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Human + AI: real ROI comes from collaboration

AI doesn’t change anything on its own but people do. Technology creates value only when it is accepted, trusted, and used every day. Successful projects share one thing in common: keeping people involved in decision-making. The human-in-the-loop principle means that people retain control over key decisions – both in the final solution and during the development process. Human involvement is essential in building trust, ensuring adoption, and ultimately achieving business benefits.

Why human oversight is critical in using AI

The EU AI Act requires that people oversee high-risk AI systems. Smart companies go further by applying similar principles across AI development in general – improving safety, reducing risks, enhancing usability, and, most importantly, speeding up adoption of new ways of working.

Human-in-the-loop is not just for operators and maintenance. It also helps in design, engineering-to-order processes, and other critical industrial workflows where quality, speed, and safety are paramount. When engineers understand and trust AI suggestions, they can act faster, make fewer mistakes, and focus their time on more demanding tasks.

Pilot, measure and scale

AI adoption is not a “big bang” project. While quick wins can often be achieved with pilots, proper AI model development requires both time and patience. Success requires a controlled evolution of human-AI collaboration. AI’s role usually starts as assistive and progresses to giving recommendations. Finally, once trust is established and data and models are mature enough, parts of the process can be automated. At the same time, it is crucial to ensure that the actual ways of working evolve alongside the AI.

The most successful companies follow this path:

  1. Pilot with clear intent: choose a well-defined use case with an analyzed impact and a clarified concept. Leverage a proven design process to turn the idea into a high-quality, testable solution quickly.
  2. Ensure the human role: It is not just about technology – success depends on carefully designing the human–AI workflows and ensuring a smooth adoption.
  3. Measure progress: track both business and solution KPIs (model accuracy, explainability, and user trust) as the model and workflows evolve.
  4. Scale step by step: apply lessons learned from the pilot and roll out the solution to additional plants and processes in a controlled, phased approach.

Treat adoption as a strategic priority

Leadership must view AI adoption, human-in-the-loop, and the principles of seamless human–AI workflows as strategic priorities. Trust must be built step by step, and solution KPIs should measure both technical success and human engagement.

Starting small and showing quick results is important – but so is keeping the long-term goal in mind: successful adoption, scalable deployment, and measurable business results.

Is your AI development ready to deliver measurable ROI – or is it at risk of becoming one of the 95% of projects where results remain unproven?

Talk to our experts and see how AI can create measurable value for your business.

About the author

Hanna Remula

Head of Design, Cloud and Applications

Hanna Remula is a business developer and design leader with a passion for driving meaningful transformation in industrial companies. With deep expertise in strategic design and change adoption, Remula helps organizations go beyond technology – ensuring that digital, data, and AI initiatives deliver genuine value. Remula bridges the gap between business, technology, and people across the OT–IT landscape to enterprise-wide digital, data and AI programs to deliver real impact. She is a trusted partner to industry leaders, SMEs, and global corporations shaping the future of industrial operations.