High adoption of artificial intelligence among technology companies in the middle market has not eliminated implementation challenges and operating model constraints that could inhibit sustained AI growth.
Results from the RSM Middle Market AI Survey 2026 show that many tech companies are encountering familiar obstacles such as data quality issues, talent gaps and legacy-system constraints.
Tech industry respondents described their organizations’ current state of AI adoption as follows:
Tech industry respondents said current AI investments are focused on:
Any challenges to deeper AI integration will likely be felt more acutely by tech companies since the industry has largely moved past the adoption phase and firmly operates in the implementation phase of AI’s lifecycle.
The greatest inhibitors to AI deployment among tech industry respondents were:
As to why AI pilot projects fail to scale, the top reasons attributed to failure given by tech industry respondents—whose organizations initiated AI pilots in the past 24 months and report moderate or limited success (50%)—were data quality issues (68%) and integration challenges (57%).
Tech industry respondents appear relatively less concerned about one potential inhibitor: security and privacy (25%). This finding suggests that for the tech industry, data readiness is a more pressing constraint than data risk.
Internal strategic clarity is integral for the longer-term success of AI implementation, and in this regard, tech companies are well-positioned for sustained growth, according to the survey’s findings.
Tech respondents reported strong indicators of AI governance and strategic oversight:
These figures point to a centralized, intentional ownership of AI strategies. When IT departments own the AI agenda, execution tends to be faster and more disciplined since those who develop and deploy the technology are the same people who establish the overall strategy.
This approach does contain some inherent risks, as IT-anchored AI programs often optimize for engineering wins and productivity gains without involving other parts of the organization that could re-shape how these programs operate.
Despite enthusiasm among tech companies regarding AI adoption, some key integration hurdles remain.
Fifty-five percent of tech respondents cited improved employee efficiency as the top outcome that would signal a successful AI initiative in the next 12 months. However, that aspiration clashes directly with the top challenges businesses face—data quality, talent gaps and integration with legacy systems, as previously noted.
As business leaders in the tech industry evaluate their AI strategies, targeted investment in data foundations and internal capabilities will be critical to address these potential inhibitors.
The firms that get the most from AI will align their ambitions with the strengths of their existing systems—and build from there.
Discover how organizations are approaching AI adoption, overcoming barriers and preparing to scale enterprise value.
