Building a Task-Driven Accountability Network (TAN) for the AI Era

Introduction: The Contrast Between External Platforms and Internal Coordination

A single order can organize strangers — consumers, merchants, and service providers — into a coordinated transaction. Yet inside companies, a few people collaborating on one task often require multiple rounds of coordination just to clarify who can commit. This contrast sharpens a question: if e‑commerce platforms, ride‑hailing apps, Beike’s agent network, and Haier’s Rendanheyi model already organize work through tasks and rules, why do enterprises still need a new “responsibility network”?

The answer has two layers. Within their respective domains, those practices have indeed built networks where tasks connect collaboration and rules allocate authority and accountability — worth serious study. But a mechanism that works for one business scope does not guarantee the whole organization has equally reliable responsibility relationships across different problems, risks, and participants. Judging that requires looking at how tasks originate, how authority is configured, and who catches the result when things change — not just order counts, micro‑entity counts, or software names.

What the Three Practices Changed — and What They Didn’t

Order‑Based Platforms (Meituan, Didi)

Instant‑service platforms solved large‑scale matching and fulfillment: who needs what, where, who can serve, progress tracking, and settlement. Meituan’s 2025 order‑allocation disclosure shows the system considers a rider’s existing orders, allows experience‑based sequence adjustments, and supports re‑dispatch via system suggestions or station‑manager intervention when vehicles break down. This already includes cross‑order coordination, on‑site judgment, and exception handling — not merely “algorithm dispatches, human just rides.”

However, giving a gig worker a stable identity in the network does not make the worker and the platform share the same responsibility. The rider cannot explain dispatch rules, system defects, or governance choices. Orders organize collaboration; whether they do so fairly depends on whether participants have information, appeal, and correction channels.

Agent Cooperation Network (Beike ACN)

Beike faced a different problem: a housing transaction needs multiple professional contributions. If only the closing agent gets rewarded, earlier contributors withhold listings, clients, and expertise. ACN uses role‑based division and commission rules to turn cooperation into a sustainable economic arrangement. Beike’s 2025 annual report notes agents can collaborate across stores and brands, commissions follow roles without renegotiation, and the platform adds supervision, co‑governance, training, and AI. ACN therefore not only connects agents but also defines how contributions are recognized and collaboration constrained.

Yet aligned interests do not eliminate conflict: one party wants speed, another needs thorough risk disclosure. Learning ACN means adopting contribution allocation and probing shared commitments, dispute resolution, and long‑term professional capability — not just copying a split sheet.

Rendanheyi (Haier)

Haier’s Rendanheyi links employee value directly to user value created. The 20th‑anniversary review (2025) highlights micro‑enterprise decision, hiring, and allocation rights plus link‑group contracts. Here “order” points to user value, not a narrow transaction order, and micro‑enterprises are not ad‑hoc task groups that dissolve after one job.

The key insight: people willing to own business results must gain organizational resources, trade‑off space, and contribution‑sharing mechanisms. If a company only declares “everyone is an entrepreneur” while budget, hiring, and pay stay locked in old approval chains, employees get a louder title — not real authority.

Rendanheyi itself evolves: by 2026 Haier Smart Home discusses AI, user needs, and cross‑brand, cross‑industry, cross‑organizational ecosystem collaboration. One cannot freeze Haier at “internal micro‑enterprises only” and then claim only AI‑era networks understand ecosystems.

Comparison Summary

The three practices show organizations need not rely solely on fixed positions to dispatch work. But their entry points differ; they cannot be lined up as a low‑to‑high evolutionary ladder. Existing platforms, cooperation networks, and autonomous teams can simultaneously form parts of a responsibility network; the problems they solved should be the starting point for the next build.

The Real Difference Lies Where a Single Order Cannot Reach

Entrepreneurs often ask: since external collaboration works around orders, why not turn the whole company into order‑taking?

External transactions can pre‑define service object, price, start/end, and settlement. Internal work often must first answer “what is the problem worth solving?” — a new product exploration, a risk investigation, a public‑rule revision. The starting point may be just a signal, with no defined delivery path. If work must wait until result and actions are standardized before entering the system, the organization excludes the very work that needs judgment most.

Internal contributions also span different cycles: a professional veto may cut current revenue but avoid future loss; training, data maintenance, and rule correction cannot be instantly billed to a single order. Turning all contributions into bidding, piece‑rate, and instant revenue sharing makes visible delivery faster while the capabilities supporting that delivery grow thinner.

Therefore, order mechanisms, autonomous teams, and organization‑level responsibility networks cannot replace each other. One micro‑enterprise can own multiple tasks; one task may pass through several micro‑enterprises. An order can trigger a fulfillment task and also spawn an unforeseen investigation or rule revision. Enterprises must organize these links, not force all work into one document shape.

Mycelium Organization shifts the authorization focus from “what a position usually manages” to “who commits to this piece of work, on what basis, and how is it verified.” Positions retain professional capability, long‑term duties, and talent development; tasks bring capability to the concrete problem. When an employee enters a task they gain the corresponding role and permissions; when the task ends, their professional identity and the organization’s cultivation responsibility do not dissolve.

Testing a Responsibility Network: The North Hyde Substation Fire

In March 2025 a fire at the UK’s North Hyde substation closed Heathrow Airport for most of a day. NESO’s July 2025 review found that the faulty component had shown water contamination in 2018 oil tests, yet no proportionate action was taken. The airport’s fallback plan to switch to other supply points required 10‑12 hours — a condition known inside the airport technical team but not sufficiently shared with the energy company.

The gap was not lack of data, departments, or plans: the anomaly was detected, recovery methods existed. The break occurred where they did not sufficiently enter subsequent actions and cross‑organization arrangements. Power restoration and airport operations resumption are interdependent yet distinct outcomes; knowing the progress of the first does not equal knowing when the second commitment can be honored.

This incident does not prove a new org name prevents disaster, but it puts the test of a responsibility network in plain sight: who turns a risk signal into a must‑advance task? Who holds the full dependency map? Who makes relevant institutions understand shared consequences? Who continues to hold unresolved risks? These questions do not presuppose AI.

Five Continuous Links to Examine a Network

Using the Mycelium lens, a network can be checked along five consecutive links:

Can the problem enter action? Customer input may not be a ready order; field discoveries may not fit existing job descriptions. The organization must allow employees to raise issues, have authorized deciders start work, and clarify who responds and with what support — not let “you found it” become “you own it alone.” When cause is unclear, owning the investigation task is more honest than promising full resolution.

Does the owner get the conditions to act? Can they mobilize relevant expertise, obtain necessary facts, use resources within their authority, and find someone to make trade‑offs when boundaries are crossed? They own the responsibility to push the complete result forward — not to sign for every specialist, nor to absorb system‑design and management errors.

Can changes propagate along responsibility links? When a judgment fails, which dependent tasks are affected? Who must re‑confirm? Which actions can continue? There must be clear grounds. On handover, the organization confirms the successor, their authority, and open items. On closure, unfinished maintenance, remediation, and external obligations get a new owner — they cannot vanish with a “done” status.

Can shared rules resolve local conflicts? When resources are scarce, professional opinions clash, or multiple customer promises conflict, the decision cannot go to the loudest voice or the best‑connected. The core must provide common facts, professional baselines, and an authorized adjudication path. Major decisions state their costs and adjust affected teams’ resources and metrics. Local optimum ≠ global optimum; putting global optimum above local optimum is not demanding unconditional concession but requiring the organization to own the trade‑off.

Does one job improve conditions for the next? Effective methods must document applicable scope, be independently usable by qualified people not on the original task, pass independent acceptance, and be adopted and maintained by authorized owners. Failure samples must also be preserved. From solving one problem to standardizing a class of problems lies verification and adoption — not just a retrospective or a successful demo.

These links already exist in current governance; the five checks are not an international certification standard. The UK government’s November 2025 response to the Heathrow incident already assigned remediation to accountable bodies with deadlines and oversight. TAN’s value lies in whether these links run continuously in daily tasks, not in renaming existing effective governance.

When the five links connect, “strong core, live frontline” becomes an understandable organizational arrangement: the core guards direction, professional rules, public capabilities, and major trade‑offs; the frontline gets space for on‑site action and improvement proposals; frontline results test the core, and rules constrain rule‑makers and interpreters. Network flexibility comes from reliable shared conditions, not from absence of management.

AI Expands Execution Capacity — and Amplifies Ambiguous Authorization Risks

Task responsibility networks can exist before AI; project charters, authorization systems, work instructions, and professional collaboration already perform some of these functions. AI changes the game: more work that used to rely on people understanding context, searching information, and handing off can now be continued by digital capabilities, lowering the cost of crossing position boundaries.

Lufthansa Group’s July 2025 disclosure: five gates at Frankfurt Airport used a visual AI system named seer to record jet‑bridge docking, baggage loading, refueling times, providing process data to partners. A change in one step becomes a timelier basis for downstream adjustments. This shows AI improves state acquisition and sharing, but does not prove machines have earned airport‑operation decision rights. Seeing a delay is different from deciding resource reallocation, changing commitments, and accepting risk. For suitably equipped LLMs and agents, the same separation applies: generating a handling plan ≠ authority to execute; invoking a system ≠ the action complies with business agreements.

Previously, an experienced employee might pause on seeing an anomaly, use background knowledge, then verify with a colleague. Digital employees executing continuously cannot be assumed to understand unwritten tacit agreements. Multiple agents each completing local goals may turn one ambiguous authorization into a chain of actual actions. It’s not that humans don’t err; machine speed, volume, and cross‑system reach make arrangements that depend on ad‑hoc rescue more fragile.

Hence the Task Contract (TC) becomes TAN’s minimal dynamic authorization carrier, pinning result, boundary, judgment, evidence, and escalation to a concrete piece of work. It lets people know what they committed to, and lets digital employees act within explainable, bounded ranges. It is not a customer contract; signing a name does not grant full force — force still comes from effective organizational systems and authorizations.

Execution‑type tasks: state which rule applies, what counts as correct completion.

Judgment‑type tasks: state whether evidence is sufficient, which uncertainties must be retained.

Decision‑type tasks: state who has authority to accept which trade‑offs.

Creation‑type tasks: leave room for exploration, use stage‑gate validation to decide continued investment.

A single work item may contain several types; one cannot pre‑label a task as “creation” or “execution” just because the participant is a programmer or a digital employee. They can share five basic semantics but must not share a rigid acceptance template. Task ID identifies the work instance; structure version explains field interpretation; content version records how agreements change. Correct ID ≠ correct model understanding. Enterprises must test behavior with real boundaries and exceptions, constrain actions with system permissions — not release on a model’s “I understand.”

Digital employees may participate in execution, discrimination, and verification; ultimate accountability remains with named individuals and organizational entities: result owners continuously organize delivery; professionals own professional judgments; system owners own digital capability configuration and maintenance; authorizers own major risk trade‑offs. A final click‑confirm cannot transfer all prior responsibilities to that one person.

Thus the AI era needs a further‑evolved responsibility network — not because old practices suddenly fail, but because more work flows between humans and machines, and relationships once held by interpersonal tacit agreement must become continuously fulfillable operating conditions.

Building TAN: Start from One Complete Piece of Work

Constructing TAN does not begin by removing departments, changing job grades, or buying a new platform. Instead, pick one class of complete work worth improving, let the authority‑responsibility arrangement face the test of customer results; once a reusable method emerges, expand the scope.

Define the outcome first, not the actions. Managers, business, and professional staff jointly specify who is served, what improvement is sought, and what evidence signals completion. If the final solution is still unknown, agree on the investigation/exploration phase’s outcome and investment boundary. When user needs are not yet understood, breaking the task finer only accelerates deviation.

Trace the real work to find breakpoints. Distinguish whether the problem stems from missing capability, data errors, or unclear authority. If facts are missing, add facts; if qualified people are missing, add capability. Do not use a task contract to substitute for these inputs. Keep effective order systems, project setups, and autonomous teams; focus on what promise and condition are missing when work passes from one owner to another.

Form the Task Contract and lock in authorization. Business owners convene participants to clarify five things: what result to deliver; resource and risk boundaries; who makes which judgments; acceptance criteria; who takes over exceptions. Cross‑department resources require a commitment from someone with allocation authority. If decisions stall, there must be backup and further escalation — not forcing frontline owners to bear all consequences of higher‑level indecision.

Connect to adjacent tasks and rehearse changes. Check upstream condition failures: who is impacted, how are downstream commitments handled, can digital employees stop out‑of‑scope actions? Verify the successor truly has time and authority. A system notification only proves a message was sent, not that responsibility was caught.

Retain validated parts, then decide on scaling. Complete customer delivery first; temporary permissions expire as agreed; subsequent obligations have clear owners. Methods worth spreading get separate verification and maintenance owners — don’t make customers wait for the whole learning project to finish. When experience moves from the original team to other qualified teams and still yields reliable results without depending on the original authors’ constant firefighting, there is grounds for wider adoption.

The Mycelium OS (MOOS) is meant to carry task identity, shared basis, effective authorization, execution evidence, and exception takeover in continuous operation. It should connect to authoritative records of existing business systems so valid agreements can follow task handoffs — not replicate another set of order, inventory, and financial facts. Projects still organize longer‑cycle goals and resources; work items can record tasks and bind contracts; positions and professional communities still develop people and maintain standards. Each object does its job; they should not be merged into one thing just because TAN is adopted.

Software makes agreements easier to execute, but cannot replace managers honoring authorizations: nominal authority that still requires personal connections; public experts who can be called but have no reserved time; employees who contribute methods but are evaluated only on personal output — if these relationships don’t change, the system merely records the old obstacles more completely.

Measuring Success: Shared Results and Sustained Capacity

A task network’s worth cannot be judged by number of tasks created, agents connected, or single‑task handling time — local optima don’t automatically sum to a global optimum. An hour saved in one link may reappear as rework, re‑check, or cleanup downstream.

Enterprises can keep a pre‑transformation baseline and compare tasks of similar risk and complexity: are customer promises more reliable? Has cross‑node waiting decreased? How long from anomaly detection to actual takeover? Have rework and legacy issues dropped? Look beyond averages at the slowest and failed tasks, because the easiest successes may mask where responsibility is hardest to continue.

Costs must be counted fully: model calls, human review, collaboration, maintenance, and remediation all belong to the cost of completing a task. Reduced manual volume cannot be directly written as corporate gain. Where possible, keep comparable task groups to separate improvement from model upgrades, staff熟练度, or changes in authorization and collaboration arrangements; without such comparison, honestly label results as pilot observations.

Human changes must also enter this ledger: can cross‑task contributions get recognition? Will raising a professional objection be punished? Do result‑owners simultaneously receive resources and rewards? Do newcomers still have guided opportunities to practice judgment? These determine whether the network sustains. Piece‑rating every job does not mean seeing every contribution; maintaining public capabilities and helping others avoid errors may be the most valuable work.

Task‑ification does not mean turning employees into contractors. Labor relations, professional belonging, and long‑term development can stay stable; what adjusts around tasks is specific participation and action permissions. A company cannot demand employees act like entrepreneurs while retaining all decision rights and upside, then transfer failure risk to individuals.

If a pilot only adds a reporting layer, critical judgments still bypass the system, professionals cannot stop automated execution, or governance costs chronically exceed gains — simplify, pause, or change the method. If existing project governance solves the problem at lower cost, acknowledge its effectiveness. Theory’s value is helping enterprises make better arrangements, not forcing every improvement to wear its label.

Conclusion

Meituan, Beike, and Haier are worth learning from because they each turned work identification, collaboration contribution, and operational authority into arrangements that run at scale. They are not outdated templates waiting for a new theory, nor can any single mechanism qualify the whole company as one organizational form.

The next construction in the AI era is to bring these effective experiences into more types of work: so someone responds when tasks are still unformed, someone judges when work is changing, responsibility continues across organizational and system boundaries, and completion leaves reliable capabilities for the next job. TAN describes precisely this responsibility network that must be actually built and continuously tested.

Enterprises need not turn all work into orders, nor eliminate all positions first. What truly needs changing is the arrangement where power and resources stay fixed while responsibility is demanded to flow endlessly along tasks.

When a task reaches a person, they should receive not only the requirement but the conditions to get it done; when a task leaves a person, the organization must not let the commitment leave with them. Only by grounding these conditions can flowing tasks become organizational capability instead of another pressure‑transfer mechanism.