Global AI spending is forecast to reach $2.52 trillion in 2026, a 44% year-over-year increase, according to Gartner. Despite this surge, research suggests up to 90–95% of AI projects fail to deliver measurable value. As generative AI slides into Gartner’s “Trough of Disillusionment,” boards are questioning returns and demanding financial clarity.
Gartner’s John-David Lovelock argues that this phase is not a collapse but a correction. Organizations must move away from speculative “moonshot” AI experiments and focus on three practical priorities.
First, build capacity deliberately. AI infrastructure investment is accelerating, with AI-optimized servers and data center expansion driving major spending increases. Enterprises must decide whether to own AI infrastructure, rely on hyperscale cloud providers, or consume AI through APIs. The strategic question is ownership: which AI capabilities differentiate the business and require control, and which can be treated as commodities.
Second, create strong partnerships. Most organizations should leverage existing technology partners instead of building bespoke AI systems. AI adoption in 2026 will increasingly come through established enterprise software stacks. Structuring partnerships around value-based or outcome-based pricing aligns incentives and improves accountability for ROI.
Third, avoid random exploration. Unfocused experimentation leads to stalled pilots. Successful AI programs tie initiatives directly to defined business outcomes, supported by clean data, redesigned workflows, and internal stakeholder alignment. Scaling AI requires coordinated effort across partners, processes, and governance structures.
The shift from hype to disciplined execution defines the next stage of AI maturity. Enterprises that prioritize infrastructure clarity, partner alignment, and outcome-driven strategy will convert AI investment into sustainable business value.
Source:
https://www.zdnet.com/article/how-to-target-your-ai-investments-gartner/
