I. The Easiest Battles in AI-Powered Office Work Are Already Nearly Won
Recently, several leading large-model companies have simultaneously declared: “We aim to build platforms—not obsess over single applications.” Meanwhile, once-high-flying point-solution AI creation tools are repeatedly hitting roadblocks in the primary market. Procurement teams are even more pragmatic: enterprise executives’ attitudes toward AI office tools have shifted from last year’s “blind experimentation” to a cold, hard-nosed “ROI evaluation.”
When these seemingly fragmented industry snapshots are pieced together, a harsh truth emerges: the easiest battles in AI-powered office work have essentially already been won.
Over the past two years, large models have targeted only the “low-hanging fruit”—sweet spots characterized by high digitalization and high tolerance for error. Writing marketing copy, generating supporting images, polishing PR drafts—these isolated creative tasks have indeed been executed flawlessly, even fostering an illusion that “productivity is about to explode.”
But this is fundamentally *not* how enterprises truly operate.
Once document-writing efficiency hits its ceiling, marginal returns for point-solution tools plummet off a cliff. Large-model companies are no longer the sole protagonists—because real business pain points have never resided in these isolated creative steps.
AI-powered office work has officially entered deep water.
Today’s adversary is no longer “how to generate a piece of text,” but “how to route that text accurately to the next approval node”; no longer “how to summarize a meeting,” but “how to embed meeting conclusions into a multi-dimensional spreadsheet—and simultaneously schedule follow-ups on the calendar.”
The illusion of point-solution breakthroughs is crumbling. Enterprises don’t need a smarter chat box; they need a collaborative foundation capable of handling complex business workflows—a platform where third-party AI assistants and intelligent work agents can truly run. And it is precisely this battlefield—where infrastructure like Lark begins taking charge.
II. The Illusion of Point-Solution Breakthroughs and the Chasm in SOP Execution
Over the past two years, an optimistic illusion has pervaded the industry: “Just plug in a large model, and enterprise processes will instantly restructure themselves.”
This sounds energizing—but it simply doesn’t hold up under scrutiny.
Anyone in the field knows full well: core enterprise operations aren’t about producing elegant text inside a chat box—they’re about executing complex, cross-departmental, cross-system, and even cross-legal-entity SOPs.
Once AI-powered office work enters deep water, the shortcomings of point-solution tools become fully exposed.
How do we break down data silos? How do we enforce strict permission isolation? How do we bridge process gaps? All answers remain elusive. A third-party AI assistant might perfectly distill the key insights from a 10,000-word article—but if those insights cannot flow directly into the approval workflow or trigger follow-up scheduling on the calendar, it remains nothing more than a “pretty toy.”
This isn’t due to poorly engineered prompts—or insufficient intelligence in the large model. It’s because the model lacks “right-of-way” to move within the enterprise’s internal systems.
If AI-generated suggestions cannot translate into concrete execution actions, they’ll forever remain “advisors”—never stepping onto the “front line.”
This is the illusion of point-solution breakthroughs. What enterprises truly crave isn’t yet another set of isolated smart plugins—but a stable, robust collaborative foundation capable of reliably supporting business workflows.
On this foundation, instant messaging serves as the nervous system; multi-dimensional spreadsheets form the skeletal structure; documents, calendars, video conferencing, and approvals constitute the musculature. Only when intelligent work agents and various AI office tools can seamlessly integrate with these foundational, commonplace capabilities can AI compute power truly flow through SOPs’ capillaries—reaching every operational endpoint across the enterprise.
There are no shortcuts in deep water. To tackle this tough challenge, what’s required isn’t point-solution explosions—but infrastructure depth.
III. How a Collaborative Foundation Enables Deep-Water AI Deployment
Large models are power plants; point-solution AI tools are electrical appliances; and the collaborative foundation is the power grid.
Without the grid, even the most advanced appliance is scrap metal. While many remain captivated by the illusion of “large-model point-solution breakthroughs,” savvy enterprises have long been quietly building infrastructure. Lark’s instant messaging, multi-dimensional spreadsheets, documents, calendars, video conferencing, and approvals—capabilities that sound utterly unglamorous and even mundane—are precisely the indispensable “construction environment” for AI deployment.
Industry insiders know full well: promises of “fully automated, system-wide integration” are mostly hype. This isn’t due to technical limitations—it’s because business workflows are too complex for machines to intuit human intentions.
True deep-water deployment relies on intuitive, scenario-based human actions.
For example, after enduring a lengthy video conference, an employee instinctively invokes a third-party AI assistant within the associated document to convert the recording into a structured meeting summary. Or, a sales representative uses AI office tools alongside multi-dimensional spreadsheets to clean up a batch of messy customer data—then, upon verification, simply selects the relevant rows to initiate approval.
There’s no magical “automatic routing”—only seamless, human-intuitive integration.
Lark’s role here isn’t to steal the spotlight from intelligent work agents—but to lay down a sufficiently robust pipeline. Even the most powerful compute must travel along the nervous system of instant messaging, the skeletal framework of multi-dimensional spreadsheets, and the muscular pathways of approvals—to finally permeate the capillaries of business operations. Lark forcibly transforms AI from a “toy” into a “tool,” and turns “suggestions in a chat box” into “actions embedded in workflows.”
But this is merely the standard answer.
In reality, you won’t find two enterprises with identical SOPs. When standardized foundations collide with wildly diverse, customized business needs, official capabilities alone inevitably fall short. This forces a far more practical question: how can the foundation retain sufficient openness to meet those non-standard demands in deep water?
IV. Countering the In-House Development Impulse—Becoming Infrastructure for the AI Era
Every company with even modest technical resources, after gaining access to several large-model APIs, inevitably succumbs to one illusion: “Let’s build our own AI office system.”
This isn’t technological confidence—it’s pure ignorance of the sheer complexity involved in organizational collaboration.
The impulse to build in-house is always seductive—but reality quickly delivers a lesson: building a conversational bot takes three days; making it read attendance data accurately, update project progress in multi-dimensional spreadsheets, and strictly enforce compliance checkpoints within approval flows? That takes at least three months. The sunk cost of reinventing the wheel often cripples entire digital transformation teams.
A smart collaborative foundation never tries to directly confront clients’ “in-house development impulse”—instead, it smoothly steps in to take over.
Lark’s solution is refreshingly direct: fully “infrastructure-ize” itself. Rather than stubbornly claiming ownership of all deep-water scenarios, Lark opens its ecosystem wide—enabling third-party AI assistants, intelligent work agents, and AI office tools to plug in and run.
Enterprises no longer need to painstakingly build account and permission systems from scratch. Employees simply connect and use various tools atop the Lark foundation—issuing commands to third-party AI assistants via instant messaging, letting intelligent work agents populate analysis results into multi-dimensional spreadsheets, or using the calendar to assist with meeting time coordination.
On the surface, third-party tools appear to be working their magic—but underneath, it’s Lark’s collaborative infrastructure doing the heavy lifting.
When foundational capabilities like documents, video conferencing, and approvals are thoroughly componentized, Lark evolves beyond being just “a software vendor selling standard features.” It becomes infrastructure enabling both in-house development and ecosystem integration.
Following this logic, the decisive battleground in the AI era has already shifted. Competition is no longer about “whose point-solution model is smarter”—but about “who can become the socket enabling all smart brains to connect at low cost.”
V. Reality Is Complex—But the Main Thread Is Clear
We must acknowledge: the second half of AI-powered office work will inevitably be a chaotic coexistence.
There is no black-and-white, wholesale replacement—only the long-term parallel existence of two distinct types of solutions.
On one hand, high-tolerance-for-error, point-solution creative scenarios will continue thriving. Writing marketing copy, generating supporting images, summarizing long articles—employees will fluidly switch among various third-party AI assistants and lightweight AI office tools, enjoying the immediate dopamine rush delivered by large models.
Point-solution tools won’t die. They’ll permeate every corner where rigorous validation isn’t required—like capillaries.
On the other hand, the moment business operations enter rigorously enforced SOP execution, cross-departmental data flows, or touch the deep-water zone of permission governance, the “dead-end street” dilemma of point-solution tools instantly reveals itself.
This isn’t due to suboptimal prompts—or insufficient model intelligence. It’s dictated by the immutable physics of organizational collaboration.
When analytical outputs from intelligent work agents must seamlessly flow into the next business node—or when conclusions from a video conference must be captured as documents and converted into follow-up calendar items—the capillaries completely fail.
At that moment, what you need is the aorta.
For enterprises genuinely seeking to reshape organizational efficiency with AI, the evaluation criterion is brutally simple: whoever can support complex business workflows—whoever enables “humans” and “intelligent work agents” to collaborate seamlessly within the same context—is the protagonist.
Lark’s instant messaging, multi-dimensional spreadsheets, documents, calendars, video conferencing, and approvals—these seemingly basic, foundational capabilities—are the rebar and concrete poured to construct that aorta.
AI’s breakneck acceleration must ultimately settle onto rational infrastructure construction.
When the tide recedes, point-solution tools dazzling with isolated brilliance may still earn applause at trade shows—but only the quiet, resilient collaborative foundation that bears the weight of all complexity will remain firmly anchored in enterprises’ core business flows—becoming the electricity, water, and coal of the digital age.