Many of the podcasts I listen to today begin with the disclaimer, “guaranteed human.”
Why does this matter? In one sense, it's a show of solidarity with content creators and a way to protect intellectual property. That's noble, and I support that idea. But as AI-generated content becomes indistinguishable from human-created work, the label risks becoming performative rather than meaningful.
Perhaps the bigger question is about work itself. What does it mean for work to be "guaranteed human" when AI is woven into everything we do? What role do humans play in an AI-enabled workplace? And how do organizations adapt and invest wisely when the tools keep changing faster than anyone can measure their impact?
Every company I speak with is struggling with these questions and wondering how to make the right investments.
Self-disruption is preventing AI ROI
The greatest obstacle to achieving ROI with AI is self-disruption. Things change so quickly that organizations pivot to new objectives before they have the opportunity to measure the output of the last one.
Ask yourself: how many agents deployed six months ago are you still using? For many, that answer is none.
The agents themselves are ephemeral. They will evolve much faster than the platforms they rely on, and they will be replaced by newer, more capable versions.
Four key waves in the evolution of the workspace
So how do you invest wisely in something that keeps changing? The answer is to zoom out. The tools change fast, but the way work evolves follows a pattern we've seen before. Every major shift in how we work has followed the same trajectory: a new tool emerges, becomes essential, and eventually gets absorbed into a larger platform that becomes the new center of gravity. Understanding this pattern helps us see where durable value lives, even when the tools themselves are temporary.
1. Email
The first wave seems so long ago as to be hardly relevant, but there was a time when email did not exist. It cannot be understated how drastic a change email introduced in defining what "work" is in modern business. Email products became the workspace where you both received assignments and delivered work products. For the first time, the work itself lived in a digital container. It remains a ubiquitous connection point between the individual and their employer, even as subsequent waves have followed.
2. Content creation platforms
The second wave was content creation platforms, notably Microsoft Office and Google Workspace. These suites didn't kill email; they absorbed it. Outlook became one tab among many. The design paradigm matters here: software companies created suites of related tools managed from a single console. This console captures the "time" of the employee as much as it facilitates access to the tools that produce the work. The platform, not the individual tool, became the workspace.
3. Collaborative messaging
The third wave is collaborative messaging, specifically Teams and Slack. The adoption of these work patterns may have been accelerated by the pandemic, but they were already well on their way to reshaping how organizations work together. Once again, the new wave absorbed the last. Teams didn't replace Office; it became the interface through which you accessed Office. Slack didn't replace your tools; it became the connective tissue between them.
4. The agentic workspace
This brings us to the fourth wave: the agentic workspace. This is the emerging model of work where humans define objectives and agents execute across tools. The human is the conductor. The agent is the instrument. The job to be done is what matters.
The pattern holds: Do not assume that the tools used today to produce "work" will themselves be replaced. They will evolve along with the infrastructure of AI.
But while the paradigm is clear, the platform is not. OpenAI, Anthropic, and others are leading with core AI technology, but there is no guarantee they will be best positioned to host the agentic workspace. The traditional enterprise players—Microsoft, Salesforce, Google, AWS—are all vying to be the platform for human engagement with AI as well as for autonomous workloads.
Where to place your bets
Thoughtful providers will develop their own agentic workspaces, leveraging the tooling they are simultaneously building to expose value in both intra-product and inter-product scenarios. Deciding on investments requires understanding both approaches for any given vendor.
But the agents themselves are not the bet. They're ephemeral. They'll be replaced by newer, more capable versions faster than you can measure their ROI.
Focus investments on two areas:
Data quality and completeness. The data that supports agent workflows is the foundation. Repeatable, deterministic work performs best with well-structured data—and that's where agents will provide the greatest value early.
Curation and insight definition. Defining meaningful insights that inform the decisions humans need to make will remain critical. Agents can surface information. Humans must decide what matters.
Rethinking time to value
Traditional ROI models assume stability. You invest, you deploy, you measure. But AI doesn't work that way because the tools change before you’ve finished measuring results.
This new era demands a different mindset: agile AI implementation. Shorter cycles. Smaller bets. Continuous iteration. Time to value becomes less about "when does this pay off" and more about "how fast can we learn and adapt."
The organizations that treat AI as a fixed investment will struggle, and those that treat it as a continuous evolution will thrive. The goal isn't to get it right the first time. The goal is to build the muscle for constant adaptation.
This is why data and curation matter more than any individual agent. Agents come and go. The ability to feed them well-structured data and extract meaningful insights remains. That's where durable value lives.
Where humans fit into the new workspace
The agentic workspace redefines work entirely. Agents take on the repetitive, deterministic work that used to fill human calendars. Humans set objectives, validate outputs, and make judgment calls that agents cannot.
So what does "guaranteed human" actually mean in an AI-enabled world? It's not about who created the content, but rather about who is accountable for the outcome.
If you’re struggling with how to make the right AI investments in this new workspace, the answer may be simpler than it seems: invest in the things that make human judgment more effective. That's where durable value lives.
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