AI-powered data solution may be your answer - just make sure you have a good question
Data solution development at Insta has been strongly driven by our industrial customers’ needs. In a nutshell, we help bring together data from multiple source systems into a unified, secure cloud-based data platform.
Starting points and maturity levels in data utilization vary greatly, from organizations taking their first steps in data collection to those already operating business-critical data environments that provide AI-driven capabilities.
In recent years, the focus has shifted towards strengthening the data architecture required for AI-assisted value creation. Our customers often ask how AI could help them. To answer this, we work backwards and start with the specific problems they are trying to solve.
AI should not be applied for its own sake. In many cases, a well-designed rule-based system is entirely sufficient. AI becomes relevant when the problem involves complexity, variability, or patterns that cannot be reliably defined in advance.
From talking the talk to walking the walk
Today, interest in AI is high, but so is a healthy dose of scepticism. When managing complex industrial processes in critical industries such as energy production, the stakes are high. Companies are looking for better data analysis, improved decision-making, and clearer direction of attention. Capabilities that our data solutions already deliver in a clear, visual and actionable way.
Before AI can deliver genuine value on a broader scale, and before higher-level operational and strategic questions can be addressed, there is fundamental work to be done.
At Insta, the journey towards advanced AI capabilities has started where it should: by establishing the structural prerequisites that enable scalable, long-term results.
AI needs context to work
AI discussions often remain abstract, but implementation requires getting your hands dirty with data. One essential aspect of this is securing adequate data availability and quality.
AI performs effectively only when it operates within the right context.
Raw sensor data, such as temperatures, pressures, production volumes, doesn't mean much on its own. That is why metadata is added to help AI understand what the data actually represents and what each data point refers to: Which production line does this measurement belong to? What units does it represent? What is the relationship between different measurements?
The contextual layer transforms a flood of numbers into something AI can genuinely understand and act upon. AI may be artificial, but when applied thoughtfully, it can generate meaningful and actionable understanding.
As AI capabilities mature, its role does not end with generating analysis. In many cases, AI can also operate in an agentic manner. First as an assistant supporting human operators, and later within clearly defined boundaries as an executor of specific tasks. The progression is gradual: starting with human-in-the-loop support, then moving towards controlled autonomy once reliability has been demonstrated. In industrial environments, this evolution must always be governed by safety, transparency, and clear responsibility.
Dare to ask big questions
Beyond envisioning and planning, what concrete questions can AI realistically answer in the future?
It may start with simple operational questions like ‘Should this valve be closed?’. From there, it can move towards more complex questions and strategic simulations such as ‘What should I know about the previous work shift?’ or ‘Should we switch to a three-shift system?’ or ‘Should we open a new plant, and why?’.
The key is identifying where the greatest impact can be created.
Rather than simply automating individual tasks, attention can increasingly shift towards process improvement and long-term strategic direction. This can significantly enhance production performance and overall operational efficiency, ultimately strengthening financial results.
Meaningful impact emerges at the intersection of bold questions, deep industry insight, and technical excellence.
Each facility is unique and operates within its own business context. At Insta, we aim to deeply understand customer operations and design data solutions that align with real-world processes and deliver measurable outcomes.
Getting started with AI does not require a large-scale transformation program. Progress is often achieved through a focused proof of concept, evolving into a limited-scope minimum viable solution, and scaling further only once measurable impact has been validated. This phased approach reduces risk, builds trust, and ensures that AI development remains tightly connected to operational performance.
The objective is not to chase the latest AI trends, but commit to real-world execution. In 2026, our ambition is to build AI-powered tools that generate tangible, long-term impact.
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