The Great Debate: Will Agentic AI Kill SaaS? | Bain & Company

Introduction: The Dawn of Agentic AI and the SaaS Reckoning

For decades, Software as a Service (SaaS) has been the backbone of enterprise productivity, transforming how companies access and utilize software. But today’s tech landscape is rapidly shifting, driven by the skyrocketing capabilities of artificial intelligence—especially agentic AI, or vertical AI agents, designed to perform end-to-end business tasks once reserved for entire teams. As entrepreneurs and executives debate the future, one question echoes the loudest: Will agentic AI replace SaaS, or even make it obsolete?

In this blog post, we break down the evolution of SaaS, the transformational impact of agentic AI, practical examples from real startups, and what the research says about this disruptive phenomenon. Drawing insights from Bain & Company’s authoritative analysis and candid industry conversations, we aim to answer the pressing question: Is SaaS at risk—or on the brink of a new renaissance?

From SaaS to Agentic AI: Understanding the Shift

The journey of SaaS began with a fundamental technological shift: software moved from being a boxed desktop product to accessible, cloud-based tools. Key innovations (like the advent of XML HTTP requests and AJAX in the early 2000s) allowed web applications to mimic desktop experiences, spawning giants like Salesforce, Google Maps, and Gmail. Over the past 20 years, SaaS has dominated Silicon Valley’s venture investments, producing over 300 unicorns and soaking up more than 40% of all venture capital dollars in that span.

• SaaS’s Secret Sauce: Specialization. There is “no Microsoft of SaaS” — no single company has dominated every vertical. Instead, the ecosystem flourished with vertical SaaS offerings tailored to unique business needs.
• Barriers to Incumbents: Large tech firms were slow to enter vertical SaaS arenas. Deep domain expertise and complex customer requirements made it difficult for horizontal players to deliver compelling experiences.
• Evolution of User Experience: SaaS allowed for a 10x improvement in usability when compared to legacy, jack-of-all-trades enterprise systems.

Now, the same kind of paradigm shift is happening with agentic AI. With the rise of large language models (LLMs), companies aren’t just automating products—they’re automating functions, teams, and entire workflows. Where SaaS optimized processes, agentic AI has the potential to replace them entirely, making businesses leaner, faster, and more efficient.

Evidence and Insights: Research Perspective from Bain & Company

A study conducted at Bain & Company, The Great Debate: Will Agentic AI Kill SaaS?, provides a thorough analysis of this seismic shift. The report highlights several critical observations:

• Agentic AI is poised to substantially reduce the need for human teams in roles such as quality assurance, recruiting, customer support, marketing, and even software development.
• Agentic AI empowers smaller teams with outsized impact—making the concept of a unicorn startup with only 10 employees a plausible near-term reality.
• The financial allure: While companies have historically spent more on payroll than on software, agentic AI threatens (or promises) to reverse this allocation; the bulk of operational expenditure could shift towards AI-driven automation rather than salaries.
• Implications for SaaS models: Many SaaS unicorns of today may be disrupted by agentic AI startups that offer not just tools, but fully integrated solutions—replacing both the workflow management software and the people who use it.

Bain & Company’s research serves as a clarion call: AI agents are not a passing trend but a structural change in how businesses will operate, compete, and organize teams moving forward.

Agentic AI in Action: Practical Startup Examples

The startup ecosystem is already a proving ground for the capabilities—and impact—of agentic AI. Here are concrete examples where AI agents are upending SaaS conventions:

• QA Testing: Startups have traditionally sold SaaS tools to make QA teams more efficient. Now, agentic AI platforms outright eliminate the need for QA teams by automating test creation, execution, and analysis—removing friction and internal resistance from teams worried about their jobs.
• Recruiting: Agentic AI can now perform full-cycle recruitment, including technical screening, candidate evaluation, and communications—tasks once requiring coordination between recruiting and engineering teams. Startups employing these agents see faster adoption, free from bureaucratic pushback.
• Customer Support: While the market is crowded with AI chatbots, few deliver true agentic AI. Companies like gig.ml deploy deeply specialized systems handling tens of thousands of support tickets daily, tailored for specific industries (like on-demand delivery marketplaces), replacing massive call centers with software.
• Marketing and Dev Tools: Engineers leveraging agentic AI (rather than building traditional marketing or support teams) have achieved levels of operational scale and quality usually associated with much larger organizations.
• Examples Beyond Software: From medical billing for dental clinics to AI voice agents conducting debt collection or handling government contract bidding, these verticals show how agentic AI is thriving wherever workflows are repetitive, rules-based, and require deep domain adaptation.

These case studies reinforce a key point: The “verticalization” of agentic AI mirrors SaaS’s earlier success. As with SaaS, the biggest impact is in areas where tasks are well-defined, highly repetitive, and previously required specialized human oversight.

Will Agentic AI Kill SaaS? Emerging Patterns and Future Possibilities

Not all SaaS companies will be wiped out overnight, but the structure of the industry is changing quickly. Several patterns are emerging:

1. The End of the “Team-in-a-Box”: SaaS historically provided digital tools that still needed teams. Agentic AI offers “software + people”—an integrated solution that takes action, not just enablement.
2. From Buying Seats to Buying Outcomes: Traditional models priced software “per seat”; agentic AI pricing is more likely to be tied to outcomes delivered or workflows run.
3. Entrant versus Incumbent Dynamics: Large incumbents may dominate obvious, general-purpose agentic AI (e.g., universal voice assistants), but vertical, domain-specific agents will flourish as startups seize niches too narrow or complex for big tech.
4. Organizational Transformation: Companies will grow revenue without proportional headcount increases. Smaller, more efficient teams will become the norm—potentially making future unicorns operated by a handful of technical experts.
5. Speed and Customization: Enterprise software buyers, already used to vertical SaaS solutions, are ready to embrace agentic AI platforms faster than they adopted SaaS in the past.

That said, the “horizontalization” counter-argument cannot be dismissed: Some companies, like Rippling, are betting on broad platforms that stitch together multiple verticals, offering both depth and integration. This tug-of-war between vertical depth and horizontal breadth will define the next era of enterprise technology.

Actionable Advice: Thriving in the Age of Agentic AI

For founders, executives, and investors, navigating the rise of agentic AI requires new strategies and mindsets:

• Find Boring, Repetitive Admin Work: The sweet spot for agentic AI is in automating mundane back-office tasks that are ripe for disruption. Look for workflows that are high-volume, rule-based, and where human involvement adds little strategic value.
• Leverage Domain Expertise: Deep knowledge of the vertical is crucial. The most promising startups are those built by or closely connected to industry insiders who truly understand business pain points.
• Start Niche, Scale Fast: As Enterprises are already comfortable with vertical solutions, startups can focus on specialized offerings and achieve rapid initial traction before considering feature expansion.
• Sell Top-Down: To avoid resistance, pitch agentic AI to decision-makers (often at the executive or CEO level) with clear ROI—especially when their incentive is to reduce cost and complexity, not preserve redundant teams.
• Rethink Team Structure: Invest in engineering talent with AI and LLM skills; the post-SaaS organizations will need fewer, more technical operators and less reliance on traditional operational silos.
• Prioritize Adaptability: The market is evolving quickly, with advances in AI infrastructure, APIs, and foundational models. Stay ready to retool, partner, or pivot as the ecosystem matures.

Conclusion: SaaS isn’t Dead—but Its Future Belongs to the Agents

The age of SaaS disrupted how businesses bought and used software; the age of agentic AI threatens to disrupt SaaS itself. Bain & Company’s research and real-world startup examples underscore that agentic AI is not only enhancing efficiency but actively replacing functions, teams, and even entire business models across industries.

Still, this transformation isn’t a zero-sum game. Enterprises will continue to need both broad and deep solutions, and the best-positioned founders and organizations will combine domain expertise, rapid iteration, and a relentless focus on customer outcomes. Whether SaaS gets “killed” or evolves into a new AI-centric suite remains to be seen—but one thing is clear: the winners of the next decade will be those who harness the exponential leverage of agentic AI to build the software—and the organizations—of the future.

For a comprehensive analysis, refer to the Bain & Company study: The Great Debate: Will Agentic AI Kill SaaS?