2025 AI Adoption Trends: What DeepSeek’s Rise Means for Enterprises

With the rapid development of AI technology, the rise of DeepSeek in China not only shows its competitiveness, but also reveals several important trends in future AI development. This article will give a quick explanationThe Rise of Open Source AI Models and the Meaning of Enterprise Applications、The Risks of AI Technology Popularity、Change in corporate talent needs, andOther AI-related trends

1. What is the significance of the rise of DeepSeek for the AI industry and AI applications?

The rise of DeepSeek represents a major breakthrough in AI technology, they are inLess compute resources are used when performance compares to GPT-4 (1/30), which demonstrates the potential for AI technology innovation and foreshadows a radical change in the industry. DeepSeek's success has also driven the trend of open source AI models, enabling more enterprises to carry out AI development at a lower cost, breaking the technology monopoly of large tech companies.

In addition,If Nvidia launches Project DIGITS in May of this year, will also be a revolutionary hardware innovation that we will be able to useDeploy a local language model at a low costand will drive more enterprises to further explore local AI solutions and once again accelerate the adoption of open source AI models, which not only reduce long-term operating costs, but also address the growing importance of data security and privacy protection by enterprises.

2. what are the benefits of open source AI models for enterprises?

Open source AI models bring a number of benefits to businesses, including:

• Reduced Development Costs: Businesses can customize development based on open source models, greatly reducing technical thresholds and costs, saving 60-80% of development costs
• Accelerate Deployment Time: Open source models can accelerate deployment time by 40-50%, enabling enterprises to adopt AI technologies faster
• Enhance innovation flexibility: Turing Award winners highlight the advantages of open-source models over the AI competitions between China and the United States. The primary benefits of open-source models are their transparency and modifiability, which empower enterprises to tailor solutions to their specific needs, thereby enhancing innovation flexibility.
• Custom Development: Businesses can fine-tune AI models based on their needs and create custom AI solutions

3. What are the risks associated with the popularization of AI technology? How should businesses respond?

The ubiquity of AI technology comes with several risks, including:

• False information and the spread of Deepfake : Open-source AI lowers the threshold for generative AI technology, which can make fake news, fake content more difficult to identify
• Increased risk of automated attacks:Cyber attackers can use AI technology to develop more targeted fraud tools and malware
• Privacy and data security issues:Decentralized AI reduces the risk of data leakage, but how to operate within compliance remains a challenge
• AI Ethical and Regulatory Challenges: Businesses need to consider fairness, transparency, and potential social impact when using AI
• Businesses should establish a comprehensive risk assessment frameworkTo ensure that technology applications can both foster innovation and protect social justice and personal privacy, and establish relevant committees to develop data usage guidelines and establish decision tracking mechanisms

4. Other AI technologies worthy of attention: NPU and edge AI

What role do NPUs (neural processing units) play in AI development? How is it different from a GPU?

NPUs (Neural Processing Units) are hardware designed for AI reasoning that can perform complex AI operations with lower power consumption, especially for edge devices such as smartphones, IoT devices, etc. The main difference between an NPU and a GPU (graphics processor) is that:

• GPUs for large AI model training but high energy consumption and less efficient in reasoning operations
• NPU is designed for AI reasoning with lower energy consumption and greater efficiency, suitable for performing AI tasks on edge devices. The development of NPU drives the rise of edge AI, enabling more complex AI applications to run directly on endpoint devices, reducing reliance on cloud computing

In addition, NPU's reconfigurable architecture is more flexible than ASIC (Dedicated Integrated Circuits), enabling it to adapt to different AI application scenarios and may become a key technology for AI hardware in the future.

What is Edge AI? What are its advantages?

Edge AI is a technology that decentralizes AI computing to end devices such as smart cameras, industrial robots, etc., to reduce reliance on cloud computing. Its advantages include:

• Reduce latency : EDGE COMPUTING REDUCES DATA TRANSFER TIME TO THE CLOUD, MAKING AI APPLICATIONS MORE REAL-TIME
• Reduce data security risks  : Data is processed locally, reducing the risk of leakage during data transmission
• Improve energy efficiency: Edge computing reduces reliance on cloud servers and reduces overall energy consumption
• Enhanced privacy protection : Sensitive data is handled locally to protect user privacy

5. Besides open source and NPU, what other AI technology trends are worth paying attention to?

• Decentralization and customization of AI applications: AI applications will be more decentralized and can be developed custom-built according to business needs
• Continuous evolution of Transformer architecture: Transformer model has made significant improvements in both performance and application, reducing training costs and improving compute efficiency through technology optimization
• Edge AI and Distributed Computing: Future AI could reduce reliance on cloud and improve on-premises data processing
• Stronger local computing capabilities: More complex AI applications can run directly on endpoint devices

6. What has changed in the needs of future enterprises for talent? What skills will become more important?

The company's need for talent will No longer limited to traditional technical capabilities, instead requires complex professional skills, possessed critical thinking and AI application integration capability professional talent will be more demanding, such as:

• AI Solution Architect: Responsible for designing and building AI systems
• Data Ethics Expert: Responsible for ensuring that AI applications comply with ethics and regulations
• AI Systems Integration Consultant: Responsible for integrating AI technology into existing systems

7. How should enterprises cope with the rapid development of AI technology? Our Action Suggestions

Businesses should assess AI integration opportunities early, create forward-looking technology layouts, and accelerate innovation applications. Specific action recommendations include:

1. Early Assessment of AI Integration Opportunities: Analyze business processes to identify areas where AI can be introduced
2. Develop a clear AI adoption strategy, set clear goals and route maps
3. Develop your team's AI application skills, strengthen the talent development system: provide training and resources to enhance teams' understanding and application of AI technologies
4. Assess the needs of AI applications,Develop customized AI solutions based on your business needs
5. Establish a comprehensive risk management mechanism, to ensure the effective implementation of AI

8. Support and services we provide: AI Agent ＆Integration Automation Expert

As PGi experts who have accumulated over 6 years of RPA process automation, our team can help you:

1. Assess the needs of AI applications in your business: Learn more about your business and identify AI application scenarios
2. Design customized AI solutions: Develop custom AI models and tools based on your specific needs
3. Provide technical introduction and team training support: Ensure the smooth implementation of AI solutions and improve the operability of the team
4. Integrated Consulting and Introduction Framework for AI Agent and Automation:We offer both cloud-based and land-based solutions for a variety of flexible applications in no-code scenarios

The future belongs to those who prepare for it today. In the face of the AI wave, now is the best time for businesses to take action. With a deep understanding of these key trends, businesses can gain a head start in the next wave of technology revolution and create a lasting competitive advantage.

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Basic Glossary Reference:

• A.I. Artificial Intelligence: Techniques that simulate human intelligence through computer programs, including the ability to learn, reason, solve problems, etc.
• GPT-4 : Advanced large language model developed by OpenAI, known for its powerful text generation and comprehension abilities
• Open source model Open-Source Model: A model with open source code that allows users to modify, use, or distribute, facilitating technology sharing and innovation
• Computing Resources Computational Resources: Hardware resources used to perform computational tasks, including processors, memory, etc.
• NPU Neural Processing Unit: A hardware processor designed to accelerate AI computing with high performance and low power consumption
• GPUs (Graphics Processing Unit, graphics processor): Originally used to process images, but also often used to accelerate AI model training
• Edge AI (Edge AI): Drive AI computing to data-generating devices instead of centralized cloud servers for efficiency and security
• Transformer architecture (Transformer Architecture): A deep learning model architecture widely used in areas such as natural language processing and computer vision
• Deepfake: Fake videos or audio generated by AI are difficult to distinguish from fake and can be used to spread fake messages or scams
• algorithms Algorithm: A set of clear steps or rules to solve a problem or complete a task
• Digital Transformation: Companies use digital technology to improve processes, increase efficiency, and innovate business models
• Data Privacy: to protect personal information from unauthorized access, use, or disclosure
• ASIC Application-Specific Integrated Circuit: Integrated Circuits tailored to specific applications
• Decentralized AI (Decentralized AI): Decentralize AI computing across multiple devices or network nodes, reducing reliance on a single center