Agentic AI is fundamentally transforming SaaS
Agentic AI is running inside tools you probably use. Cursor’s code editor? AI writes code there. ServiceNow is resolving support requests with agents. Workday is generating journal entries. Adobe’s platform is writing and deploying ad copy, end to end.
What matters here is trajectory. Foundation models are evolving fast, accuracy is getting better, and costs are dropping sharply. OpenAI’s latest model, o3, dropped in price by 80% in just two months. That cost movement is massive. In a few years, routine tasks across your organization, anything rules-based and repetitive, could shift from being handled by people using apps to being executed by AI through APIs. And when that happens, the fundamental architecture of how your company gets work done will break from the old model of “humans using software.”
This matters at the executive level. If you still think of SaaS as software that your team interacts with directly to get linear benefits, that assumption is now outdated. What you’re dealing with next is a fully dynamic system that runs itself, makes decisions, and executes on your behalf. This changes your cost structure, changes how you compete, and reshapes what customers expect. Ignore it, and you’ll fall behind faster than you think.
Different SaaS workflows will experience varying levels of AI disruption
AI will not hit every part of your business the same way. Some parts of your SaaS stack will get more valuable. Others will shrink. You need to know which is which.
Workflows that still rely on human judgment and deep domain logic (like Procore’s project accounting or Medidata’s clinical-trial randomization) are harder to replicate. These are your strongholds. In this space, AI should be used to improve productivity, but keep pricing premium. You’ve got tight control over workflow logic and the risk boundaries are high. Competitors can’t just plug into this with a general-purpose AI model and replace your edge.
On the other hand, workflows exposed through open APIs, like list building on HubSpot or task boards on Monday.com, are a risk zone. Third-party agents can latch onto these and siphon off value. They’ve got high AI penetration. If you’re here, move fast. Launch your own agents. Tighten partner integrations. Start locking down your strategic API surfaces before someone else does.
Some parts of your software might become growth engines. This is where your company owns rare data or logic and the task is highly automatable. Think Cursor’s AI-driven coding or Guidewire’s insurance claims systems. Automate aggressively. Shift your pricing, drop seat licenses, sell results. The market doesn’t want access; it wants outcomes.
And then, there’s the battleground: workflows where automation is easy, and AI penetration is already happening. This is where incumbents lose if they stay static. Tier 1 support in Intercom, invoice processing at Tipalti, or time-entry approvals in ADP, these are straightforward tasks. They’re already replicable. You need to decide whether you’re going to supply the unique data these agents rely on or become the platform orchestrating those agents. You can’t sit still. If you’re not actively replacing your own features with agents, you’re leaving a door open to competitors who will.
SaaS leaders need to map where their core business falls within these workflows. Then align their product and go-to-market strategies accordingly. Don’t wait to react, start planning the shift now. If something’s at risk of being commoditized or cannibalized, act before the market does it for you.
Mapping workflows by automation and AI penetration potential
You can’t manage what you can’t see. If you’re running a SaaS company, or using one at scale, you need hard visibility into where AI can have the most impact, and where it might erode your position. That comes down to two key dimensions: how automatable a user’s task is, and how easily AI can penetrate that workflow behind the scenes.
Focus first on user tasks. If a workflow involves structured repetition, low context switching, and a clear dataset, agents can take over quickly. Tasks like ticket triage and invoice entry? These are already deep into automation territory. In workflows like these, value is likely to shift toward performance and scale, not headcount or interface design.
Then look at your system from the AI’s perspective. Is your workflow observable to external tools? Do you operate on standard industry formats? Are third-party models already trained on patterns similar to yours? These variables signal how vulnerable, or exposed, you are to AI penetration. The easier it is for an external model to understand and re-enable your workflow, the faster your moat shrinks. The more you depend on proprietary data, customized configuration layers, or regulatory constraints, the more defensible you still are.
This type of mapping isn’t optional. It allows you to see where AI is going to expand your market, by making previously unreachable processes efficient, and where it’s going to compress it by sidestepping your value proposition. You can’t afford to treat AI as a feature layer. It’s an infrastructure reality. Map your workflows. Quantify their potential. Act before an AI-native player redirects your customer flow.
Agentic AI introduces a three-tiered control stack
The old software stack centered around apps and dashboards that humans controlled. What’s coming next is a different architecture, one that’s controlled by agents, not people. You need to understand that structure if you’re going to build or stay relevant in this environment.
The foundation is what we call the system of record. That’s the authoritative source of truth. It’s your ERP system, your financial ledger, your HR database, whatever holds your critical transactions and applies the rules. This layer is hard to replicate. It has compliance baked in, long data histories, and structured access policies that are expensive to rebuild.
Stacked on top of that are agent operating systems. These orchestrate execution. They direct which agents carry out which tasks, in what order, and retain critical context. Today, companies like Microsoft (with Azure AI Foundry), Google (Vertex AI Agent Builder), and Amazon (Bedrock Agents) are building these. Their current edge comes from model access, GPU availability, and pre-integrated toolchains. But this middle layer won’t stay open for long, whoever controls orchestration owns the workflow.
At the top, you have outcome interfaces, systems that accept plain language from users and translate it into agent instructions. These interfaces show results back to the user and are embedded in communication channels people already rely on: Teams, Slack, in-app chat, mobile push. If you own the habitual user interface layer, you’re one step closer to owning the feedback loop that scales machine delegation.
Right now, most of these tiers are siloed. They don’t speak one fluent language. Each tier communicates in fragments. That’s the bottleneck. Companies like Anthropic and Google are already trying to fix this, Anthropic’s Model Context Protocol (MCP) and Google’s Agent2Agent (A2A) aim to standardize how agents interact. But they don’t solve for meaning. They don’t define what an “invoice” is, how the concept “payment approval” maps into real workflows, or what validators need to be involved.
The missing layer is semantic. The industry needs a common vocabulary and structure that allows autonomous agents to perform tasks without relying on custom integrations for every use case. That’s the opportunity. And whichever platform builds this semantic layer and drives broad adoption will pull a disproportionate share of the next wave of SaaS margin.
Emerging agent communication protocols could dictate industry dominance
Right now, there’s momentum behind protocols that allow AI agents to communicate across systems, Anthropic’s Model Context Protocol (MCP) and Google’s Agent2Agent (A2A) are prime examples. These standards handle how agents package API calls, tokens, and responses. They’re useful, and they solve part of the problem. But let’s be clear, they don’t go far enough.
MCP and A2A create a way for AI tools to interact across platforms, but they don’t define shared meaning. Agents still lack semantic clarity. They don’t inherently know what an “invoice” is, or how a “compliance check” relates to a data table or API endpoint. That’s a fundamental limitation. Without semantic standardization, defining key object types, states, and actions, agents remain brittle. Every new integration becomes a custom job.
This is the gap. That layer, where process concepts are described in a common, machine-readable language, is still open. Whoever closes it and builds an accepted semantic model for business workflows is going to set the direction for AI agent ecosystems. Once you own the standard object definitions, you’re not just part of the workflow, you structure it. That level of influence scales quickly, and the network effects are real. Early leadership here won’t just tip the market, it’ll reshape who captures the value created by agent-based systems.
Executives should be paying attention. If you’re not helping define the semantic standard for your industry, you’re at risk of being defined by it. That means others set the rules, harvest the data, and monetize edges you once controlled.
Incumbents can maintain leadership by integrating AI into product roadmaps
It’s clear AI is changing the structure of SaaS markets. But this is not a question of winners or losers by default. Incumbents can lead, if they’re willing to move with intent.
First, AI must be built into the core roadmap. It cannot be an add-on or side feature. Look at what your product really helps customers do, and automate it. Build toward outcomes. Shift from “helping users accomplish tasks” to “having the system do it for them directly.” That’s where the value is. Don’t wait for startups to do it faster.
Second, use your data edge. Proprietary, structured, deep data, how people interact with your platform, domain-specific behavior, transaction histories, these are assets most AI startups don’t have. Make sure your models train on it, use it, and protect it. Do not give external platforms permission to learn from your data unless you control the outcome. Workday is already positioning itself as a secure orchestration layer for both human and AI workflows. That’s a viable model to study.
Third, experiment with monetization now. Seat-based pricing isn’t going to make sense when AI handles the workload. Buyers will ask, “Why am I paying per user when no user touches this task?” Follow the lead of Intercom and Salesforce, move toward outcome-based pricing. Charge for results, tickets resolved, entries posted, campaigns optimized, not usage. You’ll stay ahead of pricing pressure and build long-term margin defense.
Finally, influence the standards. Guidewire and ServiceTitan are already putting schema definitions into the ecosystem. It’s selective open-sourcing, giving just enough to shape the direction of industry interoperability without handing away strategic control. That’s how you earn influence in the next wave of SaaS: not just by being faster, but by being harder to replace.
The opportunity is right in front of us. If you’re sitting on valuable data, running key workflows, and controlling platform access, your default position is strong. But legacy advantage doesn’t entitle you to the future. You still need to act, on the roadmap, on pricing, on platform control, and on ecosystem standards.
Building AI literacy and operational fluency across the business is key as AI becomes foundational
You can’t lead in an AI-first world without internal fluency. That means every core function in your company, product, engineering, sales, customer success, needs to understand what AI can do, what it can’t, and how it integrates into your workflows. Not from a distant theoretical perspective, but through real, functional knowledge that directly informs daily decisions.
Hiring a few machine learning engineers or adding an “AI Labs” team doesn’t achieve this. You need distributed capability. Your product leads should know how to scope and prioritize feature-level automation opportunities. Your sales teams must be able to explain AI-driven capabilities to senior buyers with clarity. Your customer support and onboarding teams need to communicate how agentic workflows reduce friction and deliver value. If you expect customers to trust the AI operating inside your platform, your own people must be able to explain it with confidence.
You also need to train differently. This includes onboarding internal teams into prompt design, reinforcement learning concepts, and standards in model evaluation. Some of this knowledge can be built, some will need to be bought. Either way, the intention has to be company-wide. The better your internal fluency, the easier it becomes to prioritize infrastructure improvements, identify new data-driven opportunities, and scale trustworthy automation.
Customers also need education. Many don’t fully understand how AI inside SaaS platforms actually operates. You need to take responsibility for shaping that literacy. Clarity builds trust. It reduces resistance. It accelerates adoption of your AI-powered workflows.
It’s about becoming an organization where using AI is second nature, where operating with intelligent agents, training models, and evaluating outcomes are normal parts of the business rhythm. Companies that build this fluency now will have a structural advantage, one that compounds over time. They won’t just utilize AI, they’ll operate with it as a fundamental layer of capability. That’s where the market is going. Be ready.
Final thoughts
Agentic AI is automating workflows that used to be core to the SaaS value proposition. What you once delivered with people and dashboards is now being executed, end to end, by autonomous systems talking directly through APIs.
As an executive, you’re operating a data-driven intelligence layer. If you don’t control that layer, its workflows, its semantics, and its outcomes, you risk becoming a background utility in someone else’s AI platform.
Decisions now carry more weight. Build AI into your roadmap. Protect and weaponize your proprietary data. Redesign pricing around outcomes. Define standards before others define them around you. And build internal fluency across every part of your company.
There’s no neutral stance here. You either shape the next phase of SaaS or follow the version someone else builds. If you’ve got the foundation, now’s the time to lead.
