Latest news with #Altair
Arabian Post
2 days ago
- Business
- Arabian Post
Engineering Giant Siemens Propels Simulation Frontier with Altair Deal
Siemens has completed its acquisition of Altair Engineering in a £8.3 billion all-cash transaction, signalling a significant evolution in its industrial software strategy. The integration of Altair's high-performance computing, data analytics and simulation portfolio into the Siemens Xcelerator platform represents a major leap in the company's ambition to lead in AI-powered engineering. At the heart of this consolidation lie clear financial and strategic benefits. The deal, offering Altair shareholders US $113 per share, reflected a near 19 percent premium on its pre-announcement price. Siemens anticipates that digital revenues will rise by about €600 million in its 2023 financial results, with annual revenue gains projected at US $500 million in the medium term and potentially more than US $1 billion over the long term. Siemens president and CEO Roland Busch underlined the move as essential to cementing Siemens' industrial software position. He described Altair as a 'diamond, a unique opportunity' with complementary strengths across regions—Altair strong in the US, Siemens dominant in Europe and Asia. Managing board member Cedrik Neike noted Siemens' capacity to continue making strategic acquisitions, heralding a new phase of expansion in the software arena. ADVERTISEMENT Altair, founded in 1985 and headquartered in Troy, Michigan, specialises in computer‑aided engineering software such as HyperWorks, as well as in cloud‑based simulation, IoT, AI and HPC services. Its client base spans industries from automotive and aerospace to consumer electronics, underscoring its global relevance. The company has been delivering an average revenue growth of roughly 12 percent per year—a pace Siemens is keen to elevate. By integrating Altair's capabilities into the Xcelerator platform, Siemens intends to offer an end-to-end, AI-driven portfolio that unifies simulation, data analysis, and HPC tools. The synergy is expected to extend Siemens' leadership in virtual product development, enabling engineers to create digital twins, test design iterations virtually and optimise product performance before physical prototyping. The acquisition deepens Siemens' exploration into AI-enhanced design. With manufacturing industries under pressure to produce sustainable, efficient and safe products, the ability to simulate and analyse complex systems virtually becomes increasingly critical. Combining embedded hardware and software from Siemens with Altair's software tools could significantly reduce time‑to‑market and R&D costs. Integration planning is underway. Altair's CTO, Sam Mahalingam, and Siemens executive Jean‑Claude Ercolanelli are spearheading the combined simulation and test‑solutions portfolio, which will roll under Siemens Digital Industries Software. Users at Siemens' Realize Live Americas 2025 conference have been briefed on the roadmap for merging software suites, enabling interoperability and expanding cloud‑native simulation options. Market reaction to the announcement has been largely favourable. Analysts point out that this represents Siemens' third‑largest acquisition, second only to its Varian Medical Systems deal, and the biggest yet in its software division. Investors are eyeing enhanced earnings per share within two years post‑closing—a benchmark similar to the Varian acquisition. Competition in the simulation software space remains intense, with rival consolidations such as Synopsys' US $35 billion takeover of Ansys earlier this year. Siemens' strategic bet on Altair appears timely, as industrial users increasingly demand seamless digital‑real world integration and advanced simulation capabilities, underpinned by AI and HPC. Altair's established presence in North America, combined with Siemens' engineering and manufacturing legacy worldwide, paves the way for extensive cross‑selling opportunities. High‑impact use‑cases in electric vehicle development, aerospace, and electrification of train systems could showcase the full power of the unified platform. The scale of the transaction means full technical, organisational and cultural integration may unfold gradually. However, Siemens has signalled its commitment by placing Altair's CTO within its senior digital industries hierarchy to ensure sustained innovation and continuity. With simulation, AI and digital twins becoming indispensable in modern engineering, Siemens' completion of the Altair acquisition marks a strategic inflection point. The expanded toolkit will serve as a catalyst for manufacturers aiming to embrace advanced engineering methodologies, reflecting Siemens' broader vision of becoming the leading 'One Tech Company' in the digital age.
Time of India
4 days ago
- Business
- Time of India
Blackstone buys South City Mall, Colombo project for 3,250 crore
Kolkata: South City Mall changed hands in what was one of the largest real estate deals in the city and the first mall deal. Blackstone, one of the world's largest alternative asset management funds, has acquired South City Projects, the holding company of the mall, for Rs 3,250 crore. Tired of too many ads? go ad free now Along with the mall, the $1.1 trillion global asset manager has also acquired unsold inventories of South City's Colombo project — Altair, according to sources. South City School, however, will remain with erstwhile promoters. South City Projects was owned by a consortium that included Emami, Merlin Group, Sureka Group, Shrachi Group, Jugal Khetawat and JB Group. It was learnt that South City Mall was valued between Rs 2,700 crore and Rs 2,800 crore while the remaining Rs 450 crore-500 crore was for the Colombo project. For Blackstone, the acquisition represents a strategic entry into the Kolkata market. While the firm already owns 18 malls across 14 Indian cities, the acquisition of South City Mall marks its first major retail investment in eastern India's commercial capital. Blackstone manages assets worth Rs 9.6 lakh crore worldwide and has deployed Rs 1.7 lakh crore in India's office space and hotels. Anarock Group, a leading independent real estate services firm, served as the exclusive transaction adviser for both acquisitions. "This transaction represents more than an acquisition — it's a vote of confidence in eastern India and Bengal's economy and retail ecosystem," said Sushil Mohta, chairman of Merlin Group and director of South City Projects. He highlighted the unique nature of South City Group as a consortium of six prominent Kolkata business families. "We have collaborated successfully for over 25 years. Our consortium itself is a case study in sustained partnership and shared vision," he added. Tired of too many ads? go ad free now Prakash Bachawat, a key stakeholder in South City Projects who stewarded the transaction, expressed enthusiasm about the partnership. "This investment will undoubtedly accelerate the growth trajectory of both assets while maintaining their premium market positioning," he added. Asheesh Mohta, head of real estate acquisitions - India, Blackstone, said: "We are thrilled to strengthen our presence in India and invest in this iconic asset. South City Mall is a place where the community comes together — it's the definitive destination in Kolkata for shopping, dining, leisure and entertainment." Located on Prince Anwar Shah Road, South City Mall is a retail powerhouse generating an average annual turnover of Rs 1,800 crore. The mall is spread across 12.5 lakh square feet with a gross retail area of 6.5 lakh square feet. The mall sees 30,000-40,000 footfalls on weekdays and 75,000-1.5 lakh on weekends. It houses around 150 stores anchored by major brands such as Shoppers Stop, Pantaloons, Spencers and a six-screen Inox multiplex and the region's largest food court. It also hosts premium brands such as Zara, Tommy Hilfiger, Armani, Calvin Klein and Adidas, alongside dining favourites Chili's, Mainland China as well as a Starbucks.
Cision Canada
10-06-2025
- Automotive
- Cision Canada
Altair Announces ATCx AI for Engineers 2025 Global Virtual Event
Event will showcase AI-powered engineering, smart manufacturing, and scaling intelligence with HPC and AI TROY, Mich., June 10, 2025 /CNW/ -- Altair, a global leader in computational intelligence, announced it will host ATCx AI for Engineers 2025, a global virtual technology conference, on June 26. The event will bring together design, simulation, and manufacturing engineers from around the world to explore the transformative potential of artificial intelligence (AI) in product development. ATCx AI for Engineers will explore real-world applications of AI across product development, manufacturing, and high-performance computing (HPC). Through expert-led keynotes, technical sessions, interactive workshops, and networking opportunities, attendees will explore practical applications of AI that can elevate their work and impact. "Whether you want to speed up simulation, streamline production, or make better decisions faster, this virtual event will give you the strategies, tools, and insights to bring AI into your workflows and push the boundaries of what's possible," said Ravi Kunju, chief product and strategy officer, Altair. "ATCx AI for Engineers will show how organizations can apply AI in ways that matter." The event features an impressive lineup of speakers and panelists from some of the world's leading companies, including Hyundai, Whirlpool, and Lucid Motors. Attendees can choose from three parallel tracks: Where Physics Meets AI – explore how AI is reshaping engineering, including speeding up design, improving accuracy, and enabling smarter decisions with tools like generative design, digital twins, and AI copilots. Accelerating AI Adoption for Smart Manufacturing – deep-dive into how manufacturers are using AI to optimize operations, reduce costs, and accelerate innovation with tools like predictive maintenance and intelligent automation. Simulation-Driven Design – learn how HPC powers AI at scale and how AI makes HPC faster and smarter. See real-world examples shaping the future of computing. ATCx AI for Engineers will be held in two time zones across the AMER, LATAM, EMEA, and APAC regions, and will offer live translations in Spanish, French, Italian, Portuguese, German, Japanese, Chinese, and Korean. For more information and registration, 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
Forbes
05-06-2025
- 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
05-06-2025
- 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.
