The landscape of enterprise software is on the cusp of a fundamental transformation, driven not by human users but by autonomous AI agents. As these digital workers begin to scope, purchase, and operate software on behalf of companies, they are challenging decades-old business models and forcing a radical rethink of value, pricing, and security. This shift promises unprecedented productivity gains but also introduces complex new risks around governance, trust, and return on investment that businesses are only beginning to grapple with.
The End of the Per-Seat Era and the Rise of Agentic Licensing
The traditional Software-as-a-Service (SaaS) model, built on per-seat licensing where companies pay for each human user, is becoming increasingly obsolete in an agent-driven world. AI agents do not occupy a "seat" in the conventional sense; they transact, execute workflows, and act autonomously within systems. This fundamental shift is inverting software economics. Experts like Michael Mansard of Zuora note that while SaaS has near-zero marginal costs, AI incurs high, variable operational costs. Consequently, pricing models are evolving towards usage-based or outcome-based structures. For instance, Zendesk now charges USD 1.50 per customer service case resolved by its AI, directly tying cost to delivered value. This new paradigm, termed "agentic licensing," represents a new contract between vendors and the software itself, focusing on the license for an AI to act rather than for a human to access.
Example of Outcome-Based AI Pricing:
- Vendor: Zendesk
- Service: Customer Service Agentic AI
- Pricing Model: USD 1.50 per successfully resolved customer case.
The Tangible Business Value and the Elusive ROI
The potential value of AI agents is immense, promising to automate routine tasks and amplify human decision-making. Companies like Lowe's have seen "tangible" ROI by deploying AI agent companions for store associates, leading to the "fastest-adopted technology" among its workforce. Analysts predict that by 2028, at least 15% of day-to-day work decisions could be made autonomously by agents. However, realizing this value at scale has proven difficult. A stark MIT study from 2025 found that 95% of businesses weren't seeing a clear return on their generative AI investments. The challenge lies in moving from isolated pilots to production-scale systems that genuinely reshape business economics. Success in 2026, according to PwC's Dan Priest, will depend on CEOs precisely identifying high-impact areas for AI and pursuing them with focused execution, rather than spreading investment thinly.
Current State of Agentic AI Adoption (Per Deloitte Survey of 500 US Tech Leaders):
- Exploring Solutions: 30%
- Piloting Solutions: 38%
- Solutions Ready to Deploy: 14%
- Actively Using in Production: 11%
The Paramount Challenge: Security, Governance, and the "Rogue Agent"
The single biggest bottleneck to the rapid deployment of AI agents is not technical capability but risk management. Security and governance have leapfrogged data and compute power as the primary concerns for executives. The core paradox, as highlighted by Dev Rishi of Rubrik, is that moving fast requires trust, but building trust takes time. The nightmare scenario is a "rogue agent"—an AI that operates outside its guardrails, potentially causing significant financial, operational, or reputational damage. Experts like Experian's Kathleen Peters warn that "something bad is going to happen," leading to headlines, increased regulation, and difficult conversations about liability. In high-stakes fields like healthcare, human oversight remains non-negotiable. Rakesh Jain of Mass General Brigham emphasizes that while agents can accelerate decisions, "there has to be a human involved," with doctors providing final validation.
Future Predictions:
- Gartner (2028): Predicts at least 15% of day-to-day work decisions will be made autonomously through agentic AI (up from 0% in 2024).
- Forrester (2026): Predicts 30% of large enterprises will make AI fluency training mandatory.
- Gartner (2027): Forecasts over 40% of agentic AI projects will be canceled due to cost, unclear value, or inadequate risk controls.
Building the Infrastructure for an Agentic Future
For businesses to safely harness AI agents, they must build a robust control plane. This involves more than just pricing changes; it requires rearchitecting platforms and instituting new governance frameworks. Key requirements include establishing clear identity and accountability for each agent, implementing systems to ensure agents operate within policy guardrails, and creating detailed audit trails to explain decisions and errors. Companies are also wrestling with a build-versus-buy dilemma, weighing the benefits of custom agents against the integrated solutions offered by major platforms like Salesforce and ServiceNow. Furthermore, a critical piece of the puzzle is workforce education. Forrester predicts that by 2026, 30% of large enterprises will mandate AI fluency training to drive adoption and mitigate the risks that arise from poorly informed human-AI interaction.
A Transitional Year Ahead
The year 2026 is poised to be a pivotal one for AI agents in the enterprise. It will be a year of operationalization, where the focus shifts from experimentation to integrating agents into the core fabric of business operations. While agents will remain imperfect, companies are expected to develop clearer benchmarks, guardrails, and playbooks. The winners will be those who can navigate the complex transition from seat-based SaaS to outcome-based agentic models while building the necessary trust and security infrastructure. The transformation promises a new era of exponential productivity, but its full realization hinges on overcoming the substantial challenges of risk, value measurement, and organizational change that currently stand in the way.