Many enterprises are off to the races with agentic AI, snapping up technology in areas such as software development, customer support, IT operations and cybersecurity. But the return on this type of AI investment remains murky for some: A very sizable 94% of respondents said their organizations lacked a consistent, enterprise-wide framework for evaluating AI’s ROI.

What’s more, only 16% of respondents said they see a positive ROI on more than half their AI projects, while 31% see ROI on less than a quarter.

Those were some of the key takeaways from our just-published Battery Ventures State of Enterprise Tech Spending survey, which includes responses from 100 senior technology leaders representing more than $66 billion in annual technology spend. Overall, the survey still indicated a robust appetite from enterprises to continue spending on AI. Trends in what types of technology these leaders are evaluating, testing and buying—including new AI tools and platforms—provide key insights for tech startups trying to boost sales and scale their companies.

Indeed, enterprise spending on new technologies, notably AI, is brisk today: Seventy-six percent of respondents said they’re already in production with generative AI, with 100% planning to deploy AI programs within two years. Not a single respondent said they were cutting AI spending. An impressive 81% plan to increase overall tech spending over the next 12 months, up from 65% in December 2025.

In addition, 49% of respondents said they’re actively deploying agentic AI, up from 33% six months ago. Conviction in this type of tech remains high among buyers, with 50% of respondents saying they’re already scaling agentic workflows across business functions.

But the sobering news is that many of these buyers are still struggling with AI ROI, at least at the enterprise level. Of those who said they still don’t have an enterprise-wide, AI ROI framework, 42% said they were measuring ROI inconsistently across the organization; 43% were still defining their measurement approach; and nine percent reported they still have no way to measure ROI yet.

Still, 53% of enterprises saw a clear ROI on AI in general terms, with the industries most convinced of AI’s financial and operational impact being IT and healthcare.

Other findings:

• Humans remain firmly in the AI loop today. Of our respondents, 70% said humans were overseeing or making final decisions on their AI projects; only five percent said they expect fully autonomous systems to be deployed in their enterprises in the next 12 months.
• Not all spending on AI is net new. Seventy-eight percent of the survey’s respondents said they are funding AI at least partly by reallocating existing tech budgets. Forty-six percent said they blend net-new spending with reallocation.
• With AI technology changing so rapidly and enterprises still experimenting with use cases, enterprise contract lengths have shortened significantly. Only 29% of our respondents said they were signing tech contracts of 25 months or longer. Historically, 64% of organizations signed contracts of 25 months or .
• Still, enterprises seem to believe AI represents a core, long-term, structural shift in their operations, particularly in software development. In the survey, 84% said they expect AI to materially reshape their software-delivery cycle within two years. This is one factor driving increasing technology budgets overall.
• And AI has risen to the top of our respondents’ list of their top five enterprise priorities. Right now, those priorities are generative AI; agentic AI; cloud infrastructure; data warehousing; and cybersecurity.
• Finally, we saw some emerging trends in which LLMs enterprises are increasingly turning to. A strong 75% of respondents ranked Claude, from Anthropic, among their top three model choices, ahead of Microsoft (72%) and OpenAI (58%). Still, Microsoft’s Azure OpenAI/Copilot product remains the single most-used model.
• Code generation and testing are the top engineering re-evaluations by a wide margin: 82% of enterprises are re-evaluating code generation and refactoring, and 75% are re-evaluating testing and QA automation. These are the two highest numbers in the entire re-evaluation dataset. Code security and vulnerability detection is at 70%. Together these three paint a clear picture of where AI is restructuring the engineering stack and where opportunities for AI startups exist.
• Security review is the biggest breaking point in the AI SDLC, according to the survey. When asked to name the biggest SDLC breaking points as AI agents become more common in the delivery pipeline, respondents named security review and vulnerability management as their top concerns, followed by compliance and auditability and developer skill degradation.
• AI governance is moving to the C-suite, but ownership is still fragmented. Twenty-five percent of enterprises now have a “chief AI officer”, up from 23% in Q3 2025. But 54% still have AI sitting under existing tech leadership with no dedicated owner, and seven percent have no formal AI leadership role at all.

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Les informations contenues dans ce commentaire de marché sont basées uniquement sur les opinions de Scott Goering, Evan Witte et Nick Elsner, et rien ne doit être interprété comme un conseil d'Investissements. Ce matériel est fourni à titre d'information et ne constitue en aucun cas un conseil juridique, fiscal ou en matière d'investissement, ni une offre de vente ou une sollicitation d'une offre d'achat d'une participation dans un fonds ou un véhicule d'investissement géré par Battery Ventures ou toute autre entité Battery. Les opinions exprimées ici sont uniquement celles des auteurs.

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