There’s a narrative gaining momentum in engineering circles that goes something like this: AI coding tools have made developers so productive that the entire SaaS stack is about to get disrupted. Engineers are shipping features faster, and they’re extrapolating that into a future where traditional SaaS platforms like Salesforce, ServiceNow, and SAP become obsolete.
I’ve written about this acceleration before. In my post when I was exploring one of the vibe coding platforms Replit, I explored how AI-powered development tools are genuinely transforming what’s possible for prototyping and building certain types of applications. I built my own GTD app for under $100, something that would have cost tens of thousands just a few years ago. The engineers aren’t wrong about the productivity gains; they’re wrong about what those gains actually mean for enterprise software.
In my opinion enterprise SaaS platforms won’t be disrupted by AI. They’ll be the primary vehicle through which organisations adopt AI in the first wave of transformation. I’ve written about this in a previous post about where we are with AI adoption.
The End for Non-Enterprise SaaS
Let me be clear: there absolutely will be disruption. The overpriced, underutilised SaaS tools I wrote about in my Replit piece are vulnerable. You know the ones; the apps that charge a fortune for features nobody uses, where the price-to-functionality ratio makes you wince every time the renewal comes up. Those are ripe for replacement with custom-built alternatives using AI development tools.
But Salesforce? ServiceNow? SAP? Those aren’t going anywhere. Not in this first phase of AI adoption, anyway.
Why Enterprise SaaS Will Be the Vehicle for AI Adoption
In my piece on AI adoption and white-collar professions, I outlined three paradigms of AI transformation. We’re currently in Phase 1: the Human-First AI Transformation, where humans use AI tools to enhance productivity. This is the ‘sprinkle AI on everything’ phase.
Enterprise SaaS platforms will dominate this phase for three fundamental reasons that have nothing to do with technical capability and everything to do with how large organisations actually operate.
1. Procurement Inertia and Existing Vendor Relationships
Large organisations have established procurement patterns. They have vendor management processes, contractual frameworks, and relationships that have been built over years or even decades. When Salesforce offers an AI upgrade, it fits perfectly into the existing buying pattern. The vendor is already approved, the legal framework is in place, and the procurement team knows exactly how to process the purchase.
Compare that to the alternative: convincing leadership to embark on a massive transformation programme to replace core systems with custom-built alternatives. Which path do you think a risk-averse enterprise will choose?
It’s far easier to buy the AI upgrade than to disrupt and re-engineer your entire operating model. This isn’t a technical decision; it’s an organisational one.
2. Cost Arbitrage in the Short Term
Yes, AI development tools can make building software cheaper. But you know what’s even cheaper in the short term? Clicking ‘upgrade’ on your existing SaaS subscription.
A wholesale transformation programme comes with massive costs: change management, training, integration work, data migration, the inevitable delays and overruns. Even if you’re using AI to accelerate development, you’re still looking at months or years of effort and significant investment.
Meanwhile, the SaaS provider offers AI capabilities as an add-on or upgrade. The CFO does the maths and it’s not even close. The transformation programme might have better economics in the long run, but ‘in the long run’ is doing a lot of work in that sentence. Most organisations will take the certain, immediate cost savings of the upgrade over the uncertain, distant payoff of a transformation.
3. Risk Transfer and Accountability
This is perhaps the most underestimated factor. When you buy AI capabilities from your SaaS provider, they’re on the hook for making sure it works safely, securely, and in compliance with regulations. They assume the liability.
When you build it yourself, even with AI accelerating the development, you own all the risk. If something goes wrong, if there’s a security breach, if the AI makes a mistake that costs the company money or reputation, that’s on you.
For regulated industries, this calculation is even more stark. You can’t just spin up an AI agent that touches customer data or financial records without proper governance, compliance, and audit trails. The SaaS vendors are already compliant, already audited, already certified. They’ve done the heavy lifting on regulatory frameworks.
The Skills Gap Nobody Wants to Talk About
The engineer’s perspective assumes that organisations have access to competent technical teams who can wield these AI development tools effectively. This is, to be generous, an optimistic view of the current state of enterprise IT.
Most large organisations don’t have the engineering talent to build and maintain sophisticated AI solutions. They’re already struggling to keep their existing systems running, to hire good developers, to retain the talent they have. The idea that they’re suddenly going to pivot to building bespoke AI-powered replacements for their core systems is fantasy.
The SaaS providers, on the other hand, do have that talent. They have entire teams dedicated to integrating AI capabilities into their platforms. They can amortise that investment across thousands of customers. It’s the classic economics of software at scale.
What Actually Gets Disrupted
So if the enterprise SaaS platforms aren’t getting disrupted, what is?
The bloated, overpriced tools that deliver marginal value. The niche SaaS products charging enterprise prices for commodity functionality. The internal tools that were built by consulting firms years ago and have been limping along ever since. Those are all vulnerable to being replaced by AI-accelerated custom development.
I’ve seen this firsthand. In my recent businesses, there were multiple instances where we paid thousands of dollars monthly for SaaS tools that delivered functionality we could now build ourselves in a weekend with Replit or similar platforms. The price-to-functionality ratio was so skewed that the economics of building our own made sense.
There’s absolutely a shake-up coming for the SaaS industry. Companies will start scrutinising their tool stack more carefully, identifying what’s truly irreplaceable and what could be rebuilt in-house. The mid-tier, feature-poor, overpriced SaaS companies should be worried.
But the enterprise platforms? They’re in a different category entirely.
The Organisational Transformation Reality
Having lived through the digital and agile transformations over the last 20 years, I can tell you that organisational change takes far longer than anyone predicts. Technology is easy; changing how organisations work is hard.
In the early phases of agile adoption, organisations dabbled rather than truly transforming. They stuck ‘agile’ on the front of everything but underneath, it was the same old processes. It took years, sometimes decades, before genuine transformation happened, and only then through either fear of disruption or visionary leadership.
We’re seeing the exact same pattern with AI. We’re in the dabbling phase, the ‘sprinkle AI on everything’ phase. True transformation, the kind that would involve replacing core enterprise systems, is still years away. We can’t even say we’ve reached late mainstream adoption of the Human First AI paradigm yet, because established patterns and methodologies are only just starting to emerge.
What This Means for the Future
The engineer’s narrative about AI disrupting the entire SaaS stack isn’t wrong; it’s just premature and incomplete. Disruption will happen, but it will be selective and slower than predicted.
In the first phase of AI adoption, enterprise SaaS platforms will strengthen their position by becoming the delivery mechanism for AI capabilities. They’ll upgrade their platforms, add AI features, and make it easy for organisations to adopt AI without the risk and complexity of transformation programmes.
Later phases might look different. As we move into the Human-Supervised AI paradigm and eventually toward more autonomous AI systems, the economics and capabilities might shift enough to make replacement viable. But that’s years away, and by then the landscape will look completely different anyway.
For now, if you’re an engineer convinced that Salesforce is about to be disrupted by AI-powered startups, I’d suggest you’re underestimating the power of organisational inertia, the complexity of enterprise needs, and the value of established vendor relationships.
And if you’re working at an enterprise SaaS company, I’d suggest you’re in a stronger position than the doom and gloom narratives suggest, as long as you’re aggressive about integrating AI capabilities into your platform. The organisations that succeed will be those that make AI adoption as frictionless as possible for their enterprise customers.
The revolution is coming, but it’s going to be an upgrade, not a replacement.
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