Sunset
As businesses navigate an increasingly digital landscape, generative AI is becoming the cornerstone of enterprise applications. This transformation promises to increase operational efficiency, drive innovation and reshape the way organizations interact with technology. Understanding these changes is essential for executives and technology leaders who aim to stay competitive in a rapidly evolving market.
Here are the top five generating AI trends for 2025:
1. From AI-Infused to AI-First Applications
Generative AI, a branch of artificial intelligence that creates new content, is moving beyond simple integration into existing applications. In 2024, many applications began to include generative AI as additional features, such as built-in chatbots or assistant agents. The transition from AI-first to AI-first applications is predicted to deepen in 2025, with AI becoming integral to application design. Developers will treat AI as an integral part of the application stack and rely on large language patterns for intelligent workflows. Generative AI will no longer be limited to chatbots or AI assistants that use RAG to answer questions. Instead, it will be a core pillar of modern applications.
An example of this trend is how coding assistants are evolving. While assistants like GitHub Copilot and Tabnine were available as plugins and add-ons, AI-first integrated development environments like Cursor and Windsurf tightly integrated code generation into the native development workflow. This trend of embracing generative AI will naturally extend to software beyond coding tools and IDEs.
Key takeaways – 2025 will mark the beginning of AI first application development trends.
2. Setting up the Service as Software
The concept of service as software is another key development. Traditionally, software empowered users by providing information and insights, leaving the execution of tasks to business users. Customer relationship management systems, for example, provide valuable data and analytics, but require users to negotiate with customers and customize proposals or contracts manually. In contrast, AI agents are advancing to bridge this gap by handling these last-mile activities. These agents can act on the insights provided by the software, effectively automating tasks that previously depended on human intervention. The integration of AI agents with software-as-a-service platforms creates a new paradigm where services are delivered through software, significantly impacting both SaaS providers and IT services by improving automation and reducing the need for manual processes.
This trend will significantly impact SaaS, forcing enterprises to rethink how they implement internal workflows and decision-making processes. The traditional SaaS pricing model, which is subscription-based, will transform into a results-based pricing model. In the new model, customers only pay for workflows and tasks that an AI agent can perform autonomously, bringing them to a logical closure.
An early example of this trend is Salesforce’s Agentforce, where customers can build AI agents that take action based on insights and intelligence suggested by CRM. In the insurance vertical, Software-as-a-Service means that customers will hire claims processing agents and pay only for those claims that are processed without a dispute or conflict.
Key Achievements – Generative AI transforms the SaaS industry with AI agents capable of completing tasks.
3. Inclusion of speech and real-time interaction
Real-time interaction and speech integration are set to revolutionize user experiences with enterprise applications. Introducing speech capabilities into tools like ChatGPT has already demonstrated the potential for more natural and intuitive user interactions. By 2025, AI agents will understand spoken language and generate audio content in real time. This advancement minimizes reliance on rapid engineering, allowing users to interact with AI agents until they achieve the desired result.
For example, a sales representative can verbally instruct an AI agent to generate a personalized sales proposal. The agent will then respond dynamically to refine the document based on ongoing feedback. This level of interoperability increases usability and accessibility, making enterprise applications more responsive to user needs.
Key Achievements – AI agents and agent workflows extend beyond text by integrating speech and real-time conversations that feel natural and user-friendly.
4. Generative user interfaces drive the next generation user experience
The rise of generative user interfaces represents a significant advance in the way users interact with applications. Historically, the primary interfaces for generative AI have been text-based conversational or spoken interactions. By 2025, applications will increasingly adopt dynamic user interfaces that adapt based on user interactions and logical workflows. Generative UI enables applications to automatically generate interface elements, such as forms, dashboards, or visualizations, tailored to specific user needs and actions.
Companies like Vercel and Bolt.new are at the forefront of this movement, developing platforms that allow the creation of highly adaptable and personalized user experiences. This change increases user engagement and streamlines workflows by providing interfaces that evolve in real-time to meet changing demands.
Key Takeaways – Generative UI increases engagement and streamlines the extraction of meaningful insights through personalized and logical workflows.
5. Enterprise Agent Integration replaces Incremental Return Generation
Integrating AI agents into enterprise workflows is poised to replace incremental generation with feedback as the dominant approach to improving LLMs. While RAG focuses on providing context to reduce inaccuracies in language models, the emphasis in 2025 will shift to embedding agents directly within enterprise applications. This integration allows agents to perform specific tasks within the software environment, leveraging enterprise data and workflows to deliver more accurate and relevant results.
For example, an AI agent integrated into a financial planning tool can access real-time market data and execute trades based on predefined strategies, providing a more seamless and effective solution than traditional rag-based assistants . This evolution underscores the importance of deep integration between AI agents and enterprise systems to drive meaningful business results.
Key Takeaways – AI agents will be embedded in enterprise applications, accessing real-time data and performing actions beyond RAG.
Briefing
Predicted generational AI trends for 2025 present business opportunities and challenges. Integrating AI into core application design and service delivery can increase efficiency, cost savings, and improve user experiences. However, organizations must address potential challenges, including integration complexity, security concerns, and the need to improve employee skills to work alongside AI technologies.
Advances in generative AI suggest a transformative impact on various aspects of technology and business operations. By proactively engaging with these trends, organizations can position themselves to harness the potential of AI while effectively navigating the associated challenges.
