AI budgets are set to rise by 36% this year as businesses ramp up their AI investments, with average spending topping $1 million annually. However, many companies are struggling to attract or retain the talent needed to scale their AI initiatives.

AI-skilled professionals are commanding salaries between $150,000 and $200,000, and 35% of companies say rising compensation is the biggest barrier to filling AI roles. Meanwhile, a lack of qualified candidates, particularly in cloud and data engineering, continues to slow project timelines and fuelling internal capability gaps. In total, 32% of companies cite skills shortages, and 13% say they don’t have the in-house expertise to even assess applicants effectively.

Amid the rush to invest, boardrooms are also facing growing uncertainty about ROI. Only half of respondents said they were confident in their ability to evaluate returns from AI initiatives, even as they continued to scale their spending. The disconnect between ambition and capability is fuelling concerns about whether organisations are building sustainable, measurable AI success.

Stuart Harvey, CEO of Datactics, commented: “AI success doesn’t come from throwing money at tools alone, but by the people who build, manage, and govern those systems effectively. Companies are chasing specialist hires while overlooking the fundamentals of governance, explainability, and the quality of the data feeding their models, which, without these, the cost of AI rises while clarity around ROI vanishes.

“Instead of entering bidding wars for scarce talent, organisations should be looking into investing in long-term capability by upskilling their existing teams in data quality, model oversight, and governance. AI performance depends on the integrity of the data it processes, and the people who understand how to prepare and structure data responsibly that will make AI trustworthy, scalable, and compliant.

“The companies that will win in the next phase of AI adoption aren’t just the ones with the biggest budgets, they’re the ones with the most disciplined, data-driven foundations. Without strong data foundations, even the most advanced models will fail to deliver meaningful results.”

Analysts expect a stronger focus on building transparent, auditable AI systems that can demonstrate value under scrutiny. However, without investing in internal expertise and data infrastructure, many businesses risk inflating their AI costs without improving their outcomes.

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