We've seen this pattern before. Consumer excitement, SMB adoption, then enterprise transformation. SaaS followed the same arc, but it didn't just digitize work. It introduced structure, workflows, and reporting that reshaped how businesses operated. Software became a service you could hire.

But SaaS had a problem. It promised productivity gains that were hard to measure. An HR team might roll out onboarding software, but employees still chased approvals across tools. The jobs still needed human touch to get finished.

AI is different. Rather than just improving perceived productivity, it's completing jobs and delivering outcomes. SaaS promised efficiency, AI promises getting the job done. We expect it to draft the document, resolve the ticket, and complete the task with speed and accuracy. It's shifting execution from the user to the agent.

Why Enterprise AI Has More Upside

Enterprise work is messier than consumer tasks. The workflows are complex, the stakes are higher, and the potential impact is really enormous! Analysts see that potential playing out like this: a May 22, 2025 report from The Research Insights forecasts the global AI market soaring from $279 billion in 2024 to $1.81 trillion by 2030 as sectors from automotive to healthcare race to embed the technology.

When enterprises adopt AI, they don't just experiment, they need to integrate deeply, the risks (and rewards) are higher. And for those of you that like this level of complexity (like me!) this is fun work to do.

For example, take Harvey, the legal AI that functions like a junior associate. It doesn't just answer questions. It drafts memos, cites precedent, and integrates into firm processes. Or Abridge, which listens to doctor-patient conversations and generates clinical notes in real time. Hours of typing and paperwork disappear, freeing physicians to focus on diagnosis and care. AI alone won’t get to the outcome these lawyers and doctors need, it has to read and update data deeper in the tech stack.

These systems aren't perfect yet. They require oversight, training, and careful implementation. But they're moving beyond productivity tools toward systems that take more and more ownership of entire workflows and integrated into enterprise architecture.

AI Agents Activate Existing Systems

AI isn't replacing SaaS, it's activating it. Most enterprises keep their existing systems as the foundation. AI agents sit across these systems and act on the data that's already there.

TaxGPT works within existing tax software, interpreting logic and filing returns automatically. Salient handles loan collection calls, logs outcomes, and updates CRMs. The infrastructure stays intact. The agent moves through it, completing work that used to require human navigation between tools.

This solves a problem SaaS created: interface overload. Enterprise software brought capability, but it also brought more screens, more clicks, more fragmentation. Each tool added friction.

Now users can express what they need in plain language. The agent handles the rest by navigating tools, filling fields, completing tasks. It's more efficient and less exhausting for users.

From Productivity Tools to Outcomes

The shift is fundamental. Instead of paying for access to tools, companies will pay for completed work. Did the agent(s) deliver? Did the job(s) get done quickly and correctly? That's the new value equation.

As Bret Taylor, former co-CEO of Salesforce and founder of Sierra.ai, puts it: success comes from the deep domain of understanding the customer's job to be done.

Devin, an AI software engineer from Cognition Labs, shows how this works in practice: aiming to transform software development. Unlike coding assistants that help developers write code, Devin is working to complete the entire software development workflow. It takes a ticket, understands requirements, writes code, tests it, debugs issues, and commits the final solution. The results aren't perfect yet, but the direction is clear: agents moving from support to execution of complete jobs and broader workflows. The more it understands and connects the jobs to be done of developing a production grade software product, the higher value it creates.

What This Means for Leaders

This isn't about replacing people. It's about relieving them. Enterprise work is full of routine tasks that add friction but little value. AI agents are starting to peel those layers back so humans can focus on judgment, edge cases, and high-trust interactions.

The transformation will be gradual and uneven. Current AI has limitations. Some tools will overpromise. Some teams will resist. But the pattern is emerging: AI is moving from assistive to active, taking on complete workflows rather than just supporting them.

The strategic question for leaders isn't whether this shift will happen—it's how to position your organization to capture the value. 

Start by mapping your highest-cost, most repetitive (and possibly boring) workflows. Where are your expensive talent spending time on work that doesn't require human judgment? Those are your first targets for AI agents.

Three immediate actions to take:

Audit your workflow complexity. Identify processes that require jumping between multiple systems, repetitive data entry, or routine analysis. These multi-step workflows are where AI agents create the most value. See my previous post on creating a job map!

Calculate the true cost of manual work. Don't just look at software costs, factor in the hourly value of the people doing the work. A marketing manager spending 10 hours per week on reporting represents $50,000+ in annual opportunity cost!!

Start with pilot projects that integrate with existing systems. The most successful enterprise AI implementations work within your current tech stack, not as replacements for it. Look for agents that can activate the (clean) data you already have.

The companies that figure this out first will have a significant advantage. Not just in efficiency, but in freeing their people to do the work that actually matters: strategy, creativity, and high-value decision-making that drives business results.

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