2026年定义未来的七大AI趋势

Is your business ready for AIin 2026? Artificial Intelligence is no longer confined to the testing phase; it's rapidly becominga cornerstone of business operations across all industries. In fact, companies are not According to Gartner, by 2026, AI will no longer be a "nice-to-have" technology butwill become standard business practice, moving beyond optional pilot programs With AI spending expected to reach $500 billion globally by 2024,organizations that prepare now are positioning themselves to capture Those who delay will risk falling behind as competitors harness AI to gain operationalefficiencies and strategic advantages. As we approach this critical inflection point, it’sessential to understand which AI trends will define the business landscape. Here Infrastructure spending shifts to inference1 Companies are rebuilding their data centers around AI inference (when trainedAI models make predictions and decisions for real users), rather than training,reflecting one of the latest AI trends. This shift from just training new models reflects Gartner projects AI inference server spending will grow 42% annuallythrough 2028, while training server growth remains 24%. Training happens once or periodically when building models. Inference happenscontinuously when those models serve users, process transactions, or makedecisions. The volume difference is massive—a trained model might run millions of Inferencing and servicing Source: Gartner The diagram above illustrates that the Machine Learning pipeline flows frominitial data preparation through training to model deployment. However, the realbusiness value occurs in the final "inferencing and servicing" stage. This is wheredeployed models continuously process live enterprise data to generate predictions, The infrastructure requirements are different, too. Inference needs low latencyand consistent availability. Training can be batched and delayed. This drives demand Power consumption creates immediate constraints. AI inference workloads consume30-100 kilowatts per rack compared to 7-10 kilowatts for traditional servers. Most By 2028, Gartner estimates that over 80% of AI infrastructure spendingwill support inference workloads. Organizations that plan for inference-focused architecture today will deploy AI faster “partners who've already navigated the financial pitfalls and operational chaos.Choose your AI partner based on their experience with the messy realities, notjust their technical capabilities. Yaroslav MotaHead of AI Excellence at N-iX FinOps practices evolve to handleAI complexity2 AI project budgets consistently miss their targets, representing one of the mostconcerning AI industry trends affecting organizations today. Gartner research reveals that generative AI initiatives can experiencebudget and cost estimate overruns of up to 1000%. This isn't an outlier; it's becoming the norm for organizations attempting AIimplementations without proper cost controls. The cost variations stem from AI's multifaceted nature. Projects involveinfrastructure and cloud resources, model hosting and usage fees, data workloads,and application development. The dominant method of using GenAI models isthrough cloud providers. These services use pricing based on parameters that are Traditional IT cost management falls short because it wasn't designed forconsumption-based AI services. Most organizations lack visibility into AI spending The financial impact is forcing change. By 2027, Gartner predicts that 60%of large enterprises will adopt and apply FinOps practices for their AI initiatives. The 2025 Gartner CIO and Technology Executive Survey found that57% of respondents attach high importance to helping business areas However, the 2023 Gartner Financial Governance and Sustainability Survey revealedthat 69% of organizations with financial governance programs aren't using tools Organizations implementing AI-specific FinOps practices early report betterbudget accuracy and lower overall costs than those using traditional IT financial Agentic AI transforms business operations3 Organizations are rapidly adopting AIagents that can make decisions and takeactions autonomously, making this one Gartner predicts that by 2028, 33%of enterprise software will include Agentic AI refers to goal-drivensoftware entities authorized byorganizations to make decisions and actsemiautonomously or autonomouslyon their behalf. Unlike robotic processautomation, agentic AI doesn't require The business case is compelling. By 2030, AI agents will autonomously make 15% of day-to-day supplychain decisions, freeing humans to focus on critical decisions. In customer service, AI agents handle complex workflows that previously requiredhuman intervention. Furthermore, AI will hold 67% of B2B procurement by 2030,requiring companies to structure their offerings as machine-readable data instead Agentic AI systems use memory, planning, sensing, tooling, and guardrails tocomplete tasks a