Enterprise AI Agents – The Business Apps of the Future

The last decade saw large enterprises embrace Software as a Service (SaaS) and purpose-built applications to streamline operations and gain data-driven insights. But there is a new kid on the block: enterprise AI agents. These intelligent systems are poised to revolutionize how work gets done, going far beyond the capabilities of traditional business apps. This blog explores the key capabilities of enterprise AI agents and what they mean for the future of enterprise IT.

March 5, 2025 Mick Hollison

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Data: going beyond rows and columns

Traditional business apps primarily deal with structured data – neatly organized in rows and columns within databases. Enterprise AI agents, however, thrive on all types of data – structured, unstructured, and semi-structured. Sure, these intelligent agents can peruse a multitude of databases in search of understanding and hidden patterns. But they can also analyze customer feedback from social media, understand the context of emails, and even extract valuable information from video calls. This ability to process and synthesize diverse data types unlocks a wealth of previously untapped intelligence, leading to more comprehensive insights and better decision-making.

AI agent analyzing diverse data types, including structured, unstructured, and semi-structured data, for enhanced decision-making.

Execution: just do it

Yesterday’s business applications largely focused on analysis and reporting. The only view they provided was the one in the rearview mirror. They provided dashboards and visualizations, but humans were responsible for taking action. AI agents, on the other hand, are designed to do work. They can automate tasks, schedule meetings, respond to customer inquiries, and even proactively identify and resolve issues. Think of an enterprise AI agent that not only identifies a potential supply chain disruption but also automatically reroutes shipments to mitigate the risk. This shift from analysis to action is a profound change.

Diagram illustrating the shift from analysis-driven business apps to AI-powered action, automating tasks and resolving issues proactively.

Continuous learning: The key to evolution

Traditional apps remain static unless manually updated. This makes them too inflexible for the everchanging work of dynamic businesses. Enterprise AI agents, by contrast, must continuously learn. What’s really fascinating is that these agents aren’t just learning, they must evolve – adapting and improving with each new piece of data. Machine learning algorithms can help them to adapt and improve their performance over time. As they interact with more data and complete more tasks, they become more efficient, accurate, and capable.

This constant learning loop ensures that enterprise AI agents remain relevant and effective in a dynamic business environment. Ultimately, this makes AI agents more like smart, flexible people and less like obtuse, rigid enterprise software.

AI icon with graduation hat symbolizing continuous learning and evolution in enterprise AI agents.

Security and compliance: a new landscape

While traditional SaaS apps also raise security concerns, AI agents introduce new challenges. How do we ensure the data used to train the agent is secure and compliant with regulations like GDPR or HIPAA? How do we prevent bias in AI algorithms? How do we maintain transparency and auditability when agents are making autonomous decisions? These are critical questions that businesses must address to ensure responsible AI adoption. Robust security frameworks, data governance policies, and Explainable AI techniques will be essential.

The evolving enterprise IT stack

The rise of AI agents will significantly impact the enterprise IT stack. We’ll see increased demand for:

Modern data platforms: To synthesize structured, semi-structured and unstructured data for AI consumption and execution.
AI/ML infrastructure: To support the training and deployment of AI models.
Agent management platforms: To oversee and control the behavior of multiple AI agents.
Integration tools: To connect AI agents with existing enterprise systems and workflows.

As important as these changes are, they pale by comparison to the cultural change that enterprise AI agents will require. If business users can build and run sophisticated enterprise AI agents without custom coding requirements, exactly what is the role of enterprise IT? Well for starters, IT must deliver the operational stability and continuity that will be required without slowing down the business. Second, IT must keep the data house in order – without clean, accurate data the risks for the business increase under this new world order. And lastly, IT must ensure that human oversight is incorporated into these agentic workflows for mission critical decision making.

This shift will require IT teams to develop new skills in areas like data science, machine learning, data management, and responsible AI.

What is the impact of AI agents on major software and application vendors?

The established enterprise software giants are already reacting to this trend. We are seeing:

Microsoft, Salesforce, Google, Oracle, Workday, ServiceNow: Integrating AI agent capabilities into their existing platforms. Think of Salesforce’s push to use AI agents to automate and improve business interactions with their customers or ServiceNow’s efforts to automate business workflows with AI agents.
Snowflake, Databricks, MongoDB: Providing the data infrastructure needed for enterprise AI agents. Their platforms will be crucial for bringing AI to governed data – enabling teams to run analytical workflows on unstructured data, develop agentic apps, and train models. And, of course, they have infrastructure agents of their own too.

Enterprise AI Agents Impact on Major Vendors in Business Software.

AI agents are the future of enterprise software

The future of enterprise software is not just about apps; it’s about intelligent agents that augment human capabilities and drive business transformation. The companies that successfully embrace this shift will be the leaders of tomorrow. The question is not if enterprise AI agents will reshape the enterprise, but how quickly and how effectively businesses will adapt to this new reality.