For decades, business software has been built around human operators. Tools like spreadsheets, email, CRM systems, and call center software became the invisible scaffolding of modern organizations. If you can’t imagine a world without them, it’s not because they’re permanent—it’s because they’ve been the only way we’ve known how to work.
But as AI matures, the question isn’t how do we get better spreadsheets? It’s why do we need spreadsheets at all? The future of work will not be defined by incremental improvements to yesterday’s software—it will be defined by AI agent runtimes: the new execution environments where digital workers operate, coordinate, and evolve.
The End of Human-Centric Workflow
Traditional enterprise systems—ERP, CRM, ticketing tools, even email—were designed as human coordination layers. They help us capture data, pass tasks around, and monitor progress. In short: they exist because people had to be the ones doing the work.
With AI agents, that logic collapses. Agents don’t need a CRM to remember customer details, or a ticketing system to stay on task. They don’t need a call center dashboard to handle inbound requests—they are the call center.
Robb Wilson, co-author of Age of Invisible Machines and founder of OneReach.ai, frames it bluntly: “We won’t be trading in our spreadsheets for better spreadsheets. We’ll be getting rid of them”. Agentic AI doesn’t slot into existing workflows—it makes many of those workflows unnecessary.
As media theorist Marshall McLuhan once said, “We shape our tools, and thereafter our tools shape us.” (McLuhan, 1964). For decades, spreadsheets and CRMs shaped how organizations thought about problems. AI runtimes reverse this pattern: instead of shaping us, they dissolve the constraints of the old tools entirely.
What Is an AI Agent Runtime?
An AI agent runtime is an environment where autonomous, tool-using agents can live, collaborate, and execute complex tasks. Think of it as the operating system for digital workers.
Frameworks like LangChain or Microsoft’s Semantic Kernel provide building blocks to create agents.
Runtimes provide the space where those agents can actually run, interact with tools, and scale to enterprise use cases.
This distinction matters. A framework is like a programming language. A runtime is like the computer that runs the program. Without runtimes, enterprise agents remain prototypes, locked inside demos or limited workflows.
Why Spreadsheets Don’t Survive This Shift
Spreadsheets epitomize human-first tools. They exist to help people structure, calculate, and cross-reference data. But agents don’t need the interface layer—we built spreadsheets so humans could see and manipulate what machines already do well.
The same is true for ticketing systems, CRMs, and ERP tools. They’re coordination prosthetics—interfaces designed for human limitations. Once agents coordinate tasks with one another, those prosthetics fade away.
In this sense, agent runtimes are not “new software categories” layered on top of the stack. They are replacements for the stack itself.
From Model-Centric to Runtime-Centric
Over the past two years, attention in AI has been dominated by model releases: GPT-4, Claude, Gemini, LLaMA. Benchmarks like MMLU or HumanEval made headlines, but they were only part of the story. The real question now is: how do we make models useful at scale?
That’s where runtimes come in. Gartner predicts that “through 2027, over 40% of enterprise AI projects will be abandoned before deployment due to complexity and lack of orchestration” (Gartner, 2025). In other words: building an agent isn’t the hard part—operationalizing it is.
MIT researchers reached a similar conclusion, noting that 95% of AI agent prototypes fail to reach production because organizations lack the infrastructure to manage them (Forbes, 2025). Runtimes are the missing execution layer that turns fragile demos into sustainable systems.
Enterprise Implications: A New OS for Work
Consider customer service. Today, a company uses:
- A CRM for storing records
- A call center suite for handling tickets
- Analytics dashboards for monitoring performance
In an agent runtime, those become redundant. Agents interact directly with customers, retrieve information from knowledge bases, and coordinate among themselves to escalate issues—all without handing tasks back to humans.
This is not about replacing humans entirely—it’s about reorganizing work around persistent digital collaborators. Enterprises will measure success not by how many systems they license, but by how effectively they orchestrate agents in runtime environments.
We can already see signals of this runtime-driven leverage in how businesses compete and consolidate. When a company acquires another, the biggest friction is usually operational: overlapping HR systems, duplicate compliance processes, redundant call centers. Historically, those costs were absorbed over years. With agent runtimes, they can be neutralized in months. Imagine onboarding thousands of employees or harmonizing disparate customer data without armies of analysts—agents do the stitching in real time.
On the competitive side, the advantage is just as stark. A company with runtime-enabled operations can strip away entire categories of overhead—ticketing, reconciliation, reporting—while rivals remain bound to legacy tools. That advantage compounds: the runtime operator can reinvest savings into growth, undercut competitors on pricing, or accelerate innovation. The message is clear: in markets where runtimes become the operating layer, the leverage will either be used to integrate faster or compete harder.
Competing Visions: Platforms vs. Toolkits
Different players are approaching this shift from different angles:
- Microsoft is embedding agents across Office and Dynamics, signaling a transition but still tethered to legacy productivity metaphors.
- LangChain and open-source frameworks provide flexibility but require heavy engineering lift to make them production-ready.
- OneReach.ai has built its platform from the ground up as an agent runtime, focused on orchestration and integration across enterprise systems.
This tension—between building block frameworks and fully operational runtimes—mirrors earlier eras of software. Just as operating systems eventually standardized personal computing, runtimes will become the “OS” of organizational AI.
The Puzzle of Adoption
The irony is that while platforms like OneReach.ai very purposefully make agent runtimes possible—and a heck of a lot easier than they’d otherwise be—they also reveal just how incredibly hard adoption really is. Interestingly, technology itself is no longer the biggest barrier. The real puzzle is people: the mental models they cling to, the organizational habits they’ve built, and the fears that come with unfamiliar territory.
Imagine standing in 1993 and telling a board of directors, “the internet is the solution to your distribution, communication, and marketing challenges.” The idea would have sounded absurd. How could a patchwork of university networks replace warehouses, call centers, or print campaigns? The barrier wasn’t the technology itself—it was people’s inability to reimagine their work through a new medium. And even once you got them on board, there was still an immense amount of unfamiliar, uncharted work required to advance that vision. Not an easy task—even with the right tools in place and alignment at the top.
Enterprises aren’t just swapping out one tool for another. They’re learning to operate in a new world where software doesn’t just support their teams—it’s functionally members of their team. That requires reframing risk, rethinking governance, and building trust in systems that feel alien to many leaders. Technology may be ready, but the cultural readiness to embrace it lags far behind.
Thinking AI-First: From Tools to Teammates
The bigger challenge isn’t technical—it’s mental. Enterprises must learn to think AI-first, not tool-first. That means imagining workflows that don’t exist for human operators.
As Don Scheibenreif of Gartner said on the Invisible Machines podcast: “The enterprise that figures out how to treat AI as a team member, not just a tool, will have a lasting advantage.” (UX Magazine, Invisible Machines Podcast).
The companies still clinging to spreadsheets and CRMs will be outpaced by those designing entirely new work patterns around AI runtimes.
This shift is exactly what the AI First Principles initiative argues for: that organizations need to rebuild their assumptions around AI, rather than retrofitting old processes. Contributors to the initiative include a co-author of the Agile Manifesto, as well as leaders from Google, NASA, Meta, and Frog. Their collective point is clear—thinking AI-first is as much about new organizational DNA as it is about technology.
Preparing for the Post-Spreadsheet Era
How should leaders prepare?
- Audit dependency on human-first tools. Where are you relying on systems that exist only because humans needed coordination layers?
- Experiment with orchestration. Don’t just prototype agents—test how they interact in multi-agent environments.
- Rethink metrics. Success won’t be measured in “seats” or “licenses” but in organizational outcomes driven by agent collaboration.
- Upskill teams. Training shifts from “using tools” to “designing for orchestration.”
This is not another software upgrade cycle—it’s the dismantling of an entire layer of enterprise software.
Conclusion: A World Without Spreadsheets
Imagining work without spreadsheets or CRMs feels almost impossible—just as imagining a world without typewriters once did. But the logic is the same. When a new operating layer emerges, the tools built for the old layer disappear.
AI agent runtimes are that layer. They don’t just help us do old things faster—they make whole categories of old things irrelevant. If you can’t picture a world without today’s enterprise software, you’re not yet thinking AI-first. But you will need to.
Sources & References
- Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027 (Gartner, 2025)
- MIT report: 95% of generative AI pilots at companies are failing (Forbes, 2025)
- Robb Wilson & Josh Tyson, Age of Invisible Machines (Wiley, 2025)
- UX Magazine & Invisible Machines podcast interviews (Don Scheibenreif, Cassie Kozyrkov, Dan Goldin, etc.)
- Marshall McLuhan, Understanding Media: The Extensions of Man (New York: McGraw-Hill, 1964), p. 7
- AI First Principles (2024)
