Latest news with #industrialAI
Forbes
2 days ago
- Business
- Forbes
IFS Puts Hard Hats On ‘Industrial AI' Workforce
Engineer holding helmet on site Road construction For the development of modern transportation ... More systems, Technician worker hold hard hat safety first Product differentiation proliferates across all sectors. We segment our goods and services into different classes, the business world likes to differentiate across different industry verticals, then there is a basic clarification (in most markets) as to whether a commercial offering of any kind should be demarcated as either a business or a consumer product. In the world of artificial intelligence, we separate out product differentiation in a more applied way by virtue of what any individual piece of AI is supposed to do for us. There's reactive AI that responds to stimuli in as close to real-time as it can, predictive AI which (as it sounds) aims to create predictions based on pattern recognition from past events… and of course there is generative AI, which is quietly taking over a new role as a generator/creator in various roles. Forging Industrial AI While the cloud services provision sector is keen to differentiate between general-purpose clouds and industry-specific services (such as a marketing cloud for instance), the next iteration of AI reflects this trend could see it named after the environment that it works in, rather than the precise job or tasks it performs. That's if enterprise cloud services platform company IFS has its way; the company has set about attempting to coin the term 'industrial AI' as a homage to its enterprise resource management and field service management heritage, along with its essentially hands-on approach to working with mission-critical assets and processes. With ambitions to further its stance in this space, IFS has now acquired Silicon Valley-headquartered agentic AI specialist theLoops. IFS insists that its acquisition of theLoops marks a shift from enterprise software that tracks work, to building and deploying functional software that actually performs workplace jobs, tasks and wider workflows within the context of real-world operational enterprises. According to Mark Moffat, Chief Executive Officer, IFS, this is not a digital buddy, virtual assistant or some form of quirky workplace chatbot with a smart robotic process automation underbelly. This, he says, is an enterprise-grade agentic AI platform with security and governance, designed to deliver what he pledges to be 'radical productivity improvements and measurable return on investment' in applied industrial settings. With a promise to deliver what he calls 'real value capture' in working industrial environments, Moffat says that the addition of this newly acquired agentic AI firm will help create digital teammates who can perform real work functions across industries spanning manufacturing, energy, utilities, construction and engineering, as well as aerospace and defense. 'These industrial agentic AI 'workers' understand their business responsibilities from day one; they are agents that speak the industrial language appropriate to the industry they are located in. They can follow rules and operate securely in their workflows,' said Moffat. Semantic Environmental Awareness TheLoops acquisition will enable IFS to create and deliver multi-agent environments where autonomous AI agents are both composable and governed. They will be 'semantically aware of their operating environment' and so eminently suited to being applied in regulated, asset-intensive sectors. The industrial AI agents IFS now envisages (IFS would say enabling) will be able to participate in real enterprise workflows side-by-side with humans; adhere to customer-defined security, data access, and compliance standards; and outwardly collaborate with specialized agents across integrated domains. 'AI is disrupting our world, but nowhere is the potential impact more pronounced than in the Industrial setting. IFS's acquisition of theLoops is addressing a huge opportunity for asset-intensive and service-obsessed industries, where agentic decision making will enable organizations to rethink their digital workforce, so they can improve the way they serve their own customers. IFS is well-positioned to lead this shift in each of the industries it serves - bringing intelligent automation that's not just smart, but situationally aware and operationally impactful,' suggested Aly Pinder, research vice president for aftermarket services strategies, IDC Somya Kapoor, CEO of theLoops (who now retains her position inside of IFS) has spoken of what she thinks could be a new era of automation, where intelligent agents never rest - continuously scanning for improvements, making and executing critical streamline operations, increase capacity and free up skilled workers for higher-value tasks. According to Kapoor, this goes beyond traditional AI's pattern recognition and prediction to enable truly autonomous decision-making and execution. 'We're creating autonomous AI agents that understand industrial complexity. They identify required work, determine execution pathways and implement software services with rigorous standards for security, ethics and scale.. This isn't experimental; it's transformational,' said Kapoor. Industrial AI, Competitive Analysis IFS has championed the industrial AI tagline or slogan for a while now, but can the firm lay claim to any substantial degree of genuine leadership in this domain? Certainly, the company is structured not just as an ERP provider, but also as specialist in field service management, enterprise asset management and human capital management with manufacturing project management functions manifesting themselves across its platform toolset. That rather leads the firm not just to be a systems of record and systems of transaction company (how the industry normally categorizes a core ERP player), but also a systems of operations specialist for real world factory floors. But despite that bedrock focus, IFS isn't the only IT vendor known for industrial software technologies. You don't have to look far to find German-born industrial automation company Siemens. My first job was with Siemens in its radar division, but the company is mentioned here for its MindSphere internet of things AI platform and its pedigree in digital twins, smart factories. Staying in Germany, Schneider Electric probably wouldn't be offended to be referred to as a smart buildings and industrial automation company. Its AI services are aligned towards tasks including energy management and smart manufacturing. The firm is also known for its EcoStruxure platform, an open and interoperable technology architecture built to bring digitized services energy management and operation control systems. It was a decade ago that stories focused on General Electric (GE Digital) and its Predix platform as detailed here. To reiterate the capabilities found pat GE, works to help connect industrial assets across the internet of things and the wider world of machinery and equipment to to the cloud and to each other. It does this for asset performance management and operations optimization, not dissimilar to the focus currently seen at IFS. Other contenders in this market include ABB, known for its process optimization technologies and AI-powered robotics. Bosch doesn't just make handdrills and drillbits, it also makes industrial automation and mobility products. More centalized on industrial AI for tasks like redictive maintenance and asset performance are Uptake, and SparkCognition. As an additional note, remember that IBM has a specific iteration of Watson AI for industrial applications; Microsoft has Azure AI for industrial analytics (working in partnership with companies including Honeywell); Google Cloud has services dedicated to AI-enhanced vision-based inspection in industrial settings and AWS has its Lookout for Equipment and Lookout for Vision brands for automated quality inspection. A New Industrial Agent? Has IFS actually come forward with a new class of agentic AI here? The naysayer might say it is potentially possible to custom-align any form of agentic service into any job or task, that's why agentic services are always described as essentially non-deterministic, capable of drawing data resources from any source (both small and large language models) and remain adaptable on an ongoing basis. Conversely, a more positive view might suggest yes, this is industrial AI because there are ERP and field service management companies aplenty, but few have defined their target verticals to be as narrowly dedicated as IFS has (key sectors for the company are named above, but think aerospace and engineering from first principles), which is a focus that IFS has 'positively restricted' its focus to for many years. To apply a standardized agentic AI service that works in a call center and stick it on an oil rig operations center is not always sensible; it takes more than a crowbar (virtual or real) to make that happen. This is not chatbots and copilots, this is a case of AI agents with yellow hard hats.
Forbes
10-06-2025
- Business
- Forbes
Unpacking The Link Between Data And Industrial AI
Heiko Claussen is Chief Technologist at Emerson's Aspen Technology business, leading its AI research and technology strategy. In most conversations, data and AI are inextricably linked. The narrative tends to be that organizations are not using AI well if they don't have quality data from the field feeding into AI models. While this may be true in many industries and organizational contexts, it's far from universal. In fact, in industrial contexts, purpose-built industrial AI can be highly effective based solely on first principles and simulation models. But while purpose-built industrial AI designed for industries like oil and gas as well as chemicals may not require data from the field, that's not to say data is unimportant. Ensuring engineers, plant personnel and IT/OT leaders have access to centrally managed, contextual data is critical to deriving data-driven insights across the business. With the proliferation of modern digital tools and sensors, industries are collecting more data than ever before. As the volume of industrial data continues to grow, data management tools are fast becoming a key technology, helping companies sort through vast pools of data to better understand what information they have—all of which translates to higher levels of operational excellence. Forward-looking industrial organizations understand the growing data landscape, how well-managed data can improve their business and even the nuanced relationship between data and industrial AI. For decades, companies have collected data in one form or another, beginning with paper records collected by hand for sensors and integrated systems. What was once a trickle of information has become a deluge in recent years. Estimates from the World Economic Forum suggest that industries generated as much as 130 zettabytes of data in 2023. Despite collecting huge amounts of data, surveys show relatively little data actually gets used. A Forrester Research study found that companies use just 12% of their data for analysis and less than 30% of companies say they can translate the data into action. One reason why data is not used as well as it could be can be traced back to the fact that data is often highly siloed. In industrial environments, it's collected and stored across different plants and systems with different formats, tags and protocols, making it difficult to coordinate the use effectively. One way organizations are mitigating siloes and simplifying the process of aggregating vast amounts of data is through agnostic centralized data management tools with fewer connectors and extractors across various systems. With the ability to be hosted anywhere, ingest data from anything and feed data into anything, such tools help make data available to users when, where and how they need it. But advanced data management tools' arguably most important capability is adding crucial context to raw data. Context offers companies a more holistic understanding of their data—where it comes from, what sensors and units of measure it represents, when it was collected and more. Today's industrial use cases require not just a stream of binary values, but also a lot of metadata, such as specifications, plans, schedules and work orders. All this data needs to be stored in the right context for it to be useful. Data contextualization can be automated with an industrial data management tool. Because these tools inherently know the data's location and associated context, industrial organizations can eliminate the challenging, manual task of moving data and applying context afterward. By eliminating this step, industrial organizations can more quickly achieve efficiencies and fuel digital transformation initiatives that help them overcome increasingly complex technological and environmental challenges. AI is one example of a digital transformation initiative that has risen to the top of organizations' priority lists. Purpose-built industrial AI solutions can be applied to a host of operational challenges, from monitoring and analyzing emissions to automating mundane tasks to aiding engineers' decision making. As previously described, much of this can be achieved without field data, thanks to first principles, simulation models and deep industry domain expertise that enables guardrails that keep AI results safely within real-world constraints. In fact, an asset shouldn't be deployed in the field without such an approach. Industrial organizations eager to begin reaping the benefits of AI often don't have the luxury of waiting as field data is collected over time before training a model and controlling the asset. Despite industrial AI's effectiveness as is, asset data can help take AI results to the next level. By refining an AI model with in-context operating data from an industrial data management platform, organizations are beginning to close the simulation-reality gap and help predict future outcomes based on past observations. For instance, AI applications for predictive maintenance can be improved by identifying asset abnormalities and building models based on both normal and abnormal operations. The powerful combination of industrial AI and the right data from the field can effectively supercharge companies' operational excellence initiatives, making it easier to create and sustain meaningful AI models that give companies a significant competitive edge. Whether it's helping to optimize existing processes, identifying ways to increase efficiency or informing the design of new processes, there should be little doubt that data is—and will continue to be—a critical resource for industry. While data's benefits for AI and other business improvements may be known, the question that lies ahead is how companies will make the transition to more effective industrial data management tools. Replacing a web of siloed, global data connections that were likely set up by different personnel years earlier can feel overwhelming. Among other challenges, companies must maintain business continuity and avoid disrupting established processes that require traceable product information, test results and more. By initially configuring a new data management tool as a real-time backup or redundant control system alongside current connectivity architecture, then removing previous connectivity incrementally after a validation period, industrial organizations can safely implement centralized data management tools. Ultimately, this approach is faster, easier and more secure than setting up many peer-to-peer connections With the right data management tool and strategy in place, organizations can effectively use their data, enhance AI applications and fuel ever-more advanced use cases to help them remain ahead in an uncertain macro-environment. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Associated Press
09-06-2025
- Business
- Associated Press
XMPro Named as a Sample Vendor in Gartner® Report: Customer Trust Is a Critical Barrier to Agentic AI Adoption
Leading Industrial AI Platform Recognized for Reliability Tools that Build Trust in Agentic AI Systems 'Our hybrid approach to agentic AI, combining advanced LLM models with proven industrial AI techniques, directly addresses the trust challenges that organizations face when deploying autonomous systems'— Pieter Van Schalkwyk DALLAS, TX, UNITED STATES, June 9, 2025 / / -- XMPro, a leading provider of industrial AI and intelligent business operations solutions for asset-intensive industries, today announced it has been recognized as a Sample Vendor in the Gartner® report, 'Emerging Tech: Customer Trust Is a Critical Barrier to Agentic AI Adoption' published on June 2, 2025.* XMPro was recognized as a Sample Vendor. According to the report, 'Gartner research reveals that a top inhibitor to agentic AI adoption is a lack of customer trust. Vendors that offer observability tools, reliability controls and explainability features will emerge as near-term winners in the agentic AI market.' According to the report: 'Many interviewed providers did not use GenAI for task execution; rather, they used classical AI technologies, such as ML models and rule-based logic. For example, using LM for reasoning but ML for task execution. This hybrid approach to agentic AI embeds transparency and reliability into task automation.' 'We believe, our inclusion in this Gartner report validates our focus on building trusted industrial AI solutions that deliver real operational value,' said Pieter van Schalkwyk, CEO of XMPro. 'Our hybrid approach to agentic AI, combining advanced language models with proven industrial AI techniques, directly addresses the trust challenges that organizations face when deploying autonomous systems in critical industrial environments.' Industry Context: The Trust Challenge in Agentic AI The Gartner report reveals critical insights about agentic AI adoption: • 'The study interviewed 20 agentic AI providers, of which a majority (more than 50%) cited customer trust as a top challenge to driving customer adoption.' • 'By 2028, less than 10% of agentic AI deployments will operate unsupervised, up from less than 1% in 2025' • 'the market is currently favoring semisupervised, simple task automation over more autonomous, complex agentic task automation.' XMPro's Approach to Trusted Industrial Agentic AI Through Composite AI: XMPro's intelligent business operations solution (iBOS) addresses the trust challenges identified in the Gartner report through its unique Composite AI framework that combines six complementary AI methodologies: •Truth-Grounded Architecture: Every AI recommendation passes through multiple validation layers including first-principles validation, symbolic rule enforcement, evidentiary reasoning, and multi-agent cross-checks, ensuring decisions are safe, explainable, and trusted •Hybrid AI Integration: Combines Generative AI for insight synthesis with Symbolic AI for rules-based intelligence, First Principles Models for physics-based validation, and Causal AI for root-cause discovery, addressing the report's emphasis on combining language models with classical AI •Agentic AI with Bounded Autonomy: Orchestrates coordinated teams of specialized AI agents that observe, reason, plan, and act, with configurable human oversight and flexible autonomy controls based on operational risk tolerance •Real-Time Industrial Observability: Built-in monitoring, tracing, and alerting mechanisms designed specifically for mission-critical industrial environments where equipment failure or safety incidents have major consequences •Domain-Specific Industrial Intelligence: Pre-configured for aerospace & defense, manufacturing, mining, oil & gas, utilities, and other asset-intensive industries, translating domain expertise into formal logic structures with clear, auditable reasoning chains •Multi-Agent Collaboration with Guardrails: Supports collaborative multi-agent teams that work together on complex industrial problems while enforcing safety protocols and operational constraints Organizations can learn more about XMPro's industrial agentic AI solutions at *Source: Gartner, 'Emerging Tech: Customer Trust Is a Critical Barrier to Agentic AI Adoption,' Danielle Casey, Alfredo Ramirez IV, Anushree Verma, Akhil Singh, Aakanksha Bansal, 2 June 2025 Gartner Disclaimer: 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. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. About XMPro XMPro is a leading industrial AI company that helps enterprises achieve measurable business outcomes through intelligent operations. The company's Multi-Agent Generative Systems (MAGS) platform combines industrial digital twins with trusted agentic AI to optimize operations, improve asset performance, and enhance decision-making in complex industrial environments. XMPro serves Global 2000 companies in manufacturing, mining, energy, utilities, and other asset-intensive industries, helping them navigate their industrial AI transformation journey with confidence. Wouter Beneke XMPro email us here Visit us on social media: LinkedIn Facebook YouTube X Legal Disclaimer: EIN Presswire provides this news content 'as is' without warranty of any kind. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
National Post
05-06-2025
- Business
- National Post
Southwest Power Pool (SPP) Partners with Hitachi to Develop Advanced AI Solution for Critical Power Transmission Reliability and Flexibility Challenges
Article content End-to-end use of industrial AI and advanced computing infrastructure to help significantly speed up safe integration and use of additional energy sources supporting central U.S. power grids. Article content Initial partnership objectives are to reduce generator interconnection analysis times by 80% while facilitating more informed decision-making. Objective to be achieved via advanced AI solutions from Hitachi, powered by NVIDIA's accelerated computing platform. Integrated solution comprised of multiple Hitachi capabilities including an AI-based power simulation algorithm, Hitachi-iQ-accelerated calculations, augmented simulation modelling, predictive analytics, as well as design and engineering services. Wide-ranging impacts to address imminent U.S. energy infrastructure needs by increasing planning processes' speed and efficiency; enabling SPP to better resolve energy capacity shortages, increase grid reliability, and improve emergency response capabilities. Subsequent partnership objectives to address alternative energy integration challenges and power transmission constraints. Article content SANTA CLARA, Calif. & LITTLE ROCK, Ark. — Hitachi, Ltd. (TSE:6501, 'Hitachi') and Southwest Power Pool, Inc. (SPP) today announced a strategic partnership to solve critical and imminent problems slowing the modernization of U.S. energy infrastructure. The partnership will produce an integrated AI-based solution that accelerates generator interconnection (GI) by reducing study analysis times by 80% while also informing faster, higher-quality decision-making by GI customers. This will markedly improve SPP's ability to facilitate the addition of its 14-state region's generating capacity to keep pace with increasing demand for electricity. Article content U.S. energy demands are rising by 2 to 3 percent annually *1, driven by data center growth, expanding manufacturing, and electrification. Data centers alone are projected to consume up to 12 percent *2 of U.S. electricity by 2028, versus 4.4 percent in 2023. Such trends drive an alarming supply and demand gap as generating capacity margins in the SPP footprint could decline from 24 percent in 2020 to just 5 percent in 2029 unless an intervention occurs. Article content That intervention starts with end-to-end technical innovation, first at the point of generator interconnection. Currently, the U.S. generates 1.28 terawatts of power *3. More than twice that generated amount waits in a queue as unusable backlog caused by today's grid interconnect process. The long wait times are due to exhaustive, time-consuming analysis and simulation studies required to ensure that new energy source introductions don't compromise existing grid reliability, stability, or performance. Article content To address this gap, the three organizations will combine their industry and technical expertise. The partnership draws on multiple Hitachi competencies for a complete solution: Method's design services; GlobalLogic's software engineering services; Hitachi Energy's energy portfolio management asset modeling solutions; Hitachi R&D's AI-based energy grid algorithm; and Hitachi Vantara's integrated storage and compute platform Hitachi iQ, built on NVIDIA accelerated computing, networking, and AI software. Article content As the regional transmission organization (RTO) framing the project, SPP will guide the integration of these technical solutions and services, leveraging its deep expertise in energy grid optimization. As a reliability coordinator prioritizing operational and customer experience improvements, SPP's input will also ensure the project outcomes align with industry-wide requirements and regulations. 'Our nation's demand for electricity has risen sharply in recent years following a long period of slow growth. Our industry has struggled to keep up with this sudden and significant shift,' said SPP President and CEO Lanny Nickell. 'There are a lot of would-be power producers out there waiting to connect to the grid, but yesterday's systems and technology haven't been sufficient to enable us to bring incremental capacity online fast enough. It's time to fix that, and SPP is proud to work with Hitachi and NVIDIA, two AI industry leaders who have the means to help realize a vision of a better energy future for our nation.' Article content The integrated solution is an industrial AI system differentiated by its advanced proprietary AI algorithms and high performance enabled by Hitachi iQ's enterprise AI solution stack which sit at its core. Ultimately, dynamic AI-driven technologies will be applied to various study areas, such as: Article content The partnership with Hitachi and NVIDIA runs parallel to other improvements underway at SPP, including a from-the-ground-up reimagining of its transmission planning processes to align them with current and future industry needs. Together, these technological and process innovations are expected to set high-water marks in the electricity industry for generator interconnection, mid- and long-term planning, long-term forecast accuracy, analysis and deployment of additional grid-enhancing technologies, and more. Article content 'This initiative is about reimagining the electricity production and distribution process through the lens of modern AI technology,' said Frank Antonysamy, Chief Growth Officer, Hitachi Digital. 'Real-time data access is needed to create truly realistic scenarios caused by new generator introductions. The AI solution we're all developing will provide that data, among other advantages. SPP can then make significantly quicker, better-informed decisions that will increase overall ROI while better serving the nation's population with accessible power. We're proud to be a part of this important three-way collaboration addressing such a crucial problem.' Article content 'Interconnection process acceleration is critical to meet the unprecedented demand on our grid,' said Marc Spieler, Senior Managing Director of the Global Energy Industry at NVIDIA. 'Using advanced NVIDIA accelerated computing and AI, Hitachi and SPP are helping speed interconnection studies to bring essential infrastructure online faster.' Article content The project's phase one milestones are expected to be completed by winter 2025/26. They include initial systems acceleration, data management processes optimization, and the introduction of AI-augmented simulation modeling among other goals. Article content *1: *2: *3: About SPP Southwest Power Pool, Inc. ( is a regional transmission organization: a not-for-profit corporation mandated by the Federal Energy Regulatory Commission to ensure reliable supplies of power, adequate transmission infrastructure and competitive wholesale electricity prices on behalf of its members in 14 states. SPP ensures electric reliability across a region spanning parts of the central and western U.S., provides energy services on a contract basis to customers in both the Eastern and Western Interconnections, and is expanding its RTO and developing a day-ahead energy market in the west. The company's headquarters are in Little Rock, Arkansas. Article content About Hitachi, Ltd. Article content Article content Article content Contacts Article content Media Contacts Article content Article content Heather Ailara Article content Article content PR Manager Article content Article content Hitachi Digital (NA and EU) Article content Article content +1-973-567-6040 Article content Article content heather@ Article content Article content
Trade Arabia
26-05-2025
- Business
- Trade Arabia
IFS to showcase AI innovations for utility asset management
Leading enterprise software company IFS will be showcasing its latest industrial artificial intelligence (AI) capabilities for asset management at the World Utilities Congress which kicks off tomorrow (May 27) in Abu Dhabi. At the three-day event, IFS will showcase how it is helping utilities modernise aging infrastructure, enhance operational resilience, and accelerate sustainability goals through a fully integrated Asset Lifecycle Management (ALM) approach. A key highlight will be IFS's application of Industrial AI in transforming Asset Management. By shifting from reactive maintenance strategies to predictive and prescriptive approaches, IFS's solutions allow utility companies to proactively manage their assets, reduce downtime, optimize investments and boost long-term performance. Founded in 1983 by five university friends in Sweden, IFS today has grown into a global leader with over 7,000 employees in 80 countries. Earlier this year, IFS launched its latest release, IFS Cloud 25R1, which brings powerful AI capabilities directly to utility professionals, whether in the field or in the control centre. This platform is engineered to address the modern challenges faced by energy, water, and infrastructure organizations, helping them manage assets, people, and performance with unprecedented efficiency. During the three-day event, the Swedish group will also be actively taking part in two key panel discussions. Vijay Jaswal, CTO, APJMEA will be joining the 'The Next Frontier: Revolutionizing Water Infrastructure with Smart Innovation and Collaboration' panel on May 28 and Andrew Sutherland, Senior Vice President at IFS, will join the 'Driving Scale and Responsible Growth in Carbon Markets' panel on May 29. With the region's push towards renewable energy, smart grids, and digital transformation, utilities in the Middle East are under increasing pressure to modernise their operations while ensuring service reliability. IFS is at the forefront of this transition, offering solutions that enable smart asset and workforce management, optimised service delivery, and data-driven decision-making, said its top official. "With IFS Cloud 25R1, we are putting industrial AI to work in the places it matters most: on the grid, in the pipeline, and out in the field. This release empowers utilities to predict, prevent, and perform at a level never before possible," remarked Jaswal. "Additionally, utility organisations across the Middle East are facing immense pressure to balance rapid infrastructure expansion, energy transition goals, and service reliability. IFS is committed to supporting these organisations with an integrated platform that ensures they remain competitive, compliant, and aligned with both national visions and global energy targets," he added. Unlike traditional, fragmented point solutions, IFS unifies critical functions such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Field Service Management (FSM), Artificial Intelligence Platforms (AIP), and Industrial AI into a single composable platform.
