It is an uncomfortable question. It is also the right one.

The answer is that Agentic AI is not the end of SaaS—but rather it is most likely the end of SaaS as a category defined by applications. In fact, in some companies, this process is already well underway.

Consider what this looks like in practice. Today, a finance team logs into an enterprise resource planning (ERP) app, a treasury platform, and a banking portal to reconcile invoices across systems. Tomorrow, an agent could do the same work overnight, pulling from each source, flagging exceptions, and surfacing activities that solely require human judgment. The vendor is no longer selling accounting software seats. It is selling reconciled invoices. That is the shift, multiplied across each corner of the enterprise—from finance and customer service to supply chain and IT operations.

The momentum is well documented. Deloitte Insights’ latest State of AI in the Enterprise survey shows that 74% of companies expect to use Agentic AI at least moderately within two years. This includes using AI agents to set goals, reason through multi-step tasks, coordinate work with humans and other agents, and act with limited supervision. SaaS, which has served as the operating layer for HR, finance, customer relationships, and supply chain, sits squarely in the path of this shift.

The impact of Agentic on SaaS can be broken down into three main areas:

• Pricing. Today’s per-seat (or users) and consumption models were built for a world where humans are the unit of work. Agents can break that assumption. One person supervising a team of agents can produce far more output. As such, pricing models that are dependent on per-user fees can have redundant SaaS stacks or unused software licenses. Even adding AI features like inference or token usage to support scaled AI adoption may lead to unpredictable bills and cost over runs. Hybrid models combining licenses with outcome- or value-based components as well as some usage component may become the standard.
• Interfaces. When agents do the work, the application’s user interface (UI) becomes a centralized digital command center—or control tower—not a workspace. Users may spend less time inside applications and more time setting goals, reviewing outputs, and intervening when judgment is required. That raises a question SaaS providers have not yet answered: where does that control tower live? Inside one vendor’s walled garden or in a neutral orchestration layer that sits above all of them? The question is important, even as UIs likely lose importance as the move to headless architecture accelerates, with multiple ways to interact with enterprise software the norm (and not necessarily directly through the software platform).
• Orchestration. Large enterprises will most likely not run their business on a single vendor’s suite of agents. They may run many, many agents from a combination of sources—some from incumbent suites, some from cloud-provider AI platforms, some from AI-native companies, and some built in-house. It’s not about who has the SaaS provider with the smartest agent. It is whoever owns the orchestration layer above the agents. As such, some SaaS providers are racing to embed agents into their existing suites and frameworks. Others are positioning their productivity surfaces as the universal control plane for other software. A few are acquiring AI-native companies to fill capability gaps. And a wave of newer entrants is betting that orchestration may eventually commoditize the underlying applications altogether.

As SaaS providers explore various Agentic strategies, here are some actions organizations can take to prepare:

• Establish a data foundation built for agents. Access, observability, lineage, and governance are the prerequisites to deploying agents safely at scale. Most enterprises overestimate how ready their data is and the gap can show up the moment agents start acting on it.
• Renegotiate the relationship, not just the contract. Organizations should consider building Agentic clauses into their SaaS contract renewals now, including the portability of agents and data, transparency on consumption, and the right to integrate with non-vendor orchestration layers.
• Decide on orchestration architecture deliberately. Organizations should carefully consider whether they should bet on a single vendor’s agent ecosystem, build a neutral orchestration layer of their own, or run a federated model. Without decisive action, an organization may find themselves having to live with architectural consequences.
• Train the workforce as agent supervisors, not agent users. Jobs are shifting from operating software to managing a hybrid workforce of humans and agents—setting goals, validating outputs, and stepping in when judgment is required. This is a cultural shift, not a software upgrade.

The largest SaaS providers have deep footprints, sticky data, and trusted relationships. They are not going anywhere. The opportunity for enterprise leaders is not to bet on which platform wins. It is to use this moment to redesign work, removing the friction that may have accumulated from layered SaaS investments and putting humans back on the highest-value problems.

The enterprises that lead the next decade are the ones that treat Agentic AI as a structural shift to architect for—not a feature to consume. The window to make these decisions deliberately is open now, but it’s not likely to stay open for long.

To learn more about SaaS, AI agents, and the latest trends in technology, see Deloitte Global’s TMT Predictions 2026 or visit the TMT industry page on Deloitte.com.