The “Smith Eating Noodles” Moment of General-Purpose AI
A viral case has recently swept the industry: “AI generating a million-dollar project proposal in one click.”
At first glance, the elaborate architecture diagrams and market analyses seem impressive—many believe they’ve finally found the ultimate productivity booster for cost reduction and efficiency gains. Yet the moment this proposal is tested on the front lines of actual business operations, it immediately falls apart.
It’s all boilerplate.
There’s no historical data to back it up; no anticipation of hidden pitfalls in cross-departmental collaboration; not even a basic grasp of the company’s existing approval workflows or budget checkpoints. Large language models race through a vacuum—and what they spit out is nothing but “correct yet meaningless platitudes.”
This scenario strongly resembles early AI-generated videos of “Smith eating noodles.” His chopstick grip looks textbook-perfect; his chewing motion is flawlessly executed—yet the noodles pass straight through his chin. Every movement is technically correct, yet the final output feels eerily off.
General-purpose AI operating solo is the “Smith eating noodles” of enterprise office work.
It’s a superbrain with no hands or feet: capable of writing an impeccable “swimming guide,” yet utterly incapable of making even a splash when you toss it into real water. This is today’s most painful industry reality: AI disconnected from business systems remains a toy—not a tool.
When you try to deploy a third-party AI assistant to drive real projects, it doesn’t know how your schedule is laid out in the calendar, how budgets are calculated in multidimensional spreadsheets, or where the requirements document lies buried in some group chat. It can only guess—and enterprise-grade operations simply cannot tolerate guesswork.
To truly embed large models into complex business workflows, we must connect them to a “nervous system” fed by real data. In our validation, that nervous system is none other than Lark.
The Breakthrough: Giving Agents “Lark’s” Hands and Feet
If general-purpose large models are “superbrains without limbs,” then the breakthrough strategy is refreshingly simple: Don’t force the brain to lift weights—equip it with an exoskeleton.
In our practice, that exoskeleton is the integrated combination of “intelligent work agents + the Lark ecosystem.” The idea is straightforward: let the agent focus on what it does best—“thinking” and “orchestrating”—and delegate “execution” firmly and reliably to Lark.
The intelligent work agent interprets your vague intent—such as a casual request like “Help me move the Q3 marketing project forward”—and breaks it down into precise, actionable instructions. Meanwhile, Lark serves as the solid execution foundation, reliably supporting those actions with its everyday capabilities: instant messaging, multidimensional spreadsheets, documents, calendar, and video meetings.
We no longer expect large models to “reinvent the wheel”—for example, forcing them to manually sketch a crude timeline or dump a long, dizzying block of plain text into a chat window. Instead, we empower AI to call upon professional tools via an open ecosystem:
- Need scheduling coordination? Let the agent invoke Lark Calendar to create an event and align all relevant participants.
- Need resource inventory? Let the agent write structured data directly into a Lark Multidimensional Spreadsheet.
- Need to capture consensus? Let the agent generate a well-formatted meeting summary inside a Lark Document.
Think of it as hiring a world-class production manager (the agent)—who doesn’t need to carry the camera himself, but instead precisely coordinates lighting, cinematography, and art direction (i.e., Lark’s suite of capabilities). A tangled, chaotic business workflow is thus simplified into discrete, executable Lark actions.
Once large models learn to harness the Lark ecosystem, AI assistants—previously confined to “armchair strategizing” inside chat windows—finally grow hands and feet capable of reaching into real business operations.
When the right actions are taken, the gears of business workflows truly engage.
Proof Point 1: From “Bland Summaries” to “Multidimensional Data Drilling”
At the close of every major project, the most dreaded step is always the post-mortem review.
If you feed a pile of disorganized meeting notes directly to a general-purpose large model and ask it to “write a summary,” it will almost certainly produce a bland, formulaic report. It may appear logically sound—but it’s hollow, full of empty jargon, and utterly devoid of business context.
An AI summary built without real data is like cooking a meal without ingredients—a structure suspended in midair.
But when we hand this task to an intelligent work agent integrated with the Lark ecosystem, the entire approach transforms. Instead of asking AI to “invent from thin air” while sitting inside a chat window, we send it into the field to “fish” for authentic business data.
First, the AI assistant identifies core dimensions for the review—such as conversion funnels, customer acquisition costs, and channel ROI. Once you approve the framework, the agent begins orchestrating Lark’s underlying capabilities. It aggregates fragmented information scattered across various sources and writes it directly into a Lark Multidimensional Spreadsheet. What used to take half a day of manual copy-pasting now instantly becomes a structured data foundation.
Next, the agent links and embeds these spreadsheet data views directly into a Lark Document, generating a rich, insightful post-mortem report.
The most captivating part is the “drill-down experience.” When you see a conclusion like “conversion rate dropped sharply for Channel X” inside a Lark Document, you no longer need to hunt through old spreadsheets. Simply click the linked data card—and instantly drill down to the raw data details, revealing the root cause behind each anomaly.
From bland, text-only platitudes to structured reports with live data drilling.
This is what AI-powered office work should look like: large models extract logic and insights, while Lark’s multidimensional spreadsheets and documents carry the real, tangible data. AI stops being a charismatic speaker who only delivers pretty words—and becomes a pragmatic operator who walks you through the numbers.
Proof Point 2: “Bullet-Time” Cross-Departmental Coordination
If data-driven reviews help AI “audit your books,” then cross-departmental coordination helps AI “disassemble a precision timepiece.”
Anyone who’s managed large-scale projects knows how maddening it is to align multiple stakeholders’ timelines. Product teams set schedules; engineering teams check availability; legal teams review contracts. When several departments’ calendars collide, it’s like a set of misaligned gears grinding to a halt. The traditional workaround? Manually ping people one by one, flood group chats with “@everyone” messages, and eventually cobble together a time slot amid the chaos.
Now, intelligent work agents have ended this crude, artisan-style approach.
Faced with conflicting schedules across multiple parties, you no longer need to manually compare calendars. AI-powered office tools can quickly identify shared free time slots and recommend optimal scheduling options. All you need to do is approve. Then—with a single tap—you create the event in Lark Calendar, inviting all relevant participants.
What follows is even more seamless.
Once the meeting is scheduled, what about related resource requests and budget approvals? No need to leave the current interface or hunt for approval workflows. You can launch a Lark Approval process directly from that calendar event. As soon as the approval form is submitted, Lark Instant Messaging delivers it—like a guided missile—to every key stakeholder at precisely the right moment.
No unnecessary group-chat noise. No frantic “Where’s this process stuck?” questions. Who needs to see what—and who needs to approve what—is arranged with absolute clarity.
It’s like cinematic “bullet time.” Amid the complexity of cross-departmental collaboration, chaotic information flows slow down, decompose, and reassemble with surgical precision. Every action is captured accurately; every handoff is perfectly synchronized.
The large model clarifies order from chaos—the Lark ecosystem pulls the trigger in reality.
When AI truly grows collaborative hands and feet, those once-frustrating cross-departmental tugs-of-war become calm, precise surgical procedures.
Any Business Scenario—Ready to Build, Right Now
When we stop treating AI as just a conversational chatbox—and start seeing it as a “super-brain” embedded within the Lark ecosystem—our perspective shifts entirely.
We no longer obsess over how dazzling any single model’s response might be. Real control comes from seamless integration at the infrastructure level. Lark’s video meetings, calendar, documents, multidimensional spreadsheets, and instant messaging—once siloed building blocks—have now become the most intuitive toolkit in the hands of third-party intelligent work agents.
You don’t need to understand complex coding logic—just your own business. Any business scenario can be assembled on the spot.
After your morning video meeting, you simply ask the agent to distill key conclusions and distribute them. In the afternoon, while drafting a document, you summon your AI office tool to pull this quarter’s anomaly data precisely from a multidimensional spreadsheet. In the evening, while planning your calendar, you ask your intelligent assistant to quietly align the free time of key cross-departmental stakeholders.
No clunky system integrations. No convoluted configuration paths. Humans define the goal; AI orchestrates Lark’s foundational capabilities; and the result lands smoothly in front of you. When all the right actions are taken, the outcome emerges naturally.
This is no longer a pie-in-the-sky “future office” sketched on a PowerPoint slide—it’s the daily reality taking root across countless enterprises today.
So where is the ceiling for this integrated approach?
Perhaps the answer lies in the workflow of a business leader’s upcoming annual strategy meeting next week.