Companies and investors continue to pour billions of dollars into artificial intelligence. According to Gartner, worldwide AI spending is forecast to total $2.59 trillion in 2026, a 47% increase year-over-year. Just under half of that investment (45%) is going toward AI infrastructure, including the data centers needed to support AI’s growth worldwide.
As AI investment accelerates, so do expectations for results. Tech executives frequently highlight advances in their AI capabilities. You have probably seen numerous press releases claiming how their new AI systems can manage complex workflows fully autonomously. But while AI adoption continues to expand, new research suggests many organizations are still grappling with the challenges of turning ambitious AI goals into production-ready solutions.
The Intense Pressure to Overstate AI Progress
According to a new AI Execution Gap survey from BairesDev, a leading software development company, approximately 79% of top American technology leaders report pressure to overstate or exaggerate AI progress to meet the expectations of other executives and stakeholders. Nearly 47% of respondents say most of the pressure comes from the board of directors or the company’s highest-ranking executives.
“Most organizations are not failing at AI because they lack ambition or investment,” said Nacho De Marco, CEO and Co-Founder of BairesDev. “The pressure to show results before the foundations are ready is real, and it comes from the top.”
Stakeholders are reportedly starting to demand a faster return on their investments. To justify large capital expenditures, companies are increasingly prioritizing quick, visible results while foundational work involving security, data privacy, and regulatory compliance often receives less attention. Over half (51%) of respondents cited these three issues as the biggest obstacles to delivering AI successfully.
The consequences of top-down pressure show up in project outcomes. Roughly 88% of respondents reported that changes in executive priorities had disrupted at least one of their AI initiatives in the last year. About 54% reportedly had at least one AI initiative behind schedule before reaching production, and 34% claimed that the scope of one or more AI projects was reduced before they could be delivered.
“The execution gap shows up when teams are asked to deliver production-ready AI without the data, governance, security, and integration foundations to support it,” said Justice Erolin, Chief Technology Officer of BairesDev.
Frequent shifts in executive priorities can create additional challenges for engineering teams. When organizations redirect resources toward new AI initiatives before existing projects are fully validated or integrated, deployments can become more difficult to scale, secure, and maintain over the long term.
The Real Delivery Timelines for AI Projects
While executives communicate AI progress to investors and stakeholders, engineering teams are ultimately responsible for turning those promises into production-ready systems. The survey found that 66% of tech leaders needed four or more months to move an AI project from pilot to production, with 27% needing seven to twelve months and 9% needing thirteen months or longer. That is a significant investment of time for any initiative, and it suggests production is consistently harder to reach than initial plans account for.
These challenges are hindering the movement of AI projects from pilot to production, with 54% of respondents reporting at least one initiative that arrived significantly behind schedule, even as organizations continue to move AI projects into production. At the same time, 73% report at least one initiative that launched on schedule during the past year. Together, these findings suggest that while organizations are making progress with AI deployments, many still face challenges translating pilot projects into scalable production systems.
What is Slowing Down AI Innovation?
Tech leaders have a responsibility to ensure that their AI systems do not cause data privacy breaches, intellectual property leakages, or non-compliance with evolving government regulations. That is why vigorous testing and quality control measures are required before any new AI deployment.
Almost half (46%) of tech leaders surveyed had concerns about data quality and readiness. After all, generative AI models depend on accurate, well-managed data pipelines to deliver high-quality results to end users. But since many enterprises still run legacy systems with disorganized data and information, it is difficult to upgrade those systems for AI without spending time fully vetting the data first.
The complexity of integrating enterprise legacy systems with large language models is another huge setback, according to 43% of survey respondents. Most of these legacy systems use decades-old software that came out before AI even existed. Engineering teams have to spend a lot of time working through the technical friction between the old and new systems.
Furthermore, about 32% of respondents reported significant talent shortages in the AI sector. That concern aligns with broader labor market trends. ManpowerGroup’s recent survey of 39,000 employers across 41 countries found that 72% of employers struggle to fill roles, with AI skills ranking as the hardest-to-find capability for the first time. As demand continues to outpace supply, AI deployment timelines are likely to remain under pressure.
The Bottom Line
The findings of BairesDev’s AI Execution Gap survey indicate that many organizations are still building the foundational infrastructure required for reliable, production-ready AI. Notably, 83% of respondents report positive ROI from their AI initiatives — though most measure it through operational metrics like employee productivity and efficiency rather than hard-dollar returns, so the full financial picture will take longer to come into focus.
Long-term success will depend less on ambitious promises and more on disciplined execution. Companies that prioritize governance, data readiness, security, and realistic timelines are better positioned for sustainable results than those chasing the next trend. Ultimately, the future of AI will be determined not by who makes the boldest claims, but by who can consistently turn experimental projects into secure, compliant, and scalable business solutions. The challenge for many organizations is ensuring that execution keeps pace with expectations.
Featured image source: Magnific
