Workday's Last Workday? AI and the Future of Enterprise Software

The enterprise software landscape, dominated for decades by behemoths like Workday, is on the precipice of a seismic shift. This isn't just about incremental updates; it's a fundamental re-platforming driven by AI. The core implication is that systems designed for a pre-AI era, while historically defensible and deeply embedded, are now vulnerable. This analysis reveals that the "least loved" but "most important" enterprise tools face obsolescence, not because they fail at their original purpose, but because AI unlocks entirely new paradigms of interaction and efficiency. Leaders in enterprise technology, HR, and IT should pay close attention, as understanding this platform shift offers a significant advantage in navigating the coming wave of disruption and identifying opportunities for innovation.

The Unloved Backbone: Why AI Demands a Reimagining of Core Enterprise Systems

For years, the enterprise software market has operated on a predictable cycle: a major platform shift, like the move from on-premise servers to cloud computing, creates a massive opportunity. Companies then build their businesses around these new primitives, becoming deeply entrenched and highly defensible. This was the era that saw the rise of giants like Workday, Service Now, and Salesforce, systems that became the backbone of how businesses manage critical functions like HR, IT service management, and customer relationships. Their defensibility was rooted in their complexity and the sheer difficulty of replacing them, evidenced by their stellar gross dollar retention rates.

However, the user experience within these systems often lagged far behind. Joe Schmidt highlights this disconnect, noting that while Workday is crucial for managing vital employee data, interacting with its interface is a universally disliked experience.

"I am the insurance person here in Andreessen Horowitz. I have no idea what my benefits are, no idea. I went in actually not too long ago to actually try to find my compensation information in Workday. It took me six and a half minutes. Like, I am a technology investor."

This sentiment is echoed by many who have struggled to navigate these systems for basic information, a stark contrast to the intuitive interfaces common in consumer software. The core issue, as Schmidt points out, is that these systems were built for internal teams and IT administrators, not for the end-user employee or the evolving demands of modern work. The underlying business logic and data are critical, but the way users interact with them feels frozen in time, often resembling interfaces from 2005.

The AI Catalyst: From Point Solutions to Agentic Futures

The current platform shift, driven by AI, changes the game entirely. It's no longer about building incremental point solutions on top of existing, legacy backends. Instead, AI enables a fundamental rethinking of how these core systems should function and interact. The key difference lies in the emergence of "agentic" capabilities -- AI agents that can perform tasks on behalf of humans, fundamentally altering workflows, permissions, and internal operations.

This new paradigm allows companies to approach CIOs and CHROs with a compelling proposition: not just a slightly better version of the existing system, but a fundamentally different way of doing work. An AI-native replacement can offer a vastly improved employee and customer experience, a different cost profile, and the potential for significant savings and growth. This is why, according to Schmidt, the "obituaries" are being written -- they represent opportunities for replatforming.

"For the first time, you can actually go to a CHRO or you can go to a CIO and say, the way that this core system works for you today can be so fundamentally different, and we can change the actual way that the work is being done by your team."

The challenge for insurgents is to build these AI-native systems with feature parity for the enterprise, a feat that has historically been incredibly difficult. However, the architectural advancements and the capabilities of AI agents make this a plausible, albeit challenging, endeavor. This isn't just a niche opportunity; it's seen across various critical enterprise categories, including ITSM, CRM, and even ERP, with companies like Rillit demonstrating this shift.

The "Hotel California" Effect: Why Entrenchment Meets Its Match

The deeply embedded nature of systems like Workday has historically made them almost impossible to displace. This is the "Hotel California" effect: "You can check out any time you like, but you can never leave." Companies are locked in due to the massive investment in implementation, data migration, and the intricate web of integrations. Ripping and replacing these systems has been prohibitively expensive and time-consuming, often taking 12 months or more and requiring significant consulting spend.

However, AI is changing this calculus. The prospect of deploying AI-native systems in as little as 30 to 60 days, a fraction of the historical timeline, presents a compelling case for change. This dramatically reduced deployment friction, coupled with the promise of genuinely transformative capabilities, can overcome the inertia of existing solutions.

"The pattern repeats everywhere Joe looked: distributed architectures create more work than teams expect. And it's not linear--every new service makes every other service harder to understand. Debugging that worked fine in a monolith now requires tracing requests across seven services, each with its own logs, metrics, and failure modes."

The "AI revenue" often reported by incumbents, such as Workday's AI ARR, is viewed by some as more of a "procurement innovation" than a fundamental shift in product capability. While Workday is actively investing in AI, including acquisitions and internal development, the question remains whether these efforts can truly evolve the core user experience and unlock agentic capabilities as effectively as a ground-up, AI-native approach. The true opportunity lies in building systems that are not just AI-enabled, but AI-first, designed from the ground up to leverage agents and deliver fundamentally new experiences.

The Anthropological Lens: HR Software as a Mirror to Societal Shifts

The focus on HR software as a battleground for this transformation is particularly telling. Schmidt argues that HR systems are uniquely positioned to reflect broader societal and technological shifts. They are deeply intertwined with how individuals experience their work, their benefits, and their place within an organization. As AI introduces new platform shifts, it also reshapes buying and procurement norms, and critically, social norms around work and interaction.

The widespread adoption of AI agents will necessitate a robust understanding of roles and permissions within an enterprise. This makes core HR systems, which house this critical data, a natural foundation for an agentic future. The challenge for CIOs will be managing permissioning and tracking across an increasingly complex ecosystem of tools, both built and adopted.

"As you have more agents doing work on behalf of humans, understanding their roles and permission inside of the company is going to be critical. And so, how do you actually use what is like one of the most critical pieces of data inside the enterprise to inform that agentic future?"

This dynamic is already playing out in adjacent fields like ITSM. The race is on for companies to capture this opportunity, and incumbents like Workday are fighting to adapt, evidenced by their strategic moves. The ultimate payoff will be when every employee has access to an AI-native HR tool that transforms their daily work, marking a significant leap forward in how businesses operate and how humans interact with technology.

Key Action Items

• Immediate Action (0-3 Months):

  • Evaluate existing enterprise software: Conduct a critical assessment of current core systems (HR, ITSM, CRM) for user experience friction and outdated interfaces. Identify specific pain points that employees consistently report.
  • Pilot AI tools for workflow augmentation: Experiment with existing AI-powered tools that can automate or assist with specific, high-friction tasks within current systems. Focus on immediate problem-solving.
  • Educate leadership on AI's potential for replatforming: Share insights on how AI is enabling new architectural possibilities and the potential for fundamental workflow transformation, not just incremental improvements.

• Medium-Term Investment (3-12 Months):

  • Explore AI-native HR/ITSM solutions: Begin researching and evaluating emerging AI-native platforms that offer a fundamentally different approach to core enterprise functions. Prioritize solutions with agent-first designs.
  • Develop an AI strategy for permissioning and data governance: Proactively plan how to manage user roles, permissions, and data access as AI agents become more integrated into workflows. This is crucial for security and compliance.
  • Foster a culture of experimentation with AI agents: Encourage teams to explore and adopt AI agents for productivity gains, while establishing guidelines for responsible use and data security.

• Longer-Term Investment (12-18+ Months):

  • Plan for strategic migration to AI-native core systems: Develop a phased roadmap for migrating away from legacy systems towards AI-native alternatives, focusing on areas where the value proposition is strongest. This requires significant upfront planning and investment.
  • Invest in internal expertise for AI integration: Build or acquire the necessary skills within your IT and HR departments to effectively implement, manage, and leverage AI-native enterprise software.
  • Embrace the discomfort of change for lasting advantage: Recognize that adopting AI-native systems will involve initial disruption and learning curves. This upfront discomfort is precisely what creates durable competitive advantages by deterring slower-moving competitors.