In this article, we cover Haier AI, in other words, how and in what way Haier is using and developing its AI capabilities.
- Part 1 – The RenHanDeyi approach
- Part 2 – The Haier Business Model
- Part 3 – How Haier Delivers Scenarios: Smart Solutions
- Part 4 – The Orchestration Engine of Haier
- Part 5 – Haier Culture, Purpose and Strategy
- Part 6 – How Haier Harnesses AI (this article)
- Part 7 – How To Transform To Ecosystem Organizing (coming soon)
- Part 8 – Strategies and Tools For Ecosystem Organizing (coming soon)
Most companies are introducing AI into organisations designed for another age.
AI is being inserted into functions, workflows and approval structures where information still moves upwards and decisions move downwards. The technology may change the work, while leaving the organisation around it largely untouched.
Haier starts from somewhere different.
Over several decades, RenDanHeYi has broken much of the company into autonomous microenterprises. These units operate close to users, carry responsibility for their results, and combine through ecosystem micro-communities when an opportunity requires capabilities from several teams or partners.
Haier has also built the digital infrastructure connecting much of this system. COSMOPlat connects factories, suppliers, customers, and other participants.
Connected appliances generate usage data. Smart home platforms connect products and services. Smart factories continuously produce operational data.
AI is now being added to this architecture.
That changes the question.
What happens when AI enters an organisation already designed around autonomous units, user signals, shared platforms, distributed decisions and ecosystem coordination?
Haier’s AI strategy is increasingly moving beyond using AI to speed up isolated activities. It is applying AI across products, homes, factories, and platforms, while creating the conditions for intelligence to move across a distributed system.
The interesting part is not simply the technology.
It is what that technology can connect.
Table of Contents
From Digital Connection to Intelligent Connection
Haier could not pursue this strategy without the digital foundations built before the latest wave of AI.
COSMOPlat was launched as an industrial internet platform connecting users, manufacturing, suppliers, and other participants. It is estimated that Haier now connects around 900,000 enterprises, 30,000 developers and 5,000 partners.
That scale matters.
A traditional factory generates information inside separate production systems.
A connected factory can combine information about machines, orders, materials, quality, suppliers, and demand.
AI can then interpret those signals together.
The same principle applies in the home.
Haier’s connected appliances and platforms such as SmartHQ and HOn provide data about how products are being used. Sensors add information about conditions, settings, faults and routines.
These connections allow Haier to move through three stages.
- Connected products and factories create data.
- AI turns the data into predictions, patterns and decisions.
- The surrounding system determines whether anyone can act on them.
The last stage is easily overlooked.
An AI system can predict a production problem. If the people receiving that prediction cannot change the schedule, find another supplier or allocate resources, its organisational value remains limited.
Haier has already distributed much of that authority.
That is why its organisational model matters to the AI story.
AI Makes Zero Distance to Users More Continuous
Zero distance to the user is one of the core principles of RenDanHeYi.
The original idea was organisational. Remove layers between employees and users so that teams can understand problems directly and respond without waiting for instructions from above.
AI adds another dimension.
It can reduce the information distance between what users are doing and what Haier can understand.
HomeGPT provides a good example.
Haier developed HomeGPT specifically for the home rather than using a general language model alone. It combines user requests with information about the surrounding environment and connected appliances. The system can then coordinate responses across devices.
Consider a simple request to watch a film.
Instead of treating this as a command to one device, Haier describes HomeGPT interpreting the intended scenario. Lighting can change, notifications can be adjusted and connected devices can work together around the experience.
The same principle appears in cooking.
A user can ask an AI enabled oven to prepare a particular style of pizza. The system can search for the appropriate recipe and coordinate connected appliances around the task.
This is more significant than adding voice control to an appliance.
The unit of value changes.
The user does not necessarily want a clever oven, light or refrigerator.
They want an outcome.
- A meal.
- A comfortable night’s sleep.
- A suitable environment for watching a film.
- A home that requires less effort to manage.
That connects directly with Haier’s scenario strategy from earlier in this series.
AI helps interpret the desired outcome and coordinate the products and services required to deliver it.
Products Start Learning From Use
Haier is also making individual products more adaptive.
Its AI enabled air conditioners can learn from temperature selections, routines and sleep patterns. Rather than requiring the user to repeatedly specify the same settings, the product progressively learns preferred conditions.
The mechanism is straightforward:
Use → data → learning → adjustment → further use
Every interaction can improve the next one.
This changes the relationship between product and customer.
Traditional products largely leave the factory with fixed functionality.
Connected products can be updated.
AI enabled products learn from use.
Haier has described its smart appliances as evolving towards what it calls AI machines, connected through platforms such as SmartHQ and capable of continuous improvement.
That creates another valuable asset.
The installed product becomes a source of continuing information about real behaviour rather than simply the end of a manufacturing process.
For Haier, this can feed a much larger learning system.
Product data can reveal recurring problems. Service information can reveal failure patterns. User interaction can reveal changing behaviours.
Those signals can then influence future products, services, and scenarios.
The learning loop starts extending beyond the appliance.
AI Is Already Changing Haier’s Factories
The industrial side of Haier provides some of the clearest evidence that this is more than an AI vision.
COSMOPlat combines industrial data, connected machines and AI across manufacturing.
Machines continuously generate operating information. AI can identify abnormal patterns, anticipate problems and support maintenance before equipment fails.
One Haier plant, by combining COSMOPlat with AI, increased productivity by about 20 percent while reducing downtime by about 30 percent.
This illustrates a wider shift.
Traditional maintenance reacts after something goes wrong. Connected equipment allows condition monitoring.
AI adds prediction.
The sequence becomes:
Machine signal → abnormal pattern → predicted failure → intervention
Production becomes more responsive as well.
AI supports planning, deep learning is applied to quality control, and generative AI assists with three-dimensional modeling.
These applications attack different problems.
Prediction reduces disruption.
- Machine vision can identify defects that are difficult to detect consistently at speed.
- Generative systems can shorten parts of design and engineering work.
- Planning systems can combine demand, capacity and operational constraints.
Taken individually, these are familiar industrial AI applications.
Their significance at Haier comes from the connected environment surrounding them.
Data from one stage does not have to remain trapped there.
From Better Operations to Faster Opportunity Discovery
AI can also change how Haier finds new opportunities.
This is where the microenterprise structure becomes particularly relevant.
In a traditional organisation, opportunity discovery often moves through several stages.
- Market researchers identify a trend.
- A strategy team interprets it.
- Senior leaders decide whether it matters.
- Innovation teams investigate it.
- Operating functions eventually execute.
Haier has already pushed much of this responsibility towards microenterprises operating closer to users.
AI increases the amount of information those units can interpret.
A team can potentially combine signals from product use, customer interactions, service records and operational information rather than relying on occasional research alone.
Different AI capabilities contribute in different ways.
- Pattern recognition can reveal behaviours that repeatedly occur together.
- Prediction can identify changing demand or emerging operational problems.
- Generative AI can rapidly explore possible designs and scenarios.
- Reasoning systems can help compare options and constraints.
Haier’s existing applications in planning, modelling and quality analysis show pieces of this capability already appearing across the organisation.
The strategic shift is from periodic research towards continuous sensing.
But this does not mean handing innovation to machines.
A pattern is not an opportunity until someone understands why it matters.
AI may detect that behaviour has changed.
People still need to understand the context, decide whether the problem deserves solving and determine what value can be created.
The advantage comes from combining machine scale sensing with teams close enough to users to interpret the signal.
AI Shortens the Distance Between Signal and Action
This is where Haier AI becomes more interesting than a collection of simple AI case studies.
Imagine two organisations receiving the same insight.
In the first, an AI system identifies a change in customer demand.
- The insight goes to marketing.
- Marketing prepares a report.
- The report reaches management.
- Operations must then be consulted.
- Procurement needs approval.
- Technology has another priority.
- Finance releases the budget later.
The AI found the opportunity quickly, BUT the organisation responded slowly.
Haier has spent years shortening many of these distances.
A microenterprise already owns an outcome. It has greater authority to respond.
An EMC can bring several capabilities together around a scenario.
COSMOPlat provides access to data, production and ecosystem resources.
The significance of AI is therefore not just that Haier can sense faster.
It can increasingly connect sensing with an organisational system designed to act.
That is a much harder capability for competitors to copy than access to a language model.
The Bigger Opportunity Is Coordination
The same organisational model also creates a problem.
Decentralisation increases coordination complexity.
The more decisions Haier pushes towards autonomous units, the more those units need to find capabilities, information, resources and partners without relying on managers to connect everything manually.
Haier addressed much of this before AI.
Microenterprises can access group assets rather than automatically owning them. Internal markets help allocate resources. EMCs combine different units around shared opportunities. Platforms create visibility across the wider system. AI can strengthen these mechanisms.
Consider predictive maintenance.
COSMOPlat collects machine information continuously. AI can identify an emerging failure and connect that information with service activity before the machine stops.
That is already a basic form of intelligent coordination. A signal does not merely produce a dashboard. It influences another part of the system.
The same principle can eventually extend much further.
- Demand information can connect with capacity.
- A supply problem can connect with alternative resources.
- A customer problem can connect with relevant expertise.
- A new scenario can connect with internal and external capabilities.
This is where AI could have its greatest organisational effect.
It reduces the information and search costs created by decentralisation.
That matters because hierarchy traditionally performs much of this coordination.
Managers decide who should talk to whom, where resources should go and which problem deserves attention.
If digital platforms and AI can perform more of the discovery and coordination work, Haier can preserve autonomy without rebuilding layers of management.
From AI Tools to AI Agents
Agentic AI takes this logic another step.
A conventional AI tool analyses or recommends.
An agent can monitor a situation, determine that action is required, formulate a plan, use digital tools, and act within defined limits.
For a distributed organisation, that is potentially significant.
An agent can monitor a production commitment and flag when capacity becomes insufficient.
Another could identify an alternative resource.
A logistics system could test the effect on delivery. A person could then approve the change, or the system could act automatically where permission already exists.
That is very different from asking a chatbot to summarise a document.
Haier’s current evidence needs careful handling here.
Haier does not operate a system in which agents coordinate thousands of microenterprises and EMCs. But it is progressing fast and testing AI agentic systems.
Haier is developing more intelligent products, applying AI across industrial operations and describing smart products that continue to learn through its connected platforms.
The next organisational step is clear, however.
AI moves from: understanding to recommending to acting
Once that happens, decision rights become as important for machines as they are for people.
How Haier’s AI Turns Feedback Into Organisational Learning
The real value of all these applications comes when they connect into a loop.
Consider the air conditioner again.
- A user chooses a temperature.
- The system records the choice.
- The AI learns the pattern.
- The product adjusts its future behaviour.
- The user responds again.
- The product learns again.
Now extend that principle beyond the product.
- A repeated behaviour appears across users.
- The pattern reaches the relevant microenterprise.
- The team investigates why it is happening.
- A new feature or scenario is developed.
- Production changes.
- Customers use the new solution.
The resulting behaviour creates new evidence.
The loop becomes:
Interaction → data → interpretation → decision → action → outcome → new data
That is organisational learning rather than simply machine learning.
The distinction matters.
Many companies have sophisticated analytics but weak learning loops.
- They collect data.
- They generate reports.
- They produce recommendations.
- Then the process stops.
A learning loop only exists when evidence changes action and the result of that action becomes evidence for the next decision.
Haier’s architecture gives it many of the ingredients required to make that loop shorter.
Haier AI Could Strengthen Human Autonomy
This creates a striking fit with RenDanHeYi.
Small autonomous units face a practical problem.
Autonomy is valuable, but complexity can overwhelm a small team.
People need to monitor users, understand markets, find expertise, coordinate partners, manage operations and make decisions.
Traditionally, organisations respond by adding specialist functions and management. That eventually recreates hierarchy.
AI offers another route.
It can absorb some of the search, analysis and coordination burden while leaving decision authority with the microenterprise.
A small unit can therefore gain access to capabilities that previously required a larger administrative structure.
This is consistent with Haier’s broader organisational direction. RenDanHeYi removed large numbers of traditional managerial positions and shifted responsibility towards entrepreneurial teams.
Used this way, AI does not replace autonomy. It makes autonomy easier to sustain at scale.
But the same technology creates the opposite possibility.
AI Could Also Recreate Hierarchy
A distributed organisation connected through digital platforms produces enormous visibility.
That can empower teams. It can also become an infrastructure of control.
A central AI system could continuously score microenterprises, rank decisions, restrict choices and determine which actions are acceptable.
Recommendations could gradually become rules. Managers would no longer need to issue instructions. The software would do it for them.
The organisation chart could remain decentralised while decision authority quietly returns to the centre.
This is the deepest contradiction Haier faces.
RenDanHeYi is built around maximising human value and giving people responsibility for creating user value.
AI can only support that philosophy if it expands meaningful human agency.
The test is therefore not simply whether an AI system improves efficiency.
Does it increase the capacity of the microenterprise to understand, decide and act, or does it increasingly decide on the microenterprise’s behalf?
That boundary will become harder to manage as agents become more capable.
Local Intelligence Can Produce System Wide Problems
Another tension comes from optimisation.
Microenterprises are designed to be entrepreneurial. AI systems also need objectives.
Put those two together carelessly and local optimisation can become dangerous.
An AI system supporting one unit might reduce inventory because this improves its financial performance.
Another unit may depend on that inventory to maintain service levels.
Both decisions can appear rational locally while damaging the wider customer outcome.
The same problem can occur between production, logistics, service and external partners.
This is why the EMC and scenario logic remains important. The object being optimised cannot simply be the efficiency of one unit.
It must remain connected to the outcome the ecosystem exists to create.
AI therefore increases the importance of shared metrics, clear commitments and governance rather than removing the need for them.
Agents Make Accountability Harder
Agents introduce another problem.
Suppose a future automated action uses data from one ecosystem partner, a model managed by a platform team and an agent operating for a microenterprise.
The action causes a failure.
- Who is accountable?
- The data provider?
- The model owner?
- The unit that authorised the agent?
- The person who established its limits?
- The ecosystem orchestrator?
These questions become much harder when automated decisions cross organisational boundaries.
Haier’s distributed model makes the issue particularly important because responsibility is deliberately spread across autonomous actors.
AI governance therefore cannot sit separately from organisational governance.
The two are becoming the same design problem.
Leadership Becomes the Design of Decision Rights
Part 5 of this series examined how Haier changes the role of leadership.
AI pushes that change further.
When teams have more autonomy and machines can make more decisions, leaders cannot personally supervise every action.
Their role moves towards defining the conditions within which action can occur.
- They need to decide where AI can act automatically.
- Where it can recommend but not decide.
- Where a person must approve.
- Where certain information cannot be combined.
- Which outcomes machines are allowed to optimise.
- When a local decision must give way to the wider ecosystem.
- How an automated decision can be challenged.
- And who remains accountable when something goes wrong.
This is a move towards constitutional rather than supervisory leadership.
That is a useful distinction.
The leader increasingly designs the rules of the game rather than playing every move.
AI makes those rules more consequential because software can apply them continuously and at enormous speed.
What Haier Changes About the AI Question
The usual management question is:
Where can we use AI?
Haier suggests a more demanding question:
What must the organisation be able to do if AI is going to create value beyond individual tasks?
This changes where leaders should look.
An AI system can identify a changing customer need in seconds.
- That creates little advantage if the signal remains inside one function.
- It creates little advantage if another department controls the required data.
- It creates little advantage if the capability required to respond cannot be found.
- And it creates little advantage if approval must climb several levels before anyone can act.
Haier has spent decades addressing these organisational frictions.
- Microenterprises place responsibility closer to users.
- EMCs combine capabilities around outcomes.
- COSMOPlat connects data, production and external participants.
- Connected products create continuing user and operational signals.
AI is now increasing the intelligence running through these connections.
The significance lies in how the pieces fit together.
Signal → interpretation → decision → capability → action → outcome → learning
Many organisations possess the individual pieces.
Far fewer connect the whole chain.
Start With Decisions Rather Than AI Use Cases
This produces a practical lesson for leaders.
Do not begin with a catalogue of AI applications.
Begin with an important customer or operational outcome.
Then identify the decisions that determine whether that outcome is achieved.
For each decision, ask:
- Where does the relevant signal originate?
- Who receives it?
- Who has authority to respond?
- What capabilities are required?
- What information must move?
- What currently slows the response?
Only then ask what AI should do.
This avoids an increasingly common problem.
Companies can become very good at producing intelligence that their organisation is unable to use.
Move Authority Closer to the Signal
Faster sensing matters only if somebody can respond.
This may be one of the most important lessons from Haier.
Microenterprises do not simply receive information about users. They carry responsibility for creating value for them.
That shortens the distance between:
seeing → deciding → acting
AI may therefore expose organisational weakness rather than solve it.
If every meaningful recommendation still requires several layers of approval, better intelligence simply creates a faster queue.
Leaders need to decide which decisions can safely move closer to the people receiving the signal.
Build Coordination Capability, Not Just AI Capability
Decentralising decisions creates its own problem. Teams must be able to find and combine the capabilities required to act.
This is why Haier’s platforms and EMCs matter.
AI can make a distributed system more searchable.
- It can expose dependencies.
- It can identify capacity.
- It can match problems with expertise.
- It can anticipate constraints.
- It can help several actors coordinate around one outcome.
That suggests another question for AI investment.
Do not only ask:
Can AI make this task faster?
Ask:
Can AI make it easier for different parts of the organisation to combine what they know and what they can do?
The second question has much larger strategic consequences.
Connect AI to Outcomes and Learning
AI also needs a visible connection to results.
The number of copilots deployed is not a meaningful measure of strategic progress.
Neither is the number of experiments.
The better questions are:
- Did the organisation identify an opportunity earlier?
- Did someone make a better decision?
- Did the system coordinate resources faster?
- Did the customer outcome improve?
- What new data did the result generate?
- Did that improve the next decision?
Without these links, companies accumulate AI activity.
They do not necessarily accumulate AI advantage.
From Intelligent Activities to an Intelligent Organisation
Haier has already made substantial progress with AI.
Its appliances can learn from user behaviour.
HomeGPT and Smart Home Brain connect AI with household scenarios.
COSMOPlat combines AI with industrial data.
Factories use AI for planning, maintenance, quality and production.
Generative systems are entering design and modelling. But these applications are not the most interesting endpoint.
The more important strategic move comes when the intelligence connects.
- When user sensing connects with distributed decision rights.
- When a detected opportunity connects with the capabilities needed to pursue it.
- When production signals connect automatically with service or supply decisions.
- When autonomous teams can find resources without rebuilding layers of management.
- When action produces data that improves the next decision.
- And eventually, when AI agents can participate safely in parts of that process.
That is the movement from intelligent activities towards an intelligent organisation.
Haier’s organisational architecture gives it an unusual starting point.
- RenDanHeYi already distributes authority.
- Microenterprises already operate close to users.
- EMCs already combine capabilities around outcomes.
- COSMOPlat already connects information and resources across a much wider system.
AI can increase the speed and intelligence with which those elements interact.
That leads to the central lesson from Haier.
AI does not remove the need to redesign organisations. It makes organisational design more important.
A company can buy many of the same models as its competitors.
What competitors cannot acquire nearly as easily is an organisation where signals move quickly, decisions sit close to the problem, capabilities can combine across boundaries and actions feed a continuing learning loop.
That may prove to be the more durable advantage. And it leads directly to the next question in this series.
If organisations want AI to coordinate people, capabilities and resources around outcomes rather than functions, how do they redesign themselves to operate that way?
That is the challenge of ecosystem organising.
References
- Frynas, J. G., Mol, M. J. and Mellahi, K. (2018) ‘Management innovation made in China: Haier’s RenDanHeYi’, California Management Review, 61(1), pp. 71–93.
https://wrap.warwick.ac.uk/id/eprint/106383/ - Hamel, G. and Zanini, M. (2018) ‘The End of Bureaucracy’, Harvard Business Review, November–December.
https://hbr.org/2018/11/the-end-of-bureaucracy - Schoemaker, P. J. H. and Kuhn, J. S. (2021) ‘Haier: ecosystem leadership’, Strategy & Leadership, 49(5), pp. 16–22.
https://doi.org/10.1108/SL-09-2021-0087 - Chen, L., Xie, H., Yang, W. and Xiao, L. (2022) ‘Data Space Based on Mass Customization Model’, in Otto, B., ten Hompel, M. and Wrobel, S. (eds.) Designing Data Spaces. Cham: Springer, pp. 437–450.
https://link.springer.com/chapter/10.1007/978-3-030-93975-5_26 - Steiber, A. and Alvarez, D. (2025) ‘AI driven digital business ecosystems: a study of Haier’s EMCs’, European Journal of Innovation Management, 28(8), pp. 3966–3984.
https://www.emerald.com/ejim/article/28/8/3966/1249830/ - Su, H., Li, L., Tian, S., Cao, Z. and Ma, Q. (2025) ‘Innovation mechanism of AI empowering manufacturing enterprises: case study of an industrial internet platform’, Information Technology & Management, 26(3).
https://doi.org/10.1007/s10799-024-00423-4 - World Economic Forum (2025) ‘Global Lighthouse Network 2025: World Economic Forum Recognizes 12 New Sites Driving Holistic Transformation in Manufacturing’, 16 September.
https://www.weforum.org/press/2025/09/global-lighthouse-network-2025-world-economic-forum-recognizes-12-new-sites-driving-holistic-transformation-in-manufacturing/ - World Economic Forum (2026) ‘Global Lighthouse Network Recognizes 23 New Sites, Launches AI Platform for Industrial Transformation’, 15 January.
https://www.weforum.org/press/2026/01/global-lighthouse-network-recognizes-23-new-sites-launches-ai-platform-for-industrial-transformation/ - Haier Group (2024) ‘Global Brand Creation, Comprehensive Leadership: Haier Smart Home Releases New Achievements at AWE’, 15 March.
https://www.haier.com/group/tech/news/20240315_236324.shtml - Haier Group (2025) ‘Haier Consolidates Its Position as One of the Most Valuable Global Brands and the Only IoT Ecosystem Brand in the World’, 16 May.
https://www.haier.com/global/press-events/news/20250516_264576.shtml - Haier Group (2025) ‘Haier Smart Home 2025 Ecosystem Conference: AI Vision Upgrades the Smart Home’, 20 March.
https://www.haier.com/press-events/news/20250320_257358.shtml - Haier Group (2026) ‘COSMOPlat Industrial AI Full Stack Capabilities Unveiled at WAIC 2026’, 21 July.
https://www.haier.com/press-events/news/20260721_293355.shtml
