Technology companies focusing on artificial intelligence (AI) initiatives are investing significantly in core capabilities like data quality, governance, AI-ready talent, and change management. A recent report by Gartner highlighted that these companies invest up to four times more in these foundational areas, despite only 39% of technology leaders being confident about the financial returns from their AI investments.
The report emphasized six key shifts necessary for realizing the value of AI, including developing AI-first data and analytics capabilities, restructuring teams for human-AI collaboration, and enhancing context and data infrastructure to support AI systems. It also stressed the importance of integrated engineering practices, trust-based governance models, and moving towards long-term value creation instead of traditional return on investment metrics.
Organizations with advanced AI-ready data and analytics capabilities are experiencing significantly higher business outcomes, with up to 65% improvement in areas like revenue growth and cost optimization, according to Gartner. Rita Sallam, a Distinguished VP Analyst at Gartner, highlighted the pivotal role of Data and Analytics (D&A) leaders in achieving AI value ambitions. She mentioned that D&A leaders must focus on building strong foundational capabilities, including trusted data and context-driven intelligence to create value effectively.
Challenges persist in AI implementation, with only 23% of IT leaders expressing high confidence in managing security and governance while deploying generative AI tools. Sallam emphasized the criticality of trust in AI models, stating that without trust in the data, outputs, and decisions of AI models, the value derived from AI is compromised.
Another recent report pointed out a surge in job cuts within tech firms in the first quarter of 2026, with over 73,200 layoffs announced by 95 companies. Major companies like Snap Inc., The Walt Disney Company, Meta Platforms, and Oracle Corporation have initiated layoffs as part of operational streamlining efforts to reduce costs and shift resources towards artificial intelligence.
