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5 Generative AI Trends To Watch Out For In 2025

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As businesses navigate an increasingly digital landscape, generative AI is becoming the cornerstone of enterprise applications. This transformation promises to enhance operational efficiency, drive innovation and reshape how organizations interact with technology. Understanding these shifts is crucial for executives and technology leaders who aim to stay competitive in a rapidly evolving market.

Here are five key generative 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 incorporating generative AI as supplementary features, such as embedded chatbots or auxiliary agents. The transition from AI-infused to AI-first applications is anticipated 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 models for intelligent workflows. Generative AI will no longer be confined to chatbots or AI assistants that use RAG to answer questions. Instead, it will be an essential 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 plug-ins and add-ons, AI-first integrated development environments like Cursor and Windsurf tightly integrated code generation into the native development workflow. This trend of natively embracing generative AI will extend to software beyond coding tools and IDEs.

Key takeaways - 2025 will mark the beginning of AI-first application development trends.

2. The Rise of 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, offer 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 upon the insights provided by software, effectively automating tasks previously dependent on human intervention. Integrating 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 enhancing 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 pricing model of SaaS, which is subscription-based, will transform into an outcome-based pricing model. In the new model, customers only pay for workflows and tasks that an AI agent could autonomously perform, 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 the insights and intelligence suggested by the CRM. In the insurance vertical, Service as Software means that the customers would employ agents for claim processing and pay only for those claims that were processed without a dispute or conflict.

Key takeaways - 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 the reliance on prompt engineering, allowing users to interact with AI agents until they achieve the desired outcome.

For example, a sales representative could verbally instruct an AI agent to generate a customized sales proposal. The agent would then respond dynamically to refine the document based on ongoing feedback. This level of interaction enhances usability and accessibility, making enterprise applications more responsive to user needs.

Key takeaways - AI Agents and agentic workflows extend beyond text by integrating speech and real-time conversations that feel natural and user-friendly.

4. Generative User Interfaces Drive Next-Gen User Experience

The rise of generative user interfaces represents a significant advancement in how users interact with applications. Historically, the primary interfaces for generative AI have been text-based chat or speech 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 the specific needs and actions of the user.

Companies like Vercel and Bolt.New are at the forefront of this movement, developing platforms that allow for the creation of highly adaptable and personalized user experiences. This shift enhances user engagement and streamlines workflows by providing interfaces that evolve in real-time to meet changing requirements.

Key Takeaways - Generative UI enhances engagement and streamlines extracting meaningful insights through personalized, logical workflows.

5. Enterprise Agent Integration Replaces Retrieval-Augmented Generation

Integrating AI agents into enterprise workflows is poised to replace Retrieval-augmented generation as the dominant approach for enhancing LLMs. While RAG focuses on providing context to reduce inaccuracies in language models, the emphasis in 2025 will shift towards 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 outcomes.

For instance, an AI agent integrated into a financial planning tool could access real-time market data and execute trades based on predefined strategies, offering 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 outcomes.

Key Takeaways -AI agents will be embedded in enterprise applications, accessing real-time data and performing actions beyond RAG.

Summary

The forecasted generative AI trends for 2025 present business opportunities and challenges. Integrating AI into core application design and service delivery can increase efficiency, cost savings and enhance user experiences. However, organizations must address potential challenges, including integration complexities, security concerns and the need to upskill employees to work alongside AI technologies.

The advancements 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 leverage AI's potential while effectively navigating the associated challenges.

Investors Take Note: 5 Companies Using AI Agents To Drive Innovation

Artificial intelligence (AI) is reshaping the global economy and remains a major investing theme in 2025. From automating complex tasks to uncovering hidden insights in vast datasets, businesses stand to reach new levels of productivity. And this transformation across various industries is just getting started.

Investors have the opportunity to buy the stocks in companies that are leveraging this next-generation technology as a long-term growth driver. Here are five cloud software companies to keep an eye on while they develop innovative and unique AI agents with significant potential.

Image source: Getty Images.

1. Adobe

Adobe (ADBE 2.31%) is recognized for its market-leading creative tools like Photoshop, Illustrator, and Premiere Pro, which are staples for visual-media professionals and enthusiasts alike.

These platforms are perfect for showcasing the power of Adobe Firefly AI, with capabilities that can still be described as magic. Features like text-to-image generation and generative fill have been game-changers for the creative industry, and Adobe is capitalizing on strong demand.

The stock has been volatile, down about 35% from its 52-week high. Yet the company's fundamentals remain solid, evidenced by record fiscal 2024 results showing 11% revenue growth and 15% higher adjusted earnings per share (EPS). With continued financial momentum expected in 2025, this recent share-price weakness may present an attractive entry point for investors seeking exposure to this AI leader.

2. CrowdStrike

CrowdStrike (CRWD 0.38%) has established itself as a dominant force in AI-powered cybersecurity through its Falcon platform, which uses advanced machine learning for proactive threat detection and automated response capabilities.

The cloud-native solution has gained significant traction among organizations by delivering comprehensive protection across endpoint security, identity protection, threat intelligence, and exposure management within a single agent.

Shares of CrowdStrike have gained roughly 27% over the past year, reflecting strong growth and earnings momentum. For fiscal 2025, Wall Street analysts project revenue to climb by 29%. Along with an expected 22% increase in earnings per share (EPS), this indicates a robust outlook that should continue to reward shareholders.

3. Docusign

Docusign (DOCU -0.20%) didn't invent the e-signature but has revolutionized digital agreement technology, transforming how businesses handle legal documents. The company's latest innovation is the integration of generative AI into its Intelligent Agreement Management (IAM) platform; that creates a more comprehensive suite of solutions that allow organizations to generate AI-based customized documents and automatically manage agreement workflows.

The effort to diversify beyond electronic signatures appears to be paying off. Shares of Docusign have risen to their highest level since early 2022, highlighting a resurgence for the company with a refreshed growth outlook. What I like about the stock is its combination of solid fundamentals and category leadership, with expansion opportunities internationally.

4. Microsoft

It's difficult to discuss artificial intelligence without mentioning Microsoft (MSFT -0.41%). The company's early investment in and ongoing partnership with OpenAI (creator of the groundbreaking AI chatbot ChatGPT) has secured its position as an AI leader.

Through Microsoft 365 Copilot, the company integrates large language models (LLMs) across its Office productivity suite, embedding powerful AI capabilities within familiar tools like Word, Excel, and Teams. AI capabilities are powering Microsoft's Azure cloud platform, offering enterprise customers the infrastructure to build, train, and deploy custom AI models.

This combination of AI-enhanced productivity tools and cloud computing exposure makes Microsoft stock one to buy and hold for the long run.

5. SoundHound AI

There's a good reason that the share price of SoundHound AI (SOUN 0.58%) has risen by more than 640% over the past year. The company has quickly established itself as one of the most exciting players in AI innovation, with its conversational intelligence technology that enables natural voice communication between people and devices.

SoundHound powers hands-free infotainment systems for several major automakers. The company is now bringing that success to the retail and restaurant industries, where its voice AI automates ordering and payment systems. The company sees a significant opportunity in broader customer service applications.

Growth trends have been impressive, with Wall Street analysts projecting a 96% revenue increase in 2025. While the stock commands a pricey valuation premium, and will remain speculative until the company achieves consistent profitability, SoundHound's expanding market presence deserves to be on your investing radar.

Dan Victor has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Adobe, CrowdStrike, Docusign, and Microsoft. The Motley Fool recommends the following options: long January 2026 $395 calls on Microsoft and short January 2026 $405 calls on Microsoft. The Motley Fool has a disclosure policy.

5 Ways Data Teams Must Lead In AI-driven Organizations

The future of work requires data teams to lead with data governance, ops, and products that make data reliable and discoverable for business users and use cases.

Generative AI has inspired a surge of interest in using data to improve the accuracy of business decisions. Business managers, data analysts, and citizen data scientists can now use prompts instead of SQL queries to ask questions, interact with large language models rather than dashboards, and scan ML-generated recommendations instead of exploring data for insights.

According to the 2024 AI at Wharton report, 72% of respondents were using genAI at least once a week. Over 80% of respondents working in IT, business intelligence, customer service, marketing, operations, and product development stated that using genAI had a medium-to-high impact in their work.

Data teams and specialists—including data scientists, engineers, architects, and data governance specialists—should take the opportunity to provide more data services to departments adopting genAI. These early and mid-adopters are using genAI tools, automation, machine learning capabilities, and data visualization to redefine the future of work.

According to Deloitte's State of Generative AI in the Enterprise report (Q3/2024), 75% of organizations have increased their technology investments for data lifecycle management to support genAI initiatives. The top actions taken include enhancing data security, improving data quality, updating governance frameworks, and increasing collaboration with cloud service providers or IT integrators.

"Data teams are transforming the future of work within their organizations by democratizing data access and ensuring a solid foundation for data-driven decisions," says Irfan Khan, president and chief product officer of SAP Data & Analytics. "Through the management, governance, and analysis of data, they do more than automate calculations or create dashboards; they uncover deeper insights and help employees perform their tasks more efficiently while reducing the backlog of demands on resource-strapped IT departments."

Below are five ways data professionals can support data discovery and transformation for business teams adopting generative AI.

Make data security non-negotiable

Security is a growing challenge for data governance. According to a recent third-party risk management study, 61% of companies reported a third-party data breach or security incident—a 49% increase over the last year. Data access governance is a critical first step to protecting the organization as business teams aim to become more data-driven while leveraging LLM capabilities.

"Imagine your data environment as a sprawling mansion—everyone wants a key, but you can't just hand out a master key to every room," says Amer Deeba, GVP of Proofpoint DSPM Group. "Data access governance is about giving each user the exact key they need; no more, no less."

Deeba recommends, "Start by discovering and cataloging all your data assets so you have a clear understanding of what's stored, where, and its sensitivity. With this foundational insight, you can enforce least privilege principles, ensuring users access only what they need, supporting zero trust, and minimizing risks to valuable and sensitive information."

When there's a high business demand for capabilities, data teams have much more opportunity to require non-negotiable data practices such as improving unstructured data security, performing third-party risk assessments, and defining AI governance policies.

Extend data quality to LLM document processing

Data teams are responsible for ensuring that unstructured data sources go through data cleansing, preparation, and cataloging as more business teams want to use them in RAGs and LLMs.

"The future of work depends on data-informed decision-making, with prioritization exercises often anchored in the accuracy and timeliness of data," says Jeremy Kellway, VP of engineering for analytics, data, and AI at EDB. "Data teams must ensure that the data feeding analytics and AI applications truly reflect the organization's goals, and in RAG AI applications, documentation prep is a critical step in determining what data is appropriate to drive meaningful outputs."

Steps to create robust data pipelines for unstructured data include entity extraction, sentiment analysis, and bias detection. Before LLM technology, natural language processing for data extraction required a mix of document parsing, keyword searches, and leveraging specialized algorithms for sentiment and bias. Generative AI and machine learning offer more advanced capabilities for document processing.

"Employing AI at all levels of the data pipeline can jump-start new projects and get them to provide business value faster," says Colin Dietrich, data scientist at SADA. "AI and ML can act as accelerators throughout the data warehousing, curation, and publishing processes. They can automate the creation of derived data, improve predictive algorithms, and enhance decision-support products with natural language."

Empower citizen data scientists by centralizing data

Going beyond security non-negotiables and LLM document processing, data teams should consider their data management strategies and how to enable easier and faster access to data sources. Among the data management technologies architects consider are data warehouses, data lakes and lakehouses, and data fabrics. Regardless of the technology, ease of use for citizen data scientists and business teams is key.

"Data fabric, an architectural approach simplifying data access and enabling quality data for real-time analytics, is transforming how teams work by enabling citizen data science—empowering more departments to create, access, and leverage data through user-friendly dashboards," says Midhat Shahid, VP of product management at IBM. "By fostering a self-service culture, they equip every department to contribute to and act on data-driven decisions, creating a scalable business culture grounded in data."

Before LLMs, the primary use cases for citizen data scientists were developing dashboards, conducting data discovery steps on new data sources, and performing ad-hoc queries. Today, business teams and data scientists have expanded needs, including developing RAGs, embedding knowledge in SaaS LLMs, and leveraging AI agents. Data teams should have APIs available to primary data sources and knowledge repositories available to use in these and future use cases.

"Integrating LLM knowledge with enterprise data unlocks predictive insights and enables real-time decisions, turning information workers into proactive decision-makers and catalysts for innovation," says Ariel Katz, CEO of Sisense. "Data teams must evolve from gatekeepers to enablers, offering data API services that abstract complexity and empower every creator—whether pro-code, low-code, or no-code—to embed analytics effortlessly."

APIs are not just for accessing data sources. When data teams create visualization components, machine learning models, RAGs, and AI agents, having robust and easy-to-use APIs should be the first way to deliver the service.

Michael Berthold, CEO of KNIME, says having guardrails around data quality and access is important before putting models in production. "Companies are realizing that models can make bad predictions or leak sensitive information. Effective tools help govern data flow, model use, and add safeguards to reduce these risks."

Establish data marketplaces to simplify data discovery

Data teams should consider citizen data scientists as one of their end-user personas, but other less technically advanced business users also must be able to discover and access data sources. Using data catalogs and creating data dictionaries is an important first step for enabling broader data access. In the process of establishing data marketplaces, organizations can take the opportunity to scale their self-service data and AI programs.

"Layers of IT and governance bureaucracy are slowing down data access and making it harder to speed new innovations, improve supply-chain logistics, and deploy innovative AI applications," says Moritz Plassnig, chief product officer at Immuta. "With the acceleration and adoption of AI, killer apps are no longer the focus; data is the new app, and data teams have the power to enable anyone in the organization to become data consumers by cultivating an internal data marketplace that automates discovery and access, while still providing enterprise-grade governance and security."

Data marketplaces can be an accelerating capability in industries where integrating several primary high-volume data sources is needed for many departmental use cases. Companies in manufacturing, construction, energy, and other industrials can use data catalogs and marketplaces to aggregate and simplify using real-time data sources for decision-making in marketing, field operations, supply chain, finance, and other departments.

"Data teams are essential in industries like manufacturing, where data is abundant but hard to navigate," says Artem Kroupenev, VP of strategy at Augury. "Their role isn't just about making data operational; it's about empowering everyone to become a data scientist by ensuring data is accessible, easy to use, and impactful."

Develop data products that foster collaboration

Marketplaces aren't only for discovering, accessing, and integrating data sources. Data teams may now consider their advanced dashboards, machine learning models, LLM capabilities, and AI agents as data products and manage them as product development initiatives. Each product has a defined customer segment, value proposition, and strategic objective, which can be defined in a vision statement and managed through a product roadmap.

Pete DeJoy, SVP of products at Astronomer, adds, "The concept of data products has evolved from a buzzword into a crucial element of modern data-driven organizations. This alignment with physical product and supply chain analogies helps clarify the end-to-end data lifecycle, bridging communication gaps between technical and non-technical teams."

As more business teams become data-driven and AI becomes an increasingly important business capability, the lines separating data and business teams are blurring. The future of work requires data teams to restate their mission and deliver enhanced data governance, dataops, marketplaces, and data products that service more departments and use cases.

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