Engine Roaring, Yet Organizational Momentum Stalls
On September 11 in Dujiangyan, Sichuan Province, a deep dialogue exploring the truth behind organizational intelligence will commence.
Truly embedding AI into an organization’s business workflows is today’s most pressing preoccupation for managers. Large-model parameters showcased at exhibitions keep hitting new highs; employees in offices are already adeptly summoning third-party AI assistants to write code or draft copy. Individual productivity gains are visibly dramatic—yet zoom out to examine overall organizational effectiveness, and reality feels starkly underwhelming: business processes aren’t accelerating in sync, and cross-departmental collaboration remains mired in friction.
AI has entered enterprises—but it hasn’t yet fused into organizations.
Companies invest heavily in the smartest “brains” available, yet often overlook the “skeleton” and “nervous system” required to sustain those brains’ operation. Fundamentally, this is a systemic failure of power transmission. Standalone AI office tools are like individual high-horsepower engines, capable of explosive productivity gains within isolated scenarios. But engine roar does not equal vehicle forward motion.
Without a robust collaborative foundation serving as the driveshaft, even the most powerful engine can only spin idly—its power utterly unable to penetrate departmental silos. Lark provides widely recognized collaborative capabilities—including instant messaging, documents, calendar, multi-dimensional tables, and video conferencing. Its approval capability, continuously expanding as an organizational management module, already supports integration with core workflows. Yet, amid the current absence of holistic governance oversight, many enterprises’ intelligent work agents continue orbiting at the periphery of systems—failing to lock tightly into core business flows.
Tools celebrate; organizations watch from the sidelines. When the intelligence of point solutions far exceeds an organization’s capacity to connect them, a chasm emerges. That computing power—unconverted into organizational momentum—ultimately becomes little more than expensive digital toys.
Why does this power spin idly? Where exactly does the disconnect occur?
The Collaboration Gap: Four Key Struggles Facing Intelligent Agents at the Organizational Periphery
Industry research reveals an awkward truth: over 80% of enterprises have purchased AI tools, yet fewer than 10% have truly integrated them into core business flows. Organizations don’t lack smart “external brains”—they lack the “neural network” that enables those external brains to direct hands and feet.
A third-party AI assistant may instantly generate a marketing plan inside a chat window—but it doesn’t know who should receive it next. An intelligent work agent may precisely predict inventory risk—but it cannot directly trigger procurement actions within the system. The root cause isn’t insufficient model intelligence; it’s a broken collaborative foundation.
Let’s unpack this organizational-level “cerebral infarction”:
Why does information flow always lag by half a beat?
Insights generated by intelligent agents often die inside isolated interaction windows. They cannot seamlessly interweave with instant messages, nor transform automatically into actionable items on calendars. Once information reaches people, manual “human-powered” transfer is still required—causing the gears of workflow to jam immediately.
Why dare no one take ownership or make decisions?
In rigorously governed organizations, every action maps to clear lines of authority and responsibility. Today’s AI, however, mostly operates as an undocumented “ghost resident”: lacking a digital identity and unable to embed itself into corporate approval systems. Without formal authority and accountability backing, intelligent agents remain perpetual advisors—not decision-makers or executors.
Why does data vanish the moment it’s generated?
Human–machine interactions and business simulations ought to become valuable organizational assets. Yet without deep integration with multi-dimensional tables and documents, such data flows away like water through sand. Without memory, AI cannot retain learning—and the organization cannot inherit it.
Why do capabilities stagnate in place?
Business outcomes fail to close the loop. Real-world feedback generated upstream never flows back to the backend intelligent work agents for iterative improvement. Without feedback mechanisms, AI remains stuck at factory settings—unable to evolve alongside actual business contexts.
These scattered breakpoints at the periphery are quietly eroding the efficiency dividends technology promises. To bridge this gap, we must lay a railway network for intelligent agents—one that runs straight through the heart of business operations.
Laying the Railway Network: Enabling Intelligent Work Agents to Run Along Core Business Tracks
So how do we build this railway? Lark’s choice is not to compete in the “gateway-building” race—creating yet another isolated entry point—but instead to diligently construct an organizational collaboration railway network. AI should never be just a standalone personal add-on; it must become an organizational node tightly meshed into business gears.
Only when third-party AI assistants genuinely integrate into the Lark ecosystem does real transformation begin.
Deep insights generated by intelligent work agents cease being inert text trapped in chat windows—and instead land directly in Lark documents, ready for team annotation, discussion, and co-creation. Information flow instantly shifts from private, individual domains into shared, organizational ones. Critical to-dos generated by AI can be conveniently copied by users into the Lark calendar to create scheduled items—or linked as notes to existing calendar entries—enabling seamless human–machine execution planning.
AI-generated decision recommendations can be rapidly converted by users into Lark approval forms, completing process loops within Lark’s structured authority-and-responsibility framework. Raw computational horsepower is thus safely housed within organizational governance boundaries. As for voluminous analytical data, it ultimately settles into multi-dimensional tables. Feedback from business simulations can be manually entered—or captured via multi-dimensional table templates—as structured data, supporting continuous tracking and retrospective analysis of AI application effectiveness. With memory, AI gains continuity—and so does the organization.
Instant messaging, documents, calendar, video conferencing, multi-dimensional tables, and approvals—when these foundational, widely understood capabilities interconnect and operate in concert, they form the underlying track upon which AI runs. Only once that track is laid can intelligent work agents truly run along core business tracks.
An Open Ecosystem: Charting Real-World Routes for Industry-Specific AI Deployment
Once the railway is built, it shouldn’t host just one special train. A true railway network must support express services and heavy freight across hundreds of industries.
When third-party coding-assistant agents integrate into the Lark ecosystem, they cease being isolated geek toys. Developers outline architecture within Lark documents—and intelligent work agents naturally fill in logical details. After code submission, developers effortlessly notify stakeholders via Lark instant messaging. Technical gears thus lock tightly into the organization’s collaboration chain.
Now consider frontline operations. When vertical-industry intelligent customer service assistants integrate with Lark approvals, AI-identified high-risk customer complaints no longer yield merely cold analytical reports. Employees can instantly launch a Lark video conference—and push accountability tickets directly into the approval workflow. Frontline crises instantly translate into executive decision directives.
Various AI office tools resemble locomotives of different models. As long as they mount Lark’s standardized-gauge chassis, they can sprint at full speed along the organization’s core business lines. Whether R&D, marketing, finance, or legal—every vertical scenario’s intelligent work agent finds its own entrance onto the main line.
Lark has no intention of building every locomotive itself. It focuses on one thing only: laying the sturdiest, most interoperable railway track. Ecosystem prosperity has never depended on who monopolizes all the scenery—but on who defines the fertile soil where everything grows. When intelligent applications from hundreds of industries converge here, AI deployment gains tangible, real-world coordinates.
Digital Water Conservancy: Channeling AI Floodwaters to Irrigate Organizational Fertility
No matter how loudly engines roar, without proper driveshaft engagement, kinetic energy never reaches the wheels.
The explosion of large AI models resembles snowmelt surging down the Min River at its mountain exit. Computing power and model parameters are the source waters—but without channels to guide them, this floodwater breaches embankments and becomes a deluge that drowns business operations. Over two millennia ago, Li Bing tamed the river not by damming it, but by following its natural course—transforming the unruly river into the life-giving source that irrigates the Chengdu Plain. Today, Lark’s instant messaging, documents, calendar, approvals, and video conferencing constitute precisely this digital-era water conservancy project.
It doesn’t create water—it builds channels.
Third-party AI assistants and intelligent work agents represent converging tributaries; Lark’s collaborative foundation is the crisscrossing irrigation canal network. When AI computing power flows orderly along message streams and approval workflows, it ceases to be a spectacular yet disembodied technological marvel—and instead becomes nourishing rain, precisely drip-irrigating every business node.
Water arriving does not guarantee harvest. The deep-water zone of AI deployment is, at its core, a philosophical reconstruction of organizational governance. Technology can approach omniscience—but organizational judgment—how many percentage points to discount, how many days to extend payment terms, which defects warrant rework—must always reside within authentic authority-and-responsibility frameworks. What Lark provides is precisely the riverbed enabling AI to run steadily within organizational rules.
Let intelligence ride the rails. Let floodwaters nourish fertile fields.
September 11, Dujiangyan, Sichuan Province. The answers await—in person.