Agentic AI is a disruptive force poised to complement SaaS
Let’s be clear, agentic AI is going to change how software works. But it’s not the doomsday scenario people claim. SaaS won’t vanish. It will evolve.
Agentic AI refers to systems that act autonomously on your behalf. These agents don’t just follow commands, they can assess data from various sources, decide what to do, and execute it. There’s been mainstream talk, including comments by Microsoft CEO Satya Nadella, suggesting that most business apps, essentially CRUD platforms (Create, Read, Update, Delete)—will collapse into these AI layers. He’s right about the shift in logic from the app layer to the AI agent layer, but he also made it clear on the BG2 podcast that SaaS isn’t going away. Applications like Excel and Word don’t become useless, they become smart canvases for these agents.
That’s the real picture. Today, SaaS platforms are still systems of record. They store workflows and trusted data. If you’re running a company, you already know how deeply embedded processes are inside platforms like Salesforce or ServiceNow. Removing them overnight? Not possible. Agents might take over the user interface, handling input without human clicks and keyboard routines. But the logic and data still sit inside those platforms.
This will lead to deeper integration over time. Not elimination. If you run a SaaS company or rely heavily on one, this is your new path: optimization through intelligent agents, not obsolescence.
According to Gartner, by 2028, about 15% of daily business decisions will be made by autonomous agents. That’s up from zero today. That’s not a minor shift. It means automation at scale, but it doesn’t mean SaaS evaporates, it becomes more intelligent, faster, and less intrusive.
Agentic AI’s direct interaction with SaaS interfaces
Most work today is still filled with repetition. Data entry, reporting, system updates, these tasks aren’t strategic. They’re necessary, but they’re drag. Agentic AI doesn’t just speed them up. It eliminates the need for people to handle them altogether.
These systems take action without waiting for a command. They don’t care whether the work is spread across five SaaS platforms or one. Give the agent access; train it with workflows; and it acts. This changes how scale works. Instead of hiring more people or adding more licenses, you assign an agent. Pricing shifts. Productivity scales. Latency drops.
There’s quantifiable impact already. At SnapLogic, CTO Jeremiah Stone said agentic AI led to a 90% cut in data entry and reporting time for their Salesforce environment. That’s not about cutting headcount, it’s about unlocking time. Let your people focus on solving real problems instead of moving information between forms.
If you’re managing enterprise infrastructure, you’ll see this trend soon enough. Expect fewer manual interactions, even with platforms your teams depend on. This translates directly into cost savings, fewer SaaS licenses, less training for interfaces, and reduced process friction.
The play isn’t to rip out your suite. You automate interaction with it. That’s where agentic AI comes in. It slashes time costs and gives leadership more capacity to think, not just operate.
The transition’s underway. Smart execs should be defining where AI integration shortens the path to business outcomes, without assuming existing infrastructure has to be dumped. Most of it just needs augmentation.
Agentic AI offers more flexibility
Traditional software often breaks at the surface. Change the layout of a form, and suddenly workflows collapse. Fields end up in new places, dropdowns change, validations fail. This is where agentic AI takes a very different path.
Agents don’t rely on UI. They operate through APIs or backend logic, which means they’re not affected by cosmetic changes. If a form or document input structure changes, the AI adapts, by learning. It rewires how it understands the input without needing to rewrite code rules. More usage trains the models. And more training makes agents more effective over time.
Mike Wertz, Program Engineering Lead at Aptia, laid it out well. His team developed agents that automatically understood changes in data input windows, even if human users would have struggled to adjust quickly. That kind of responsiveness is now built into your workflow logic, not coded in post-change updates.
For executives leading digital operations, this is a major shift: AI agents introduce resilience. You reduce your dependence on manual patching and scripted fixes. Your systems begin to self-correct. The more your team uses the agents, the smarter they get. That’s not just additional efficiency; it’s operational agility.
This also unlocks a shift in how you handle system maintenance. IT doesn’t need to intervene every time a layout changes. Human support moves to oversight and exception handling. Execution stays autonomous and stable.
Businesses can expect a better return on automation investments, not just because output speeds up, but because those automations aren’t brittle anymore. They keep working even when change happens.
Agentic AI is dependent on the quality and security of data
Agentic AI isn’t perfect. And it’s not plug-and-play. Under the hood, it’s often powered by large language models (LLMs), which are probabilistic systems. That means the output is only as good as the inputs. If the training data is skewed, incomplete, or compromised, the agent will make flawed decisions.
This isn’t just a risk to efficiency, it could be a liability. AI that’s acting autonomously can misinterpret data trends, trigger actions based on wrong assumptions, or expose sensitive information if poorly secured. When you give an agent access to business-critical systems, you’re extending trust. So your data quality and access controls must be locked down first.
Tom Coshow, Senior Director and Analyst at Gartner, pointed out that most enterprises aren’t ready to go full-scale with agents yet because their data isn’t “in shape.” He raised two key concerns: biased or inaccurate data leads to bad outcomes, and unsecured agents could violate permissions by accessing restricted information across systems with inappropriate authority.
This is a serious point for C-suite decision-makers: if you’re building or buying AI capability, start with internal data readiness. Clean datasets. Role-based access controls. Security layered from user to database. Without that, AI doesn’t amplify intelligence, it amplifies risk.
It’s important to know that the space is still early. Enterprises are experimenting, iterating, and tightening control. But if you don’t treat data as a product, intentional, managed, clean, you’ll get unpredictable results from even the smartest agents.
Adoption should be staged and security-first. Evaluate models, test outcomes, and control decision rights. Use AI agents where trust is earned, not just where speed is desired. That discipline, early on, pays dividends as the tech matures.
Major SaaS providers are proactively integrating agentic AI
SaaS platforms aren’t sitting still. They see the shift to agentic AI, and the best ones are already embedding it directly into their systems. This isn’t defensive, it’s strategic. Companies like Salesforce and ServiceNow are building no-code, agent-friendly tools that allow their enterprise customers to create automation within the platform, instead of looking elsewhere.
This move keeps key workflows and data securely within their ecosystems. It also removes friction for business units that need automation but don’t want to deal with the overhead of standalone AI development. If you’re a platform provider, building this in means customers don’t churn to third-party AI solutions. If you’re a customer, using built-in agents is faster, more secure, and fully integrated with your existing stack.
The market is already fragmenting. According to Tom Coshow, Senior Director and Analyst at Gartner, there’s growing diversity, from startups to hyperscalers to private, enterprise-built agents. That means deployment will take many forms, but the trend is consistent: more automation, built into more SaaS products, with greater customization.
For C-suite leaders, this is an inflection point. You don’t need to abandon your current vendors. But you do need to evaluate which ones are building automation into the core of their offering, and which aren’t. If your platform can’t support agentic workflows or doesn’t have a roadmap for AI integration, you’re behind. And your competitors will start moving faster while your teams remain stuck in manual processes.
The relationship between SaaS platforms, AI agents, and the people using them is going to evolve iteratively, not all at once. The systems will co-exist and improve together. That’s the path forward, automate inside your systems, not in isolation. Stay aligned with vendors that can incorporate agentic AI natively, and your infrastructure stays future-proof without overhauling your tech stack.
Key executive takeaways
- Agentic AI complements rather than replaces SaaS: Leaders should view agentic AI as an enabler that enhances SaaS platforms rather than threatens them. While agents automate user interactions, SaaS remains essential as the system of record and workflow engine.
- Workflow automation reduces cost and boosts efficiency: Executives should invest in agentic AI to eliminate repetitive tasks and reduce SaaS licensing overhead. Automating multiple platforms through a single agent drives measurable savings in time and operational complexity.
- AI agents improve adaptability across changing systems: Leaders should prioritize AI-driven systems that can self-adjust to evolving data structures and interfaces. This flexibility reduces IT maintenance burdens and sustains workflow continuity.
- Data quality and security are critical to AI success: Decision-makers must ensure clean, unbiased, and secure datasets before rolling out agentic AI. Poor data or weak access controls can lead to operational errors and regulatory risks.
- SaaS vendors are integrating AI to stay competitive: Stay aligned with platforms embedding AI agents natively. Doing so ensures sustainable automation without abandoning enterprise infrastructure or retraining teams on new tools.
