This article was written by David Crawford, Chris McLaughlin, Purna Doddapaneni and Greg Fiore . The original article was published by Bain & Company. You can find the article here.
Disruption is mandatory. Obsolescence is optional.
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:
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:
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).
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:
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:
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).
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).
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).
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).
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.
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).
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?
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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