Assistant, collaborator, fully autonomous: The three upcoming eras of AI in enterprise software 

Imagine a user interface for a business intelligence platform. Typically these interfaces include a dizzying array of tabs, sidebars, dropdowns, sliders and other UI elements. A new user wouldn’t know where to look to find what they need, and even an experienced user might find themselves hunting for a lesser-used feature. 

Now, imagine instead of all these components, the screen features a simple text box. The user can enter a prompt such as “Generate a chart that shows the year-on-year subscription numbers over the past decade from the age 20–30 demographic in the EMEA region.” And, presto, the chart materializes. 

We’re not there yet. But this future is not far off.

Over the past decade, software companies have focused on enhancing user experience (UX) by improving user interfaces (UI), simplifying workflows and reducing the number of clicks required to complete tasks. These advancements have increased productivity, boosting software adoption rates and reducing task completion times.  

However, enterprise software still requires users to invest time in learning and adapting to different systems, particularly when transitioning from legacy applications. Moreover, inconsistencies in design across platforms further complicate user training and adoption. 

To address these challenges, organizations often deploy extensive change management programs, but these initiatives sometimes fail to deliver the wanted benefits due to poor user acceptance of new systems. This failure often stems from inadequate training, resistance to change and the complexity of transitioning from legacy systems.

AI is going to change all of this. 

Let’s explore how we expect enterprise applications to evolve across three distinct eras driven by agentic AI advancements, from incremental improvements to complete autonomy. 

We are presently in the early stages of integrating agentic AI into enterprise software. While these changes enhance user experiences, they primarily complement existing UIs rather than replacing them. 

Embedded AI-powered assistance: Autocompletion of descriptions and details by using generative AI, recommendations based on machine learning and retrieval of relevant information by using Retrieval-Augmented Generation (RAG).

Conversational interfaces: Chatbots and copilots enable task completion through natural language commands.

Customizable platforms: Platform-as-a-Service (PaaS) solutions empower customers to build tailored AI functions using Large Language Models (LLMs) available on the platform.

While these enhancements improve productivity, traditional UIs remain integral to user interaction and significant user involvement is still required.

In this era, enterprise applications move toward a more intelligent and collaborative framework. Conventional UI takes a backseat, becoming a tool primarily for IT professionals and super-users. AI agents automate most human-to-software interactions, providing step-by-step guidance and highlighting bottlenecks. However, human input is still required for critical decisions. The core features anticipated in this stage include:

Agents as interfaces: Conversational interfaces dominate user interaction, minimizing reliance on traditional UIs.

Interagent communication: AI agents across different software platforms communicate seamlessly using standardized protocols akin to HTTP.

Dynamic integration: Manual integrations between software products become obsolete, as AI agents exchange information in real time. This shift allows IT professionals to focus on higher-value tasks such as strategy and innovation, rather than routine maintenance and troubleshooting. Organizational workflows also become more agile, as seamless communication between AI agents reduces bottlenecks and accelerates decision-making processes.

The final evolutionary stage envisions enterprise applications that are almost entirely autonomous, requiring minimal human intervention. Users define goals and AI agents collaborate to achieve them within predefined organizational guardrails. Key characteristics include:

Goal-oriented AI agents: Users specify objectives and AI agents execute tasks end-to-end.

Configurable guide rails: Adaptable guidelines outline task boundaries, decision points and authorization requirements. These can be configured using natural language, eliminating the need for specialized IT skills. For instance, administrators might input simple instructions such as "Route all invoices over USD 10,000 for finance department approval," and the system would generate the appropriate workflow.

However, potential limitations can include language ambiguities, where vague or poorly phrased commands might result in unintended configurations. Ensuring accuracy and providing fallback mechanisms, such as guided prompts or validation steps, are critical to addressing these challenges.

On-demand UIs: Dynamic interfaces are generated as needed for decision-making or information presentation.

Unstructured data management: Information is primarily captured in unstructured formats but is transformed into structured or semistructured data for analytics and reporting. 

Streamlined architecture: Enterprise applications consist of two main components—specialized AI agents and configurable guide rails with scope boundaries. Data is be stored in centralized, organization-wide repositories removing the need for application-specific data repositories.

In this era, productivity gains are projected to be substantial, with more benefits including reduced total cost of ownership (TCO) for enterprise applications and data management.