For decades, enterprise software has aimed to enhance productivity, primarily succeeding in transactional areas like approval routing, data synchronization, and invoice automation. However, the more complex tasks of synthesizing unstructured information, exercising judgment, and crafting nuanced written outputs have historically remained with humans. This dynamic seems to be changing as AI agents are increasingly entering enterprises not as experimental tools but as systems capable of executing intricate, multi-step knowledge work, which warrants attention regarding organizational operations.
From Assistive Tools to Autonomous Agents
The initial phase of enterprise AI focused on assistive technology, where generative AI tools helped users draft documents faster, summarize content, and produce initial drafts. A notable transition is underway, with AI agents now able to plan, execute, use various tools, and delegate tasks across systems with minimal human intervention, operating through established workflows rather than merely responding to prompts.
According to McKinsey’s research on the economic potential of generative AI, this technology could automate work activities that account for 60 to 70 percent of employees’ time, a significant increase from previous estimates related to conventional automation. This potential is primarily concentrated in language-intensive, judgment-heavy roles, suggesting that the next wave of productivity gains may come more from desk-based knowledge workers than from traditional shop-floor operations.
Increasing Enterprise Adoption
Although it may be premature to label agentic AI as mainstream, adoption is progressing more rapidly than often assumed. A 2025 PwC survey of senior U.S. executives revealed that 79% of companies are adopting AI agents in some capacity, with many reporting genuine productivity increases as a result. Budget allocations are also shifting in favor of this technology.
Deloitte’s “State of AI in the Enterprise” report corroborates this trend, indicating that more AI projects are moving beyond experimentation into production, with organizations increasingly reporting measurable impacts. While many companies are still navigating this transition, the overall trajectory points to steady progress.
Demonstrable Value in Complex Workflows
Agentic AI is particularly beneficial in workflows characterized by high volume, high complexity, and reliance on institutional knowledge. Consider the significant time investment involved in tasks such as responding to detailed RFPs, conducting due diligence requests, or locating the appropriate content to support business deals. Each of these tasks relies on accessing dispersed organizational knowledge, applying contextual judgment, and generating sufficiently accurate outputs to maintain professional relationships.
AI agents excel in executing these types of tasks by presenting relevant information, identifying gaps, and creating coherent initial drafts for human users to finalize. The emphasis is less on replacing expertise and more on streamlining the time necessary to leverage it effectively. Gartner predicts that by 2028, one-third of enterprise software applications will integrate agentic AI, indicating that vendors failing to adapt may lag behind.
Transforming Software Interaction
A noteworthy aspect of the agentic shift is its potential impact on the utilization, pricing, and evaluation of enterprise software. Instead of navigating complex modules and dashboards, users are increasingly interacting with agents that operate on platforms. This change positions software more as an infrastructure layer and establishes the agent as the primary interface.
Consequently, this shift raises questions regarding pricing structures. PwC’s 2026 AI Business Predictions suggest a gradual transition from per-seat licensing to outcome-based pricing models. While still in early stages, this trend reflects a broader reevaluation of the value that enterprise software is expected to provide.
Importance of Governance for Scalability
Despite the compelling case for agentic AI, practical challenges loom. Deloitte found that only a small fraction of organizations have mature governance frameworks in place for autonomous agents, and issues regarding data quality remain prevalent. The reliability of these agents is contingent upon the accessibility of accurate content, necessitating that organizations invest thoughtfully in their foundational knowledge architecture, not merely the AI technology applied atop it.
For teams representing their organizations to clients, partners, and buyers, outdated or inconsistent information can lead to significant trust erosion. Therefore, ensuring high-quality underlying knowledge content emerges as a strategic concern rather than just a technical detail.
Establishing Strong Foundations
Although agentic AI is unlikely to revolutionize every aspect of enterprise operations instantaneously, some current enthusiasm may require adjustments based on what is deployable at present. Organizations that focus on building structured, clean knowledge architectures and choose suitable platforms for these challenges stand to reap substantial compounding benefits in the coming years.
For those willing to establish the right foundations, agentic AI presents one of the most substantial productivity opportunities of this generation.
Manish Bafna serves as SVP of Engineering at Responsive. The views presented are solely those of the author, and ETCIO does not necessarily endorse them. ETCIO is not liable for any damage caused directly or indirectly to any person or organization.
