2025企业软件：AI应用趋势评估报告

What’s Inside 1Introduction CHAPTER 1 2Enterprise AI:Hype Meets Reality CHAPTER 2 5Competitive Analysis andVendor Impacts CONCLUSION 12Outlook: Execution, Cost inFocus has Arrived Introduction The enterprise software space is undergoing afundamental shift as AI agents make inroads in more andmore corporate workflows. Heading into 2026, leadingvendors are positioning themselves as comprehensiveagentic enterprise platforms, moving beyond automationand into true digital workforce capabilities at scale. Withcapex to implement AI tools surging across enterprises,management teams are focused on proving return oninvestment and productivity improvements. Despite the high stakes, AI integration remains complex,with organizational readiness a primary barrier. Adoptionpatterns show variance in deployment strategies, withorganizations realizing that AI implementation aloneis not enough to unlock true value. Expert analysishighlights execution challenges, with high failure ratesfor projects that lack clean data or clear metrics forsuccess. Effective AI usage in enterprise softwarerequires structured training, clear governance, andactive change management. This report examines the enterprise functions where AIis already proving valuable and explores the strategiesthat differentiate successful scaling efforts from thosethat falter after initial trials. CHAPTER 1 Enterprise AIAdoption Patternsand DemandAccelerants Usage of AI platforms and tools is proliferating across enterprises.As of 2025, 85% of organizations have integrated AI agents inat least one workflow, with 40% allocating budgets exceeding$1 million for AI agent initiatives. Many large enterprises are alsoplanning expenditures of $5 million or more in the next year foragent deployments. Generative AI is now embedded in workflowsfor many large enterprises and has become a core strategicpriority for executives. Several converging tailwinds are fueling this growth. Governanceby design has emerged as a growth engine, with Microsoft’s pivottoward “governance as foundation” signalling broader marketdemands. Another shift is demand creation, with rising AI ACV/ARR demonstrating strong commercial momentum at companieslike Oracle. Broker research in AlphaSense shows customersincreasingly shifting from summarization workflows (which requireone or two AI assists per task) to multi-step agentic workflows(29 to 30 assists). The infrastructure underpinning AI is improvingrapidly, with hyperscalers rushing to add more capacity asAI capabilities advance. Microsoft says it will raise its total AIcapacity by more than 80% this year and double its data centerfootprint over the next two years. Related Reading: Beyond the Current Surge: Long-Term Outlookfor Data Center Equipment Growth Though optimism is building into 2026, other data paints a morenuanced picture of adoption. Compliance costs, security risks,GPU and capacity constraints, and ROI proof requirements stillfactor heavily into enterprise decision-making. Broker researchshows just 17% of CIOs say they have adopted any sort of agenticAI, while 42% plan to do so within the next year. Separate surveydata finds only 18% of organizations have fully deployed AI intoproduction, despite 97% having a budget for agentic AI. Highfailure rates underscore the difficulty in sustaining production-grade AI deployments, with Gartner expecting over 40% ofagentic AI projects to be canceled by the end of 2027. Moreover, there is still room for further implementation of genAIworkflows. Broker research and enterprise statistics available inAlphaSense show that while most organizations now utilize one ormore genAI tools, just 1% of C-level executives say their firm hasreached genAI “maturity.” Even where fully deployed, measurablebusiness impact remains concentrated in a few well-definedworkflows — those with access to clean data, clear metricsdefining success, and established best practices. GenAI adoption patterns differ significantly by region. Uptakeis strongest amongNorth American firms, with a focus ondriving efficiency gains. Yet 42% of these firms acknowledgethat their genAI tools are still at an early stage. European firmsface structural challenges, including slow procurement cycles,fragmented digital strategies, and resource limitations that delayadoption timelines. In the Middle East, AI deployment centers onautomation, cybersecurity, and conversational tools. The disconnect between adoption and real impact is stark, with awhopping 95% percent of enterprise AI solutionsfailing to reachsustained deployment. Enterprises are increasingly realizingthat AI is not plug and play, and many are failing to bridge thegap between pilot programs and sustained, meaningful usage.Enterprises often start with narrow deployments, with only afraction of use cases reaching full-scale implementation. Evenwhere AI has been deployed successfully, it is often in the form ofgeneral-purpose large language models rather than task-specificag