Dramatic phrases like ‘Saas is dead,’ ‘SaaSpocalypse,’ and others have dominated recent discussions in the IT world.
However, that narrative is premature, according to Deloitte. Yes, in 2026, established SaaS vendors will face competition from AI-native ones, the firm forecasts, but the real story is that traditional enterprise software continues to grow as it becomes more intelligent, agentic, and outcome-focused. And, in the end, this can benefit IT buyers.
“The reality is far more nuanced” than the sensationalistic language, said Ayo Odusote, Deloitte’s software and platforms leader. “What’s driving that narrative is change in how enterprise software delivers value, primarily due to the rise of agentic and AI-native capabilities.”
SaaS isn’t dead, it’s just changing
In 2026, as the panic around SaaS vitality persists, Deloitte anticipates that established software players will focus on becoming full-stack, end-to-end agentic platforms that can build, run, orchestrate, and govern agents across numerous functions.
These legacy SaaS models (with subscription or seat-based pricing, and often rigid workflows) are being challenged by AI-first and AI-native companies offering highly specialized and industry-specific capabilities at potentially lower cost, noted Odusote. These smaller players are beginning with simpler and/or neglected workflows, but will likely evolve to more complex areas, while incumbents will pivot to hybrid pricing models and AI-infused tools.
“It’s important to avoid overgeneralizing ‘SaaS,’” Odusote emphasized . “Dev tools, cybersecurity, productivity platforms, and industry-specific systems will not all move at the same pace. Buyers should avoid one-size-fits-all assumptions about disruption.”
For buyers, this shift signals a more capability-driven, outcomes-focused procurement era. Instead of buying discrete tools with fixed feature sets, they’ll increasingly be able to evaluate and compare platforms that are able to orchestrate agents, adapt workflows, and deliver business outcomes with minimal human intervention.
In this landscape, Odusote advised buyers to consider:
- Value beyond feature parity: How does a tool improve actual business outcomes rather than just automating existing tasks?
- Total cost of ownership (TCO): New pricing models, whether hybrid, outcome-based, or usage-based, will require “more careful” budgeting and forecasting.
- Interoperability and governance: As agents increasingly operate across systems, integration and safe governance will be key.
- Compute cost impact: AI workloads have different cost dynamics. “You’ll need to understand how those costs flow through pricing and your infrastructure budget,” said Odusote.
For most enterprises, core enterprise resource planning (ERP) and customer relationship management (CRM) systems won’t go away; they’ll evolve with added agentic capabilities and, in many cases, “subsume smaller boundary systems because it becomes easier and cheaper to build that functionality into the core platform,” said Odusote.
A new layer will emerge, Deloitte forecasts: Essentially, it will be “[an] enterprise AI operating system” that will govern, orchestrate, and control AI agents, as opposed to disconnected tools. Buyers should begin thinking about who owns that layer and how it integrates with their broader technology architecture, Odusote advised.
IT buyers will benefit from simplified integration, since one platform reduces the complexity of stitching multiple tools together; provides centralized control over agent behavior, security, and compliance; and offers scalability across functions without the need for point solutions for each use case.
Going forward, Deloitte predicts, AI-native companies may lead with specialized agility, but incumbents will offer enterprise-grade stability and scale with added agentic capabilities. Traditional companies bring “integration depth, enterprise-grade controls, regulatory experience, and large installed bases,” said Odusote. Meanwhile, AI-native vendors will likely lead with greenfield architecture and rapid innovation.
“Many enterprises will ultimately balance both approaches rather than choosing one exclusively,” said Odusote.
What pricing might look like
Because the economics of AI are “quite different and more complex” than cloud migration, outcomes and return on investment (ROI) will require as much focus as possible, Odusote noted. “Value cannot just be incremental,” he said. Also, IT buyers may expect AI‑embedded software costs to be similar to those of traditional products, which can complicate “nascent hybrid pricing models.”
Buyers will likely have increased leverage in certain segments due to competitive pressure among new and established providers, Odusote said. New entrants often come with more flexible pricing, which obviously is an attraction for those looking to control costs or prove ROI.
At the same time, traditional SaaS leaders are likely to retain strong positions in mission-critical systems; they will defend pricing through bundled AI enhancements, he said. So, in the short term, buyers can expect broader choice and negotiation leverage.
“Vendors can no longer show up with automatic annual price increases without delivering clear incremental value,” Odusote pointed out. “Buyers are scrutinizing AI add-ons and agent pricing far more closely.”
At the same time, IT buyers are looking to avoid agent sprawl, or having “five different vendors, each deploying autonomous agents without centralized governance,” Odusote said. This can also create leverage, as buyers look to consolidate vendors or negotiate broader enterprise agreements.
“That said, buyers should prepare for more complex pricing discussions, understanding not just list prices, but how costs scale with AI usage and performance,” said Odusote. Cost frameworks that consider new metrics, efficiency, quality of growth, and fiscal caution may likely override traditional performance benchmarks.
Important questions for buyers
Ultimately, buyers should “elevate their questioning” beyond just features to capability, risk, and value delivery, Odusote said. He suggested that buyers ask:
- How does the vendor’s tool use AI agents to improve specific business outcomes?
- What metrics and KPIs should be used to measure ROI?
- Can the vendor explain their pricing model? How do costs scale with usage or outcomes?
- What data governance, security, and compliance controls are embedded for agentic operations?
- How open and interoperable is a vendor’s architecture with existing systems and data estates?
- How does a provider’s tool fit into a customer’s broader agent governance and orchestration strategy? Are they expanding into adjacent boundary systems? And if so, how does that affect overall vendor footprint and architecture?
In the end, buyers should treat AI adoption as a strategic business transformation, not just a tech procurement exercise, said Odusote. They must also build internal capabilities for AI governance and risk management. “Don’t let agents operate unchecked; your team should own safe, ethical use.”
“Ultimately, the biggest opportunity isn’t replacing core systems overnight,” he said. “It’s transforming the thousands of boundary applications and manual processes around them, turning service and manual work into software through agentic models.”
This article originally appeared on CIO.com.
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