Buying the System, But Can’t Move the Data
A SaaS system purchased for $10,000 requires 20 person-days just to export a single report.
You think you’re buying a system—but in reality, you’re renting a data prison.
Sales uses one system; finance, another; inventory management runs on yet another standalone backend. The more software you buy, the more fragmented your data becomes. Employees’ core daily work devolves into copying, pasting, and re-verifying across endless browser tabs—effectively becoming “human APIs.”
Five hundred mouse clicks won’t yield a single automatically updated summary table.
You can afford $10,000—but still can’t move your data. System vendors erect high walls using closed APIs and “custom development” billed by the day, effectively controlling how enterprise data flows—and turning customers’ business data into their own moat.
You own the login credentials; they control the data.
Until he decided he’d no longer serve as this cyber搬运工.
If the systems won’t open up, then he’d break down the walls from the outside. He set his sights on Lark.
As an open collaboration platform, Lark natively includes instant messaging, multi-dimensional tables, documents, calendar, and approval workflows. These foundational, widely understood capabilities happen to be the ideal “thread and needle” for stitching together fragmented business systems.
He refused to remain shackled by rigid processes. Instead, he intended to orchestrate third-party intelligent work Agents within the Lark ecosystem—to gradually reclaim data locked away on isolated islands.
Thus began an overnight shift dedicated to data sovereignty.
Three AI Assistants, One Long Night Shift
At 2:00 a.m., the first “night-shift engineer” clocked in.
This is a third-party intelligent work Agent specialized in processing long-form text. The protagonist dropped a 114-page cross-border compliance contract into a Lark document, expecting it to rapidly extract key risk-control clauses.
For the first 80 pages, it performed flawlessly—until it hit a four-layer-nested financial earn-out table.
Then, it began confidently fabricating nonsense.
Misaligned figures, broken logical chains—it resembled an intern who’d blacked out after too much sake. The protagonist didn’t frantically hammer the “stop” button, nor did he rage helplessly at the screen. He understood a harsh truth: The theoretical specs of an AI tool never equal immediately usable productivity.
Models hallucinate. They stall. They freeze completely when confronted with complex structures.
But he had Lark Documents.
Here, a document is never merely a place to type—it’s a vessel for knowledge accumulation and a buffer zone that accommodates failure. The protagonist issued a new instruction to the Agent: “Stop parsing. Draft a handover document.”
Within minutes, a comprehensive handover document appeared on the page.
It clearly recorded verified clauses, the exact coordinates of the stalled table, and two attempted extraction paths—both of which had failed. This wasn’t just an error log; it was a “minefield map” for the next colleague stepping in.
Human–AI collaboration isn’t about delegating everything to machines—it’s about handing over responsibilities with grace.
The first Agent logged off, disappointed—but the cost of trial and error didn’t vanish. It was carefully encapsulated as intermediate assets inside the Lark document.
Third-party AI office tools can integrate with Lark—for example, receiving instructions via Lark bots and reading handover content from designated Lark documents.
The protagonist triggered a task by @-mentioning a bot within Lark, completing the human–AI handover.
500 Attempts Inside Multi-Dimensional Tables
The second intelligent work Agent is a “straight shooter.”
It quietly read the handover document and plunged headfirst into the deep waters of Lark’s multi-dimensional tables. Its mission: clean and compare massive volumes of legacy-system data. Sounds simple—until you try it.
“Field type mismatch—error.”
“Association rule conflict—retry.”
“Logical infinite loop—error.”
Rows and columns in Lark’s multi-dimensional tables became Sisyphus’s hill—the boulder perpetually rolling back down.
It tirelessly attempted rule matching—only to repeatedly slam into the wall of inconsistent formatting.
Ten attempts. Fifty. Two hundred…
The status bar refreshed mechanically—and the protagonist’s blood pressure rose in tandem.
Under pure manual labor, such monotonous repetition would’ve driven even the most seasoned operations staffer to hurl their keyboard. But machines don’t crash—they simply record every failure, faithfully.
The protagonist wasn’t idle either. Watching the ever-growing stack of error logs inside the multi-dimensional table, he敏锐ly detected an underlying structural flaw in the data.
“Stop. Convert all date formats uniformly to timestamps, then run again.” He typed the new command.
AI handles tireless trial and error; humans steer the course before hitting a dead end.
Attempt #98. #142. #389…
Finally, on attempt #400+, the once-blinding sea of red gave way to a crisp, uniform stripe of green.
It ran successfully.
Those 500 attempts weren’t wasted. Lark’s multi-dimensional tables support structured logging and archiving of critical parameters and results throughout the trial-and-error process—enabling teams to reuse and iteratively optimize later. Every failed matching rule became a stepping stone for the next data flow.
A tool’s ceiling isn’t defined by its code—but by its user’s patience to verify and adjust direction, even after 500 errors.
The protagonist exhaled deeply. But he knew it wasn’t over yet. The data had been cleaned—now it needed to run through real business workflows.
Final Closure: Message Streams and Approval Workflows
No matter how cleanly data is scrubbed, left idle in a table, it remains nothing more than silent characters.
It needs to flow—to be seen, confirmed, and stamped with human intent.
The third intelligent work Agent quietly assumed the night shift. Its task was modest: perform cross-validation on the final results in the multi-dimensional table and generate an analysis report comprehensible to humans.
No flashy magic—just methodical comparison and line-by-line document generation.
The moment the report was generated, the protagonist didn’t wrestle with “deep API integration.” He simply pushed the output directly to Lark Approval.
Then he switched to a Lark instant messaging group, @-mentioned relevant business leads: “Data flow is complete—take a look?”
That’s it.
No need to toggle between multiple disparate systems. No need to copy-paste links into various chat windows.
Content generated by third-party AI office tools can be pushed—via Lark’s open API or bot capabilities—into Lark Approval, group chats, or documents, seamlessly integrating into everyday collaboration.
Ten minutes later, the group replied with “OK,” and the approval node advanced accordingly.
The protagonist opened Lark Calendar, quickly created a retrospective meeting, and attached a Lark Video Meeting link to the event.
“3 p.m. tomorrow—let’s discuss this new workflow.”
From 500 attempts inside a multi-dimensional table…
to a single “@” mention in a message stream…
to a meeting invite on the calendar.
This isn’t some sci-fi fantasy of “fully automated system closure.”
It’s people mastering tools.
AI sprints relentlessly in the shadows; Lark’s foundational, intuitive capabilities handle closure visibly and reliably.
Buying a system gets you only software—but when you stitch together fragmented AI outputs into daily work using messages, approvals, and calendars, you truly reclaim control over your data.
Everyone Is Cultivating Their Own Agent Workflow
The moment the calendar invite for the retrospective meeting was sent, the “data reclamation campaign” had, in fact, concluded.
No—more precisely, it had just begun.
In the past, enterprises spent heavily on business systems—often acquiring what amounted to digital mausoleums.
Data remained locked inside closed table structures; employees functioned like assembly-line workers, mechanically clicking “submit” within rigid forms.
Now, things have changed.
Within Lark’s open ecosystem, everyone is “cultivating” their own workflow.
Frontline sales reps use instant messaging and third-party intelligent work Agents to uncover customer intent; operations staff drop messy AI-generated data straight into multi-dimensional tables for cleaning; product managers co-create requirements with AI office tools inside documents, then formalize conclusions using approval workflows.
No complex system integrations. No hair-pulling API configurations. Just natural, seamless handovers, flows, and knowledge accumulation between people and AI—built atop Lark’s infrastructure.
As an open collaboration platform, Lark provides foundational capability interfaces—including messaging, documents, multi-dimensional tables, and approvals—that empower enterprises to independently integrate and orchestrate diverse AI office tools, building human–AI collaboration workflows aligned with their unique business logic.
It enables seamless integration of third-party AI assistants, re-knits fragmented business data, and transforms “tacit knowledge”—which used to vanish when employees left—into durable, organization-wide digital assets.
Buying a system gets you someone else’s code—but when you gradually embed your business logic, AI collaboration habits, and core data into your own collaborative soil, you finally achieve true data sovereignty in the digital age.
Tools become obsolete. Models evolve. Today’s flashy AI gimmicks will inevitably become tomorrow’s baseline expectations.
What truly holds value is the human–AI collaboration methodology your team has battle-tested and refined through countless iterations.
Can you buy a system but not move your data?
No—here, the rules are yours to define.
You own your data.