Today, enterprise software is designed to augment human capabilities. In the near future, agentic AI will drive a paradigm shift. Instead of purchasing tools to assist with specific functions, organisations will rely on autonomous agents to handle tasks end-to-end. This will eliminate traditional processes, workflows, and operations, requiring organisations to fundamentally rethink how they operate. These changes will have far-reaching implications for how organisations operate, software vendors model their solutions, and the skills organisations need to cultivate.
What Does a Tierless Future Look Like?
Agentic AI introduces the concept of systems of agents: autonomous digital assistants performing specific functions within a larger ecosystem. These agents bypass traditional interfaces—such as forms, dashboards, and workflows—by capturing and interpreting data at its source. This creates richer datasets and a seamless experience, with the interface itself fading into the background.
In this tierless architecture, data flows freely across systems, unimpeded by the bottlenecks of siloed applications. Work happens organically, and outcomes are delivered without users needing to consciously interact with software in the traditional sense. This shift not only redefines technology but also necessitates a new approach to organisational operations.
Reshaping the Enterprise Software Model
For incumbent software vendors, this transformation is challenging. Legacy software companies must fundamentally rethink their architectures and pricing models. Instead of offering comprehensive, monolithic packages, they must pivot to modular, result-oriented portfolios of agents. This shift demands not only technical innovation but also a philosophical change. Success will depend on their ability to prioritise customer outcomes over traditional feature lists.
For those who adapt, the potential reward is enormous. The Total Addressable Market (TAM) for AI-enabled software, driven by automation and labour replacement, dwarfs anything seen before.
However, these projections assume smooth execution. Achieving labour replacement at scale requires flawless automation and deep organisational trust—both of which are far from guaranteed. While the TAM is tempting, realising it will be far more challenging than many anticipate.
The Limits of AI Optimism
But to what extent are we going to see labour replacement? While the opportunities are vast, they are often overstated. Agentic AI and automation are not silver bullets. Much of the enterprise data these systems depend on is poorly structured, inadequately catalogued, or outright missing. Similarly, many processes are only partially documented, with human ingenuity filling critical gaps that AI cannot yet replicate.
These limitations underscore the risks of automating suboptimal processes or building systems based on incomplete data. While AI can mask flaws in the short term, these cracks will inevitably surface. Enterprises that rush to replace human processes wholesale with AI risk exposing inefficiencies they may not yet have recognised.
Reimagining Processes, Not Just Automating Them
The real potential of agentic AI lies not in automating existing processes but in enabling entirely new ways of working. This requires a ground-up rethink of workflows, unbundling and rebundling them to focus on outcomes rather than legacy structures.
For the workforce, this shift presents both challenges and opportunities. Routine tasks will increasingly be automated, but new roles will emerge around designing, supervising, and optimising these reimagined processes. The skills of tomorrow’s workforce will centre on creativity, critical thinking, and collaboration with AI systems, rather than repetitive execution.
Roles historically focused on operating and maintaining monolithic systems will need to evolve. Skills in configuring, managing, and co-developing modular agent systems will become critical. Employees will transition from operational tasks to higher-value activities such as strategic oversight and exception handling.
In addition, entirely new roles will emerge—focused on auditing, training, and supervising autonomous systems. As the interface becomes less visible, employees must become experts in understanding what happens “under the hood” of these agents, ensuring they align with business goals.
The Realistic Road Ahead
The dismantling of enterprise software will be a slow and uneven process. Organisational inertia, budgetary constraints, and decision-making cycles will ensure that changes occur incrementally. However, the trend is clear: the future belongs to modular, outcome-oriented systems of agents.
In the near term, enterprises must balance ambition with pragmatism. Success will depend on building strong foundations—organising data, rethinking processes, and preparing the workforce to thrive in hybrid environments. For employees, this means reskilling and embracing new roles that require closer collaboration with AI tools.
Ultimately, the shift from tools to outcomes will redefine enterprise software—and the way we work with it. Those who adapt will reap the rewards, while those who cling to outdated models risk being left behind. The dismantling of enterprise software is not just a technological transformation; it is a call to rethink how businesses deliver value in a world increasingly driven by outcomes.
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