2026年人工智能状况报告_AI执行时代（半年刊）_44页_2mb

2026 State of AI: Bi-Annual Snapshot Summary

Core Content

The 2026 State of AI report highlights the evolution of AI from a competitive advantage to a standard component in software development. The focus has shifted from building foundational models to delivering differentiated AI products at the application layer. This phase is marked by increased emphasis on execution, cost management, trust, and go-to-market strategies as key factors in establishing a durable competitive edge.

Main Points

• AI Maturity: The AI market has matured, with a growing focus on scaling AI into economically viable products rather than just experimentation.
• Application Layer Innovation: Over 70% of companies are now building vertical AI applications, emphasizing domain-specific workflows over generalized intelligence. 49% of teams cite application-layer innovation as their primary source of differentiation.
• Multi-Model Strategies: Companies are increasingly adopting multi-model strategies, using an average of 3.1 model providers to balance accuracy, cost, latency, and customization.
• Open Source and SLMs: Open-source models and small language models (SLMs) are gaining traction due to their cost-effectiveness and sufficient accuracy for automation tasks.
• Hallucination Mitigation: Reinforcement learning techniques such as RLHF and RLAIF are being used to improve performance and reduce hallucination risk as products scale.
• Data Foundations: Despite increased investment, most companies still report their data foundations as only "mostly" or "somewhat" ready, particularly at enterprise scale, indicating data readiness remains a key bottleneck.
• AI Economics: AI is taking a larger share of product investment, with high-growth companies allocating ~57% of their R&D budget to AI. Gross margins on AI products are projected to improve to ~52% in 2026, driven by cost management and model selection.
• Pricing Models: There is a shift toward consumption- and outcome-based pricing, with ~35% and ~18% of companies using these models respectively. Hybrid pricing models are emerging as the most pragmatic approach.
• GTM Strategies: Hybrid go-to-market (GTM) approaches are gaining popularity, combining enterprise selling with product-led experiences. Partnerships and channel strategies are becoming critical for pipeline generation and post-sale implementation.
• Talent and Workforce: AI-fluent talent is becoming more important, with a decline in administrative roles. R&D teams lead AI adoption, showing tangible productivity gains from AI tools.
• Forward Deployed Engineers (FDEs): FDEs are increasingly used to bridge the gap between product development and customer delivery, playing a crucial role in scaling AI solutions.
• Internal AI Adoption: AI is being used across various functions to improve productivity, testing, documentation, and content generation, with ~36% of code now written with AI assistance in high-growth companies.
• ROI Measurement: Companies are now measuring AI ROI through productivity gains, cost savings, and revenue uplift, rather than just experimentation.

Key Insights

• Agentic AI: Larger companies, especially those with $500M+ revenue, are leading in agentic AI deployments due to operational maturity, customer demand, and infrastructure capabilities.
• Model Selection: OpenAI remains the most widely used model provider, but Gemini has emerged as the second most popular.
• Execution Bottlenecks: Data readiness and model evaluation are still challenges, with manual feedback and limited automated frameworks being the norm.
• Cost Drivers: As AI products scale, model inference costs become the primary cost concern, while talent costs trend down.
• AI as a Force Multiplier: AI is not just a tool for cost-cutting but is also enhancing productivity and shaping workforce composition across organizations.

Summary of Changes

• Pricing Models: Companies are moving toward outcome- and consumption-based pricing, with ~37% planning to change their AI pricing model in the next year.
• Compensation Structures: AI has led to changes in compensation structures, including new commissions and adjusted quotas.
• GTM Complexity: The complexity of AI sales and the use of proof-of-concept (POC) phases are prompting a reevaluation of sales and compensation models.
• FDE Usage: FDEs are being used more frequently to support customer onboarding and product delivery, with median usage at ~40% of customers.

Conclusion

The 2026 State of AI report underscores that AI leadership is now defined by disciplined execution and strategic differentiation at the application layer. As AI becomes more integrated into product development, economic viability, trust, and scalability are becoming central to success. Companies that focus on cost management, multi-model strategies, and customer-centric GTM are better positioned to sustain long-term competitive advantages.