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When software as a service (SaaS) first emerged 25 years ago, it revolutionized software by moving it to the cloud and speeding up feature delivery. Now, a fresh discontinuity is at hand. Generative and agentic AI—tools that can reason, decide, and act—are already:

• drafting code in Cursor's AI code editor;
• handling support tickets in ServiceNow;
• preparing journal entries in Workday Financial Management; and
• writing ad copy in Adobe’s Experience Cloud.

These aren’t experimental one-offs. The cost curve trajectory of foundation models is accelerating downward even as accuracy improves. OpenAI’s latest frontier reasoning model (o3) dropped 80% in just two months. In three years, any routine, rules-based digital task could move from “human plus app” to “AI agent plus application programming interface (API).”

SaaS providers know this strategic problem is urgent, but it’s also addressable. Product leaders must answer several strategic questions:

• Which user workflows can AI and agents automate? To what extent?
• Which SaaS workflows can be handled by AI and agents?
• Where will AI increase the size of the software market, and where will it cannibalize?
• Where are incumbents and new entrants favored?
• What investment priorities will shape the outcome in their favor?

In our work with clients, we see five broad possibilities for any given SaaS workflow: No AI, AI enhances SaaS, spending compresses, AI outshines SaaS, and AI cannibalizes SaaS (see Figure 1).

Figure 1

Potential for AI to automate tasks and penetrate workflows

To navigate these risks, executives should evaluate workflows according to two independent characteristics: the potential for AI to automate SaaS user tasks and the potential for AI to penetrate SaaS workflows. Mapping workflows against these characteristics can help identify value at risk and plans to capture it before it migrates elsewhere.

Six indicators can help companies understand the degree to which AI and agents can replace or further assist users: task structure and repetition, risk of error, contextual knowledge dependency, data availability and structure, process variability and exceptions, and human workflow and user interface dependency.

Where these indicators suggest a high potential to automate SaaS user activity, the AI disruption tends to expand the market, offering significant opportunity to capture top-line growth (see Figure 2).

Figure 2

Six additional indicators help identify which SaaS workflows are most easily replicated (and potentially captured) by AI and agents: external observability, industry standardization, proprietary data depth, switching and network friction, regulatory/certification barriers, and agent protocol maturity. The higher a workflow’s AI penetration potential, the easier it is for a clever AI wrapper to siphon usage and margin (see Figure 3).

Figure 3

Four strategic scenarios

By plotting products and workflows, SaaS providers can estimate which scenario from Figure 1 most resembles the impact of AI. This maps out across four strategic scenarios (see Figure 4).

Figure 4

• AI enhances SaaS (low user automation, low AI penetration): These workflows are core strongholds for incumbents. These still rely on human judgment, and rivals struggle to mimic the logic behind them. Think of Procore’s project cost accounting or Medidata’s clinical-trial randomization—both require deep domain knowledge, strict oversight, and regulated data flows. Incumbents should use AI to boost productivity while protecting the unique data that sets them apart. Price the time savings at a premium.
• Spending compresses (low user automation, high AI penetration): These workflows are like open doors that expose incumbents to new risks. People still play a role, but third-party agents can hook into exposed APIs and siphon value. Examples include HubSpot’s list building or the task boards on Monday.com. To defend and salvage value and customer influence, incumbents must launch their own agents fast, deepen partner integrations to raise switching costs, and limit access to critical end points.
• AI outshines SaaS (high user automation, low AI penetration): These workflows will be an incumbent’s growth gold mines. Here, companies hold exclusive data and rules, giving them a head start on full automation. Cursor's AI code editor and Guidewire’s claims adjudication are good examples. Leaders should build solutions with end-to-end agents, shift pricing from seat-based to outcome-based, and train sales teams to sell business results, not just features.
• AI cannibalizes SaaS (high user automation, high AI penetration): These workflows will be battlegrounds. Incumbents should have the advantage, but to keep it they will need to proactively replace SaaS activity with AI. Incumbents that fail to do this risk disruption, obsolescence, and losing out to entrants. Tasks such as Intercom’s Tier 1 support, Tipalti’s invoice processing, or ADP’s time-entry approvals are easy to automate—and just as easy for others to copy. Winners will be the organizations that scale agent orchestration best. Most companies must pick a lane: Either become the neutral agent platform or supply the unique data that powers it. Only a few giants (Salesforce, for example) can realistically do both.

Bottlenecks and the need for common syntax

SaaS unbundled suites of apps and services. Agentic AI is now rebundling control on a three-layer stack (bottom to top): systems of record, agent operating systems, and outcome interfaces.

• Systems of record sit at the base of the stack—the source of truth. They store core business data, manage who can access it, and enforce rules that keep everything consistent—from approvals to compliance checks. Their edge lies in unique data structures, long histories of activity, and built-in regulatory logic that would be costly for others to replicate.
• Agent operating systems sit in the middle tier, orchestrating the actual work. They plan tasks, remember context, and invoke the appropriate tools for users and agents. Early versions include Microsoft’s Azure AI Foundry, Google’s Vertex AI Agent Builder, and Amazon Bedrock Agents. Today’s advantage hinges on GPU scarcity, proprietary AI models, and tightly integrated toolchains that speed up deployment.
• Outcome interfaces form the top layer. These translate plain language requests—such as “close my books” or “replace pump 17”—into agent actions and share updates back through tools such as Teams, Slack, or custom mobile apps. Their power comes from being woven into daily routines and delivering a trusted, intuitive user experience.

As models have become more powerful, communication across layers and across vendors has become the bottleneck. Vendors have stepped into this void to improve syntax. Anthropic’s Model Context Protocol (MCP) and Google’s Agent2Agent (A2A) standardize the way agents package tool calls, security tokens, and results as they move among layers. But they don’t provide a shared vocabulary (that would define terms such as invoice), policy, or work order—nor do they show how those concepts map to APIs, tables, and approval gates.

The emergence of these standards (MCP and A2A) has shown strong network-effect dynamics—for instance, lightning-fast tipping points, winner takes most. We expect that the standard for this semantic layer will be similar. In other words, the first semantic layer that creates an industry-wide standard to enable an invoice.bot to talk to a payment.bot, for example, will reshape the AI ecosystem and direct a large next wave of value.

SaaS incumbents are well-positioned to lead—if they move fast. This will require high-stakes strategic bets—such as selective open-sourcing or a shift in the monetization model—and will yield a unique, durable industry influence position. Win here, and your platform becomes a marketplace, earning revenue even when someone else’s agent takes the action. Miss it, and you risk exposing your IP and becoming a silent back end while the semantic gatekeepers harvest the margin (see Figure 5).

Figure 5

Strategic priorities for SaaS leaders

Will AI and agents disrupt SaaS? Yes. In some cases, that disruption will grow the market; in others, it will commoditize the market. In some cases, the disruption will favor incumbents; in other cases, it will favor new entrants. Disruption is mandatory, but obsolescence is optional. What can SaaS executives do to navigate this opportunity?

• Make AI central to your roadmap. Look for the key jobs that your software helps users accomplish, and deploy AI to automate and speed them up. Take a customer-centric view: Identify repeatable tasks that smart agents can handle, and implement those before your customers look elsewhere. This could mean integrating off-the-shelf models or training your own model with your data. Turn your product into a “do it for me” experience, and help customers see the ROI. Embed AI deeply, stay at the center of the workflow, and deliver more value.
• Turn unique data into your edge. Your data is your moat. While models such as GPT-4o are everywhere, the real value lies in the proprietary data you own—usage patterns, domain-specific content, and transaction history, for example. Double down on capturing and using this data to deliver results no outsider can match. And protect it. If you connect with other AI platforms, make sure your terms stop them from learning from your data and cutting you out. The aim: Become the best source of truth for a key process or data set. Workday’s positioning as a secure hub for managing both human and AI workflows is a good model.
• Shape investment and competitive plans across the four strategic scenarios: core strongholds in which AI enhances SaaS, open doors in which spending compresses, gold mines in which AI outshines SaaS, and battlegrounds in which AI cannibalizes SaaS.
• Decide your strategy for addressing the semantic gap for your industry.
  • Get your house in order: Standardize how you define key objects within your own platform. This sets the foundation to either join or lead the next generation of industry-wide agent platforms.
  • Open source early, selectively: Publish schemas in which you already lead—as ServiceTitan and Guidewire do. Doing nothing cedes definition power to others; giving away too much puts competitors on a fast track. In standards wars, early movers with a practical solution often win.
  • Make it hard to copy: Build unique constraints—for instance, approval flows, state transitions, and compliance rules—right into your data model. Any external agent should have to validate through your system of record.
  • Rally the ecosystem: Standards stick when vendors, customers, and cloud platforms align. Bring the group together, shape the agenda, and offer real code to become the default leader.
• Rethink pricing for an AI-first world. Seat-based pricing may not fit when AI is doing the work. If an agent replaces a human task, customers will expect to pay based on outcomes, not log-ons. Start experimenting with pricing tied to results: tasks completed, tickets resolved, AI outputs generated. Leaders, such as Intercom and Salesforce, are already shifting in this direction. The fundamental shift is to stop charging for access and start charging for work done. Stay flexible as you learn what your customers value most.
• Build AI fluency across the business. AI needs to be a core capability, not a side project. That means helping your teams understand what AI can and can’t do, hiring or training the right talent (from machine learning engineers to prompt designers), and building a culture that’s excited about innovation. Everyone—from product to sales—should be able to explain how your AI features work and what value they deliver. And that fluency should extend to customers, too. Help them understand and get the most out of what you’ve built. In the end, your organization should be as comfortable using AI as a new hire is with a browser.

Write the next chapter before your competitors do

AI is disrupting SaaS, creating upsides and downsides. By tailoring investments and strategic plans to each workflow’s strategic context, anchoring to the new platform layers, and investing in semantic gaps that affect your developers, today’s leaders can shape the future—not chase it.

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