Is SaaS Under Threat? Enterprise Strategy & the Next Wave of AI Growth

For the past several years, AI has dominated enterprise technology conversations. Organizations have experimented with copilots, generative AI applications, and increasingly autonomous agents. But as the technology matures, the conversation is shifting.

The question is no longer simply, "What can AI do?"

It's where AI should create value, how enterprises should support it, and what happens to the technology and operating models built before AI.

The latest episode of ConstellationTV brings together Constellation Research analysts to explore those questions from three different angles. The conversation moves from the future of SaaS and the impact of AI on enterprise applications, to the broader priorities now facing corporate boards, to practical opportunities for AI agents to transform revenue-generating work.

Is SaaS Really Under Threat From AI?

The episode opens with The Great Debate, featuring Larry Dignan, Esteban Kolsky, and Martin Schneider.

Their central question is provocative: Is the SaaSpocalypse over?

AI introduces a different kind of disruption for enterprise software. Instead of simply adding another capability to an existing application, AI can change how people interact with software and how work moves across applications.

If an AI agent can coordinate a task across multiple systems, employees may spend less time navigating individual applications. That raises fundamental questions about the future role of SaaS, the value of application interfaces, and how software vendors will differentiate in an increasingly AI-driven market.

But the debate isn't as simple as AI replacing SaaS.

Enterprise applications contain data, workflows, business rules, integrations, and processes that organizations depend on. Those systems may become more important to AI, even as the way people interact with them changes.

That creates a more nuanced future for enterprise software.

The application may no longer be the primary interface between a worker and the business process. Instead, AI could increasingly become the interface and orchestration layer spanning the applications beneath it.

What does this mean for enterprise technology leaders?

The important question isn't whether an application has AI features.

Leaders should ask:

• What role will this application play in an AI-driven operating model?
• What data and workflows does it control?
• Can AI agents securely access and act on that information?
• Can it participate in workflows beyond its own application?
• How will its value change as employees interact with software differently?

The Great Debate suggests that AI may change the role and economics of SaaS without making enterprise applications obsolete.

AI Is Becoming Part of a Bigger Enterprise Agenda

The episode then shifts from the future of enterprise software to a broader executive question: What should be on the enterprise agenda now?

In The Board, Esteban Kolsky examines how AI is becoming one part of a broader set of priorities for enterprise leaders.

AI remains an important catalyst, but it no longer needs to consume the entire executive agenda. Organizations are balancing AI investments against issues such as resilience, sustainable growth, workforce capabilities, infrastructure dependencies, trusted information, security, and accountability.

That represents an important maturation of the AI conversation.

During the early stages of generative AI adoption, organizations were largely asking what the technology could accomplish. Now, executives have to determine where AI belongs in the operating model and how to measure whether it is actually creating value.

That means AI strategy increasingly intersects with:

• Business outcomes. AI investments need to contribute to growth, efficiency, resilience, or another measurable enterprise priority.
• Infrastructure. AI workloads introduce new dependencies around computing capacity, architecture, and cost.
• Data and trust. AI systems need access to reliable, governed information if organizations expect them to make decisions or take action.
• Workforce transformation. As AI takes on more work, organizations need to rethink roles, skills, and how humans interact with increasingly autonomous systems.
• Governance and accountability. The more autonomy an AI system has, the more important it becomes to establish clear boundaries around what it can do and who is accountable for its actions.

The takeaway is straightforward: AI strategy is becoming enterprise strategy.

Five Ways AI Agents Can Transform Revenue Work

The final segment gets much more tactical.

Martin Schneider explores how organizations can move AI agents beyond individual productivity use cases and into the lead-to-order process, where AI has a direct connection to revenue.

His research identifies five opportunities:

1. Signal-Based Prospecting

   AI agents can identify signals that indicate a potential buying opportunity and help sales teams prioritize where to focus.

2. Lead Qualification and Routing

   Agents can evaluate leads against relevant criteria and help determine where they should go next, reducing manual work and improving speed.

3. Deal Acceleration and Risk Mitigation

   AI can help identify potential obstacles within deals, surface relevant information, and support teams in moving opportunities forward.

4. Proposal, Quoting, and Invoicing

   AI agents can help automate parts of the process between opportunity and order, reducing friction and potential revenue leakage.

5. Post-Sale Expansion

AI can continue looking for signals after the initial sale, identifying opportunities for expansion and additional customer value.

The significance of these use cases isn't simply that AI can automate individual tasks. It's that agents can participate across an entire business process.

That is a meaningful shift from the traditional copilot model. Instead of asking, "How can I use AI to do my job faster?" organizations can begin asking:

"What parts of this business process can AI reliably own?"

From Copilots to Enterprise Workflows

That question connects all three segments of the episode.

• The Great Debate examines what happens when AI changes the way people interact with enterprise software.
• The Board examines what happens when AI becomes embedded in the broader enterprise strategy.
• The lead-to-order research examines what happens when AI agents begin taking responsibility for pieces of actual business workflows.

Together, they point to a larger transition:

AI is moving from a feature people use to a capability organizations operationalize.

That transition creates new requirements for technology leaders.

AI needs reliable data. It needs access to the right systems. It needs security and governance. It needs clear boundaries. And organizations need a way to evaluate whether AI is actually improving business outcomes.

Human judgment remains critical as well. The opportunity isn't necessarily to remove people from workflows. It's to determine which work machines can handle reliably and where human expertise remains essential.

What Should Enterprise Leaders Be Watching?

The next phase of enterprise AI will not be defined solely by who has the best model or the most AI features.

Leaders should be watching several interconnected shifts:

• How AI changes software consumption.
  The traditional application interface may become less important as agents increasingly act across systems.
• How organizations measure AI value.
  Experimentation is giving way to expectations around revenue, efficiency, resilience, and measurable business outcomes.
• How agents move into core workflows.
  The biggest opportunity may be less about individual productivity and more about redesigning how work gets done across the enterprise.
• How governance evolves.
  Greater AI autonomy requires greater attention to security, accountability, permissions, and oversight.

• How humans and AI work together.
  The organizations that succeed will need to determine where AI can operate independently and where human judgment remains essential.

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The Bottom Line

Enterprise AI is moving into a more consequential phase.

The technology is no longer something organizations can evaluate in isolation. AI is beginning to reshape applications, operating models, executive priorities, and business processes.

That doesn't mean every application becomes obsolete or every workflow becomes autonomous. Enterprise leaders need to rethink where technology creates value and how AI fits into the systems that already run the business. The question becomes how leaders will deliberately redesign the enterprise around it.

Watch the latest episode of ConstellationTV for the full conversation with Constellation Research analysts on the future of SaaS, the evolving enterprise AI agenda, and five opportunities for AI agents to transform revenue workflows.