Author: ICONIQ
Compiled by: Tim, PANews
The development of artificial intelligence has entered a new chapter: from a hotly debated topic to practical implementation. Building scalable AI products is becoming the key battleground for competition. The 2025 AI Status Report (Builder's Handbook) shifts the focus from technology adoption to practical implementation, providing an in-depth analysis of the complete solution from conception and implementation to large-scale operation of AI products.
Based on exclusive research findings from April 2025 involving 300 software company executives and in-depth interviews with AI leaders in the ICONIQ community, this report provides a tactical roadmap aimed at transforming the intelligent advantages of generative artificial intelligence into sustainable business competitiveness.
The report distills five key chapters and how they will help teams actively build AI applications.
1. The artificial intelligence product strategy has entered a new stage of maturity
Compared to companies that only integrate artificial intelligence into existing products, AI-led companies are bringing products to market faster. Data shows that nearly half (47%) of AI-native companies have achieved critical scale and are confirmed to have market fit, while only 13% of companies integrating AI products have reached that stage.
What they are doing: intelligent agent workflows and vertical applications are becoming mainstream. Nearly 80% of AI-native developers are laying out intelligent agent workflows (i.e., AI systems capable of autonomously performing multi-step operations on behalf of users).
How they are doing it: Companies are converging on multi-model architectures to optimize performance, control costs, and match specific application scenarios, with each respondent averaging 2.8 models in customer-facing products.
2. The evolving AI pricing model reflects unique economic characteristics
Artificial intelligence is changing the way businesses price their products and services. According to our survey, many companies are adopting a hybrid pricing model that adds a usage-based billing on top of a base subscription fee. Some companies are also exploring pricing models that are entirely based on actual usage or customer outcomes.
Currently, many companies still offer AI features for free, but more than one-third (37%) of enterprises plan to adjust their pricing strategies in the coming year to make prices more aligned with the value customers gain and their usage of AI features.
3. Talent strategy as a differentiated advantage
Artificial intelligence is not just a technical issue but also an organizational issue. Currently, most top teams are forming cross-functional teams composed of AI engineers, machine learning engineers, data scientists, and AI product managers.
Looking ahead, most companies expect that 20-30% of their engineering teams will focus on artificial intelligence, with this ratio expected to reach up to 37% in high-growth companies. However, survey results show that finding suitable talent remains a bottleneck. Among all AI-specific positions, hiring AI and machine learning engineers takes the longest, with an average filling time exceeding 70 days.
There are differing opinions on recruitment progress. Although some recruiters believe progress is going well, 54% of respondents indicate that progress is lagging, with the most common reason being a lack of qualified talent resources.
4. The surge in artificial intelligence budgets reflected in the company's profit and loss statement
Companies adopting artificial intelligence technologies are investing 10%-20% of their R&D budget into the AI field, and companies across all revenue ranges are showing a continuous growth trend by 2025. This strategic shift increasingly highlights that AI technology has become a core driving force in product strategic planning.
As the scale of artificial intelligence products expands, the cost structure often undergoes significant changes. In the early stages of product development, human resource costs are usually the largest expense item, including recruitment, training, and skill enhancement costs. However, as the product matures, cloud service costs, model inference costs, and compliance costs will account for a major portion of expenditures.
5. The scale of internal artificial intelligence applications in enterprises is expanding, but distribution is uneven
Although most surveyed companies provide around 70% of employees with access to internal AI tools, only about half of them actually use these tools regularly. In larger, more mature organizations, the challenge of encouraging employees to use artificial intelligence is particularly prominent.
High adoption rate companies (i.e., more than half of employees using AI tools) have deployed artificial intelligence in an average of seven or more internal application scenarios, including programming assistants (77% usage), content generation (65%), and document search (57%). The efficiency gains in these areas range from 15% to 30%.
The AI tool ecosystem, while still fragmented, is gradually maturing
We surveyed hundreds of companies to understand the technical frameworks, libraries, and platforms currently running in production environments. This report is not a simple ranking but a true reflection of the tools adopted by developers across different fields.
Here is a brief overview of the most commonly used tools arranged in alphabetical order:
