Latest news with #Altair
Forbes
2 days ago
- Business
- Forbes
3 No-Code AI Tools Changing How Financial Institutions Innovate
AI tools are changing the way we collaborate with teams. AI in financial services has moved past the hype, but implementation still stalls where it matters most: data quality, internal capabilities, and practical governance. To understand what's working in the real world, I spoke with three leaders building the next generation of no-code and low-code AI tools: Christian Buckner, Head of Data and AI at Altair which owns RapidMiner; Michael Berthold, CEO and co-founder of KNIME; and Devavrat Shah, professor of AI at MIT and co-founder of Ikigai Labs. What emerged was a clear playbook for banks, insurers, and fintechs looking to leverage AI safely and effectively. Start by fixing your data before chasing models. Use AI to amplify your domain experts, not sideline them. Prioritize explainability and guardrails over novelty. And stop chasing flashy chatbot demos, instead, build focused, contextual tools that do the unglamorous work of planning, reconciling, and forecasting. This is what it looks like when financial institutions take AI seriously. Christian Buckner: Christian Buckner, Head of Data and AI at Altair The biggest obstacle to effective AI isn't regulation, risk, or technical know-how. It's data. All three speakers echoed the same frustration: siloed systems. Whether you're in banking, insurance, or asset management, chances are your data lives in too many places, governed by too many people, in formats no one trusts. AI can't fix that. In fact, it only amplifies the mess if used too early. Christian Buckner emphasized that real progress starts with integrating and contextualizing data. He highlighted the use of knowledge graphs to unify previously disconnected systems, calling it the foundation for safe and scalable automation. 'You need a contextual model that includes both internal data and external rules like regulatory constraints,' he said. 'Once that's in place, automation becomes far more reliable, and hallucinations from generative models can be eliminated entirely.' Devavrat Shah agreed. 'We have a ton of data sources and workflow tools, but the AI tooling that connects them is still missing,' he said. 'What we need are specialized, purpose-built models that live within the enterprise and understand the specific tasks they're built to support.' Devavrat Shah, professor of AI at MIT and co-founder of Ikigai Labs. We talked at length about trust, not in theory but in practice. Can a bank trust AI to approve a mortgage, flag fraud, or run a forecast? Shah made it clear that AI is not a crystal ball. 'AI is not perfect by design. It provides directional information. The key is to treat it like an input, not an answer,' he explained. He drew a parallel with betting strategies: if AI has a 51 percent edge, you don't bet everything. You diversify, manage risk, and make decisions accordingly. Michael Berthold added that the context in which AI is used determines how much trust is appropriate. 'If I'm looking for trends in data, the model doesn't need to be perfect. But if I'm forecasting revenues or hiring, it needs to be very accurate,' he said. He stressed the importance of transparency. 'Too many systems give you a result with no way to dig into how it was calculated. That's unacceptable in finance.' Buckner noted that governance must be built into the data layer itself. 'You define who sees what, what models can do with that data, and how outcomes are evaluated. Then you can add traceability so every action is auditable,' he said. 'If the model steps outside its boundary, the request fails. That's how you build trust.' Michael Berthold, CEO and co-founder of KNIME There is a lingering misconception that no-code platforms are simplistic. But as Berthold put it, 'We see teams move from massive Excel macros to KNIME workflows that are faster, safer, and auditable. It's not about removing complexity, it's about handling it responsibly.' He emphasized that tools like KNIME let users build automated workflows without knowing how to code, while still requiring them to understand the logic behind each model. 'Data literacy is key. You don't need to know how a method is implemented, but you need to know what it does,' he said. Buckner expanded on this, describing how RapidMiner lets non-technical teams act independently without losing oversight. 'If you can empower your domain experts to tweak visualizations or run their own analysis, you eliminate bottlenecks,' he said. 'Meanwhile, expert users can focus on the high-value, high-impact problems.' This dual-mode approach enables collaboration rather than isolation. As Buckner explained, 'Business teams can move quickly without compromising security or quality, because they're operating within guardrails defined by the platform.' When the conversation turned to model architecture, all three leaders rejected the idea that bigger is always better. Shah, in particular, was clear: 'The current model where a few companies own massive models and everyone else consumes them is not the endgame. The future is small, contextual models that live within the enterprise.' These role-based agents are more efficient, cheaper to run, and far less risky. They can live inside a firm's firewall, interact directly with structured internal data, and avoid the data leakage concerns associated with using external APIs. Berthold noted that even predictive AI applications like credit scoring or risk simulations don't need large models. 'You can build highly effective predictive models from existing datasets, and you can run 'what if' simulations to explore different decisions without exposing data to the cloud.' This is especially appealing to risk-averse financial institutions, which can now apply AI without compromising control or regulatory compliance. KNIME, Altair RapidMiner and Ikigai Labs, three no-code platforms for enterprise AI, analytics, and ... More decision automation. All three experts agreed that AI's real power lies not in automation for its own sake, but in augmentation. Berthold predicted that interaction models will shift away from the current 'chatbot everything' trend. 'The next frontier is AI that quietly observes and offers meaningful suggestions, like a co-pilot, not a search bar,' he said. Shah described this as a natural evolution of the human-machine relationship. 'We used to have to learn the machine's language. Now the machine is learning ours. That opens the door to more intuitive, collaborative systems,' he said. But he was quick to add a caveat: 'Explainability is now just as important as accuracy. If people don't understand the output, they won't use it. Period.' Buckner framed the future in terms of speed and scale. 'You can onboard ten AI agents faster than hiring one new analyst,' he said. 'But it's not about replacing people. It's about giving your team leverage to work smarter, faster, and with more confidence.' From these three perspectives, several clear lessons emerge for banks, insurers, and fintechs seeking to implement AI safely and effectively: Start with data integration, not model training: Building a contextual foundation using knowledge graphs or structured workflows pays dividends. Most failures stem from poor data hygiene, not bad AI to amplify domain experts, not replace them: No-code tools allow risk, finance, and compliance staff to build their own workflows while reserving complex tasks for data explainability and governance: AI outputs should be traceable, auditable, and embedded with compliance rules. If a model can't explain itself, it doesn't belong in a financial chase flashy use cases: Many of the most valuable applications are 'boring' internal optimizations, budgeting, forecasting, reconciliation, not chatbot front models are often better: Focused, context-aware AI agents tied to specific roles or workflows are easier to deploy, govern, and in data literacy: Giving tools to business users without training is a recipe for failure. Literacy enables responsible experimentation. The real winners won't be the firms chasing headlines or pouring money into the biggest model. They'll be the ones quietly building robust, interpretable systems that let humans and machines work side by side. And if this conversation was any indication, that future is already under construction. For more like this on Forbes, check out How AI, Data Science, And Machine Learning Are Shaping The Future and Who Owns The Algorithm? The Legal Gray Zone In AI Trading.
Forbes
2 days ago
- Business
- Forbes
Three No-Code AI Tools Changing How Financial Institutions Innovate
KNIME, Altair RapidMiner and Ikigai Labs, three no-code platforms for enterprise AI, analytics, and ... More decision automation. AI in financial services has moved past the hype, but implementation still stalls where it matters most: data quality, internal capabilities, and practical governance. To understand what's working in the real world, I spoke with three leaders building the next generation of no-code and low-code AI tools: Christian Buckner, Head of Data and AI at Altair which owns RapidMiner; Michael Berthold, CEO and co-founder of KNIME; and Devavrat Shah, professor of AI at MIT and co-founder of Ikigai Labs. What emerged was a clear playbook for banks, insurers, and fintechs looking to leverage AI safely and effectively. Start by fixing your data before chasing models. Use AI to amplify your domain experts, not sideline them. Prioritize explainability and guardrails over novelty. And stop chasing flashy chatbot demos, instead, build focused, contextual tools that do the unglamorous work of planning, reconciling, and forecasting. This is what it looks like when financial institutions take AI seriously. Christian Buckner: Christian Buckner, Head of Data and AI at Altair The biggest obstacle to effective AI isn't regulation, risk, or technical know-how. It's data. All three speakers echoed the same frustration: siloed systems. Whether you're in banking, insurance, or asset management, chances are your data lives in too many places, governed by too many people, in formats no one trusts. AI can't fix that. In fact, it only amplifies the mess if used too early. Christian Buckner emphasized that real progress starts with integrating and contextualizing data. He highlighted the use of knowledge graphs to unify previously disconnected systems, calling it the foundation for safe and scalable automation. 'You need a contextual model that includes both internal data and external rules like regulatory constraints,' he said. 'Once that's in place, automation becomes far more reliable, and hallucinations from generative models can be eliminated entirely.' Devavrat Shah agreed. 'We have a ton of data sources and workflow tools, but the AI tooling that connects them is still missing,' he said. 'What we need are specialized, purpose-built models that live within the enterprise and understand the specific tasks they're built to support.' Devavrat Shah, professor of AI at MIT and co-founder of Ikigai Labs. We talked at length about trust, not in theory but in practice. Can a bank trust AI to approve a mortgage, flag fraud, or run a forecast? Shah made it clear that AI is not a crystal ball. 'AI is not perfect by design. It provides directional information. The key is to treat it like an input, not an answer,' he explained. He drew a parallel with betting strategies: if AI has a 51 percent edge, you don't bet everything. You diversify, manage risk, and make decisions accordingly. Michael Berthold added that the context in which AI is used determines how much trust is appropriate. 'If I'm looking for trends in data, the model doesn't need to be perfect. But if I'm forecasting revenues or hiring, it needs to be very accurate,' he said. He stressed the importance of transparency. 'Too many systems give you a result with no way to dig into how it was calculated. That's unacceptable in finance.' Buckner noted that governance must be built into the data layer itself. 'You define who sees what, what models can do with that data, and how outcomes are evaluated. Then you can add traceability so every action is auditable,' he said. 'If the model steps outside its boundary, the request fails. That's how you build trust.' Michael Berthold, CEO and co-founder of KNIME There is a lingering misconception that no-code platforms are simplistic. But as Berthold put it, 'We see teams move from massive Excel macros to KNIME workflows that are faster, safer, and auditable. It's not about removing complexity, it's about handling it responsibly.' He emphasized that tools like KNIME let users build automated workflows without knowing how to code, while still requiring them to understand the logic behind each model. 'Data literacy is key. You don't need to know how a method is implemented, but you need to know what it does,' he said. Buckner expanded on this, describing how RapidMiner lets non-technical teams act independently without losing oversight. 'If you can empower your domain experts to tweak visualizations or run their own analysis, you eliminate bottlenecks,' he said. 'Meanwhile, expert users can focus on the high-value, high-impact problems.' This dual-mode approach enables collaboration rather than isolation. As Buckner explained, 'Business teams can move quickly without compromising security or quality, because they're operating within guardrails defined by the platform.' When the conversation turned to model architecture, all three leaders rejected the idea that bigger is always better. Shah, in particular, was clear: 'The current model where a few companies own massive models and everyone else consumes them is not the endgame. The future is small, contextual models that live within the enterprise.' These role-based agents are more efficient, cheaper to run, and far less risky. They can live inside a firm's firewall, interact directly with structured internal data, and avoid the data leakage concerns associated with using external APIs. Berthold noted that even predictive AI applications like credit scoring or risk simulations don't need large models. 'You can build highly effective predictive models from existing datasets, and you can run 'what if' simulations to explore different decisions without exposing data to the cloud.' This is especially appealing to risk-averse financial institutions, which can now apply AI without compromising control or regulatory compliance. All three experts agreed that AI's real power lies not in automation for its own sake, but in augmentation. Berthold predicted that interaction models will shift away from the current 'chatbot everything' trend. 'The next frontier is AI that quietly observes and offers meaningful suggestions, like a co-pilot, not a search bar,' he said. Shah described this as a natural evolution of the human-machine relationship. 'We used to have to learn the machine's language. Now the machine is learning ours. That opens the door to more intuitive, collaborative systems,' he said. But he was quick to add a caveat: 'Explainability is now just as important as accuracy. If people don't understand the output, they won't use it. Period.' Buckner framed the future in terms of speed and scale. 'You can onboard ten AI agents faster than hiring one new analyst,' he said. 'But it's not about replacing people. It's about giving your team leverage to work smarter, faster, and with more confidence.' From these three perspectives, several clear lessons emerge for banks, insurers, and fintechs seeking to implement AI safely and effectively: Start with data integration, not model training: Building a contextual foundation using knowledge graphs or structured workflows pays dividends. Most failures stem from poor data hygiene, not bad AI to amplify domain experts, not replace them: No-code tools allow risk, finance, and compliance staff to build their own workflows while reserving complex tasks for data explainability and governance: AI outputs should be traceable, auditable, and embedded with compliance rules. If a model can't explain itself, it doesn't belong in a financial chase flashy use cases: Many of the most valuable applications are 'boring' internal optimizations, budgeting, forecasting, reconciliation, not chatbot front models are often better: Focused, context-aware AI agents tied to specific roles or workflows are easier to deploy, govern, and in data literacy: Giving tools to business users without training is a recipe for failure. Literacy enables responsible experimentation. The real winners won't be the firms chasing headlines or pouring money into the biggest model. They'll be the ones quietly building robust, interpretable systems that let humans and machines work side by side. And if this conversation was any indication, that future is already under construction. For more like this on Forbes, check out How AI, Data Science, And Machine Learning Are Shaping The Future and Who Owns The Algorithm? The Legal Gray Zone In AI Trading.
Yahoo
5 days ago
- Business
- Yahoo
Altair to Showcase AI-Powered Engineering, Smart Manufacturing, and Connected Aerospace Solutions at Paris Air Show 2025
Live demonstrations will highlight how computational intelligence is shaping the future of flight TROY, Mich., June 2, 2025 /PRNewswire/ -- Altair, a global leader in computational intelligence, will demonstrate the transformative power of artificial intelligence (AI)-powered engineering, smart manufacturing, and more at the Paris Air Show 2025, taking place June 16-22 at the Paris-Le Bourget Exhibition Centre in Paris, France. At the event, Altair will demonstrate how its solutions are reshaping the aerospace sector from concept through production to in-flight performance. "AI, data, and connectivity are no longer future concepts — they are today's competitive advantages. Altair technologies are helping the aerospace industry achieve next-level breakthroughs in performance, sustainability, and innovation," said Dr. Pietro Cervellera, senior vice president of aerospace and defense, Altair. "And now following the recent Siemens acquisition of Altair, together we will rapidly accelerate product development in aerospace." "From design, to build, to launch, the addition of Altair technology to the Siemens Xcelerator portfolio will reinforce our leadership in aerospace, complete the world's most comprehensive digital twin, and propel AI-powered innovation that will help our customers push the boundaries of innovation," said Todd Tuthill, vice president of Aerospace & Defense Industry Strategy, Siemens Digital Industries Software. As aerospace organizations race to meet demands for sustainability, efficiency, and operational readiness, Altair's AI-powered engineering, data analytics, and high-performance computing (HPC) solutions are enabling smarter design, faster development, and more agile decision-making across the entire product life cycle. Visitors to Altair's booth will experience: AI-Powered Engineering for Smarter, Faster Design: Altair is at the forefront of integrating AI into simulation and design workflows. Attendees will see how engineers can reduce design cycles, optimize structures for weight and strength, and improve aircraft performance using intelligent, AI-assisted modeling tools — all while supporting sustainable aviation goals. Smart Manufacturing and Real-Time Optimization: With aerospace manufacturers under pressure to increase throughput and precision, Altair will showcase how real-time data collection and analytics can enhance production line efficiency, reduce scrap and rework, and support smart factory initiatives. From digital thread to predictive maintenance, Altair is making manufacturing more adaptive and responsive. Connectivity Across the Aerospace Ecosystem: Altair is enabling seamless digital connectivity across the entire complex aerospace ecosystem. Demonstrations will explore how Altair supports digital twins, Internet of Things (IoT)-enabled monitoring, and cross-platform collaboration that ties together design, engineering, operations, and maintenance. These connected workflows streamline feedback loops, risk management, and innovation. Empowering Defense and Startups: Altair will also highlight how its solutions empower government agencies, defense organizations, and startups to deliver advanced programs at speed. See how the Altair Aerospace Startup Acceleration Program (ASAP) equips emerging companies with enterprise-grade tools to bring novel technologies to market faster. Altair has recently partnered with the Campania Aerospace District (DAC) to provide over 150 small and medium-sized enterprises (SMEs) and startups with access to Altair solutions, empowering them to work at the same technological level as OEMs and tier-one suppliers. At the Paris Air Show, Altair will be located at Booth H155 in Hall 2B. For more information, visit or At the event, Siemens will be located at Chalet #72– Row D. For more information, visit About Altair Altair is a global leader in computational intelligence that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair is part of Siemens Digital Industries Software. To learn more, please visit or Media contactsAltair Corporate Bridget Hagan +1.216.769.2658 corp-newsroom@ Altair Europe/The Middle East/Africa Altair Asia-Pacific Louise Wilce Man Wang +44 (0)7392 437 635 86-21-5016635,825 emea-newsroom@ apac-newsroom@ SOURCE Altair Sign in to access your portfolio
Cision Canada
5 days ago
- Business
- Cision Canada
Altair to Showcase AI-Powered Engineering, Smart Manufacturing, and Connected Aerospace Solutions at Paris Air Show 2025
Live demonstrations will highlight how computational intelligence is shaping the future of flight TROY, Mich., June 2, 2025 /CNW/ -- Altair, a global leader in computational intelligence, will demonstrate the transformative power of artificial intelligence (AI)-powered engineering, smart manufacturing, and more at the Paris Air Show 2025, taking place June 16-22 at the Paris-Le Bourget Exhibition Centre in Paris, France. At the event, Altair will demonstrate how its solutions are reshaping the aerospace sector from concept through production to in-flight performance. "AI, data, and connectivity are no longer future concepts — they are today's competitive advantages. Altair technologies are helping the aerospace industry achieve next-level breakthroughs in performance, sustainability, and innovation," said Dr. Pietro Cervellera, senior vice president of aerospace and defense, Altair. "And now following the recent Siemens acquisition of Altair, together we will rapidly accelerate product development in aerospace." "From design, to build, to launch, the addition of Altair technology to the Siemens Xcelerator portfolio will reinforce our leadership in aerospace, complete the world's most comprehensive digital twin, and propel AI-powered innovation that will help our customers push the boundaries of innovation," said Todd Tuthill, vice president of Aerospace & Defense Industry Strategy, Siemens Digital Industries Software. As aerospace organizations race to meet demands for sustainability, efficiency, and operational readiness, Altair's AI-powered engineering, data analytics, and high-performance computing (HPC) solutions are enabling smarter design, faster development, and more agile decision-making across the entire product life cycle. Visitors to Altair's booth will experience: AI-Powered Engineering for Smarter, Faster Design: Altair is at the forefront of integrating AI into simulation and design workflows. Attendees will see how engineers can reduce design cycles, optimize structures for weight and strength, and improve aircraft performance using intelligent, AI-assisted modeling tools — all while supporting sustainable aviation goals. Smart Manufacturing and Real-Time Optimization: With aerospace manufacturers under pressure to increase throughput and precision, Altair will showcase how real-time data collection and analytics can enhance production line efficiency, reduce scrap and rework, and support smart factory initiatives. From digital thread to predictive maintenance, Altair is making manufacturing more adaptive and responsive. Connectivity Across the Aerospace Ecosystem: Altair is enabling seamless digital connectivity across the entire complex aerospace ecosystem. Demonstrations will explore how Altair supports digital twins, Internet of Things (IoT)-enabled monitoring, and cross-platform collaboration that ties together design, engineering, operations, and maintenance. These connected workflows streamline feedback loops, risk management, and innovation. Empowering Defense and Startups: Altair will also highlight how its solutions empower government agencies, defense organizations, and startups to deliver advanced programs at speed. See how the Altair Aerospace Startup Acceleration Program (ASAP) equips emerging companies with enterprise-grade tools to bring novel technologies to market faster. Altair has recently partnered with the Campania Aerospace District (DAC) to provide over 150 small and medium-sized enterprises (SMEs) and startups with access to Altair solutions, empowering them to work at the same technological level as OEMs and tier-one suppliers. At the Paris Air Show, Altair will be located at Booth H155 in Hall 2B. For more information, visit or At the event, Siemens will be located at Chalet #72– Row D. For more information, visit About Altair Altair is a global leader in computational intelligence that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair is part of Siemens Digital Industries Software. To learn more, please visit or SOURCE Altair
Cision Canada
30-05-2025
- Business
- Cision Canada
Altair Named a Leader in the June 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms for Second Consecutive Year
Altair recognized for Completeness of Vision and Ability to Execute TROY, Mich., May 30, 2025 /CNW/ -- Altair, a global leader in computational intelligence, announced that Altair® RapidMiner®, Altair's data analytics and AI platform, has been positioned by Gartner as a Leader in the Magic Quadrant for Data Science and Machine Learning Platforms. The evaluation was based on specific criteria that analyzed the company's overall Completeness of Vision and Ability to Execute. "We think being recognized as a Leader for the second consecutive year further validates Altair's expertise in data science and machine learning. Our unique, world-leading solution for data preparation, AI development, orchestration, and automation, empowers organizations to turn data into intelligence faster and more effectively," said Sam Mahalingham, chief technology officer, Altair. "We continually push the limits of innovation, and now having joined the Siemens ecosystem, we will help our customers build, automate, and deploy AI faster than ever." Altair RapidMiner's full-stack AI capabilities—from low-code AutoML to sophisticated MLOps, agent frameworks, and high-speed visualization—empower organizations to quickly prototype, deploy, and scale AI applications. The platform also offers native support for SAS language execution—one of only two platforms in the world with this capability—allowing customers to preserve and extend the value of their existing analytics investments while modernizing their workflows. Another notable differentiator is Altair RapidMiner's massively parallel processing (MPP) graph engine designed to support knowledge graph creation, data fabrics, and ontology modeling at enterprise scale. According to the report, "Leaders in this market have a mature, refined and targeted company and platform strategy that incorporates and leverages GenAI and AI agents to drive their customers' business value. They see opportunities for leveraging agents that other providers may not see or have made significant investments above and beyond standard offerings. They have the capability to innovate at a speed that outperforms other vendors. In addition, they can clearly articulate how they provide value to the multiple types of personas involved in the process of building data science and machine learning models." Magic Quadrant reports are a culmination of rigorous, fact-based research in specific markets, providing a wide-angle view of the relative positions of the providers in markets where growth is high and provider differentiation is distinct. Providers are positioned into four quadrants: Leaders, Challengers, Visionaries and Niche Players. The research enables you to get the most from market analysis in alignment with your unique business and technology needs. For more information about Altair RapidMiner, visit Gartner Disclaimer Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Afraz Jaffri, Maryam Hassanlou, Tong Zhang, Deepak Seth, Yogesh Bhatt, May 28, 2025. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Altair is a global leader in computational intelligence that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair is part of Siemens Digital Industries Software. To learn more, please visit or SOURCE Altair
