A review of what is happening in 2026

Artificial Intelligence (AI) is becoming an ever more important aspect of the design, marketing, and servicing of home appliances—but not in the futuristic way that is so often depicted in the technology media.

Rather, the most interesting and relevant applications of AI in this sector are small, incremental, and operational. These are already beginning to impact the functionality, support, and management of products and customer expectations.

Industry trends in 2026

Smart appliances continue to increase in market value

The global market for smart home appliances, which are those that are connected to a network and software, is growing steadily. Current market forecasts suggest that the smart home appliance market will expand at a compound annual rate of close to 9 percent over the next decade, reaching a value of nearly USD 50 billion in 2028.

AI is a part of the Smart Appliance Evolution

Major companies continue to incorporate AI into appliances to enable convenience and efficiency. For example:

Samsung has launched new AI-infused versions of its bespoke line of appliances, featuring object recognition within refrigerators, fabric sensing in laundry appliances, and improved climate control systems.

Hisense has emphasized the increased use of AI in its ConnectLife platform, with a focus on household chore synchronization.

LG continues to enhance connectivity and integration between devices through its ThinQ platform, integrating voice assistants and automation between a range of appliances.

These are not prototype models. These are commercial models and represent the increasing role of AI as an expected component of consumer-facing home appliances.

Why AI matters beyond consumer functionality

One of the misconceptions about AI in appliances is that it is primarily about enabling consumer functionality such as recipe suggestions, voice control, or fridge cameras. The most important AI opportunities for manufacturers and retailers are related to how AI can enable after-sales service operations and infrastructure.

AI-Assisted Diagnostics

AI can assist in decoding error messages, usage patterns, or sensor data, cutting down on time spent on diagnosis and the likelihood of dispatching technicians to the wrong location.

Predictive Maintenance

Machine learning algorithms can predict a probable future failure before it occurs, such as by analysing vibration, temperature, or usage data. This enables service organizations to schedule repairs based on parts availability and minimize unscheduled visits.

AI powered Parts Forecasting and Recommendations

Using historical service data and demand insights, AI can help inform part demand decisions, including what parts are likely to be required, how many should be ordered, and where. This enables better inventory management by minimising excess inventory without creating shortages.

Natural Language Assistance for Parts Ordering

Instead of requiring precise part numbers, AI can understand problem descriptions in natural language and correlate them with probable parts. This eliminates any confusion in customer or service requests.

These use cases do not need “revolutionary” technology—instead, they enhance existing systems with analytical capabilities where human decision-making was the norm.

After sales services continue to expand

 The home appliance after-sales services market is growing on its own, separate from appliance hardware. Global market forecasts show that the overall value of after-sales services, including repairs, maintenance contracts, and service scheduling, is expected to grow throughout the latter part of this decade.

This growth has several drivers:

• Longer appliance lifespan.
• Higher consumer expectations of convenience.
• More connected devices producing service data.
• Adding more third-party and subscription-based support options.
• AI tools, especially those for diagnosis and part recommendation, will increasingly be a part of the organization of service networks.

Benefits for Finance Teams

• Better Predictability, Lower Hidden Costs.
• Cost pressures in after-sales service receive their origin in unpredictable service demand or parts ordering errors.
• Expedited shipping costs for emergency parts.
• Repeated field service visits due to incorrect diagnosis

For finance teams, it is about cost clarity and controllability rather than technology per se.

Benefits for Operations teams

• More reliable workflows, fewer Interruptions.
• Operational leaders seek fewer surprises and smoother execution.
• AI assisted diagnostic tools drop dispatching mistakes.
• Predictive insights help align service work orders with inventory readiness.
• Data driven decisions replace manual navigation of catalogues.

This supports consistent operational performance, especially at scale.

There is no single project on deploying AI in after-sales and operations. The deployment of AI is usually staged. Based on your work on information already available, collect service logs, inventory flows, and issue reports. Apply analytical models using machine learning or rule-based systems to detect patterns. Integrate into workflow requires aligning AI outputs with service scheduling, parts ordering, and inventory planning.

Measure outcomes

• Track error rates.
• Time per resolution.
• Inventory turns and.
• Service costs.

In general, organisations see value not from some big ‘AI project‘ but from iterative improvements that reduce uncertainty in established processes.

Considering current product announcements combined with market trends, we have three themes that arise:

• AI is increasingly embedded in products. Often with practical features, these are increasingly connecting sensors, software, and user control.
• Market forecasts continue to show steady growth in smart appliances and related services, supported by connectivity and AI integration.
• Operational use cases for AI in after-sales and spare parts are becoming mainstream, most especially diagnostics and predictive insights.

These developments suggest that AI in this industry is not some sort of theory. It is part of how appliances, services, and supply chains will be organized in the near term.

AI as a tool for better decisions

The question is not whether AI will matter it already does.

The pressing challenge is how organisations can use AI to:

• Improve cost visibility.
• Support operational decision-making.
• Make service workflows more reliable.
• Reduce dependence on individual ability.

About Novacept

Novacept helps home appliance manufacturers and retailers optimise their after-sales operations through AI-powered diagnostics, parts, and service intelligence. For more information on how we support operational excellence in the appliance industry,  click here to book a consultation.