The AEC industry needs a different mental model for AI.

For two decades, we've thought about technology through the lens of SaaS: rent a capability, pay per seat, train your team on the interface. Software is a tool you buy.

That framing doesn't work for AI agents.

When you deploy an AI agent, you're not licensing software. You're hiring a worker.

The Evidence Is Already Here

The shift from "AI as tool" to "AI as worker" isn't theoretical. It's happening now, and the data is striking.

McKinsey now employs 40,000 humans and 25,000 AI agents. Bob Sternfels, McKinsey's global managing partner, expects equal numbers by year-end. They're expressing org charts in both FTEs and agents deployed. This isn't a pilot program - it's how one of the world's largest consulting firms now operates.

At Anthropic, 100% of code is now AI-written. Boris Cherny, head of Claude Code, hasn't written a line himself in over two months. The result? A 67% increase in merged pull requests per engineer. That's not incremental improvement - it's a fundamental change in how software gets built.

Salesforce describes Agentforce as providing a "digital workforce" where humans and agents work together. The language matters: not "digital tools" or "AI features" - a workforce.

This isn't just marketing hype. It's changing the perception and expectations around how knowledge work should be done.

Coding Agents Are the Proof

If you want to see where AI agents are headed, look at software development. It's the canary in the coal mine for knowledge work transformation.

Claude Code grew from research preview to billion-dollar product in six months. More remarkably, Claude Code is now 100% built by Claude Code. The agent builds itself. That's not a product demo - it's production reality at one of the leading AI companies.

But here's what made it go viral: people started using it for everything other than coding. Vacation research. Building slide decks. Cleaning up email. Cancelling subscriptions. Recovering wedding photos. Even controlling their oven.

The pattern is clear: coding agents generalize to knowledge work. Give an agent access to files, context, and the right tools - it can execute on nearly any task.

Anthropic responded to this unexpected usage by launching Cowork - described as "Claude Code for the rest of your work." Same agentic architecture, accessible to non-technical users. It reads files, organizes folders, drafts documents, and executes multi-step workflows. Cowork itself was mostly built by Claude Code in 10 days.

The job isn't "write code" or "write reports." The job is "manage a team of agents that execute work with you."

This pattern will emerge in every knowledge domain. Including AEC.

The SaaS Model Is Cracking

The traditional SaaS business model - per-user, per-month licensing - was built for a world where humans interact with software through interfaces. AI agents break that model.

IDC predicts that pure seat-based pricing will be obsolete by 2028, with 70% of vendors refactoring around outcomes, consumption, or capability. That's not a minor adjustment - it's a fundamental restructuring of how software is bought and sold.

Why the shift? AI agents fundamentally alter the SaaS tech stack by replacing the logic and presentation layers that software companies rely on. When an agent can navigate, query, and act across systems, the interface becomes secondary. The value moves from "access to the tool" to "work the agent produces."

Consider this analogy: When you hire an employee, you don't pay "per seat" for them to use Excel. You pay for output. AI agents work the same way. The question isn't "how many licenses do we need?" It's "what outcomes do we need to produce?"

This has profound implications for how AEC organizations should think about AI adoption. You're not buying software. You're building a workforce.

AEC Already Understands Hybrid Workforces

Here's what gives the AEC industry an advantage in this transition: you already know how to manage hybrid workforces.

This industry knows flexible labor models. Direct employees. Subcontractors. Temporary workers. Specialized consultants. Every project involves orchestrating different types of workers with different skills, availability, and contractual relationships.

AI agents are the next category.

Procore introduced AI agents as "digital log keepers, foreman assistants, and automated action-item generators." Notice the language: these aren't features. They're roles. A "digital log keeper" has responsibilities, produces outputs, and can be evaluated on performance - just like a human worker.

When Procore acquired Datagrid, the CEO joined to lead AI strategy. His stated focus: "Building an AI that can execute, not just talk."

Execution. That's what employees do. That's the mental model shift.

What Does This Mean Operationally?

If AI agents are employees, you need the same operational infrastructure you use to manage human workers. This isn't metaphor - it's practical reality.

Job Descriptions

What tasks does this agent own? What decisions can it make autonomously? What requires human approval? These aren't abstract questions - they're the same questions you'd answer when hiring a project coordinator or document controller.

SHRM advises companies to "treat AI like any new hire by writing a job description, defining its role, and even 'interviewing' it." That means understanding what the agent is good at, where it needs supervision, and how it fits into existing workflows.

Performance Management

How do you know if your AI agents are performing? The same way you'd evaluate any worker: metrics.

KPIs for AI agents might include resolution rate, accuracy, response time, and task completion. But the specific metrics depend on the role. How do you measure whether your document review agent is performing? Probably the same way you'd measure a human document reviewer: accuracy, throughput, and error rate.

Onboarding

New employees need context. They need to understand the organization, access the right systems, and learn institutional knowledge. AI agents are no different.

Workday's "Agent System of Record" provides a framework to onboard agents, define their roles, and track their impact. The fact that enterprise HR software companies are building this infrastructure tells you where the market is headed.

Progressive Autonomy

You wouldn't give a new hire full decision-making authority on day one. The same principle applies to AI agents.

Start agents on low-risk tasks. As they demonstrate reliability, expand their scope. Trust calibration - adjusting confidence based on demonstrated performance - mirrors how you'd manage a junior team member. Prove yourself on small things, earn responsibility for bigger things.

Organizational Structure

Who manages the AI agents? Who's responsible when they make mistakes? Who decides which agents to "hire" and which to "let go"?

New roles are emerging to answer these questions: AI Agent Orchestrator, AgentOps Specialist, AI Ethics and Governance Specialist. Someone needs to manage the agent workforce. And agents themselves need clear roles within the organization - not as an afterthought, but as a deliberate part of organizational design.

The Labor Crisis Makes This Urgent

The AEC industry doesn't have the luxury of waiting to figure this out.

92% of construction firms can't find qualified workers. The industry needs 454,000 additional workers in 2025 to keep pace with demand. 25% of the workforce is over 55, meaning a wave of retirements is coming.

AI agents won't swing hammers. They won't install ductwork or pour concrete. But they can handle the knowledge work surrounding every project: document review, submittal tracking, schedule analysis, RFI drafting, compliance checking, contract analysis, knowledge search.

Consider how much time your project teams spend on administrative work versus problem-solving. Every hour a project engineer spends on administrative work is an hour not spent solving problems and delivering value. AI agents can shift that ratio dramatically - if you deploy them as workers, not just as features in your existing software.

The Question Isn't "What Software Should We Buy?"

The mental model matters. If you think of AI as software, you'll evaluate features, compare vendors, and negotiate licenses. You'll ask "does this tool do what we need?"

If you think of AI as workforce, you'll ask different questions: What roles do we need to fill? What skills should these agents have? How do we onboard them? How do we measure their performance? How do they fit into our org structure?

The second set of questions leads to better outcomes. It forces you to think about AI deployment as an operational capability, not a technology purchase.

Stop evaluating features. Start writing job descriptions.

Book a free discovery call

Want to move from “AI features” to an agent workforce that actually ships work?

In a free 45-minute discovery call, we’ll identify the first role to “hire,” where it plugs into your delivery process, and what it needs to perform reliably inside your systems.

We’ll cover:

• The 1–2 workflows creating the most drag (RFIs, submittals, document control, safety, QA, compliance, reporting)

• What data already exists (CDE, email, PDFs, BIM, photos/video, spreadsheets) and what’s missing

• Where approvals and handoffs must stay human

• A pilot plan with scope, success metrics, and guardrails

You’ll leave with:

• A clear “agent job description” for your first hire

• A short pilot plan (30–60 days) with measurable outcomes

• A rollout checklist for security, access, and auditability

Book your free discovery call here.