Accelerate Finnish Business: New AI-Powered Tool Helps Evaluate Ideas Before Costly Development Investments 19.5.2026 Tiedote suomeksi Suomi nousuun: Uusi tekoälyä hyödyntävä työkalu auttaa arvioimaan ideasi ennen kalliita kehitysinvestointeja Suurin osa tuote- ja palveluideoista epäonnistuu jo ennen kuin kehitys alkaa – ei teknologian vaan puutteellisen asiakasymmärryksen vuoksi. Suomalaiset toimijat ovat kehittäneet tähän ratkaisuksi maksuttoman IdeaTest.io -työkalun, jonka tavoitteena on madaltaa kynnystä ideoiden datalähtöisessä arvioinnissa ja näin nopeuttaa suomalaisten yritysten ideavalidointia ja kasvua. Työkalu tuo modernit innovaatiomenetelmät, kuten Jobs to Be Done -ajattelun, käytännönläheisesti yritysten arkeen. IdeaTest.io on avoin ja maksuton työkalu, joka on rakennettu Remionin, diplomityöntekijä Joona Viitasalon ja Rehtitecin yhteistyön tuloksena. Remion toimi työn mahdollistajana, Viitasalo kehitti työkalun ja Rehtitec auttoi asiakashaastatteluissa. Teknologiateollisuuden 100-vuotissäätiö rahoitti Viitasalon diplomityön osana Remionin tuoteinnovaatiotyötä. “Yrityksissä käytetään paljon aikaa ja rahaa väärien asioiden rakentamiseen. Tällaisella työkalulla voidaan välttää merkittäviä virheinvestointeja ja ohjata kehitystä kohti tarpeita, joilla on asiakkaalle todellista merkitystä,” sanoo Remionin teknologiajohtaja Miika Okko. Idean arviointi alkaa asiakastarpeesta “Suomalaisilla yrityksillä on valtavasti hyviä ideoita. Hyvä idea ei kuitenkaan yksin tarkoita, että sitä kannattaa lähteä rakentamaan. Ensin pitää ymmärtää, onko ongelma asiakkaalle oikeasti tärkeä ja miksi nykyiset ratkaisut eivät riitä. IdeaTest.io syntyi tähän käytännön tarpeeseen,” sanoo Joona Viitasalo, joka kehitti työkalua osana diplomityötään. IdeaTest.io ohjaa käyttäjän vaiheittain idean taustalla olevan työnkuvan rakenteisesta arvioinnista asiakasymmärryksen keräämiseen ja analyysiin. Työkalun käyttäjää avustetaan tekoälyä hyödyntäen ymmärtämään mitä asiakas tekee nykytilanteessa, mitä hän yrittää saavuttaa, missä suurimmat ongelmat syntyvät ja mitkä tarpeet ovat asiakkaan työtehtävän kannalta kriittisimpiä. Työkalu yhdistää suoraviivaisesti asiakkaan työn mallinnuksen, tavoitteiden ja tarpeiden tunnistamisen, tarjoaa valmiin haastattelurungon sekä rakenteisen pisteytyksen, joiden avulla tämän hetkisiä tarpeita voidaan arvioida jo tarjolla olevien ratkaisujen kautta. Työkalu muodostaa suosituksen siitä, kannattaako ideaa kehittää eteenpäin, muuttaa vai hylätä. Lisäksi se generoi automaattisesti tuoteasemointistrategian ja myyntiviestit. Potentiaaliselta asiakaskunnalta kerätty data voidaan syöttää manuaalisesti tai tuoda suoraan digitaalisista haastatteluaineistoista (esim. tekstimuotoiset nauhoitteet), mikä mahdollistaa työkalun käytön myös laajemmissa asiakastutkimuksissa ja markkinakartoituksissa. Työkalu syntyi Remionin käytännön tarpeesta IdeaTest.io syntyi Remionin innovaatiohankkeen oheistuotteena, jossa Remionin ideapankkia validoitiin todellista laitevalmistajamarkkinaa vastaan AI-avusteisesti. Työn tuloksena Remionin markkinaymmärrys kasvoi merkittävästi ja yritys sai aiempaa selkeämmän kuvan siitä, mitkä tarpeet ovat sen asiakaskunnassa keskeisimpiä. Asiakashaastattelujen ja markkinakartoitusten kautta syntynyt tieto auttoi muuttamaan hajanaisen asiakastiedon selkeäksi, datapohjaiseksi ymmärrykseksi. Näin Remion pystyi arvioimaan ideoita aiempaa rakenteisemmin, tunnistamaan lupaavimmat kehityssuunnat ja valitsemaan parhaat ideat jatkojalostettaviksi kohti konkreettisia tuotepilotteja. Avoin työkalu suomalaisille yrityksille IdeaTest.io auttaa suomalaisia yrityksiä menestymään ja kasvamaan nopeammin. Työkalu on käytettävissä ilmaiseksi verkossa ja se on julkaistu avoimella MIT-lisenssillä, jolloin se voidaan asentaa myös yrityksen omille palvelelimille. Työkalu tukee suomalaisten yritysten kasvua vähentäen epäonnistuneita tuotekehityshankkeita, nopeuttaen ideasta markkinaan etenemistä ja auttaen uusien liiketoimintamahdollisuuksien havaitsemisessa, jotka perustuvat todellisiin asiakastarpeisiin. “Ensimmäisten käyttökokemusten perusteella ideat tarkentuvat usein merkittävästi AI-analyysin jälkeen. Monissa tapauksissa keskeinen asiakastarve on eri kuin alun perin oletettiin,” Viitasalo sanoo. Lisätiedot Miika Okko, TkTCTO, Remion Puhelin: +35850 381 4966Sähköposti: miika.okko@remion.com http://www.remion.comRemion on IoT- ja data-analytiikkaratkaisuihin keskittyvä yritys, joka auttaa teollisuusyrityksiä keräämään, jalostamaan ja hyödyntämään dataa liiketoiminnan kehittämisessä. Joona ViitasaloPerustaja, Rehtitec Puhelin: +35850 443 2116Sähköposti: joona.viitasalo@rehtitec.fi http://www.rehtitec.fiRehtitec auttaa teknisiä B2B- ja teollisuusyrityksiä avaamaan tarjouspolkuja oikeisiin asiakkuuksiin ja viemään myynnistä saadun tiedon takaisin tuotekehitykseen. Media Release in English Accelerate Finnish Business: New AI-Powered Tool Helps Evaluate Ideas Before Costly Development Investments Most product and service ideas fail before development even begins — not because of technology, but due to insufficient customer understanding. Finnish innovators have developed a solution to this problem: the free IdeaTest.io tool, designed to lower the barrier for data-driven idea evaluation and accelerate idea validation and growth for Finnish companies. The tool brings modern innovation methodologies, such as Jobs to Be Done thinking, into practical everyday business use. IdeaTest.io is an open and free tool built through collaboration between Remion, Master’s thesis researcher Joona Viitasalo, and Rehtitec. Remion enabled the project, Viitasalo developed the tool, and Rehtitec contributed through customer interviews. The Technology Industries of Finland Centennial Foundation funded Viitasalo’s Master’s thesis as part of Remion’s product innovation initiative. “Companies spend a significant amount of time and money building the wrong things. A tool like this can help avoid major misinvestments and guide development toward needs that truly matter to customers,” says Miika Okko, CTO of Remion. Idea Evaluation Starts with Customer Needs “Finnish companies have a tremendous number of great ideas. However, a good idea alone does not mean it should be built. First, you need to understand whether the problem is genuinely important to the customer and why existing solutions are insufficient. IdeaTest.io was created to address this practical need,” says Joona Viitasalo, who developed the tool as part of his Master’s thesis. IdeaTest.io guides users step by step from structured evaluation of the underlying customer job to collecting and analyzing customer insights. Using AI assistance, the tool helps users understand what customers currently do, what they are trying to achieve, where the biggest pain points occur, and which needs are most critical in the customer’s workflow. The tool combines customer job modeling, goal and need identification, a ready-made interview framework, and structured scoring methods that allow existing solutions and unmet needs to be evaluated systematically. Based on the analysis, the tool provides recommendations on whether the idea should be developed further, pivoted, or discarded. In addition, it automatically generates product positioning strategies and sales messaging. Data collected from potential customers can either be entered manually or imported directly from digital interview materials (such as text transcripts), enabling the tool to support broader customer research and market analysis initiatives. Built for a Practical Need at Remion IdeaTest.io originated as a byproduct of Remion’s innovation initiative, where the company’s internal idea portfolio was validated against real equipment manufacturer market needs using AI-assisted analysis. As a result, Remion significantly improved its market understanding and gained a much clearer view of which customer needs are most critical within its target industries. Insights gathered from customer interviews and market analysis transformed fragmented customer information into structured, data-driven understanding. This enabled Remion to evaluate ideas more systematically, identify the most promising development directions, and select the best concepts for further refinement into concrete product pilots. An Open Tool for Finnish Companies IdeaTest.io is designed to help Finnish companies grow and succeed faster. The tool is available free of charge online and has been released under the open MIT license, allowing companies to install it on their own servers if desired. The tool supports business growth by reducing failed product development initiatives, accelerating the journey from idea to market, and helping companies identify new business opportunities based on real customer needs. “Based on the first user experiences, ideas often become significantly more refined after AI-driven analysis. In many cases, the core customer need turns out to be different from what was originally assumed,” says Viitasalo. Additional Information Miika Okko, D.Sc. (Tech.) CTO, Remion Mobile: +35850 381 4966Mail: miika.okko@remion.com http://www.remion.com Remion is a company specializing in IoT and data analytics solutions, helping industrial companies collect, refine, and utilize data to improve business operations. Joona Viitasalo Founder, Rehtitec Mobile: +35850 443 2116Mail: joona.viitasalo@rehtitec.fi http://www.rehtitec.fi Rehtitec helps technical B2B and industrial companies open sales opportunities with the right customers and feed insights gained from sales back into product development.
From Control to Cognition: The Shift from Automation to Autonomy in Industrial Systems 1.4.2026 Viewpoint Article The thoughts shared here are based on discussions with teams and companies across multiple sectors exploring the shift from automation toward autonomy. While the vision of autonomous systems is gaining momentum, many organizations are still working to understand what it truly requires in practice. This article reflects one perspective on how this transition is unfolding, where the real challenges lie, and how companies can approach autonomy in a more practical, system-level way. Key Takeaways Industrial systems are shifting from automation → adaptation → autonomy — this is already happening, not a future vision Autonomy is not “more automation” — it requires a fundamentally different system architecture Traditional automation works in controlled environments, but autonomy is needed to handle real-world complexity and variability Success in autonomy depends on three factors: data quality, perception, and system resilience The biggest shift is from centralized control to distributed, local decision-making Companies that succeed will not jump straight to autonomy — they will build it step by step, as a capability From Control to Cognition: Why the Shift from Automation to Autonomy Changes Everything Before joining Remion, I spent years working closely with robotics and automation. Back then, the world was quite clear: Machines follow logic. Systems execute predefined commands. Reliability comes from control. And this worked, because automation has never really been about intelligence. It has been about predictability. After a year at Remion and countless discussions with our tech teams and companies across industries my perspective has shifted. What we’re seeing is not just “better automation.” It’s a fundamental change in how industrial systems are built and expected to operate. We are moving from: control systems → adaptive systems → autonomous systems And this is not theoretical anymore. It’s already happening. The Industry Is Already Moving — Faster Than It Looks If you follow industrial trends closely, the direction is clear. Robotics and industrial AI are no longer limited to fixed, pre-programmed tasks. They are becoming adaptive, learning, and context-aware systems capable of making decisions in real time. In mining, for example, autonomous equipment is already operating continuously in hazardous environments — improving safety, uptime, and precision beyond what human-operated systems can achieve. And this is just the beginning. The real shift is not about replacing humans. It’s about changing the role of machines. Automation Was About Predictability, Autonomy Is About Handling Reality Traditional automation works extremely well, as long as the world behaves. Defined inputs Known environments Repeatable processes This where control systems shine. But reality is rarely that clean. Dust, weather, and poor visibility Unexpected obstacles Human interaction Changing environments This is where automation starts to break and where autonomy begins. Autonomy introduces three capabilities that traditional automation simply doesn’t have: Perception – understanding the environment Reasoning – interpreting what is happening Decision-making – acting without predefined instructions This is the difference between: a machine that executes vs. a system that understands and adapts What I’ve Learned: One Of The Biggest Misconception is “Autonomy = More Automation” I’ve learned that autonomy is not an upgrade from automation. It’s a different system architecture altogether. In automation: Intelligence is centralized Logic is predefined Failures are handled externally In autonomy: Intelligence is distributed Logic evolves in real time Systems must recover independently That last point is critical. Because in real industrial environments: Connectivity fails. Sensors fail. Assumptions fail. And autonomous systems must still operate. 3 Key takeaways: What actually determines success in autonomy 1. Data Quality (Not Just Data Availability) Most companies already collect data, but very few have usable data. Autonomy depends on: Contextualized signals Clean pipelines Consistent semantics Without these, even the best AI models fail and this is where many autonomy initiatives quietly die. 2. Perception (The Hardest Problem Nobody Talks About Enough) Sensors are easy to install, but reliable perception is not. Real-world challenges include: Variable lighting Dust, rain, vibration Occlusion and edge cases In theory, object detection works. In reality, it must work every time. This is why autonomy is not just a software challenge to solve, It’s a system-level engineering problem. 3. Resilience (The Real Differentiator) A simple question: What happens when your system loses connection? In quite many automated environments today → everything stops. In autonomous environments → the system continues safely. This requires: Edge decision-making Fallback logic Self-diagnostics Safe degradation In my view, resilience is the defining capability of autonomy. The Architecture Shift: From Centralized to Distributed Intelligence This is how think about the evolution from automation to autonomy. Industries operating in dynamic, real-world environments are already moving in this direction because they have to. The key adaption driver is changing conditions. When conditions constantly change, centralized control alone is not enough. Systems need more abilities to make decisions locally. The Human Question: Where Do We Fit? One topic that keeps coming up in discussion is “What’s going happen to people?” I see that the reality is more nuanced. Autonomy does not remove humans, It will change the interfaces between humans and machines. Research already points toward collaborative autonomy, where humans remain part of decision loops, especially in safety-critical situations. So the future is not: Human vs machine. It is: Human + autonomous systems. And getting this interaction right: trust, transparency, fallback – is just as important as the technology itself. Why Autonomy Matters Now (Not in 10 Years) Autonomy is not a future concept anymore. I see that it is becoming a competitive requirement for many industries, because: AI-driven predictive systems are already standard in many new operations Autonomous equipment is scaling across industries Investment in intelligent robotics is accelerating globally Companies are no longer asking: “Can we automate this?”, they are asking: “Why isn’t this autonomous yet?” What Companies Get Wrong From what I’ve seen, most companies struggle not because of technology, but because of approach. Three common mistakes: Jumping too far ahead – Trying to build “full autonomy” without solid foundations. Underestimating integration – Autonomy is not a feature. It’s a system. Ignoring operational reality – Pilots succeed. Real environments expose everything. A More Practical Way to Think About the Journey Instead of chasing autonomy as a goal, my suggestion is to think of it as a capability you build layer by layer: Reliable control systems High-quality data pipelines Assisted automation Local decision-making Scalable autonomy Every project should move you one step forward. This is how real progress happens. My Key Takeways: Autonomy Is Not About Machines – It’s About Systems That Survive Reality Automation works in controlled environments.Autonomy works in the real world and the real world is messy. This is why autonomy is hard, but it’s also why it matters, because the companies that solve this will define the next generation of industrial systems. The future is not just automated. It’s adaptive, resilient, and increasingly autonomous. And we’re only at the beginning. About the Author – Jussi Laaksonen Jussi focuses on bridging business needs with digital and data-driven solutions in complex industrial and logistics environments. With a background spanning consulting, digital product development, and supply chain operations, he brings a practical approach to building scalable systems that create real business value Sources & Related Public Content Businesswire.com & Industrial Robots Research Report 2025 Miningconferences.org mdpi.com Sciencedirect.com
From AI Hype to Real Value: Start With the Work, Not the Technology 15.3.2026 Viewpoint Article The thoughts shared here are based on observations from discussions with teams and companies across multiple industries that are currently exploring the use of AI. While interest and experimentation are growing rapidly, many organizations are still searching for practical ways to translate AI enthusiasm into real operational value. This article reflects one perspective on why that gap exists and how companies could approach AI in a more practical way. Key Takeaways AI alone doesn’t create value. Real impact comes when it improves how work, systems, and processes operate. Start with the work, not the technology. Understand the job to be done before introducing AI tools. Most organizations are still experimenting. While many companies invest in AI, only a small fraction see consistent operational value. AI works best in specific tasks. It is particularly useful where work involves large datasets, complex systems, or hard-to-detect patterns. Focus on friction points. AI can help where work becomes slow, repetitive, unpredictable, or data-heavy. Think beyond humans. AI can support human decisions, optimize machine behavior, and improve system coordination. Small improvements scale. Targeted AI support in specific parts of work can create meaningful operational gains. Measure outcomes, not deployments. Success should be evaluated by improvements in efficiency, quality, and decision-making. The companies that win with AI understand their work best. Deep knowledge of processes, systems, and jobs enables meaningful AI adoption. AI Is Everywhere AI is Everywhere we look. Every week there’s a new tool, a new demo, or a new headline about how artificial intelligence will transform work. Companies are launching pilots, defining AI strategies, and encouraging employees to experiment with new tools. But when you actually talk to people inside organizations, the reality often sounds different. Most teams are experimenting. Very few are seeing real impact. Research suggests that nearly all companies are investing in AI, yet only a small fraction consider themselves mature in how they actually use it in everyday work. There is still a noticeable gap between the excitement around AI and the value organizations are able to capture from it. Interestingly, the challenge is rarely the technology itself. More often, it’s how we approach it. A Pattern Across Industries One of the most rewarding parts of my work is the opportunity to talk with companies and teams across many different industries. Every conversation is a bit different. Different products, different markets, different organizational challenges. And that’s what makes it interesting. But over time, a pattern starts to emerge. Despite all the differences between industries and organizations, many teams are struggling with the same questions around AI: Where should we use it? What problems is it actually good for? How do we move beyond experimentation? And perhaps the most interesting observation is this: No one really has a silver bullet. Even companies that are considered advanced are still figuring things out. They are experimenting, learning, adjusting, and gradually discovering where AI actually creates value. In many ways, that’s exactly how new technologies should be approached. But at the same time, I’ve also seen approaches that are likely to lead to frustration rather than meaningful results. The Technology-First Trap One of the easiest ways to take a wrong turn in the AI journey is to start from the technology itself. When organizations begin their AI journey, the first question is often: “Where could we use AI?” At first glance, this seems like a reasonable place to start. But in practice, it often leads teams down the wrong path. When we start with technology, we start looking for places to insert it. A chatbot here. A document generator there. Some automation somewhere else. The result can easily become a collection of experiments rather than meaningful improvements in how work actually gets done. Technology should rarely be the starting point. Work should be. Topping of a a currently poor process or a concept with AI, will end with a poor outcome. Don’t start with the technology. Know your processes, and work, fix them first and then utilize new tools like AI. (Original image credits – Eduardo Ordax) Start With the Work Before the Tool A more useful question might be: What job is actually trying to get done? This idea comes from the Jobs to Be Done framework. Instead of focusing on tools or technologies, it focuses on the outcome someone — or something — is trying to achieve. Most people apply this thinking only to human work. But the same logic applies equally well to machines, systems, and processes. Think about the work happening inside organizations. Engineers analyze data to understand system behavior. Managers prepare reports to support decisions. Customer teams investigate issues and find solutions. But at the same time: Machines are trying to produce parts with consistent quality. Production lines are trying to maintain throughput. Supply chains are trying to deliver materials on time. Energy systems are trying to operate efficiently. Behind every role, machine, and process, there is a job to be done. And inside every job there are moments where work becomes slow, repetitive, uncertain, or difficult to predict. Those moments are where AI can start to make sense. Not because the technology exists. But because the work itself needs support. Where AI Tends to Work Well AI is often discussed as if it could automate entire organizations. In current reality, it tends to work best in smaller, practical parts of work — both human and operational. Especially where work involves: large amounts of data patterns that are difficult for humans to detect complex systems with many variables The same thinking applies to machines and industrial systems. Many machines already produce huge amounts of data through sensors and operational systems. AI can use this data to understand how systems behave and how they might behave in the future. In many industries, unexpected equipment downtime can cost hundreds of thousands of dollars per hour. This is one reason why predicting failures and optimizing operations with AI has become a major focus of industrial innovation. Again, the pattern is similar. AI is not replacing the system. It is helping the system operate better. Examples Where AI can Support Human Work Data analysis AI can process large datasets and surface patterns much faster than humans can. Documentation and summarization AI can help produce reports, summaries, and structured outputs that would otherwise take hours. Supporting expert work Experts often spend significant time gathering and structuring information before making decisions. AI can accelerate this part of the process. Preparing decisions AI can synthesize information, highlight risks, and present options that help decision-makers evaluate complex situations. Examples Where AI can machines and processes Predictive maintenance AI can analyze equipment data to detect early signs of failure and recommend maintenance before a breakdown occurs. This can significantly reduce downtime and maintenance costs. Process optimization AI models can analyze production data to optimize parameters such as temperature, speed, or material flow to improve efficiency and product quality. Quality inspection Computer vision systems can detect defects in products that are difficult for humans to identify consistently. Operational forecasting AI can predict demand, energy consumption, or machine load to help systems operate more efficiently. AI + Humans + Systems A lot of discussion about AI focuses on the relationship between humans and machines. Will AI replace people? In practice, the more interesting question is how humans, machines, and systems work together. Research often distinguishes between two concepts: Automation – Machines replace human tasks. Augmentation – Humans and AI collaborate to achieve better results. In real organizations, however, it’s rarely one or the other. Instead we see combinations: AI supporting human decisions AI optimizing machine behavior AI coordinating complex processes The goal is not to remove humans from the system. The goal is to make the entire system — humans, machines, and processes — work better together. A Practical Way to Introduce AI For organizations trying to figure out where AI fits, the answer does not necessarily require a massive transformation program. A simpler approach often works better. 1. Identify the job What work is trying to be done? This could be: a human task a machine function a process outcome 2. Identify the friction Where does the work become slow, repetitive, unpredictable, or data-heavy? Common examples include: information analysis decision preparation machine maintenance production variability 3. Test AI support Introduce AI to support specific parts of the work rather than trying to redesign entire processes. Small improvements often scale surprisingly well. 4. Measure the outcome Did the system improve? For example: faster decisions lower downtime higher quality improved efficiency AI should not be measured by how many tools are deployed. It should be measured by whether the work itself improves. My Key Takeaways After many conversations with different teams across industries, one thing has become clear to me. AI itself is rarely the hard part. The hard part is understanding the work. What work are people trying to accomplish? What outcomes are machines supposed to deliver? What processes are organizations trying to optimize? When we start from those questions, AI suddenly becomes much easier to place. It stops being a hype-driven technology experiment and becomes a practical tool for improving how things actually work. At the same time, it’s clear that organizations are at very different stages of their AI journey. Some are just getting started, while others are already further ahead and beginning to see real value emerge. Experimentation is necessary. It is through experimentation that teams and companies discover where real value creation opportunities exist. I also recognize that these thoughts only scratch the surface. The field is evolving quickly, and there are far more advanced opportunities emerging — from autonomous AI agents to increasingly sophisticated decision-support systems. But personally, I like to approach things from a practical perspective. If you’ve just learned how to swim, it might make sense to first practice your strokes in the shallow end before jumping straight into the deep water. The same applies to AI. The teams and companies that will succeed are not necessarily the ones with the most advanced models or the biggest AI teams. They will be the ones that understand their jobs, systems, and processes the best. Because in the end, AI does not create value on its own. It creates value when it helps people, machines, and systems do their jobs better. About the Author – Jussi Rajamäki Jussi helps companies unlock the value of machine data. Supported by a team of experienced experts and his practical experience in IoT platforms, data analytics, and digital services, he focuses on enabling scalable data-driven solutions and building new digital services. Sources & Related Public Content Airiam Blog – 11+ Practical Examples of AI in the Workplace in 2026 AI in the Workplace: Use Cases, Benefits and Risks Kore.ai Blog – What is AI in the workplace: Use cases + real-world examples (2026) IBM – AI in the workplace: Digital labor and the future of work Mckinsey 2025 Report – Superagency in the workplace: Empowering people to unlock AI’s full potential
Cyber Resilience Act (CRA) – What It Means in Practice and How Remion Integrates It into Software Development 3.3.2026 Key Takeaways The EU Cyber Resilience Act (CRA) introduces mandatory cybersecurity requirements for products with digital elements placed on the EU market. Vulnerability reporting obligations begin in September 2026, with full compliance required from December 2027. Responsibility lies with the entity placing the product on the EU market — compliance must be demonstrable and documented. CRA requires risk-based secure design, structured vulnerability management, SBOM transparency, and security updates throughout the support period. Technology partners must be able to prove compliance through documented development processes and traceable controls. Remion is strengthening its Secure Software Development Lifecycle (SSDLC) to embed CRA-aligned cybersecurity into architecture, development, CI/CD, and update processes. The result for customers: reduced regulatory uncertainty, improved resilience, and clearer shared responsibility across the supply chain. The EU Cyber Resilience Act (EU 2024/2847) introduces mandatory cybersecurity requirements for products with digital elements placed on the EU market. The first obligations, including vulnerability reporting requirements, apply from 11 September 2026. Full compliance will be required for products placed on the market from 11 December 2027. Under CRA, the responsibility lies with the party that places a product with digital elements on the EU market. This entity must ensure that the product is developed and maintained in accordance with risk-based cybersecurity requirements, properly documented, and supported with security updates throughout the defined support period. At Remion, we have assessed the impact of CRA both on our own solutions and on our customers’ operating environments. We are strengthening our development practices to ensure that regulatory requirements are systematically embedded into our solutions. Remember These Dates 11 September 2026 The first obligations, including vulnerability reporting requirements, apply from 11 December 2027 Full compliance will be required for products placed on the market from Risk-Based Cybersecurity A central principle of CRA is risk-based implementation. Cybersecurity measures must be aligned with identified risk scenarios. Controls must correspond to documented risk assessments and realistic threat models. Manufacturers must be able to demonstrate: Risk-based secure design and development Documented cybersecurity risk assessments Structured vulnerability management Security updates throughout the defined support period Transparent reporting processes This raises a practical question for our customers: Can our technology partners demonstrate compliance if required? Compliance requires a structured and documented development model aligned with applicable standards. Strengthening Our Secure Software Development Lifecycle We are strengthening our Secure Software Development Lifecycle (SSDLC) and related development practices across projects. Our focus is on ensuring that implemented cybersecurity measures are directly derived from risk assessments and that their rationale is documented and traceable. Our Focus Areas Unified Secure Development Practices Security requirements are integrated already in the specification phase. Security acceptance criteria are defined to ensure implemented controls, such as code scanning, manual testing, and review processes correspond to identified risks. Risk-Based Security Measures and Threat Modeling We evaluate attack surfaces, trust boundaries, and critical components during product design and architecture planning. Early identification and prioritization of risk scenarios help ensure that cybersecurity measures remain proportionate and appropriate to the actual risk landscape. SBOM and Vulnerability Management We are implementing Software Bill of Materials (SBOM) practices and continuous CVE monitoring to maintain visibility into third-party components. Combined with a documented vulnerability management process, this supports faster response times and improved traceability. Integrated DevSecOps Controls Static (SAST), dynamic (DAST), and dependency scanning are embedded into our CI/CD pipelines. This supports continuous security verification and auditability. Secure Update and Patch Management Processes CRA requires security updates throughout the product’s support period. We are strengthening release and OTA processes to ensure secure, controlled, and documented updates. What This Means for Our Customers Our CRA-aligned development work provides clear benefits: Reduced regulatory uncertainty Demonstrable compliance readiness Clear shared responsibility Improved long-term resilience Better supply chain transparency In industrial and connected environments, structured and risk-based cybersecurity practices are essential. At Remion, cybersecurity is integrated into product quality and lifecycle management. Our objective is to deliver secure, maintainable, and regulation-aware solutions that support long-term business continuity. About The Author –Jesse Ikola Jesse is passionate about building resilient and secure software solutions that meet both business and regulatory requirements. With hands-on experience in application development and complex technical environments, he focuses on practical cybersecurity and secure software development in industrial and connected systems.
#RemionCrew Goes Sappee! 16.2.2026 What Would Remion Be Without a Little Adventure? At Remion, we love creating opportunities to spend time together — and to try something new and exciting every time. This autumn, our team swapped laptops for helmets and hiking shoes as the #RemionCrew headed to Sappee for a day full of outdoor fun, fresh air, and laughter. From the Office to the Hills of Sappee Our journey began at the office, where the our crew gathered and made their way to Tampere Keskustori to hop on a bus headed to Sappee — leaving the city behind and heading straight into the heart of nature. Surrounded by forest trails, cozy cabins, and fresh autumn air, we knew a great day awaited us. After getting settled into our cabins, it was time to gear up for the afternoon’s adventures. The afternoon activities kicked off at rental station, where everyone could pick their favorite activity. Some jumped on electric fatbikes and sped off on scenic forest routes, while others tested their skills at the adventure park or the frisbee golf course. For those seeking a calmer pace, nature trails around Sappee offered the perfect way to enjoy the peaceful surroundings and the golden autumn colors. No matter the activity, the day was full of good vibes, laughter, and just the right amount of friendly rivalry. Dinner, Sauna, and Good Company After a few hours outdoors, #Remioncrew gathered at Ekokammi log cabin for a delicious dinner. The evening continued back at the cabins, where saunas were heated and conversations flowed. Recharged and Reconnected As the bus rolled back to Tampere late in the evening, it was clear that the day had achieved its purpose — to recharge, reconnect, and remind us that teamwork isn’t built only in meetings, but also in moments like these. Shared experiences like this are at the heart of Remion’s culture. They strengthen our bond, inspire new ideas, and remind us that great things happen when we step away from our desks together. Saddling up on the electric FatBikes Almost ready for the adventure park double checking the harnests. Enjoying the wood routes Relaxing at Ekokammi log cabin after dinner Looking for new career opportunities? Join #Remioncrew Learn more
#RemionCrew Goes Sailing 16.2.2026 What Would Remion Be Without Regatta? The #RemionCrew headed out to sea just off the coast of Helsinki to take part in a friendly Regatta between three sailboats!At Remion, we like to organize enjoyable activities together, always aiming to try something new and exciting. This time, our shared adventure was a sailing trip in the Helsinki archipelago. Friendly Competition Guided by Professional Skippers At the harbor, we went through the route and sailing instructions with guidance from the professional skippers of Nemo Sailing. After the briefing, we moved on to the boats to plan our tactics and practice race maneuvers. The crews sailed the following vessels: s/y Zorro, Swan 41, skipper Henri s/y Alice, Bavaria 49, skipper Jari s/y Stella, Finngulf 37, skipper Teemu We divided the teams so that sailing experience was evenly distributed across the boats. The race itself was held as a pursuit start, with the winner being the first boat to cross the finish line. The finish was set near Vallisaari, where we also concluded the sailing trip. s/y Alice awaits her crew. With Teemu at the helm, the mood on board was just as bright as the sunshine Vallisaari offered breathtaking views. Stunning Views of Vallisaari Completed the Day On Vallisaari, the spring outing continued with good food, sauna sessions, and enjoying the island’s beautiful scenery.Shared moments and experiences are at the heart of what we do at Remion. It’s important that the team has time to relax and enjoy each other’s company outside of work as well. The sailing trip offered the perfect opportunity for this, and the breathtaking views of Vallisaari truly completed the day.Events like this don’t just create great memories — they also strengthen team spirit. We’re already looking forward to the next adventure. Looking for new career opportunities? Join #Remioncrew Learn more
Remion Participated in the Future Mobile Work Machine Event in Tampere 16.2.2026 Remion participated in the Future Mobile Work Machine (FMWM) event at Tampere Hall on May 28–29, 2024. FMWM brought together manufacturers of mobile work machines, technology companies, and industry researchers to showcase demos and services, network, and discuss the future of the industry. At the event, Remion presented its Remote Monitoring service together with Normet, a technology company specializing in mining and tunneling solutions. “Normet has implemented remote monitoring at its control center in India. From there, the company monitors its customers’ equipment globally,” says Remion CEO Jukka Kivimäki. The remote monitoring service provides real-time supervision, usage reports, and data-driven recommendations to help maximize equipment availability and optimize performance. Normet’s own experts remotely oversee and guide local maintenance operations using up-to-date equipment usage and fault data. Normet has implemented Remion’s remote monitoring service globally through its control center–Eric Stigzelius, Senior Manager at Normet Progress Toward Autonomous Mobile Work Machines The event presentations offered valuable insights into the direction in which mobile work machine technology is evolving and how digitalization, electrification, artificial intelligence, augmented reality, and sustainability requirements will transform business operations and production environments in the coming years. A central theme of the event was machine autonomy. Remion CTO Miika Okko highlighted a presentation by Agco that reflected many of the themes discussed by various equipment manufacturers. “Pekka Ingalsuo from Agco outlined six key areas required for machine automation: route planning, vehicle control, obstacle avoidance, local navigation, process automation, and vehicle condition monitoring.” Companies specializing in machine control systems were also present, developing driver-assistance technologies. Mobile work machines are gradually moving toward partial autonomy through advanced assistance systems– Jukka Kivimäki, CEO at Remion According to Okko and Kivimäki, autonomy—both of mobile work machines and production environments—was one of the most prominent themes at the event. A Vision of an Autonomous Production Environment A presentation by Konecranes CTO Franz Schulten introduced a vision of an Industrial Metaverse — a future production environment enabling dynamic collaboration between humans and machines — which sparked interest among the Remion team. In the Industrial Metaverse, not only are devices autonomous, but the production facility itself operates autonomously by utilizing intelligent technologies and AR solutions. “The remote monitoring service we presented at the event could be one of the tools within the Industrial Metaverse. It provides visibility into equipment condition and helps maintenance personnel and operators keep machines operating efficiently,” Kivimäki explains. “At Remion, we have both the interest and the capability — along with a comprehensive understanding — to help build tools for future production environments that simplify customer operations and improve process efficiency,” Kivimäki continues. “We have strong analytics and data modeling expertise that can support the development of autonomous production environments, for example in modeling the environment itself,” Okko adds. Breaking Barriers in Mining: Normet Xrock and Remion Pioneer Autonomous Rock Breaking In the depths of mining operations, rock breaking has always been one of the toughest challenges. Dangerous, unpredictable, and often a bottleneck that slows everything down, it has long demanded human skill in environments where safety is never guaranteed.Normet Xrock, a global leader in rock-breaking solutions, set out to change that story. With its Xrock product line of breaker booms and advanced attachments, the company envisioned a future where technology could take on the danger, leaving people free to focus on higher-value work. The bold ambition: to create the world’s first autonomous rock-breaking system. Read more The Initial Hype Around Electrification and AI Is Fading — Safety and Sustainability Gaining Focus According to the Remion team, last year’s discussions at the event focused more heavily on electrification. “Now safety and sustainability were more prominent. Electrification is no longer a new concept — it’s already underway. It feels like companies are shifting their focus toward equipment safety and sustainable, responsible operations,” Kivimäki reflects. Volvo’s Deputy CEO Carolina Diez Ferrer stated in her presentation that Volvo aims to achieve carbon neutrality by 2050. “Volvo aims to bring carbon-neutral enabling equipment to market by 2040, so that these machines would already be in operation ten years before the 2050 carbon-neutrality target,” says Okko. “Hybrid solutions were also discussed. Pekka Ingalsuo from Agco noted that combustion engines cannot yet be completely replaced at this stage,” Okko continues. Okko also observed that the initial excitement surrounding artificial intelligence has stabilized. With AI, we’ve returned to realism – AI is part of future solutions, but it will not solve everything on its own– Miika Okko, CTO at Remion
#RemionCrew in Spain – Remote Work Strengthened The Team 16.2.2026 Remion’s five-person team packed their laptops and shorts and headed to Fuengirola, Spain, for a two-week remote work trip. “Our timing couldn’t have been better. In Fuengirola, daytime temperatures climbed well above 20°C, while back in Finland we were hit by a surprise return of winter,” says Petri Tuominen, Project Manager at Remion, who joined the trip. Working remotely abroad is nothing new for the company. Remion’s first trip combining work and shared leisure time took place in Marbella in 2020, just before the global COVID-19 pandemic changed the world. An Office on the Costa del Sol Finding suitable workspaces for a short remote-work period required some quick problem-solving. “One challenge was finding office space on short notice. We chose a local office through FuengirolaOffices.com, located in the Centro Finlandia building. The office had a reliable internet connection and sufficient work facilities,” Tuominen explains. The change of scenery also enabled the team to spend meaningful time together outside work. The two weeks were filled with a wide range of activities: beach time and sunshine, hiking Antennivuori, playing frisbee golf on the mountainside, sauna sessions, go-karting, a day trip to Gibraltar, tapas, a gaming museum visit, and of course exploring the Finnish communities in Fuengirola. “Our schedule was packed but enjoyable. Not a single boring moment,” Tuominen says. Deeper Interaction with Colleagues The Remion team’s experiences of remote work in Fuengirola were positive on many levels. Opportunities to get to know colleagues better feel especially valuable in today’s post-pandemic working life, where in-person office interactions have significantly decreased. “During the trip, we interacted with each other in new ways, and everyday working life took on a different rhythm during those two weeks.” From a productivity standpoint, the trip was also a success. Participants felt that the remote office environment had a positive impact on their work efficiency. Remote Work in Spain Was a Success Overall, participants gave the trip a perfect score of 5/5. Feedback collected after the trip suggests that Remion’s remote work travel may very well continue in the future. According to participants, the best parts were: “Warmth, light, spending time with great colleagues, a change of scenery, and experiencing new things.” According to Petri Tuominen, Remion’s two-week remote work trip to Fuengirola was a clear success. “The trip gave us the opportunity to work in amazing surroundings, but it also strengthened our team cohesion and gave us new perspectives and ideas.” Looking for new career opportunities? Join #Remioncrew Learn more
Digital twin lifts benefits of machine data to a new level 16.2.2026 Machine information – concerning design models, manufacturing, remote monitoring and service – is typically dispersed in different enterprise systems. Data is collected but not systematically utilized by business driver. Digital twin combines all machine lifecycle data enabling new business models, digital services and new data optimized machine design methods. How can machine manufacturers and end customers benefit from digital twin? With actual and predicted machine data of digital twin the end customer can be informed of maintenance needs, improve customer process and decrease machine lifetime cost. For machine manufacturer digital twin enables increased maintenance business and new business models and digital services enhancing whole Industry 4.0 smart factory-readiness. The remote monitoring solution developed to Mantsinen collects and transmits data from sensors in each machine to a global cloud service. The data is automatically refined and visualized according to end-user’s needs.It’s important that maintenance, product development and business managers can fully benefit from the data.–Jukka Kivimäki, CEO Remion Remion is collaborating on digital twin project with Mantsinen Group Remion is the supplier of remote monitoring for Mantsinen Group’s material handling machines. Now Remion also provides them tools to create digital twin environment which Mantsinen Group utilizes for their business model and digital service development. Watch the video of the digital twin collaboration of Remion and Mantsinen The video is produced by Business Finland.
Remion’s AI Expertise and Experience Secured Victory at Industryhack – Innovating Intelligent Laser Welding 17.2.2020 Remion’s team was able to leverage its extensive technological expertise in an Industryhack co-creation project, where the host company Coherent sought solutions to enhance its welding process. In the challenge competition, Coherent—a provider of laser and photonics solutions—was looking for a partner to develop a next-generation laser welding system utilizing sensor data and artificial intelligence. Remion claimed its first Industryhack victory in strong competition. In addition to Remion, three other Finnish AI specialist companies were selected from all applicants. The three-day challenge took place in early June. Remion’s CTO, Miika Okko, is delighted with the team’s win. Expectations for the competition were high, and the initial outlook was positive.– Coherent’s challenge focused on building an intelligent laser ecosystem, where our expertise and solutions were an excellent fit. Coherent aims to deliver added value to its customers through a more precise and optimized welding process. Miika Okko served as the team lead for Remion during Industryhack. A Three-Person Team Built on Complementary Expertise Remion’s competition team combined diverse expertise.– Our CEO Jukka brought strong business insight into building a comprehensive solution tailored to the customer’s needs. Petri, who works as a specialist, has extensive experience in designing and technically implementing AI solutions. As team lead, I guided the collaboration throughout the process, Okko explains.Okko himself has a broad background in AI research, which was also the subject of his doctoral dissertation. From Intensive Ideation to a Structured Winning Solution During the competition days, ideas evolved into a well-defined winning concept through intensive innovation, solution refinement, and mentoring.– Ultimately, three interwoven factors led us to victory: identifying the core customer need, delivering an innovative technical implementation, and presenting the solution clearly and convincingly. Through preliminary discussions, the team gained additional insights from the host company, helping them refine their direction and develop a highly targeted proposal addressing the customer’s needs.– We were also able to leverage our Regatta® platform in the solution, enabling faster and more cost-efficient implementation. Defining the solution architecture, outlining the implementation, and preparing the Proof of Concept descriptions required close collaboration—at times working late into the night.– Assembling the concept into a visually compelling format is also crucial. A good solution alone is not enough—you must be able to communicate the solution and its benefits effectively, Okko emphasizes. A new element in this year’s competition was a two-minute video presentation, through which each team had to showcase their solution in the final round. Proven Long-Term Performance of Remion’s Solutions One of the jury’s key justifications for selecting Remion in Coherent’s laser welding system development challenge was that Remion’s analytics and AI expertise and solutions have been proven effective over the long term. This makes it fast and straightforward for the company to move forward with implementing a comprehensive solution. Remion has extensive experience in developing analytics solutions across various industrial sectors, including predictive maintenance, failure forecasting, alarm and vibration analysis, and production optimization. Remion is an experienced industrial player in AI and analytics.– We have predicted usage-based maintenance needs and identified machines and data collection units behaving abnormally.– We also have strong expertise in statistical analysis of big data, as well as structuring and aggregating data. A comprehensive situational overview can be created, for example, by combining data from multiple machines, Okko explains.