AI vs. SaaS: Fighting for the Future of Enterprise Tech
- Stephanie Pflaum
- 4 days ago
- 5 min read
The pervasive narrative that "AI will kill SaaS" is fundamentally flawed. It misunderstands how AI actually creates measurable value in the enterprise. Far from being a platform killer, software is the essential infrastructure that allows AI to move beyond a simple querying tool and become a true force multiplier.
This perspective, grounded in the realities of how businesses operate and how investors evaluate companies, highlights why established SaaS businesses are uniquely positioned to win in the age of AI.
We spoke with Sammy Abdullah, Founder and Managing Partner at Blossom Street Ventures — who initially shared his thoughts on the proverbial death of SaaS in our recent podcast — to dig deeper into what’s really happening on the ground.
The Market Is Open: Fundraising, Efficiency & the Future of SaaS
In this episode of Bootstrapped: The Lighter Side, Lighter Capital CEO Melissa Widner sits down with Sammy Abdullah, Co-Founder and Chair of Blossom Street Ventures, for a candid, contrarian, and refreshingly practical conversation about the state of the market for SaaS founders.
Why the 'AI Will Kill SaaS' Theory Is Misguided
The belief that AI would replace SaaS took hold quickly when ChatGPT hit the market in 2022, where initial excitement outpaced a full understanding of the technology’s limitations.
As an investor, Abdullah calls the "AI will kill SaaS" idea "silly." It fails to recognize AI’s current shortcomings:
Technical Challenges: Issues around cost, energy consumption, security, privacy, and data management.
Reliability: AI still struggles with consistency and trust.
Lack of Structure: Without software to organize data, manage workflows, and enforce outcomes, AI's outputs are difficult to operationalize.
Realistically, AI has to be consistently useful and reliable within a business context before it can kill anything.
The Incumbent Advantage
The companies best positioned to benefit from AI are not necessarily new AI-native startups. They’re existing SaaS businesses that already possess critical foundations:
Customer & Data Moat: They have paying customers and real usage data, sales teams, and customer success teams embedded in real-world problems.
Operational Maturity: They have the engineering teams, leadership, and institutional knowledge necessary to ship, iterate, and understand pricing, ROI, and value delivery.
SaaS companies do not need AI to invent demand; they need it to enhance workflows that already exist.
“They have the customers, sales teams, dev teams, leadership, and CS teams,” said Abddulah. “They know the customer's problems, they understand how to price, they have visionary ideas and they're all tech forward. If anyone is going to succeed with AI, it will be the SaaS companies who build it into their systems to drive business use cases.”
Separating Real Adoption from AI Theater
Despite the noise, there is currently very little data-backed evidence of AI driving productivity gains large enough to meaningfully impact GDP. Much of what exists today is relegated to pockets of usefulness, not transformative scale.
“We're all just using it to plan trips and tell us how to clean our pools or what that rash on our arm really is,” said Abddulah.
Founders fall into AI theater when they lead with the technology — adding AI features without being able to articulate how they measurably improve customer outcomes. Instead of asking, “How can my SaaS use AI?” leaders should be asking, “How does my product drive ROI?”
Abddulah says, “Don't let an infatuation with becoming AI first distract you from driving real value for the customer. The customer doesn't really care how they're getting value, as long as they are getting it.”
Software is the Backbone of Enterprise Tech
The experiences of SaaS operators show that AI's success depends on the software platforms it runs on, whether for data, go-to-market, or execution.
The Data Foundation: Trust and Scale
Brighthive Co-Founder and CEO Suzanne El-Moursi emphasizes that, “AI without a strong data foundation is like building a skyscraper on sand.” When underlying data management and workflow automation are missing, AI becomes an expensive querying tool that produces inconsistent results.
Without robust data foundations, three things break down:
Trust Evaporates: Unclear data lineage, untracked transformations, and unvalidated quality make decision-makers unable to rely on AI outputs.
Speed Disappears: Teams spend the majority of their time wrangling data instead of generating insights, making the process slower than manual work.
Scale Becomes Impossible: Without systematic management, every new AI application requires recreating the wheel, leading to chaos and keeping AI trapped in pilot purgatory.
El-Moursi explains:
“Using the word "foundation" here isn't the foundation of automation, but the foundation of having a data management platform that is powering the agentic layer or AI workflows. In the beginning, Brighthive was the SaaS layer that now powers the agentic workflows — we originally built a platform that removed the friction of data workaround ingestions, data quality and cleansing, and governance to create clean data pipelines. Today we find ourselves in a unique position where that foundational platform we built before the agentic layer is actually powering more robust, more accurate, and more performant AI data workflows.”
When Brighthive customers automate the end-to-end data lifecycle, the improvements are dramatic: time-to-insight drops from days or weeks to hours or minutes. Crucially, decision velocity and confidence improve simultaneously because data is reliable and verifiable. And both of those improvements drive ROI and utilization.
“Software platforms aren’t just enablers of AI — they’re the multipliers,” says El-Moursi.
"We're seeing 75-90% reduction in time spent on routine data tasks, which means teams can focus on the 20% of work that actually requires human judgment and strategic thinking.”
The Strategy Foundation: Clarity and Consistency
In go-to-market and execution, AI introduces the risk of "graywash." As Keith Lauver, Founder and CEO of Atomic Elevator, notes, AI's native tendency is to average — to produce outputs that are statistically likely but strategically indistinct.
Without human-defined strategy and structured systems, content and insights might look smart, but they lack substance. Here’s what breaks when these guardrails aren’t there:
No Point of View: AI reenacts the median of the industry, lacking the sharp perspective required for true differentiation.
No Structure: Strategy (ICPs, positioning, offers, constraints) isn't encoded, leaving the AI to simply "free-associate."
No Feedback Loop: The system cannot learn if the output worked in the real world.
“AI needs software and human strategy — not because it’s weak — but because it accelerates what you give it, whether that’s clarity or confusion, or distinctiveness or sameness,” said Lauver.
Things improve dramatically when AI runs on a platform that encodes clear strategy and guardrails. The AI becomes an amplifier of clarity rather than a generator of noise, which leads to measurable lifts in consistency, speed (without sacrificing quality), and managerial leverage.
“The future isn't AI instead of software. It's software with an opinion, encoding a human strategy, powered by AI that refuses to drag you back to average,” says Lauver.
AI and SaaS Are Stronger Together
Does the rise of AI mean the death of SaaS? No. But it is exposing shallow products and value propositions built on "vibes" instead of measurable outcomes.
Abdullah sees a future in which SaaS will continue to provide the structure — the data models, workflows, permissions, and guardrails — and AI will enhance that foundational structure by accelerating insight, improving execution, and ultimately increasing ROI.
“If AI succeeds, and that's a big if right now, founders will find ways to make AI useful to their customers and embed it in their existing SaaS suite,” says Abdullah. “It will be a feature. It will not, however, replace the platform. SaaS will be the platform and AI will be a part of what makes the software valuable. Not vice versa.”
The companies that come out on top will be those using software — thoughtfully, measurably, and with discipline — to turn AI into something that delivers more value to customers. Because without software, AI is just a query. With it, AI becomes something people can’t live without.
