Two of the most fascinating developments shaping the future of artificial intelligence (AI) are the rise of agentic AI – systems that can autonomously reason, plan and execute complex tasks – and generative AI, which can create software and code without human intervention. These innovations promise to transform the software industry, particularly the Software-as-a-Service (SaaS) sector, which faces either a significant opportunity or a looming threat.
The evolution of AI from a supportive feature to a truly autonomous actor challenges the very foundation of how SaaS has traditionally operated and now faces pressure to adapt to the digital age or risk obsolescence.
Understanding the Shift: Agentic Workflows vs Traditional SaaS
SaaS platforms are designed as systems of record. Their role is to organise, store and present data within structured environments, facilitated by clean user interfaces and predictable interactions. These platforms require human involvement – users input data, manage workflows manually and navigate through UIs to accomplish tasks. Common examples include tools like Salesforce, Microsoft Outlook, Slack and Zoom.
By contrast, agentic workflows represent a cross-industry shift towards AI-driven task automation and decision-making. Rather than relying on humans to initiate every step, agentic AI can interpret high-level instructions and autonomously determine how best to achieve them. These agents can access databases, invoke APIs, switch between platforms, and adapt to real-time feedback – essentially becoming a digital workforce that operates with minimal or no direct human involvement.
This transformation is not simply a matter of layering AI on top of existing platforms. SaaS and agentic AI have fundamentally different foundations. SaaS is static and reactive; it is built for stability, predictability and user control. Agentic AI, on the other hand, is dynamic and proactive. It’s designed to adapt, reason and act autonomously. Integrating the two requires rethinking how systems function – from how data flows, to how decisions are made, to how tasks are initiated and completed.
The dual threat to traditional SaaS
There are two main disruptions AI introduces to the SaaS landscape, with agentic workflows potentially eventually replacing the traditional SaaS interface altogether. Rather than logging into a dashboard or app, users can interact with a digital agent via natural language. This agent can perform the same functions – retrieving data, updating records, sending communications – but does so independently, using backend services without exposing a UI to the user.
In this scenario, SaaS applications risk becoming mere data repositories – pushed to the background as AI agents control the user experience.
In parallel, generative AI is empowering users to build software on demand. Tools like Replit and GPT-4 allow individuals to create functional applications – such as CRMs or task managers – without technical expertise. These DIY applications can mimic the capabilities of SaaS platforms, but they’re more flexible, cost-effective, and tailored to specific needs. This threatens the core SaaS model, where value is traditionally derived from licensing access to static, pre-built platforms.
What does the future hold?
The future relationship between SaaS and agentic AI could evolve in one of three ways:
1. Parallel Pathways
In some sectors, agentic AI and SaaS may continue to exist independently. Regulated industries – such as finance, healthcare and defence – may favour the predictability, auditability and control of SaaS platforms. Here, compliance demands strict oversight, and AI autonomy introduces potentially unknown risks.
Additionally, deeply embedded industry-specific SaaS solutions, such as electronic health records or manufacturing control systems, offer niche capabilities that are difficult for general AI to replicate.
2. Convergence of Hybrid Systems
More likely in the short term is a gradual convergence. SaaS providers are already embedding agentic capabilities into their products to enhance user experience. For instance:
- Salesforce’s Einstein assists with sales forecasting, automates data entry and recommends next steps.
- Adobe Sensei helps marketers generate personalised campaigns and A/B test them in real time.
- Microsoft Copilot enables natural language interaction with Excel, Word and Outlook, streamlining tasks like data analysis or document generation.
According to the 2024 SaaS Benchmarks Report, 56% of companies reported that they’ve launched or tested AI features in their products within the past year. Of this group, 41% are monetising AI features, a number that’s up 9% from 2023. These overlaps suggest that SaaS and agentic AI are converging, with AI layered on top of structured SaaS foundations to deliver more autonomous, user-friendly workflows.
These examples illustrate how agentic AI is transforming UI/UX. AI doesn’t replace the SaaS platform – it enhances it, creating more intuitive, responsive and autonomous user journeys.
Additionally, agentic AI is already performing cross-platform orchestration. AI agents can schedule meetings, update project trackers and send follow-ups – all without human command.
On the infrastructure side, generative AI is powering low-code/no-code platforms that allow businesses to create their own tools. These developments suggest a world where traditional SaaS and custom AI-built software coexist – SaaS providing the foundation, and AI delivering the flexibility.
3. Agentic AI Dominates
The most radical (and in some verticals, inevitable) scenario is that agentic AI could eclipse SaaS altogether. As AI becomes more adept at interpreting goals and generating solutions, users may move away from rigid, subscription-based tools towards more fluid, outcome-based models.
In such a world, the user doesn’t pay for access to a tool – they pay for a result. Want to launch a marketing campaign? You don’t log into five different SaaS platforms; you tell your agent the outcome you want, and it handles everything – from planning to execution to reporting. This future may not be far off. Gartner predicts that by 2028, over 30% of enterprise software will have autonomous decision-making capabilities, compared to less than 1% today.
Regulatory Roadblock
Regardless of the future trajectory, AI systems – particularly agentic ones – must operate within legal and ethical frameworks. As these systems take on more responsibility, ensuring they are safe, auditable and compliant becomes paramount.
Governments and industry bodies are already developing AI regulations that stress transparency, consent and accountability. Companies deploying agentic systems will need robust governance protocols and data protection safeguards to maintain user trust and regulatory compliance.
What Should Companies Do Now?
SaaS companies must act quickly to remain relevant in an AI-first world. Embedding AI as a core feature – not just as an add-on – will become essential to survive. Platforms should evolve into agent-native systems that enable autonomous workflows, shifting from user-driven tasks to intelligent orchestration. This requires rethinking how data moves, how actions are triggered, and how value is delivered. At the same time, traditional per-seat pricing models may need to give way to outcome-based pricing that reflects the real benefits AI can provide.
To stay ahead of the curve, SaaS providers should also embrace the generative shift by offering AI-powered tools that let users build and customise their own applications. This turns a potential threat into a competitive advantage. For investors, the focus should be on whether companies have a defensible AI strategy and the flexibility to pivot toward agent-led, adaptive platforms. Those that do, will be best positioned to lead the next wave of enterprise software.
Who Will Take Control?
The future of SaaS in an agentic AI world is not black and white. All three scenarios, separation, convergence and eclipsing, are likely to play out differently across industries and companies.
In highly regulated or specialised fields, traditional SaaS will endure. In others, we’re already witnessing the rise of “service-as-software,” where the product is not the platform, but the outcome. In this world, AI agents become the new interface – and perhaps, the new vendor.
Agentic AI doesn’t mean the end of SaaS. But it does mean the end of SaaS as we know it. The companies that adapt early – reimagining not just their products, but their role in this emerging AI ecosystem – will define the next era of enterprise software. Those that don’t may find themselves outpaced, not by faster competitors, but by smarter machines.
