2025年人工智能状况报告

The Builder’s Playbook 2025 State of AI Report Follow our research Growth & EfficiencyExplore our research on best-in-class SaaS growth and Go-To-Market Series Navigating Today’s Public MarketsThe metrics that matter and the market realities of 2025 and efficiency beyond Guidesto sales, customer success, marketing compensation– and more The SaaS GlossaryA guide to understanding and tracking key SaaS metrics The ICONIQ Enterprise FiveKey performance indicators of Enterprise SaaS companies Introduction We believe that building and operationalizing AI products is the new frontier of competitive advantage – and that the voices of thearchitects, engineers, and product leaders driving this work deserve their own spotlight. While last year’s State of AI report centeredon the buying journey and enterprise adoption dynamics, our 2025 report pivots squarely to the “how-to”: what it takes to conceive,deliver, and scale AI-powered offerings end-to-end. This year’s report unpacks core dimensions of the builder’s playbook:1.Product Roadmap & Architecture: The emerging best practices for balancing experimentation, speed to market, and performance at each stage of model evolution2.Go-to-Market Strategy: How teams are aligning pricing models and go-to-market strategies to reflect AI’s unique value drivers3.People & Talent: Building the right team to harness AI expertise, foster cross-functional collaboration, and sustain long-terminnovation4.Cost Management & ROI: Strategies and benchmarks for spend associated with building and launching AI products5.Internal Productivity & Operations: How companies are embedding AI into everyday workflows and the biggest drivers ofproductivity unlock Drawing on our proprietary survey results alongside in-depth interviews with AI leaders across the ICONIQ community, the 2025State of AI report offers a blueprint for anyone tasked with turning generative intelligence from a promising concept into adependable, revenue-driving asset. Explore Our AI Perspectives Table of Contents Internal Productivity Budget40Budget Sources41AI Access and Usage42Key Purchasing Considerations43Deployment Challenges44Number of Use Cases45Top Use Cases46Attitude Towards Internal AI Adoption48Tracking ROI49 Types of AI Products9Model Usage and Key Purchasing Considerations11Top Models Providers13Model Training Techniques14AI Infrastructure15Model Deployment Challenges16AI Performance Monitoring17Agentic Workflows18 BuildingGenerative AIProducts InternalProductivity AI Product Roadmap20Pricing21AI Explainability and Transparency24AI Compliance and Governance25 Go-to-MarketStrategy &Compliance LLM & AI Application Development51Model Training & Finetuning52Monitoring & Observability53Inference Optimization54Model Hosting55Model Evaluation56Data Processing & Feature Engineering57Vector Databases58Synthetic Data & Data Augmentation59Coding Assistance60DevOps &MLOps61Product & Design62Other Internal Productivity Use Cases63 Dedicated AI/ML Leadership27AI-Specific Roles and Hiring28Pace of Hiring29% of Engineering team Focused on AI30OrganizationStructure AI Development Spend32Budget Allocation33Infrastructure Costs34Model Training Costs36Inference Costs37Data Storage & Processing Costs38AI Costs Respondent Firmographics DataSources& Methodology This study summarizes datafrom an April 2025 survey of 300executives at software companiesbuilding AI products, includingCEOs, Heads of Engineering,Heads of AI, and Heads ofProduct. Throughout this report, we alsoweave in perspectives, insights,and what we believe to be bestpractices from AI leaders fromthe ICONIQ community. In this report, select companies are referred to as “highgrowth companies” because they meet the following criteria AI Product Traction: AI product is in General Availability orScaling Revenue: At least $10M in annual revenue All industry perspectives sharedin this report have beenanonymized to protect company-level information. Topline Growth: 100%+ YoY revenue growth if <$25MRevenue, 50%+ YoY revenue growth if $25M-250M Revenue,30%+ YoY revenue growth if $250M+ Revenue AI Maturity Most SaaS companies have evolved to add new AI capabilities and products; the following pages will dive into how AI-enabled and AI-native companies are approaching product development Building GenAIProducts Stage of Primary AI Product AI-native companies are further along in the development cycle compared to AI-enabled peers, with around 47% of productsanalyzed having reached critical scale and proven market fit This begs the question whether AI-native orgs may be structurally betterequipped - through team composition,infrastructure, or funding models - tovalidate product-market fit and scaleeffectively, and perhaps leapfrogging thetrial-and-error phases that slow down AI-enabled companies retrofitting AI intoexisting workflows. Types of AI Products Agentic workflows and the application layer are the most common types of products being b