From uncontrolled embedded AI – to governed and monitored operations

When a global white goods company started embedding AI in its products, the customer experience took off. But beneath the surface there was a problem: no one could see when the models started to lose precision in real-world use. With HiQ as a partner, the customer established an operational layer for AI operations (MLOps), with structured processes and tools for model monitoring, where model behaviour in production is continuously tracked and deviations are caught early, long before they impact revenue or customer experience.

Client: Global manufacturer of household appliances
Industry: Industrial / consumer product
Solutions Area: AI operations and MLOps (AI model drift)

Global white goods giant with AI in more and more products

The customer is a leading global manufacturer of household appliances, with millions of products in homes around the world. A growing part of the offering is digital: connected products, in‑app services and internal processes driven by AI models in live production.

Internally, they already had data scientists, product teams and a clear AI focus. What they lacked was a consolidated view of how all these models actually behaved over time.

AI makes products better – until conditions change

At launch, the models performed well – accuracy was high and the effects were clear. But the world does not stand still. Data changes, users change their behaviour and environments shift.

Without structured monitoring, this meant three key risks. No one notices when a model gradually loses performance, incorrect predictions can directly affect both customer experience and costs, and model updates happen on an ad hoc basis only once problems have already become visible in the business.

The customer needed to move from “we think the models are working” to “we know exactly how they are behaving right now”.

When AI operations become as robust as the rest of production

HiQ came in as a partner for AI operations (MLOps). The goal was clear: to build a scalable structure to monitor, alert on and improve AI models in production, regardless of use case.

Together with the customer we:

Established a shared framework for how models should be followed up
Defined key metrics and measurement points for each model, both technical and business‑oriented
Set up a platform to collect, visualise and alert on deviations
Connected the monitoring to the customer’s existing ways of working and teams.

The result was a central “control panel” for AI operations, where all relevant models are visible – in real time.

Controlled AI operations where deviations are caught before they cost

Instead of chasing errors after the fact, teams now get a clear, continuous view of three things. Instead of chasing issues after the fact, teams now have a clear and continuous view of three key areas. Model performance is consistently tracked against expected levels, allowing even small shifts in accuracy to be detected early. At the same time, the data flowing into the models is monitored, so changes in distribution, drift and unexpected patterns are identified in time. In addition, data quality is safeguarded by detecting missing or anomalous values as well as broken integrations before they have an impact on decision-making.

On top of this sit clear thresholds and alerts. When something deviates, notifications are triggered to the right roles with context: which model, what problem, what impact. This allows data scientists, product owners and operations teams to act proactively rather than reactively.

AI that delivers. Not just at launch, but every day

With the new setup, the customer has increased transparency across all business-critical AI models while significantly reducing the time from initial deviation to corrective action. They have also established a structured loop for retraining and continuous improvement of the models, which in turn has reduced the risk of hidden errors that could otherwise erode both customer experience and revenue. AI has thus evolved from a collection of separate projects into an integrated part of a controlled production environment, with the same quality standards as the rest of industrial operations.

From smart algorithms to reliable, business‑critical AI in production

When AI becomes part of the business itself, it is no longer enough just to build accurate models. Without model monitoring, even the most advanced algorithm becomes a potential risk – invisible deviations can hit both experience and revenue directly.

HiQ steps in as a partner for AI operations (MLOps), helping industrial companies to build, monitor and continuously improve their models – with full control in production. That is when AI stops being a side experiment and instead becomes a stable, manageable part of the backbone of the business.

Want full control over your AI models in production? Let us show you how.