The True Benchmark of AI-Driven Drug Discovery and the Industry Relay Race
At Lark’s annual industry conference, Xie Chengrun, Chairman of CP Guohe Pharmaceutical, posed a refreshingly pragmatic question: “We’ve introduced so many AI tools—do we do so merely to boost the number of API calls reflected in our reports, or do we truly aim to shorten the time it takes for new drugs to reach market?”
This is not merely introspection from an industry leader; it’s an unavoidable reflection for the entire life sciences field. The true benchmark for measuring AI’s value has never been “how many times it’s invoked,” but rather “what tangible change it delivers.” When the industry easily falls into blind pursuit of technical specifications, we must reaffirm a fundamental truth: A mindset focused on usage volume is a relic of the digital era; a results-oriented mindset is the only valid passport to the intelligent era.
Drug development, at its core, is a high-stakes, long-cycle relay race. From the spark of insight in molecular discovery, to the delicate balancing act of clinical trials, to the razor-thin tolerances required for stable manufacturing—every leg of this relay demands flawless execution.
Yet a molecule’s efficacy in the lab does not automatically translate into clinical efficacy for patients. In this marathon relay, the greatest losses often stem not from technological bottlenecks, but from collaboration breakdowns. When research data is handed off to clinical teams, or when clinical feedback flows to manufacturing, information frequently gets lost or distorted as it crosses departmental boundaries. These invisible frictions silently erode R&D efficiency.
So how do we bridge these gaps? The answer lies not in more complex algorithms—but in tighter connections. Dismantling departmental silos, enabling seamless information flow across the organization, and transforming isolated technological breakthroughs into enterprise-wide coordination are the pharmaceutical industry’s key strategies for navigating uncertainty. A smooth handoff should naturally become the starting point for the next wave of innovation.
Four Types of Intelligence: From Point Solutions to End-to-End Integration
Following this relay race forward, AI’s evolutionary trajectory within pharma has become strikingly clear. Today, AI has deeply penetrated every facet of the industry, crystallizing into distinct application scenarios: *Molecular Intelligence* accelerates target identification and compound generation; *Clinical Intelligence* optimizes patient recruitment and real-time data monitoring; *Manufacturing Intelligence* ensures quality consistency during process scale-up; and *Management Intelligence*, woven throughout the chain, orchestrates resources across the entire enterprise.
These point solutions are indeed exhilarating. But can simply stacking localized intelligence automatically generate organizational intelligence?
The answer is clearly no. Many enterprises have adopted various third-party AI assistants and intelligent work agents, attempting to fill efficiency gaps through tool proliferation. Yet if computational results from the molecular team cannot be synced in real time with clinical designers—or if abnormal findings from manufacturing fail to instantly trigger R&D retrospectives—even the most advanced algorithms remain isolated islands of self-amusement. Tool proliferation masks, but does not heal, collaboration fractures; local optima cannot logically yield global victory.
True organizational intelligence is not achieved by physically bolting together disparate systems—it emerges as a chemical reaction when knowledge, judgment, action, and accountability flow seamlessly across functional boundaries. It requires that every experimental dataset captured and every clinical decision modeled be effortlessly integrated into the organization’s neural network.
When point solutions hit their efficiency ceiling, what’s needed is a unified foundation capable of carrying end-to-end information. This is precisely where Lark—and its open ecosystem—delivers value: by deeply interweaving foundational capabilities like instant messaging, documents, multi-dimensional spreadsheets, and video conferencing, Lark enables all AI-powered workplace tools to organically embed themselves into daily collaborative workflows—truly dismantling fragmented system barriers.
The Lark Ecosystem: A Foundation Connecting People and Intelligence
Once data silos are broken down, what infrastructure carries organizational intelligence?
The answer isn’t deploying yet another new system—it’s returning to collaboration itself. Within the pharmaceutical industry’s complex operational flows, Lark plays exactly this pivotal role. It’s far more than just a communication channel; it’s both a repository for institutional knowledge and a launchpad for action.
Urgent cross-departmental responses are rapidly coordinated via instant messaging, making information actively seek out the right people; tacit expertise accumulated over lengthy R&D cycles is codified into reusable organizational assets using documents; multi-dimensional spreadsheets construct dynamic business maps, rendering the progress status of each molecule instantly visible. When alignment across physical boundaries is required, calendar and video conferencing integrate seamlessly—ensuring discussions cut straight to the core.
But this is only the foundation. Lark’s true power lies in its open ecosystem boundary.
By integrating diverse third-party intelligent work agents and AI-powered workplace tools, Lark naturally weaves external computing power into everyday collaborative workflows. Within strictly defined authorization scopes, these third-party AI assistants can swiftly verify experimental data, compare clinical protocols, and convert labor-intensive desk work into structured decision-support inputs.
This leads us to a practical principle for human-AI collaboration: Let AI prepare the evidence; let humans handle the exceptions.
Machines anchor coordinates amid oceans of data; scientists focus their attention on those “exceptional” moments demanding intuition, ethical judgment, and creative insight. Under this division of labor, information loss during handoffs—between humans and AI, between departments—is minimized to the greatest extent possible.
Technology’s mission is not to replace human thinking—but to provide a solid foundation for human judgment.
Restructuring Collaboration for the AI-Native Organization
With the technological foundation in place, the real challenge has only just begun. Introducing third-party AI assistants and collaborative platforms isn’t about handing employees slightly sharper tools—it’s about fundamentally redesigning the interlocking relationships among people, information, processes, and accountability.
Building an AI-native organization starts with establishing three core principles.
First: Reconstruct around outcomes. Traditional digital transformation typically begins with “mapping existing processes.” AI-native transformation, however, demands that we first define deliverables and accountability boundaries—and then design collaborative workflows backward from there. Don’t obsess over “How many steps does this approval require?” Instead ask, “What evidence does this decision truly need?” Within Lark’s approval and calendar workflows, every node must point directly toward the final business outcome.
Second: Redefine human-machine roles. Machines excel at exhaustive enumeration; humans must focus on breakthrough thinking. Let intelligent work agents shoulder high-frequency tasks like searching, data comparison, and execution support—while anchoring goal-setting, complex negotiation, and ultimate accountability firmly with *people*. This is not a delegation of authority, but a sharpening of responsibility.
Third: Ensure work continuously accumulates. Fragmented communication kills organizational memory. Leveraging Lark documents and multi-dimensional spreadsheets, capture every meeting conclusion, every set of experimental data, and every reasoning logic as reusable organizational assets. Transform individual flashes of insight into team-wide, traceable experience.
These three principles form the skeletal framework of the new collaboration paradigm.
True AI-native capability isn’t about making machines increasingly resemble humans—it’s about freeing humans to truly take command. When workflows are pulled by outcomes, accountability is unambiguously defined, and experience is effectively preserved, organizations finally gain the resilience to endure and thrive across long cycles.
Upholding Responsibility Boundaries and Returning to Human-Centered Values
Amidst waves of technological advancement, what pharmaceutical companies need most is often not blind acceleration—but sober restraint. Drug development concerns human life, which means that as we embrace intelligent work agents and the Lark ecosystem, we must establish three unequivocal guardrails.
First: Data boundaries—adhere to authorization and data minimization. Core molecular structures and clinical data constitute the enterprise’s lifeline. Using Lark’s granular permission controls for documents and multi-dimensional spreadsheets, ensure data flows strictly within the minimum necessary scope. No overreach. No misuse. Let data deliver value only within secure guardrails.
Second: Decision boundaries—critical judgments must be confirmed by humans. AI can rapidly scan vast volumes of literature—but critical scientific judgments and quality release decisions must never be delegated to algorithmic black boxes. Third-party AI assistants prepare evidence; final adjudication must remain firmly in the hands of qualified scientists.
Third: Accountability boundaries—full traceability and auditability. Regulatory compliance in pharma permits zero ambiguity. Through Lark’s approval workflows and instant messaging, transform every R&D milestone confirmation and every cross-departmental collaboration into auditable, traceable organizational memory. Make decisions fully traceable. Anchor accountability unambiguously.
Upholding these boundaries keeps technology on track. As end-to-end organizational intelligence gradually coalesces, we must ultimately ask: Where does all this lead?
AI’s true significance lies in reclaiming time wasted on repetitive searches and futile trial-and-error—and returning it to scientists and physicians.
When researchers step away from sifting through mountains of literature, and when clinicians are liberated from burdensome administrative tasks, they can direct their most precious energy toward deeper disease insights and compassionate patient care. To safeguard health with professional rigor—and to honor life with solemn responsibility—this is the most grounded, enduring destination of technological evolution.