There is a lot of noise right now about SaaS-pocalypse. The market has reacted sharply, and in some cases indiscriminately. By the end of this piece, I want to put it in perspective, and make the case that for Capital Markets incumbents willing to move quickly and deliberately, this moment is an opportunity, not a threat. 

At Engine AI, we have a front row seat to this. We are an AI-native company, deeply entrenched in Capital Markets, and what we are seeing on the ground is quite different from what the headlines suggest. 

What Triggered SaaS-pocalypse 

The catalyst was the convergence of two things. First, the material improvement in LLM and related tooling quality, highlighted by Opus 4.5 release in November and broadening adoption of Model Context Protocols (MCPs). Second, and more visibly, the release of tools like Claude Cowork earlier this year, which began delivering vertical solutions, legal, cyber, and payments, in quick succession. 

The market reaction was almost automatic. Sector by sector, stocks sold off. By mid-February, an estimated trillion dollars had been wiped from software market valuations. And the question people started asking was obvious: if you can build a CRM using natural language and vibe coding, why would any organisation continue paying millions in annual per-seat licences to established software vendors? 

The selling was at times indiscriminate. It cut across verticals and business models, hitting companies with very different levels of actual exposure to AI disruption. But the more important point is this: SaaS companies were the first to feel it but and they will not be the last. This is because AI is not just disrupting a business model. It is disrupting the idea that your position in the market protects you. 

Incumbency in Capital Markets has always been hard-won. AI is now testing whether it is enough. 

From Incumbent to Disruptor 

Deploying AI in Capital Markets is genuinely hard. But that is precisely the point. The firms that can navigate the complexity are the ones that will be hardest to displace and best placed to disrupt. 

What we have learned, working closely with banks, asset managers, data technology companies, and investment platforms, is that the depth of integration required to deliver genuine value operates on multiple levels simultaneously. At the most basic level, a system needs to understand the domain. But that is just the starting point. It also needs to understand how a specific firm operates, its data, analytics and workflow framework. Beyond that, it needs to understand how individuals within that firm work, their preferences, their patterns, their risk tolerance. And finally, over time, it needs to accumulate the history of decisions and outcomes that allows it to anticipate rather than just respond. Each of those layers compounds on the one before it. The longer a system is embedded, the harder it becomes to replace. 

Delivering across all of those layers requires getting three things right simultaneously.  

Data: All of an organisation's data, internal and third-party, structured and unstructured, has to be connected and coherently integrated. The ontology, the data graph that sits behind it all, has to be seamless. You cannot vibe-code your way to that.  

Analytics: Once the data is connected, you need advanced machine learning and AI applied on top of it in a way that produces reliable, explainable outputs grounded in Capital Markets domain knowledge.  

Delivery: Insights have to reach the right people, in the right format, through the right medium, whether that is an agentic AI workflow, an API, or an intelligence interface where users can interact directly with the data. 

Sitting across all three of those layers are enterprise-grade cyber controls, granular permissioning, and compliance requirements. In Capital Markets, those are not optional. The market has underestimated how hard it is to deliver all of this coherently. That is a large part of why the sell-off was indiscriminate. 

None of this means incumbents can afford to sit still. The ones that move quickly and deliberately will define what the next era of their industry looks like. That requires a genuine mindset shift, top-down vision met with bottom-up use cases, rapid experimentation, and a relentless focus on scaling what works. 

How Engine AI Helps Incumbents Become Disruptors 

At Engine AI, we are AI-native and deeply entrenched in Capital Markets. Most firms are one or the other. That combination is what allows us to work with firms across all of those levels of integration, and to help them do something more than cut costs. 

We enable the firms we partner with to make their own products and services more valuable to their customers. We do that by turning their fragmented data into a unified intelligence layer, applying domain-specific analytics and AI engineering to make that intelligence accurate, relevant, and explainable, and delivering it through persona-specific workflows that fit how their clients actually work. When a wealth manager can answer a client's question in real time, or an analyst can surface relevant intelligence the moment it matters, the impact is tangible. Retention improves, addressable markets expand, and engagement deepens. That is what a force multiplier looks like in practice. And that is how incumbents become disruptors. 

SaaS-pocalypse has exposed a fault line. On one side, companies whose value was always more surface-level than the market realised. On the other, companies whose value sits in the intelligence, the relationships, and the outcomes they deliver for their clients. Companies building something that compounds. Those are the ones with a real moat. And a real moat does not erode when code gets cheaper.