During a recent lesson about AI with Massimo Chiriatti at Università Cattolica, a smart student asked about SaaS and Agentic AI – specifically about the potential impact on SaaS business models. I wasn’t completely satisfied with my answer to such a profound question, so I decided to think deeper and formulate a more complete response.
The Nature of the Shift
The transition from SaaS to agentic AI represents more than just another technology shift. While SaaS transformed how we distribute and consume software, agentic AI is transforming how we create it. Let’s understand why I think this transformation might fundamentally change the software industry as we know it.
The evolution from on-premise to SaaS was primarily about distribution and consumption. Software remained fundamentally the same – it just lived in the cloud instead of your desktop. But the shift to agentic AI is different. It’s not about where software lives, but about how it comes to life.
Let’s decompose this transformation. SaaS products are built around specific use cases: Salesforce for CRM, Workday for HR, ServiceNow for IT. They’re intent-specific, with predefined features and workflows. Agentic AI fundamentally changes this paradigm. Instead of adapting to the software’s logic, the software adapts to your intent. The same agent can write emails, analyze data, or create presentations – not because it has different modules, but because it understands and adapts to different needs.
At dunnhumby, we spent months building specific analytics features into our customer data platform. Each new capability required careful planning, development, and release management. An agent, instead, can create new capabilities on the fly, adapting to specific user needs as they arise. This shift from fixed functionality to fluid adaptation represents a fundamental change in how software creates value.
The Business Model Challenge
Let’s look at how this shift impacts business models. The SaaS model thrived on predictability: recurring revenues, clear value metrics, usage-based pricing. Software as a service meant exactly that – a defined service, delivered consistently, at scale.
But what happens when your software isn’t a fixed product, but a fluid conversation? At Yahoo!, we built an advertising platform that served hundred of thousands of advertisers. The value came from standardization – same features, same workflows, same interfaces for everyone. Agentic AI inverts this model. Instead of one solution serving millions, we might see millions of unique solutions, each adapted to individual needs.
This creates interesting challenges in value capture. How do you price an agent that can theoretically do anything? The traditional SaaS metrics – users, features, usage – might not apply. If an agent can (eventually) replace multiple SaaS products, does it command multiple subscriptions? More importantly, who captures this value, and how?
The Disruption Pattern
The way SaaS disrupted on-premise software offers interesting insights into how agentic AI might disrupt SaaS. But the pattern looks quite different. SaaS won by making software more accessible and cost-effective, while maintaining the same fundamental value proposition. Agentic AI, instead, is changing the value proposition itself.
Let’s analyze where this disruption is likely to start. The first wave isn’t targeting core business processes – it’s focusing on the knowledge work around them. Every SaaS tool requires users to translate their intent into system actions. At dunnhumby, our data scientists spent considerable time translating business questions into SQL queries. Agents will at some point likely eliminate this translation layer entirely – they’ll understand the intent and execute it directly.
The integration layer presents another interesting angle. The SaaS ecosystem created data and workflow silos, spawning a multi-billion dollar integration market. But agents don’t see silos – they see capabilities. If your agent can seamlessly work across systems, the need for traditional integration diminishes significantly.
The Industry Impact
Not all SaaS businesses are equally vulnerable to this disruption. The pattern isn’t random – it follows lines of resistance and opportunity.
The most vulnerable? Tools where users spend significant time translating thoughts into actions. Think about report builders, data visualization tools, basic analytics platforms. At Pubmatic, our business intelligence stack was essentially a complex translation layer between business questions and data answers. That’s natural territory for agents.
Then there’s the long tail of niche SaaS solutions. The software industry’s beauty is that (thanks to scale and standardization) it can support highly specialized tools – from email signature management to invoice processing. Each solving a specific problem well. But when one agent can handle all these tasks, the economics of specialized SaaS become harder to justify. Of course, there will be high initial training costs which might slow this down, or prevent it in some areas.
Some categories will prove more resilient. Mission-critical platforms with deep workflow embedding, compliance-heavy solutions where predictability matters more than flexibility, real-time operational systems where microseconds count – these won’t transition quickly. Their value isn’t in the interface – it’s in the reliability, compliance, and ecosystem they’ve built.
The Coexistence Question
The emergence of cloud computing didn’t kill on-premise software entirely – it created a hybrid reality that still exists today. The transition to agentic AI might follow a similar pattern, but with a very significant difference: while cloud was about location, agents are about creation and interaction.
Let’s understand what this means for the future. Most enterprises today run a complex mix of on-premise and cloud solutions, each chosen based on specific requirements around control, compliance, and capability. Tomorrow’s technology landscape will likely see a similar blend of SaaS and agentic AI, but the decision factors I think will be different: instead of choosing based on deployment models, organizations will choose based on interaction models.
Value Creation in the Agentic Era
I think it is worth giving some thought to the possible evolution of value creation in software. SaaS 1.0 created value through standardized features – the same solution for everyone, delivered efficiently at scale. SaaS 2.0 added value through ecosystems and integrations – platforms that could connect and extend functionality across solutions.
The agentic era will likely introduce a fundamentally different value creation model. At dunnhumby, our products created value by solving specific problems well – like customer analytics or retail media. But they were still bound by their predefined capabilities. Agents create value through adaptation and learning – understanding each user’s unique context and evolving with their needs.
This shift challenges fundamental SaaS foundations:
- The user interface becomes a conversation (or something else, TBD)
- Feature sets become capability spaces
- Product updates become continuous evolution (and no longer top down)
- User training becomes agent learning
Looking Ahead
The transition from SaaS to agentic AI isn’t just a technology shift – it’s a fundamental rethinking of how software creates and delivers value. Traditional SaaS vendors face a choice similar to what on-premise vendors faced with cloud: resist, adapt, or transform.
The winners in this new era won’t be those who simply add AI capabilities to their SaaS products. This will prove to be just a limited efficiency play that leaves them susceptible of disruption and eclipse. Success will come to those who reimagine what’s possible in an agent-first world. This might mean new business models, new value propositions, and new ways of thinking about software creation and distribution.
The real question isn’t whether agentic AI will replace SaaS – it’s how the software industry will evolve to embrace both paradigms. The future will not be either/or, but rather a sophisticated orchestration of both models, each applied where it creates the most value.
Let’s continue this conversation in the comments. How is your organization thinking about this transition? What opportunities and challenges do you see in this evolution?
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