AI Won't Eat SaaS. AI-Enabled Teams Will Outrun Everyone Else.

The SaaS software selloff has been steep. At the time of writing, the S&P North American Expanded Technology Software Index is down roughly 22% year-to-date. Names like Salesforce, ServiceNow, and Intuit have all taken significant hits. The narrative driving it is simple: AI is going to eat SaaS.

I never quite understood what "eating SaaS" means, but regardless, I don't think that's quite right. Yet I also think the selloff is justified.

AI changes the value of software by changing how teams work. Companies will increasingly judge software by the work it eliminates, rather than simply the workflows it records. The real divide will be between teams that integrate AI and automate their operations and teams that continue to use software in largely the same way they always have.

Stock prices reflect expectations around future earnings. When the market marks down SaaS stocks 20%+, the implied message is that these companies face meaningfully higher execution risk than they did a year ago, lowering the expected level and durability of future earnings. Prices are adjusting to reflect that.

That's a reasonable conclusion. It's different from the breathless version of the argument you'll hear from some corners of tech Twitter, which goes something like this: AI startups will rebuild every SaaS product from scratch, incumbents will be left holding the bag, and the entire category will be completely different within a few years.

The reality is more nuanced. What we're actually watching is a race between two different operating models.

It's a race

On one side of the race are teams with existing tools, established workflows, enterprise relationships, and years of accumulated domain knowledge. They may use dozens of SaaS products, but people still stitch many of those systems together by hand.

On the other side are teams integrating AI deeply into how they operate. They are connecting systems, automating handoffs, removing repetitive work, and redesigning entire workflows around what AI now makes possible.

Both sides have real advantages, and both sides have real gaps. The question isn't who wins in some abstract sense. The question is how quickly conventional teams can close the adoption gap, and how quickly AI-enabled teams can make their new workflows reliable, secure, and scalable.

To understand the race, you have to understand what each side is working with.

What non-AI teams have, for now

If you're an operator at an established company, your starting position is stronger than the AI hype suggests. One obvious advantage is customers. Of note, those customers have hopefully built lots of workflows around your product and your company.

Your teams have a certain operating shape today. Some parts are well automated by software. Other parts are still very manual, time-consuming, or require a human touch because of the risk involved.

Think about the finance team that uses an ERP but still spends hours reconciling data across systems, or the sales team that lives in a CRM but manually qualifies leads based on gut feel and a few heuristics.

Those manual, painful steps still reflect friction in the workflow. Companies have tolerated that friction for a while because the cost of redesigning the process was higher than the cost of living with the pain.

But we're in a unique moment right now.

First, AI can legitimately eliminate some of that friction, either as a point solution or by reworking the full workflow.

Second, there's so much attention on AI that companies are more open to changing how they work than they normally would be.

Two teams can use exactly the same CRM and produce completely different results. One team might manually research every prospect, qualify each lead, write every email, route every opportunity, and update the CRM by hand. Another might automate research, enrich and score leads, generate initial outreach, route opportunities, update records, and bring a person in only when judgment is required.

The difference between those teams does not come from access to software. Both have the software. The difference comes from how thoroughly one team has integrated and automated the workflow. This is exactly the kind of system we design in our sales automation builds.

The window of operational complacency that established companies have historically benefited from is narrower than it has been in a long time.

What AI-enabled teams are doing

Somewhere out there, an AI-enabled team is studying those same workflows. In reality, it's optimistic to think there's only one doing that. There are probably ten or more.

They have identified the most painful, time-consuming step and are building an automation that shrinks it dramatically or proposes a completely new way to handle it. This is the "wedge" that VCs talk about. If it's a good wedge, it will solve a real problem or friction point that the existing process has not fully addressed.

But wedges are just entry points.

An AI-powered lead qualification process might be impressive, but it still covers only one piece of the sales workflow. To create a lasting advantage, the team needs to expand from that initial wedge to cover more of the workflow.

Some of the expanded workflow will be AI-powered. Other parts will rely on traditional software, rules, integrations, and human review. The team also needs data access, security controls, monitoring, exception handling, implementation support, and everything else required to make the process dependable.

That expansion takes real time and real effort.

Yet an AI-enabled team's main advantage is how quickly it can move. These teams tend to have several structural advantages when it comes to velocity.

• Readiness to adopt. Access to AI tools is becoming widely available. The greater advantage belongs to teams willing to change their workflows, responsibilities, data practices, approval structures, and performance metrics. They are prepared to let the technology change how work gets done.
• Integrated systems. They are connecting their tools so information can move between them without depending on a person to copy, paste, interpret, and re-enter it. Integration gives AI the context it needs to operate across a workflow rather than inside one isolated application.
• Quickly building beyond the 80/20. AI is good at writing code, producing content, interpreting information, and completing routine analysis extremely quickly. The time it takes to go from a small automation to a credible end-to-end workflow is compressing.
• Fewer manual handoffs. Every manual handoff adds waiting time, communication overhead, and the possibility of error. AI-enabled teams are systematically identifying these handoffs and either automating them or creating clear exception paths for the cases that require human judgment.
• AI infused throughout the company. AI is speeding up implementation, operations, sales, marketing, customer support, finance, and other functions. The most aggressive teams are exploring where AI can create tenfold efficiencies, rather than limiting it to a chatbot or an isolated internal pilot.
• Talent density. The people drawn to AI-enabled teams are often deeply innovation-forward. These are frequently the people who were using AI tools at work long before their company had an official AI strategy. That cultural comfort with new technology compounds into a real speed advantage.
• Focused investment. Starting with one painful workflow means a team can iterate extremely quickly on that one thing, learning and shipping in tight loops. From there, it can choose which adjacent steps to automate next.

The important distinction is that these advantages are available to established companies as well as startups. A large company can become AI-enabled. A startup can still operate like a legacy organization.

The dividing line is readiness to adopt. We wrote more about that in building AI adoption habits before expecting AI leverage.

The non-AI team's real challenge

Here's the race in concrete terms.

The conventional team needs to understand how AI fundamentally changes what's possible in its workflows, reimagine those workflows accordingly, and implement those changes before AI-enabled competitors compound their speed advantage.

The AI-enabled team needs to turn early experiments into dependable operating systems. It must expand from individual automations to broader workflows while building appropriate controls, security, oversight, and exception handling.

Of note, a team does not get meaningfully ahead by simply adding AI to its existing process. We've all seen companies herald an "AI agent" or chatbot feature that feels underwhelming.

Meaningful integration requires a deeper understanding of how AI changes the workflow itself. The company must then rebuild accordingly, even when that means removing existing steps, changing responsibilities, connecting multiple systems, or redesigning the broader process.

Most leadership teams have identified where AI could improve their operations. They have done the landscape analysis, created an AI strategy, purchased tools, and allocated budget. This is all necessary work, and the thinking behind it is often sound.

The primary challenge is operational. It is about readiness and velocity.

It's one thing to decide, "We should use AI to reimagine lead qualification." It's another thing entirely to implement that in a way that works reliably, while connecting the required systems, protecting customer data, defining when a person needs to intervene, changing existing responsibilities, and persuading the team to use the new workflow.

The availability of AI is rarely the main bottleneck. The organization's readiness to adopt it is.

Data may be fragmented across systems. Processes may be poorly documented. Teams may disagree about who owns the workflow. Employees may not trust the outputs. Managers may resist changing roles or performance expectations. Security and legal teams may lack clear review standards. Leaders may announce an AI initiative while continuing to reward the same behaviours and processes as before.

Each of these issues slows adoption.

A company can provide every employee with an AI tool and still remain a non-AI company. Tool access does not automatically change the operating model.

Workflow velocity

The central issue is not only general velocity. Workflow velocity matters most.

I define workflow velocity as the speed at which the combined people, systems, and processes of an organization can move from an input to a completed outcome. Or, to put it bluntly, how quickly the team can actually get work done.

How quickly can a sales team move from identifying a prospect to creating a qualified opportunity? How quickly can finance move from receiving data to producing a reliable forecast? How quickly can customer support move from receiving a request to resolving it?

Can your teams reduce the cycle time from "we should automate this" to "this workflow runs reliably, with human review only where it adds value"?

For many companies, the honest answer is: not yet.

A team that merely uses more tools is still paying the coordination cost. People copy information between systems, chase approvals, interpret outputs, update records, and remind other people to complete the next step.

A streamlined team has integrated context, automated handoffs, clear ownership, and defined exception paths. Work continues moving unless a situation genuinely requires human intervention. Connecting those systems is exactly what an automation platform like Make.com enables when the operating discipline is in place.

Over time, the tools-only team will struggle to match the streamlined team's cycle time, cost structure, output, and learning rate. Adding another SaaS product does little when the underlying workflow remains fragmented.

This is where the weight of legacy operations really shows.

Legacy technology makes it harder to connect systems and share context cleanly. Legacy processes, including long review cycles, approval chains, and rigid procedures, slow the rate of implementation. Legacy culture means that even when leadership says "move fast," the organization's muscle memory often pulls in the other direction.

These layers actively limit the company's ability to adopt new ways of working at the speed the market now demands.

Strategy is the easier part. The harder part is transforming how work actually gets done inside the company.

Where operators should focus

If you're an operator at an established company, you have probably spent a lot of time over the last year or two thinking about your AI strategy. Which tools should you buy? Which workflows should you target? Where should you add AI capabilities?

That work matters, but it is nowhere near sufficient.

The outcome will depend on the speed and depth of adoption across the organization. The most important investment right now may be fundamentally rethinking how your teams work.

Where are people manually transferring information between systems? Where are approvals adding time but not value? Where does work sit in a queue waiting for someone to act? Which decisions are repetitive enough to automate? Which cases genuinely require human judgment? What changes to responsibilities, incentives, data access, or governance would be required?

Readiness should be measured through actual changes in the workflow. Look at cycle time, manual handoffs, cost per outcome, error rates, adoption rates, and the percentage of work completed without unnecessary human intervention.

The number of AI licences purchased is not a useful measure of transformation.

Figuring out how AI can power an individual task is important. Figuring out how AI transforms the entire workflow creates a compounding advantage.

A single automation saves time once. A redesigned workflow changes the economics of the team.

Final thoughts

AI probably will not eat SaaS. Companies will continue to need systems of record, workflow tools, security controls, domain expertise, and enterprise-grade software.

But software alone will no longer create the same advantage.

Existing companies still have real, durable strengths: customer relationships, institutional knowledge, domain expertise, established processes, and enterprise trust. Those matter, and they are difficult to replicate.

Those advantages have an expiration date when a company cannot adopt quickly enough. And "quickly enough" is getting faster every quarter as AI tools improve and AI-enabled teams become more capable of automating broader workflows.

The stock market is pricing in that execution risk, and I think that is directionally right.

The central question for every operator has moved beyond, "Do we have an AI strategy?"

Can we redesign our workflows so AI removes work rather than adding another interface? Are our systems connected well enough for automation to operate across them? Are our employees, managers, policies, and incentives ready to support a different way of working? Can we move from information to action with fewer handoffs and less waiting?

Companies that answer those questions well will do more than use better software. They will operate as fundamentally different organizations.

That is the real divide: AI-enabled teams versus teams that continue to work the old way.

If you're ever interested to learn more about how to streamline work, please feel free to get in touch with Stob.AI, or grab our free monthly AI model cheat sheet.