News & Press
Xplain Data at the Causal Data Science Meeting 2026
On November 4, 2026, Xplain Data took part in the virtual Causal Data Science Meeting 2026.
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Our Data Scientist Marco Molitor presented “Object-Centric Causal Discovery in MIMIC-IV”, demonstrating how Xplain Data’s technology helps uncover cause-and-effect relationships in complex healthcare data.
November 4, 2026 | Session 01, 10:30–11:50
The event was held online and was free to attend.
Dr. Michael Haft joined AI and Causal Inference Panel at ISPOR Europe 2026
Our CEO Dr. Michael Haft joined a panel at ISPOR Europe 2026 in Vienna, discussing the future role of AI in causal inference for healthcare decision-making.
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Our CEO, Dr. Michael Haft, participated in a panel discussion at ISPOR Europe 2026, the leading European conference for Health Economics and Outcomes Research (HEOR), which took place November 8–11, 2026, in Vienna, Austria.
The panel, “The Future of Causal Inference: How Large (or Small) Role Will AI Play?”, explored how advances in artificial intelligence are transforming real-world evidence generation and the application of causal inference in healthcare decision-making. Experts from epidemiology, data science, and the pharmaceutical industry discussed the opportunities and challenges of integrating AI into established causal inference frameworks, as well as the implications for transparency, reproducibility, and regulatory trust.
Dr. Michael Haft was joined on the panel by two distinguished experts: Miguel Hernán, PhD, Director of CAUSALab and Professor of Biostatistics and Epidemiology at Harvard (Boston, MA, United States), and Gorana Capkun, PhD, VP, Global Head of Patient Focused Real World Evidence at Merck Healthcare (Allschwil, Switzerland).
The session was moderated by Radek Wasiak, Co-Founder and Director of Adigens Health.
EVENT: Xplain Data @EFX Electronics Manufacturing Tradeshow, Oct 6–8, 2026
Xplain Data exhibited at EFX – Expo for Electronics Manufacturing (Oct 6–8, Stuttgart), where CEO Dr. Michael Haft joined a panel on AI in electronics manufacturing.
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From October 6–8, 2026, Xplain Data exhibited at EFX – Expo for Electronics Manufacturing at Messe Stuttgart (Hall 9, Booth 9B01).
On October 6, our CEO, Dr. Michael Haft, joined industry experts for the panel talk “AI in Electronics Manufacturing”. The discussion focused on how manufacturing can become the “factory of the future” – and how Causal AI can automatically identify the true causes of quality issues, enabling manufacturers to take corrective action earlier.
Thank you to everyone who visited our booth and joined the panel. Want to learn how Causal AI can improve your electronics manufacturing? Contact us.
Xplain Data Featured in Three Gartner Reports for the First Time
Xplain Data has been named a Sample Vendor for Causal AI in two Gartner Hype Cycles for banking and is featured in Gartner’s Emerging Tech Impact Radar: Disruptive Technologies on the Far Horizon.
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We are pleased to share that Xplain Data has been included in three Gartner reports published in 2026 – each of them for the first time:
- Hype Cycle for Artificial Intelligence in Banking, 2026 – Sample Vendor for Causal AI
- Hype Cycle for AI-Driven Trade Finance Transformation in Banking, 2026 – Sample Vendor for Causal AI
- Emerging Tech Impact Radar: Disruptive Technologies on the Far Horizon
Unlike correlation-based AI, Causal AI identifies genuine cause-and-effect relationships — helping banks understand why customers churn, why loans default or why fraud spikes, not just predict that they will — a shift Gartner attributes to agentic AI’s need to act autonomously, tightening explainability requirements such as the EU AI Act, and GenAI’s growing ability to surface candidate cause-and-effect links from documents and data, even though establishing causality itself still requires human validation.
Full reports are available to Gartner clients only.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and HYPE CYCLE 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 its 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’s 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.
Dr. Michael Haft Featured in UK Data & AI Magazine Issue #13
Our CEO is featured in Issue #13 of Data & AI Magazine, discussing whether Causal Discovery is the missing layer in Artificial Intelligence.
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Our CEO, Dr. Michael Haft, is featured in Issue #13 of Data & AI Magazine on “Understanding Cause and Effect: Is Causal Discovery the Missing Layer in Artificial Intelligence?”
While generative AI continues to dominate the conversation, Causal AI focuses on a different question: not just what will happen, but why it happens. Understanding cause-and-effect relationships is essential for building AI systems that are explainable, reliable, and capable of supporting better decisions.
In the interview, Michael discusses how Causal Discovery helps distinguish true causal relationships from simple correlations and why this capability will become increasingly important as AI moves from prediction to intelligent intervention.
We’re pleased to be part of this discussion and would like to thank Damien Deighan and the entire Data & AI Magazine team for the opportunity to contribute.
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Xplain Data Featured in “Industrial AI: From Pilot to Profit” Book by Boris Scharinger
Xplain Data is featured in the new book “Industrial AI: from Pilot to Profit” by Boris Scharinger (Siemens), including a Solution Brief on our Causal AI technology for production optimization.
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We are proud to be featured in the new book “Industrial AI: from Pilot to Profit” by Boris Scharinger, Senior Innovation Manager and AI Strategist at Siemens Digital Industries.
The book presents leading Industrial AI concepts, success factors, and real-world applications across manufacturing and industrial operations, and includes a Solution Brief on Xplain Data’s Causal AI technology for production optimization.
We would especially like to thank Boris personally for his continuous support over the years. From the very beginning, he invited Xplain Data to participate in the AI with Purpose Summit and has always been incredibly open, supportive, and encouraging towards innovation in Industrial AI.
Boris has been a fantastic host, moderator, and advocate for meaningful AI applications in industry, and we truly appreciate the collaboration and trust.
It is an honor for Xplain Data to be recognized in this publication alongside other innovators shaping the future of intelligent manufacturing.
Learn more about the book: amazon.de
More News
Xplain Data Research on Pressure Injuries Accepted for ISPOR 2026
Xplain Data, together with academic partners from the United States, presented new research at ISPOR 2026 in Philadelphia.
The project, “Object-Centric Causal Discovery in MIMIC-IV: A Paradigm Shift for Identifying Risk Factors for Hospital-Acquired Pressure Injuries in Real-World Clinical Data,” has been accepted as a poster presentation.
The work was developed in collaboration with Andy Wilson (University of Utah), an expert in real-world data and causal inference, and Dr. Jenny Alderden (Boise State University), a specialist in pressure injuries and board member of the National Pressure Injury Advisory Panel (NPIAP).
Background
Hospital-acquired pressure injuries (HAPIs) remain a significant challenge in healthcare. Approximately 2.5 million patients in the U.S. are affected each year. They occur in 6–10% of surgical critical care patients, with a global prevalence of around 8.4%.
The economic impact is substantial, with total annual costs in the U.S. estimated between $9.1 billion and $26.8 billion, and per-patient costs ranging from $20,900 to $151,700, depending on severity.
Approach
The project applies an object-centric approach to Causal Discovery using the MIMIC-IV clinical database. This method is designed to better capture relationships between clinical entities compared to traditional tabular models, with the goal of identifying more reliable risk factors in real-world clinical data.
Dr. Michael Haft on Data Science Conversations Podcast: Why Causal AI Matters
Our CEO, Dr. Michael Haft, was recently featured on the Data Science Conversations podcast, where he discussed the growing importance of causal AI in modern data science.
In the episode, Dr. Haft explains why predictive models alone are no longer enough. While traditional machine learning can forecast outcomes, it often fails to reveal the underlying cause-and-effect relationships required for intelligent intervention.
A practical manufacturing example illustrates the difference: predictive analytics identified failing components, but causal discovery uncovered the root cause – enabling a targeted fix.
The conversation highlights why organizations must move from correlation to causality, especially in complex domains like manufacturing and healthcare. As AI systems increasingly support operational decisions, understanding “why” becomes essential.
Listen to the full episode to explore how Causal AI bridges the gap between prediction and actionable insight.
New eBook by Prof. Elias Bareinboim: A Milestone for the Future of Causal AI
At Xplain Data, we closely follow the latest breakthroughs in Causal AI – not only because they inspire our work, but because they define the future of reliable, explainable, and truly intelligent systems. One of the leading voices shaping this future is Prof. Elias Bareinboim, a renowned computer science professor at Columbia University and Director of the Causal AI Lab.
His pioneering research focuses on teaching AI systems to distinguish correlation from causation – a capability fundamental to real general reasoning.
This week, Prof. Bareinboim shared an important message with the AI community: Scale alone will not deliver general reasoning. The next layer must be causal.
In response to recent discussions – including Ilya Sutskever’s (co-founder of OpenAI and Chief Scientist until 2024) widely cited comments on the limits of scaling – Bareinboim emphasized that true intelligence requires a structural understanding of consequences, not just patterns. Humans reason causally; AI must do the same to achieve robust generalization, explanation, and decision-making.
He warns that the stakes are too high to ignore the causal foundations any longer:
“We cannot afford another decade, or another trillion dollars, invested without addressing the causal foundations that real intelligence requires.”
To support this transformation, Bareinboim has released a draft of his forthcoming textbook, presenting a coherent blueprint for building truly Causal AI systems. The book spans the full spectrum of causal reasoning: Foundations of Causality, Understanding, Decision Making, Generalization, and Generative Models.
Access the eBook draft here: causalai-book.net
At Xplain Data, we see Bareinboim’s work as an important validation of what drives our mission: enabling organizations to make better decisions through Causal AI that goes beyond prediction to true understanding.
Xplain Data wins productronica Innovation Award 2025 Future Markets Cluster
We are thrilled to announce that Xplain Data has won the productronica Innovation Award 2025 in the Future Markets Cluster with our breakthrough solution CausalDiscoverer YieldPro! 🏆
CausalDiscoverer YieldPro takes yield improvement to a new level. By analyzing all quality-related factors across process and design, it automatically identifies the true root causes of yield and quality issues. Emerging problems and potential failure modes are detected early, enabling highly targeted actions and driving sustainable optimization in electronics manufacturing. Because we go beyond prediction – we uncover the why behind every issue.
The productronica Innovation Award, presented in cooperation with the magazine productronic, recognizes the most innovative products and manufacturing processes in the industry. All exhibitors at productronica 2025 had the opportunity to participate in this globally unique competition, now in its sixth edition. Submissions were evaluated based on technical innovation, economic efficiency, and integration into systems and processes.
The official award ceremony took place on Tuesday, November 18, 2025, during the Exhibitor Get-Together at productronica in Munich. A special thank-you goes out to the productronica team, our wonderful host Petra Gottwald, editor-in-chief of productronic, and juror Dr. Sandra Engle (VDMA) for their commitment to highlighting the technologies shaping the future.
🎉 Congratulations as well to all other nominees and award winners who continue to push the boundaries of innovation in our industry.
We are incredibly proud of this achievement and look forward to presenting our innovation to the global electronics manufacturing community.
Check out the Award Winner Video, featuring our CEO, Dr. Michael Haft.
Press Release: Xplain Data Introduced CausalDiscoverer YieldPro @productronica 2025
Xplain Data GmbH showcased its cutting-edge Causal AI solutions at productronica 2025 in Munich (November 18–21, Hall B2, Booth 369/3). The company demonstrated how its patented technology helps electronics manufacturers significantly increase efficiency, stability, and transparency by moving beyond symptoms to identify the root causes of production issues.
Featured Solutions
- CausalDiscoverer YieldPro: This platform automates root cause analysis to uncover hidden errors in processes and design. By identifying what truly impacts quality, it helps electronics manufacturers achieve higher yields and reduce production failures.
- Causal DiscoveryBot: This autonomous bot provides live production monitoring. It continuously analyzes data to detect quality risks in real time and alerts staff immediately, allowing for quick intervention before issues escalate.
“With our patented Causal AI, we make it possible to identify causes rather than just symptoms—evidence-driven, automated, and ready to use,” explained Dr. Michael Haft, CEO of Xplain Data GmbH.
Xplain Data @GetReal Conference 2025, Oct 1–2: Advancing RWE with Causal AI
Xplain Data was selected to present at the GetReal Conference 2025, which took place October 1–2, 2025, at the Crowne Plaza Hotel in Utrecht, The Netherlands.
Our abstract, “Advancing Real-World Evidence with Causal AI: A Scalable Framework for Unlocking Actionable Insights from Real-World Data”, was accepted for a poster presentation.
Session details
Type: Poster
Moderated Poster Session: Thursday, October 2, 11:45–12:30
Venue: Crowne Plaza Hotel, Utrecht
The GetReal Conference brings together experts from research, healthcare, and industry to explore the future of real-world evidence. We were proud to contribute by showcasing how Causal AI can transform the scalability, reliability, and actionability of insights derived from complex real-world datasets.
Xplain Data @Productronica 2025: Causal AI for Electronics Manufacturing Optimization
November 18–21, 2025 | Munich
50 years Productronica 2025 in Munich – and Xplain Data was part of it!
At Munich Trade Fair Center, Booth 369/3 in Hall B2, our visitors could experience how our innovative CausalDiscoverer YieldPro revolutionizes electronics manufacturing optimization. Our technology goes far beyond conventional, predictive analysis methods, delivering precise, automated Root Cause Analysis for complex manufacturing processes without feature extraction.
Intelligent Root Cause Analysis redefined!
Live demonstrations of our Causal AI platform:
- Automated identification of electronics production anomalies
- AI-powered Root Cause Analysis in real-time
- Intelligent optimization recommendations for manufacturing processes
PCEA.NET Article: Siemens achieves PCBA Quality Breakthrough with Causal AI
We’re excited to share that our customer, Siemens Motion Control, has been featured in the latest edition of the PCEA (US Printed Circuit Engineering Association) magazine for a pioneering achievement in PCBA manufacturing.
As part of a Proof-of-Concept project, Siemens’ Erlangen plant successfully broke the 5-Sigma Wall in PCBA production by leveraging Causal AI from Xplain Data.
In this initiative, Siemens implemented a holistic, object-centric analysis approach across 500 million daily data points from its highly complex PCB assembly process. This marked a shift from traditional predictive models to Causal AI, enabling the team to identify the true root causes of quality issues.
Using Xplain Data’s patented ObjectAnalytics technology, Siemens drastically reduced error rates and achieved a new benchmark in process transparency, efficiency, and product quality. The result: improved yields, reduced costs, and faster processes – with the investment paying off within a year.
Congratulations to Erik Schwulera and the Siemens team for this impressive milestone in digital transformation!
Xplain Data again recognized in 2025 Gartner AI Hype Cycle
Xplain Data has been named a sample Vendor for Causal AI in Gartner’s Hype Cycle for Artificial Intelligence, 2025, affirming its leadership in advancing a new generation of intelligent, explainable, and action-oriented AI systems.
Causal AI – now identified as an emerging, high-benefit innovation by Gartner – focuses on uncovering and applying cause-and-effect relationships, going beyond today’s correlation-based predictive and generative models. As Gartner emphasizes, “The next step in AI requires Causal AI.”
In a fast-evolving AI landscape, Gartner’s report signals a major shift: from narrow prediction to prescriptive intelligence. Current models often lack transparency – predicting outcomes without understanding the why. Causal AI addresses this gap by delivering more reliable, explainable, and autonomous AI that can prescribe effective actions, not just anticipate results.
“AI’s ultimate value comes from making better decisions and taking effective actions. Causal AI is crucial when systems need to be more transparent and reliable…”
The recognition of Xplain Data as one of 10 Causal AI vendors worldwide reflects the company’s deep focus on developing scalable Causal AI tools that integrate domain expertise, Causal Discovery, and transparent modeling – helping businesses move from data to decisions with confidence.
Why Causal AI Now? Traditional models struggle with transparency, robustness, and adaptability in dynamic environments. Causal models remain valid even when environments shift – making them ideal for fast-changing markets. Causal AI is increasingly vital for decision intelligence, smart automation, and AI agent systems.
Dr. Michael Haft, CEO: “Being named by Gartner validates our commitment to building AI that doesn’t just see patterns – but understands them.”
Press Release: Digital Transformation with Causal AI: Xplain Data joins Siemens Xcelerator
Zorneding/Munich, June 10, 2025 – Xplain Data has joined Siemens Xcelerator – an open digital business platform that helps companies accelerate their digital transformation. Participants gain access to innovative technologies and solutions through a continuously evolving partner ecosystem and an expanding marketplace.
As an open digital business platform, Siemens Xcelerator aims to support companies in their digital transformation. It does this through a curated portfolio of IoT hardware, software, and digital solutions from Siemens, all within a strong partner ecosystem.
Xplain Data was included in the Siemens program because of its patented Causal AI technology, which analyzes complex production data. This enables the joint implementation of data-driven optimization projects across the entire value chain. Xplain Data’s solution integrates seamlessly with the platform’s data and integration services, enhancing the ecosystem with advanced analytics and automation capabilities throughout the entire product and production lifecycle.
“This cooperation builds on our existing successful collaboration with Siemens in optimizing PCB production,” says Dr. Michael Haft, CEO of Xplain Data. “Our Causal AI solution unlocks new potential for improving industrial manufacturing processes by identifying the true root causes of production issues in precision manufacturing.”
Causal AI with ObjectAnalytics: Understand Root Causes instead of Correlations
Xplain Data’s Causal AI algorithms are built on the patented ObjectAnalytics technology, which provides an object-centric view of business data. All information related to a business object—such as a workpiece or machine—is consolidated into a single object. This enables holistic analysis of the entire production process without the need for manual feature selection and automatically uncovers the root causes of quality issues and process deviations.
Xplain Data wins F.A.Z. AI Innovation Award 2025
Award for Causal AI: Xplain Data has been honored with the F.A.Z. AI Innovation Award 2025 for its patented Causal AI technology. The jury praised the solution for its innovative strength, social relevance, and technological prowess.
The recognition comes from the German AI Association and the F.A.Z. Institute, acknowledging breakthrough AI solutions creating meaningful organizational and societal impact. Out of over 200 submissions, only seven projects per category reached the final stage—described as a record-breaking achievement for the company.
The technology represents a new class of transparent artificial intelligence that combines causal thinking with automated decision-making, emphasizing clarity, superior outcomes, and practical effectiveness in complicated scenarios.
The announcement occurred on June 4, 2025, at the Innovative Leaders Conference held at the DHL hub in Leipzig, themed “With the courage to change.”
The company thanked the jury and supporting partners for their contributions.
10th Rockwell State of Smart Manufacturing Report 2025 Highlights Surge in Causal AI Adoption
The 2025 State of Smart Manufacturing Report from Rockwell Automation reveals a decisive shift in how manufacturers are leveraging artificial intelligence. According to the report, “95% of manufacturers have either already invested in or plan to invest in AI/ML, including Generative and Causal AI, within the next five years.”
This growing commitment signals a move beyond experimentation to real-world deployment—with AI becoming a mainstream tool for transformation. In fact, by 2027, organizations expect AI to play a critical role in driving cost and time savings, creating operational efficiencies, and streamlining production processes.
Among the most promising developments is the rise of Causal AI. Unlike traditional AI, which identifies correlations, Causal AI understands cause-and-effect relationships. This enables manufacturing teams to move from reactive decision-making to proactive, explainable actions—making it a powerful asset for optimizing production, supply chains, and energy use.
AI-Powered Quality Control
A particularly strong application area is AI-powered quality control. For the second year in a row, it remains the top AI use case in manufacturing. Half of the surveyed companies plan to use AI/ML to support product quality by 2025, highlighting the growing need for precision in increasingly complex production environments.
Causal AI is proving essential for companies aiming to scale their operations intelligently. With “a 12% year-over-year growth in investments in Causal and Generative AI,” the shift toward smarter, more autonomous manufacturing systems is well underway.
Want to explore how Causal AI can help you boost production efficiency, reduce costs, and stay ahead of the curve? Contact us to find out more.
Rockwell Automation’s 2025 State of Smart Manufacturing Report
Xplain Data featured in Gartner Emerging Tech Impact Radar 2024
We’re pleased to share that Xplain Data has been recognized as a relevant vendor in Gartner’s Core AI Technology and the Foundation of Future Innovations report (December 2024). The report identifies Causal AI as a foundational technology for the future of artificial intelligence, with an expected impact horizon of 6 to 8 years.
Causal AI leverages cause-and-effect relationships to deliver more reliable forecasts and actionable insights, moving beyond correlation-based models. By uncovering why something happens – not just what will happen – it enables smarter, more explainable decision-making.
Gartner notes: “Causal AI identifies and utilizes cause-and-effect relationships to go beyond correlation-based predictive models and toward AI systems that can prescribe actions more effectively and act more autonomously.”
Despite its transformative potential, adoption remains early due to the complexity of the models and data requirements. However, interest is rapidly growing across domains such as economics, healthcare, and pricing.
At Xplain Data, we are proud to contribute to the advancement of this field.
“We see causal reasoning as a crucial step toward trustworthy AI,” says Dr. Michael Haft, CEO Xplain Data. “Our focus is on making causal insights accessible and actionable – strengthening decision-making at its core.”
Xplain Data @Siemens AI with Purpose Summit 2025
The AI with Purpose Summit is Europe’s premier leadership event for industrial AI, hosted by Siemens on May 20–21 at the House of Communication in Munich. This exclusive event brought together academics, analysts, entrepreneurs, industry leaders, media and technical experts for 2 days of unparalleled networking and collaboration.
With over 700 thought leaders and 100 visionary talks, the Summit addressed the key challenges and opportunities in industrial AI, showcasing how AI, domain expertise, and data revolutionize productivity, sustainability, and societal progress. Over 30 booths and showcases highlighted the most exciting startups in industrial AI.
Xplain Data is proud to have been invited again, where we showcased our Causal AI solution at our booth and gave a thought-provoking talk titled “Cracking the Why: Causal AI for Production Optimization”.
Talk: Judea Pearl on Causality and Personalized Decision Making
On March 26, 2025, Judea Pearl, a luminary in the field of Causal Inference, delivered a powerful talk at Genentech titled “From Questions to Cures.” In his address, Pearl championed a new scientific mindset, one that transcends mere pattern recognition to achieve a genuine understanding of cause and effect. He emphasized the transformative potential of answering critical questions such as: “Did the drug cause the recovery?” “Would this patient have survived without treatment?” and “If we change this policy, what will happen?”
Pearl’s insights underscore the daily challenges faced by researchers, clinicians, and policymakers.
At Xplain Data, we are dedicated to building the very tools that turn these pivotal questions into actionable answers. We are proud to be contributing to a present where more organizations can adopt causal thinking for fun, communication, profit, and science – aligning perfectly with Judea Pearl’s vision.
Xplain Data @ML Week 2024 – Breaking Barriers in Smart Manufacturing
At Machine Learning Week 2024 in Munich, Xplain Data’s CEO, Dr. Michael Haft, joined Siemens’ Erik Schwulera to present an exciting use case on how Causal AI is transforming printed circuit board production.
By leveraging our innovative Causal DiscoveryBot and ObjectAnalytics, Siemens is uncovering previously hidden error causes and driving unprecedented efficiency gains—an important step toward achieving Zero Data Engineering.
This milestone highlights the growing impact of Causal AI in smart manufacturing. Stay tuned as we continue to pioneer solutions that empower industry innovation!
Xplain Data: ChampI4.0ns Award 2024 for Innovation in Data Solutions
Xplain Data has been honored with the prestigious ChampI4.0ns Award 2024 at the Global Data Spaces Connect 2024 (GDSC24) in Vienna. The award recognizes our groundbreaking project on PCB production optimization using Causal AI, which is celebrated for its innovative approach and significant industry impact.
The event brought together over 250 global experts, decision-makers, and innovators to explore the transformative potential of Data Spaces.
Our CEO, Dr. Michael Haft, proudly accepted the award at the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology. “This recognition underscores our commitment to advancing data-driven solutions that enhance efficiency and foster innovation across industries,” said Dr. Haft.
Xplain Data remains committed to shaping a data-driven future through impactful use cases and global collaboration. A heartfelt thanks to DIO – Data Intelligence Offensive, the International Data Spaces Association (IDSA), iSHARE Foundation, Fraunhofer Austria, and all organizers for making this event a success.
Press Release: Xplain Data – IDC Innovator for Industrial AI Applications & Solutions, 2024
IDC Innovators are emerging vendors with annual revenues under $100 million that have demonstrated either a groundbreaking business model or an innovative new technology – or both. The IDC Innovators report profiles vendors that provide innovative artificial intelligence solutions, services, and techniques for industrial companies. Xplain Data is one of three vendors included in the IDC Innovators Report 2024 (Doc # EUR252446124, August 2024).
Innovators are selected by an individual IDC analyst and approved by the IDC Innovators Review Panel.
“Being recognized as an IDC Innovator is a tremendous honor. Our patented technologies and groundbreaking Causal AI methodologies continue to drive industry transformation, enabling organizations to make evidence-based decisions with unprecedented confidence to optimize production processes. We view this recognition as a validation of our decade-long commitment to revolutionizing the way companies extract value from their data.” — Dr. Michael Haft, CEO Xplain Data GmbH.
“As data complexity increases and data protection regulations become stricter, data safety is emerging as a competitive advantage at all business levels. The demand for AI tools continues to surge, requiring vendors to stay current with advancements while offering sophisticated yet user-friendly solutions and services. To differentiate themselves from their competitors, vendors must meet genuine customer needs, provide business-aligned benefits, and comply with evolving regulations.” — Jan Burian, Associate Vice President, IDC.
An IDC Innovators report presents a set of vendors – under $100M in annual revenue at the time of selection – chosen by an IDC analyst within a specific market that offer a new technology, a groundbreaking solution to an existing issue, and/or an innovative business model. It is not an exhaustive evaluation or a comparative ranking of all companies, but rather a document that highlights innovative companies in a specific market segment. IDC INNOVATOR and IDC INNOVATORS are trademarks of International Data Group, Inc.
Xplain Data Recognized in German AI Startup Landscape 2024 (Frameworks)
Xplain Data Honored in Seventh Annual German AI Startup Landscape (2024)
Xplain Data is proud to announce its inclusion for the second consecutive year in the Seventh Annual German AI Startup Landscape (2024) report, published by the renowned appliedAI Institute for Europe. This prestigious recognition highlights Xplain Data’s commitment to advancing the field of Applied AI in Germany and shaping the future of the technology for the benefit of society.
The German AI Startup Landscape is Germany’s most comprehensive analysis of the AI startup scene. The report, created in collaboration with leading academic, government, and industry partners, aims to: highlight the vibrant German AI startup ecosystem, drive the adoption of AI across various sectors, and facilitate partnerships between AI startups and established corporations.
By featuring high-quality, externally validated AI startups like Xplain Data, the report serves as a valuable resource for corporations, SMEs, and government institutions seeking trusted AI partners.
This year’s report saw a rigorous evaluation process involving over 1000 startups, all founded after 2014 with a primary AI-based business model. Stringent criteria based on data, talent, applied AI methods, scalability, and overall quality determined inclusion.
We are particularly excited to see Berlin and Munich continue to be hubs for German AI innovation, with Xplain Data contributing to this thriving landscape.
Xplain Data: 1 of 10 relevant global Causal AI Vendors in Gartner AI Hype Cycle 2024
The Gartner AI Hype Cycle ’24 is out, and Causal AI continues its steady ascent! Causal AI is no longer a brand-new concept – it’s been gaining momentum as an innovation hype over the past two years confirming its growing importance and potential to revolutionize decision-making. Xplain Data and a select few others are pioneering this space.
Traditional AI excels at identifying patterns and making predictions based on correlations in data. However, this approach has limitations. While it might predict future outcomes assuming things stay similar, it can’t explain the underlying reasons or how to influence those outcomes.
Causal AI fills this critical gap. It goes beyond prediction to reveal cause-and-effect relationships. This empowers businesses to:
- Make stronger decisions: By estimating the impact of interventions, Causal AI allows AI systems to play a more autonomous and impactful role in decision-making.
- Boost efficiency: Causal AI can leverage domain knowledge to train models effectively with smaller datasets, saving time and resources.
- Enhance explainability: Causal models capture clear cause-and-effect relationships, making AI’s reasoning easier to understand and trust.
- Build resilience and adaptability: By focusing on causal relationships that hold true even in changing environments, Causal AI helps businesses prepare for the unexpected.
- Optimize experimentation: Causal AI allows for extracting valuable insights through less expensive and time-consuming experiments.
- Reduce bias: By making causal links more explicit, Causal AI helps mitigate bias in AI systems.
This shift towards causal reasoning unlocks a new level of effectiveness for AI, enabling businesses to not just predict the future, but actively shape it.
Xplain Data @Siemens AI with Purpose Summit 2024
Art, agencies, and AI: A vibrant mix at the House of Communication in Munich’s trendy Werksviertel – and a perfect new home for the Siemens AI with Purpose Summit!
We were thrilled to be part of the Siemens AI with Purpose Summit 2024 (powered by Siemens AI Lab) in this vibrant new location.
Over two days on June 10 and 11, the agenda was packed with inspiring speakers who shared their efforts to harness AI for a better future. Industry leaders discussed AI innovation, regulation, challenges, and scaling opportunities.
The Summit provided a unique opportunity to delve into the remarkable progress being made in the world of AI. It was organized into four distinct tracks: Future of AI, Trust & Regulation, Data Challenge, and Scale Up. Each track played a critical role in highlighting the evolution of the industry. The event not only explored innovative concepts, hypotheses and visions, but also showcased numerous use cases and project implementations.
Xplain Data was proud to participate in the startup area as well as present the use case of its Causal AI solution for PCB production optimization at Siemens Erlangen in collaboration with Erik Schwulera, Lead IoT@Manufacturing. We thank all participants and organizers for making this event a resounding success.
Data Dialogue: BAdW & Xplain Data on Healthcare’s Data-Driven Future
How can we support health innovation through data sharing and AI? The future of healthcare in Germany was passionately discussed in the venerable rooms of the Bavarian Academy of Sciences and Humanities (BAdW). Two topics took center stage: the solution to the German data protection dilemma and the importance of Causal AI for quickly gaining knowledge from complex patient data, especially when this data is available across national borders.
Intensive discussions were held about working together to get closer to the common goal of simplifying the aggregation of anonymized health data, making it available for research and science and evaluating it using innovative Causal AI algorithms – in order to drive healthcare forward through innovation.
Causal AI makes skyrocketing Debut in Rockwell Smart Manufacturing Report 2024
9th Rockwell Smart Manufacturing Report
How does the future of Smart Manufacturing look? In its largest report to date, Rockwell Automation surveyed more than 1,500 decision-makers from 17 of the top manufacturing countries. The Rockwell Smart Manufacturing Report provides insights into trends, challenges and their strategies for future-proofing their operations.
The report’s findings underscore a significant trend: the pervasive role of artificial intelligence (AI) in shaping the future of manufacturing.
A notable revelation among the 2024 findings is the emergence of Causal AI, marking its official debut in the manufacturing landscape.
In the latest report, Causal AI skyrockets in several areas:
- Primary Investment Focus for the Next 12 Months: Gen/Causal AI
- Key Technology Solutions to address Workforce Challenges: Gen/Causal AI
- Exceptional Return on Investment (ROI): Second only to cloud/SaaS, Gen/Causal AI stands out as a high-value Investment.
Download the full report to discover first-hand how forward-thinking manufacturers are using cutting-edge technology like Causal AI to raise quality standards, drive sustainable growth and maintain a competitive edge in 2024 and beyond.
If you want to explore how to optimize your production with groundbreaking Causal AI technology, get in touch!
Archive (2020 – 2023)
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Dr. Michael Haft spoke at ML Week 2023, Berlin
Xplain Data CEO @Machine Learning Week 2023
ML Week 2023 Talk: From Correlation to Causation – The Hunt for the Culprit Behind Machine Failures
Causal Discovery takes us a step further than Predictive Maintenance by delving into the causes of machine failures. Our CEO discussed the unique challenges of identifying confounding factors in complex data sets and how to address them in a practical way. During the presentation, Dr. Haft presented project results such as the continuous monitoring of a cylinder head production line by our Causal DiscoveryBot, which alerts the QM team to emerging factors contributing to quality defects. The talk also included a live demonstration of causal discovery algorithms on an AWS instance hosting production data, and showed how to interactively explore the results.
The conference emphasizes the practical use of analytics such as predictive and prescriptive models, machine and deep learning, data and text mining, generative AI in industries. It covers analytical, technical, business and organizational aspects at strategic and operational levels. Sessions offer a variety of formats: Case studies, deep dives, keynotes and panels.
ML Week 2023 acts as a regional hub for the European data science community. It promotes the exchange of success stories and insights among experienced data scientists, analytics managers and AI innovators. They represent diverse industries, including manufacturing, logistics, marketing, e-commerce, finance, and more.
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Press Release: Xplain Data debuts autonomous Causal DiscoveryBot @SPS 2023
Causal DiscoveryBot: AI Innovation at SPS 2023
Xplain Data introduces its autonomous Causal DiscoveryBot at SPS – smart production solutions 2023 in Nuremberg, Germany, November 14–16
Munich, October 10, 2023 – This groundbreaking bot works in the background of a plant and continuously analyzes the data from all production steps. The goal is to find the few factors that have a causal influence on a target variable. E.g., factors that endanger the quality of the workpieces produced. If significant new causes emerge, a message is sent to the relevant stakeholders. Visit Hall 6, Booth 241F in the “Automation meets IT” area. There you will have the opportunity to learn more about the comprehensive range of Causal AI technologies from Xplain Data for production optimization.
According to a Deloitte study, the manufacturing industry generates approximately 1,812 petabytes of data per year. More than any other industry. This observational data, stored in various databases (SCADA, MES, CRM…) along the supply chain, is the basis for Xplain Causal AI solutions. These identify the factors that have a causal impact on value creation (e.g., root causes for n.i.o. products).
“Post-mortem analysis is often of limited use. With the introduction of the Causal DiscoveryBot, we are for the first time able to integrate our processes directly into the ongoing operation of a plant. This makes the bot an integral part of a production line and gives our customers the assurance that they will be alerted quickly to newly occurring failure factors,” says Dr. Michael Haft, CEO of Xplain Data.
By identifying cause and effect in Historian data, Xplain Data customers are able to:
- make more informed decisions about corrective actions in the manufacturing process,
- significantly improve the quality and sustainability of manufacturing activities, and thereby
- sustainably reduce production costs.
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Nov 14–16, 2023: Xplain Data @SPS Smart Production Solutions, Nuremberg
In November 2023, Nuremberg hosted the industry event of the year: SPS – Smart Production Solutions.
‘Bringing Automation to Life’: Xplain Data @SPS 2023 – where automation dreams come true
Xplain Data exhibited at SPS 2023 in Hall 6-241F at the “Automation meets IT” collaborative stand.
We showed the latest version of our groundbreaking Causal AI solution for production optimization through Root Cause Analysis (RCA) – without 5Why, Ishikawa (or similar) methods – and demonstrated how Causal AI can revolutionize RCA and find the needle in your data haystack.
On Tuesday, November 14, from 3:40–4:00 pm, our CEO Dr. Michael Haft took the stage in Forum Hall 8:
TALK: Understanding Cause & Effect – The Hunt for the “Whys” and “Hows” in your Process Data Historians!
Typically, proving cause & effect requires controlled interventions: an experiment in engineering. Recently, AI methods have been developed that can infer cause & effect from observational data alone, called “Causal AI”. In the case of industrial manufacturing processes, the observational data – a real treasure – is contained in the various existing databases along the product supply chain – ERP, SCADA, MES, CRM. A good starting point for manufacturing applications is, of course, SCADA.
But: which of all these SCADA parameters have a causal effect on yield (e.g., root causes for n.i.o. products)? We presented challenges, solutions & project examples where these new methods are in daily use.
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Press Release: Xplain ObjectAnalytics Database now running on Raspberry Pi 4 8 GB
Xplain Data’s ObjectAnalytics Database demonstrates scalability from the smallest computers to servers with 256 cores and TB of RAM
Xplain Data is excited to share a significant development that underscores software efficiency, particularly in its performance on compact hardware. The latest release, version 2.1 of ObjectAnalytics DB, brings forth an exciting capability: seamless operation on a Raspberry Pi 4 with 8 GB RAM. This accomplishment solidifies Xplain Data’s commitment to offering comprehensive solutions across the entire spectrum. From the compact Raspberry Pi to servers boasting 128 cores or more, the ObjectAnalytics database scales impeccably. This adaptability is exemplified in scenarios like the healthcare sector. There it efficiently analyzes billions of prescriptions and diagnostics in mere seconds, delving into causality.
ObjectAnalytics: Aggregating Data for Comprehensive Insights
Central to this achievement is Xplain Data’s patented ObjectAnalytics paradigm. This database revolutionizes data integration by presenting diverse information sources within an object-centric framework. This holistic approach allows for a comprehensive view of business objects, providing customers with an encompassing 360° understanding of their data. Furthermore, ObjectAnalytics facilitates scalable data integration and seamless AI integration, rendering it a versatile asset for various applications.
The versatility of the ObjectAnalytics database extends to its capacity to aggregate data from disparate sources, interlinking them as sub-objects, and even recursively. Notably, the recent Raspberry Pi experiment underscores the scalability of Xplain Data’s technology. This prowess spans an impressive gamut of platforms, encompassing both Raspberry Pi devices and high-performance clusters.
Empowering AI Integration in Varied Environments
A particularly striking outcome of this development is the newfound potential to incorporate intelligent AI modules within an array of environments. This includes embedding them within mobile devices, automotive control units, and even sprawling server farms like those situated in Iceland.
In essence, Xplain Data’s groundbreaking progress with the ObjectAnalytics DB version 2.1 not only amplifies software efficiency on compact hardware like the Raspberry Pi 4. It also extends the horizons for intelligent AI integration in diverse settings.
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Xplain Data recognized in AI Startup Landscape 2023
Xplain Data announces its inclusion in the prestigious AI Startup Landscape 2023, a comprehensive analysis of Germany’s thriving AI startup scene.
Published annually by the appliedAI Institute for Europe, the report sheds light on the country’s AI startup ecosystem, driving the adoption of AI technologies and fostering new partnerships between startups and established companies.
Being recognized in the “Frameworks” category is a testament to Xplain Data’s commitment and success in the Causal AI space. It is noteworthy that only a handful of German AI startups, just 11, focus on building frameworks, and even fewer, just 10, focus on developing infrastructures. Areas, that “are known to have the potential for high revenue”, according to the appliedAI Institute for Europe.
appliedAI Institute for Europe worked with industry giants such as NVIDIA and Intel, as well as twelve renowned venture capital firms, including Lakestar, Earlybird Capital, UVC Partners, and others. They analyzed over 1000 AI startups. The goal: to identify the most promising for the current landscape.
Breaking Ground in Causal AI: Xplain Data’s Achievement in the Frameworks Category
This recognition signifies Xplain Data’s commitment to advancing the frontiers of Causal AI and developing cutting-edge frameworks in the field. It underscores the company’s pivotal role in driving innovation and pushing the boundaries of AI applications in Germany and beyond. Xplain Data’s inclusion in the German and European AI Startup Landscape 2023 reinforces its position as a relevant player in the thriving AI startup ecosystem.
“We are thrilled and honored to be recognized in the Frameworks category of the German AI Startup Landscape 2023,” said Dr. Michael Haft, founder and CEO of Xplain Data. “This recognition validates our team’s hard work and unwavering commitment to pushing the boundaries of Causal AI, creating frameworks that drive innovation and create tangible value for our customers.”
Xplain Data looks forward to using this recognition to forge new partnerships and collaborate with more industry leaders. And of course make a significant contribution to the advancement of applied AI in Germany and beyond. As the AI landscape continues to rapidly evolve, Xplain Data remains committed to pioneering groundbreaking solutions that have a transformative impact across industries.
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Xplain Data: Recognized Causal AI vendor in Gartner AI Hype Cycle 2023
July 2023: Release of highly anticipated Gartner AI Hype Cycle and report
Xplain Data is proud to announce its prestigious inclusion in the list of relevant causal AI vendors in the Gartner AI Hype Cycle. This solidifies its position as a player in the rapidly evolving AI landscape.
The Gartner AI Hype Cycle has long been a trusted source for identifying industry trends. The 2023 edition offers insights into some notable developments. Foundation Models & Generative AI have reached their peak, demonstrating outstanding progress and receiving widespread acclaim from the community.
AI Hype Cycle: Causal AI on the rise
However, one of the real standouts in this year’s report is Causal AI. Over the past year, Causal AI (Causal Discovery) has experienced an unprecedented surge in importance, exceeding all expectations. In the 2022 edition, Gartner predicted a 5–10-year timeline for Causal AI’s adoption in the mainstream market. And the latest edition brings welcome news – Gartner now highlights Causal AI as a key area to watch over the next 2–5 years:
“Early adoption of these innovations will lead to significant competitive advantages and ease the problems associated with using AI models within business processes.” – Gartner’s 2023 AI report.
Business Impact (– Gartner’s 2023 AI hype cycle and report)
Causal AI leads to:
- Greater decision augmentation & autonomy in AI systems by estimating intervention effects.
- Greater efficiency by adding domain knowledge to bootstrap AI models with smaller datasets.
- Better explainability by capturing easy-to-interpret cause-and-effect relationships.
- More robustness & adaptability by leveraging causal relationships that remain valid in changing environments.
- The ability to extract causal knowledge with less costly & time-consuming experiments.
- Reduced bias in AI systems by making causal links more explicit.
Xplain Data is pleased to be recognized as a leader in the Causal AI space. This recognition reinforces our commitment to delivering innovative solutions to our customers. We believe that Causal AI has the potential to revolutionize the AI landscape and deliver transformative benefits to businesses across industries.
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Xplain Data to Power AI with Causal Insights @ Siemens AI with Purpose Summit 2023
Xplain Data, a leading provider of Causal AI solutions, successfully participated in the Siemens AI with Purpose Summit 2023. The event took place in Munich on May 16–17. It brought together renowned experts and innovators from various fields to jointly master industrial AI.
Prof. Volker Tresp and Dr. Michael Haft, CEO of Xplain Data, took the stage during the summit not only to discuss the general importance of Causal AI. The focus was on its practical application in manufacturing by identifying cause-and-effect relationships in production data. Their informative session highlighted the importance of integrating causality as a fundamental part of AI systems. It emphasized the need to focus on “why” rather than just predicting “what” will happen. And assess the potential for change.
From smart factories to predictive maintenance and intelligent quality control, AI is revolutionizing every aspect of industrial operations. But the real question is: How can we successfully integrate AI into these environments and unlock its full potential?
This was discussed by 250+ of Siemens’ most trusted partners & peers – leading minds in AI – during 1.5 inspiring days in the Munich Urban Colab.
Xplain Data is proud to have contributed to the knowledge exchange and collaborative spirit of this renowned gathering – well placed in the Future of AI Summit track, where the hottest AI trends were discussed.
Watch the video recap to get a quick glimpse of the spirit of the engaging session led by Prof. Volker Tresp and Dr. Michael Haft: Video recap
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Press Release: 84% less Scrap in Cylinder Head Production due to Causal AI
84% less Scrap and Rework: Cost Reduction in Cylinder Head Production thanks to Causal AI Algorithms from Munich-based startup
Munich, December 1, 2022 – By using Causal AI algorithms from Munich-based startup Xplain Data GmbH, Schwäbische Werkzeugmaschinen GmbH (SW) has succeeded in reducing scrap and rework rates by 84% in the production of cylinder head covers. Xplain Data provides a causal discovery process that uncovers previously unknown causes of defects and optimizes production processes.
The supply chain around Schabmüller Automobiltechnik GmbH, a Bavarian automotive supplier with a focus on series production of high-precision components, benefited from this. By eliminating the identified causes, the production defect rate in cylinder head cover production was reduced to 1.6%.
It turned out that leak testing in the manufacturing process is strongly dependent on the component temperature after the washing process: A short waiting time between these steps is crucial. Furthermore, significant quality differences were found in the casting nests of the supplied blanks. Due to the complexity of the plant, this influencing factor was previously difficult to test.
“The manufacturing industry wants to use its extensive production data to understand cause-and-effect relationships and solve problems. SW took the plunge and implemented our AI algorithms – and won,” said Dr. Michael Haft, CEO Xplain Data GmbH.
Schabmüller plans to further automate the algorithms used:
- AI-Bot: Counteracting new sources of error early on through ongoing monitoring.
- Integration of additional data along the life cycle of a workpiece into the analysis: from material composition during casting to further processing and final assembly at one of the leading German automotive manufacturers.
The goal is a 360° perspective. Full SW case study.
Causal relationships as a critical factor in process optimization
Since 2015, the Munich-based startup Xplain Data has been developing algorithms that can detect causal relationships in “real world data” (causal discovery) and use them for intelligent interventions. For example, causes of errors can be eliminated and desired effects can be generated. The prerequisite for this is an object-centric consolidation of all company data. This is accomplished by Xplain Data’s Object Analytics Database.
Xplain Data achieved initial success with “Root Cause Analysis” in the healthcare sector. Now, in collaboration with SW, Xplain Data has been able to transfer analysis methods from the healthcare sector to production data for the first time: the aim here is to identify and eliminate the causes of production errors and to perform predictive maintenance on machines. The first real-world application in industry turned out to be a great success, which is now being followed up.
Also applicable in industry: AI techniques from healthcare
Using the patented Xplain Data methods, it was possible to distinguish potential causal relationships from ordinary correlations in the healthcare environment, based on extensive patient data. For example, it identified unwanted side effects of medications, uncovered risks for future illnesses, or assisted insurance providers with CRM, rate design, and quality of life offers.
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Gartner includes Causal AI in Hype Cycles 2022 for Emerging Technologies & AI
The Gartner AI Hype Cycle™ 2022 outlines the key innovations in AI technology and techniques that go beyond the everyday AI already deployed to add intelligence to currently static business applications, devices, and productivity tools.
The Hype Cycle for Emerging Technologies is unique among Gartner Hype Cycles because it identifies key insights from more than 2,000 technologies and applied frameworks that Gartner profiles each year into a succinct set of “must-know” emerging technologies and trends. These technologies and trends have the potential to deliver a high degree of competitive advantage over the next two to 10 years.
According to Gartner, Causal AI includes different techniques that help uncover causal relationships to improve decision-making. The prediction is, it will (probably) take 5 to 10 years for Causal AI to reach mainstream adoption, but the business benefits are expected to be high. It enables new ways of performing horizontal or vertical processes that will result in significantly increased revenue or cost savings for an enterprise.
Gartner identifies a number of benefits of Causal AI, including:
- Efficiencies from adding domain-knowledge to bootstrap causal AI models with smaller datasets
- Greater decision augmentation and autonomy in AI systems
- Better explainability by capturing easy-to-interpret cause-and-effect relationships
- More robustness and adaptability by leveraging causal relationships that remain valid in changing environments
- Reduced bias in AI systems by making causal links more explicit
Causal AI is a key enabler of the next wave of AI, where AI moves toward greater decision automation, autonomy, robustness, and common sense.
– Gartner
Read more from Gartner on AI, Causal AI and the Hype Cycles 2022:
Press Release: Gartner Identifies Key Emerging Technologies 2022
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Dr. Michael Haft on the Industrial AI Podcast
Our CEO in conversation with AI expert Peter Seeberg on
- understanding causal relationships
- whether and how they can be proven using observational data
- what ObjectAnalytics is all about
- what it all means in terms of ML and AI
- how Causality can be applied in an industrial environment
… and whether a lot of information helps a lot – or not.
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Notification: US Patent Allocation
The US patent for “METHOD AND APPARATUS FOR PROCESSING A QUERY ON A PLURALITY OF OBJECT INSTANCES” was issued to Xplain Data GmbH by the United States Patent and Trademark Office on December 7, 2021. (Search for PATENT NUMBER 11194811).
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Notification: EU Patent Allocation
A European patent (UK & Germany) for “METHOD AND APPARATUS FOR PROCESSING A QUERY ON A PLURALITY OF OBJECT INSTANCES” was issued to Xplain Data GmbH by the European Patent Office on October 14, 2021.
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Press Release: Health Insurer AOK Nordost relies on Xplain Data Technology
AOK Nordost, Gesundheitsforen and Xplain Data launch innovative partnership
To perform a previously impossible cross-sectoral analysis of disease progression, German health insurance company AOK Nordost relies on Xplain Data technology to understand causal relationships. Combined with the technical and professional expertise of the Gesundheitsforen Leipzig, a leading provider of business intelligence and analytics solutions in the healthcare industry, the result is a unique offering for the German health insurance market. The new database technology places the patient at the center of the analysis and enables a holistic view of the patient. In this way, potential cause-and-effect relationships can be identified – the basis for intelligent control in the healthcare system.