the tool companies use to stake a claim in adjacent markets and reposition what they offer,” said Vincent Zosa, Partner at Grant Thornton | Stax. “If they’re not very well positioned in what they do, their portion of the tech stack can be subsumed by another firm.”
Commoditization is the new valuation risk
Given the AI-enabled valuation pressure impacting tech and SaaS companies, sponsors are reassessing the investment thesis itself: is this company's competitive strength — and its path to a strong exit — still intact given how fast the landscape around it is changing?
One threat is replication. “AI is bringing a new threat to SaaS companies that own a functional piece of software in the tech stack: somebody can take what they do and replicate it through an AI-native solution,” Zosa said. “That solution can do everything the company previously did, but in an AI-enabled architecture that is less expensive for the end-user in platform costs and in what they can charge customers.”
AI is also making it easier for companies to absorb one-off features into one, larger product. A customer’s tech stack that once required separate tools for workflow automation, data analytics and customer engagement can increasingly run on a single AI-native platform that does all three.
Customer needs for specific software are also shifting. As AI automates more work, software seat counts, pricing models and addressable markets are shifting, forcing sponsors to reassess growth assumptions embedded in the original investment thesis.
“AI is automating a lot of tasks and reducing the number of people actually using certain software,” Zosa said. “If you used to sell into 500 end users in an organization and much of that work becomes automated through AI, leaving only 50 end users, you're finding that your market is shrinking.”
Together, these factors compound: customers are spending less, and companies are working harder to compete for a shrinking budget. That's changing how tech companies approach AI strategy and operating model design.
“Many of our clients are still working through AI use case strategy, but increasingly, that means understanding all of these dimensions — building a strategy, and designing and implementing an AI operating model that will set them apart from competitors and make their case to future buyers,” Zosa said.
Redefining AI strategy for competitive positioning
To improve their valuation potential and meet the rising expectations of buyers, tech companies are re-imagining their competitive positioning and how AI fits into both their product roadmap and M&A strategy.
“There's no doubt that technology-related companies need AI embedded into their value proposition,” Zosa said. “Determining how to monetize AI-driven capabilities, or use them to gain market share, is a critical priority.”
A year ago, an AI strategy for most tech companies started and ended asking: where can we bolt AI onto what we already sell? The result was often a feature — a chatbot, a summarization tool or an "AI-powered" label on an existing product — layered onto a roadmap that remained the same.
“We’re finding the need for strategy to be more acute than ever,” Zosa said. “What’s clear is that there must be a more defensible moat, whether that comes from data, such as a system of record, differentiated expertise or stickiness from being deeply rooted in customer workflows." The jockeying for position to win in a disrupted market is what will drive tech valuation going forward.
AI strategy steps that have evolved to improve competitive positioning
- Strategy & alignment: Ensure every AI initiative ties to a specific value-creation component with quarterly checkpoints sponsors can track against the thesis.
- AI value identification & prioritization: Use cases are prioritized by feasibility and return.
- AI operating model & ownership: Clear roles, workflows and support structures embed AI into daily operations.
- Product roadmap and exit readiness: AI is speeding up product roadmap decisions, and reshaping what’s in them by surfacing product and repositioning moves.
“To ensure a competitive moat in an AI-forward market, companies are moving beyond automation toward an integrated approach across data, intelligence and orchestration,” Zosa said. “Those that differentiate are pulling in the right data, using AI to surface trends and insights, and then acting on that insight automatically, across systems. That's what solves a problem for the end user that no single tool could solve on its own — and it's much harder for a competitor to replicate."
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Proving the value of operational transformation
As portfolio companies prepare to sell, sponsors are recognizing the need to prove how AI is driving organizational impact through automation, productivity and performance.
“Buyers are examining whether targets have actually lowered costs, reshaped roles or enabled people to do more with the same headcount.” Zosa said. “It’s critical that organizations can demonstrate how they’ve moved AI from experimentation to something that actually transforms functions, processes and outcomes.”
Over the past several years, many of Zosa's clients have moved past the search for AI use cases — most, particularly in tech, already have tools in place and are successfully adopting them. Twenty-four percent of tech leaders reported they aren't experiencing adoption challenges, compared with just 3% of leaders across all industries.
But adoption doesn’t always equate to impact.
“Companies that are successfully adopting AI but are unable to measure its value are often working in silos,” Zosa said. “AI strategy, operating model design, data capabilities, adoption and deployment should be interconnected from the start.”
One reason for that disconnect is AI adoption alone doesn't reshape how work gets done. The organizations actually capturing value from AI are the ones that redesign roles around it: bringing in new tools, as well as redesigning roles to best leverage them.
Effective AI enablement follows three connected phases:
- An AI diagnostic and readiness assessment, which includes aligning executives, assessing AI’s potential impact on the market and security posture, defining product-strategy implications and developing an internal AI use case roadmap
- An AI pilot and mobilization, which includes governance, security, technical and data infrastructure readiness; designing and launching a pilot; evaluating and selecting tools; and mobilizing the organization around data foundations, tracking and functional sponsorship
- AI is adapted for results, which covers capturing pilot learnings, building a deployment roadmap, redefining operational and financial targets to reflect AI’s expected impact, redesigning workflows, roles and performance management, anchored by an AI governance council to sustain it
Client success story
AI strategy and enablement for a software development company
The challenge: A PE-owned SaaS provider for not-for-profit organizations needed to transform its engineering function with better efficiency, consistent metrics and greater AI adoption.
The approach: Grant Thornton helped the company use AI to restructure its engineering organization, tying AI adoption to key performance metrics and aligning leadership around clear priorities for where AI would drive the most value.
The result: The transformation delivered a 3x increase in productivity and scale. The team is now 90% AI-first, with AI tools writing more than 50% of the company’s code — above industry average, strengthening the company's scalability narrative and buyer appeal ahead of exit. AI adoption also grew 98%, with a 48% decrease in cost per line of code.
Protecting valuation in AI-enabled market
AI has changed what buyers are looking for, and it’s caused PE-backed companies to revisit their approach to AI-enabled growth. A tech company that treats AI as a bolt-on feature faces two key risks: its product can be replicated, and its operations can't prove the impact buyers now expect to see.
AI is becoming part of the investment thesis, not simply the product roadmap. Companies that can demonstrate differentiated products and measurable operational gains will be better positioned to defend valuation through diligence and strengthen exit outcomes.
"The winners in this next phase won't be the companies that adopted AI first," Zosa said. "They'll be the companies that used it to differentiate their products so that they become irreplaceable.”
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