Over the past decade I have written extensively about the rise of Software‑as‑a‑Service (SaaS) and how it reshaped the structure of the digital economy. In several earlier blogs I explored what I described as the “vulnerability of SaaS” in the emerging world of Agentic AI.
At the time, some readers interpreted that argument as a criticism of SaaS itself.
That was never the intention. SaaS was one of the most powerful technology and commercial innovations of the last twenty years. But every technology wave eventually becomes infrastructure for the next one. What we are now witnessing is precisely that transition.
The shift underway is not simply about better software. It is about the emergence of digital workers – autonomous, AI‑driven agents capable of performing tasks, coordinating processes, and increasingly making operational decisions. In other words, we are moving from software that people use to systems where software itself does the work.
This is the real meaning of Agentic AI. And if that trajectory continues – which the evidence increasingly suggests it will – the dominant commercial model will evolve from Software‑as‑a‑Service to something far more profound: Digital Workers‑as‑a‑Service.
The End of the Software Interface Era
To understand why this matters, we need to step back and look at how enterprise technology has evolved. For decades, enterprise software was designed around the assumption that a human user would sit at the centre of every process. Software provided the tools, dashboards, and workflows, while humans executed the tasks. SaaS refined that model brilliantly. Instead of installing complex enterprise systems, organisations subscribed to cloud platforms that were continuously updated, scalable, and relatively easy to integrate.
- Salesforce transformed customer relationship management.
- Workday modernised HR systems.
- ServiceNow digitised enterprise workflows.
But in every case the operating model remained the same: people used software. Agentic AI disrupts that assumption.
In an agentic system, the software no longer waits for instructions. It observes data, interprets goals, and executes actions autonomously. Human involvement shifts from execution to supervision. The implication is profound: the primary “user” of enterprise systems may increasingly be another piece of software. When that happens, the entire design logic of SaaS begins to change.
From Applications to Digital Labour
What makes this moment particularly interesting is that we are already seeing early evidence of the transition. In China, logistics giants such as Alibaba and JD.com have deployed AI systems that autonomously optimise supply chain routing across thousands of delivery points in real time. The system continuously adjusts warehouse allocation, delivery routes, and inventory positioning without human intervention.
In the financial sector, JPMorgan’s COiN platform analyses complex legal contracts using machine learning, performing in seconds tasks that previously required thousands of hours of manual legal work.
Meanwhile, in Europe, telecommunications operators are increasingly deploying AI agents to manage network optimisation. Rather than engineers manually monitoring network performance, autonomous systems detect anomalies, predict congestion, and automatically adjust network parameters.
Even in customer operations the shift is visible.
Swedish fintech company Klarna recently reported that its AI assistant now performs work equivalent to hundreds of customer service agents, handling millions of conversations with customers across multiple markets.
These examples are not isolated experiments.
They represent early manifestations of a new organisational capability: digital labour. Lowering the Barrier to Adoption. Despite the promise, however, the deployment of agentic systems remains uneven. Research across global enterprises consistently shows that while organisations are experimenting heavily with AI, relatively few have managed to scale autonomous agents across their operations. The reasons are not difficult to understand. Building agentic systems requires a combination of capabilities that many organisations simply do not possess: data infrastructure, orchestration frameworks, governance models, and the ability to continuously train and monitor AI systems. This is precisely where a new commercial model begins to emerge. Instead of building digital workers internally, organisations can increasingly subscribe to them. In the same way that SaaS allowed businesses to consume software without managing infrastructure, Digital Workers‑as‑a‑Service allows organisations to deploy autonomous agents without building the underlying AI architecture themselves.
The analogy with cloud computing is striking. Few companies today build their own data centres. Instead they rely on cloud providers such as Amazon Web Services, Microsoft Azure, or Google Cloud. The same dynamic is beginning to appear with agentic AI.
Specialist providers are developing domain‑specific digital workers that can be deployed across industries: compliance agents, procurement agents, supply chain optimisation agents, and financial reconciliation agents. For smaller organisations in particular, this model dramatically lowers the barrier to entry. A mid‑sized manufacturer, for example, may never build an advanced AI operations platform internally. But subscribing to a digital supply chain agent that continuously optimises production schedules is entirely feasible.
New Business Models Emerge
This is where the real strategic opportunity lies. In previous technology waves, the companies that dominated were those that recognised how to translate technical capability into scalable commercial models. Google and Amazon emerged from the early internet economy. Salesforce and ServiceNow defined the SaaS era. Agentic AI will produce its own generation of platform leaders. But the opportunity is not limited to technology companies. One of the most interesting possibilities is that organisations will begin to package their own operational expertise as digital workers. Consider a global logistics firm that has spent decades refining supply chain optimisation algorithms. Instead of simply using those capabilities internally, the company could offer autonomous logistics agents to other businesses as a service.
A legal consultancy could deploy AI agents trained on its regulatory expertise to act as automated compliance advisors for smaller companies. A cybersecurity firm could provide continuous AI‑driven threat monitoring agents that operate across thousands of client networks simultaneously. In each case, the company is no longer selling software. It is selling operational capability. That distinction matters enormously.
Governance and Trust
Of course, the rise of digital workers also introduces new governance challenges. In earlier writing I have argued that AI governance must evolve beyond traditional IT risk management. When organisations deploy autonomous agents capable of executing decisions, oversight frameworks must address transparency, accountability, and human supervision. Encouragingly, regulators and international organisations are already moving in this direction. The European Union’s AI Act establishes risk classifications for AI systems and mandates governance controls for high‑impact deployments. Similarly, the OECD and various industry bodies have developed frameworks for responsible AI deployment that emphasise auditability, human oversight, and ethical safeguards.
In practice, organisations adopting digital workers will need new internal capabilities: AI supervision roles, model validation processes, and operational guardrails. Digital workers may perform tasks, but accountability will always remain human.
Why Leaders Should Pay Attention Now
One of the most consistent lessons in technology history is that early signals of structural change are often underestimated. Cloud computing initially appeared to be simply a more convenient way of delivering software. In reality it reshaped the economics of the entire technology sector. Agentic AI may prove to be an equally transformative shift.
When digital workers become widely deployable through service models, the cost structure of organisations begins to change. Routine operational tasks can be automated at scale, allowing human employees to focus on creativity, strategy, and complex decision‑making. Importantly, this does not imply the disappearance of human work. Rather, it signals the emergence of hybrid organisations where human and digital workers collaborate.
In many ways, the future enterprise may resemble a mixed workforce composed of people and autonomous systems working together. For business leaders, the strategic question is not whether this shift will occur. It is how quickly.
Organisations that begin experimenting with agentic systems today will develop the operational knowledge needed to manage digital workforces tomorrow. Those that delay may find themselves competing against companies whose operational efficiency has been radically transformed by autonomous systems.
Conclusion
When I wrote about the vulnerability of SaaS in the age of Agentic AI, the argument was not that SaaS would disappear. Far from it. SaaS will remain a critical foundation of enterprise technology. But its role is changing. Instead of being the destination, SaaS increasingly becomes the infrastructure layer upon which autonomous digital workers operate. We are witnessing the emergence of a new organisational paradigm: the digital workforce. And just as cloud computing democratised access to computing power, Digital Workers‑as‑a‑Service may democratise access to advanced AI capability. If that happens, the next decade of business innovation will not simply be driven by better software. It will be driven by autonomous systems that work alongside us, augmenting human capability and reshaping how organisations operate. The companies that recognise this shift early will not just adopt new technology. They will redesign how work itself is done!