Jim Napolitano on 3DLive, Virtual Twins, and the Future of Immersive Product Design

Paul Danter on Welocalize Opal, AI Translation, and the Future of Global Content

Paul Danter is helping enterprise teams make global content faster, smarter, and more brand accurate. At Welocalize, Paul works on Opal, an AI powered platform designed to improve how companies translate, adapt, validate, and manage multilingual content at scale. Welocalize recently won an AI Excellence Award for its work helping global brands operationalize AI across language workflows. In this episode, Russ and Paul explore how enterprise localization has changed over the last 20 years, from traditional human translation to neural machine translation to AI powered post editing and quality estimation. Paul explains why translation is no longer just about converting words from one language to another. It is about preserving brand voice, tone, terminology, intent, and business impact across global markets. They dive into Opal and how it helps companies process millions of words while using AI to improve translation quality, route content through the right workflow, and determine when human review is still needed. Paul shares why different content types carry different levels of risk, and why a support article, product launch campaign, and brand tagline should not all be treated the same way. The conversation also covers how AI is creating more content than ever before, why enterprises need governance around multilingual workflows, and how continuous feedback from human linguists can help models improve over time. Along the way, Paul discusses content risk, quality scoring, brand sensitive workflows, reinforcement learning, AI governance, long tail languages, global support content, and why the future of multilingual AI may include living systems that monitor performance and automatically improve content across markets. Topics Covered: [00:01] Welcome and intro, Paul Danter and Welocalize’s AI Excellence Award win [00:53] Welocalize’s background in global language services [01:55] Why enterprise teams need language services to unlock global markets [02:20] How Opal operationalizes AI for translation and brand voice [03:10] Human translation, machine translation, and AI post editing [04:31] Training AI models to sound like a specific brand [05:21] How generative AI changed language quality and automation [07:47] Matching the right workflow to the right content type [09:14] Moving from academic quality scoring to content risk [10:42] Auditing enterprise content and connecting translation workflows [12:42] The role of AI in support content and customer experience [13:20] Why AI governance matters as content volume explodes [14:57] What companies underestimate about multilingual content operations [16:59] How Welocalize measures whether Opal is working [17:25] Faster turnaround times and reduced human editing effort [18:40] What early Opal deployments revealed [20:58] Building trust with enterprise content teams [21:29] Quality testing, certified languages, and human validation [23:40] Why quality estimation matters before human review [25:01] Continuous editing, feedback loops, and model improvement [25:58] Lessons other industries can learn from language AI [28:13] What multilingual AI could look like in five years [29:20] Improving source content before translation begins [30:00] Using performance data to improve localized marketing content [31:25] Advice for founders building AI in brand sensitive workflows [33:05] Language as part of closing the global digital divide [33:40] Final thoughts on Opal, AI, and the Welocalize team

Hila Segal and Roni Kandel on Learning Arc, AI Training, and Learning in the Flow of Work

Hila Segal and Roni Kandel are helping enterprises rethink how employees learn, practice, and apply new skills inside the tools they use every day. At WalkMe, they are building Learning Arc, an AI native learning solution designed to close the gap between training content and real work. WalkMe recently won an award for its work helping organizations deliver learning at the moment employees need it most. In this episode, Russ, Hila, and Roni explore why traditional corporate training often fails to translate into execution. Hila explains how WalkMe’s digital adoption platform has spent years helping employees navigate software workflows, and why customers began asking for a deeper learning experience that could build lasting proficiency, not just guide clicks. They dive into Learning Arc and how it combines AI powered content creation with in app learning delivery. Roni shares how the product helps organizations train employees before they enter sensitive systems, then resurfaces the same learning content when they are actually doing the work. The conversation also covers why learning and software adoption have historically been disconnected, how AI can help L&D teams create and refresh content faster, and why human oversight remains essential. Hila and Roni explain how Learning Arc gives authors visibility into what AI created, what source material was used, and how learning content can be refined to fit business needs. Along the way, they discuss sales training, ERP rollouts, AI literacy, multimodal learning, personalization, learner choice, enterprise software development, and why the future of workplace learning will be more contextual, flexible, and embedded directly into the flow of work. Topics Covered: [00:01] Welcome and intro, Hila Segal, Roni Kandel, WalkMe, and Learning Arc [00:45] WalkMe’s role in digital adoption and enterprise software usage [02:00] Why customers needed deeper skills and lasting proficiency [02:30] What WalkMe Learning Arc is designed to solve [03:36] Moving from traditional training to learning in the flow of work [04:26] Knowing versus doing in sales methodology and workflows [06:12] Why Learning Arc became a standalone product [06:39] Training before access to sensitive systems and processes [07:32] Why the learning and execution gap has lasted so long [08:20] Connecting L&D outcomes to actual work performance [09:25] Why the pace of workplace change is outgrowing traditional training [10:20] Using AI to create and refresh learning content faster [11:48] Early customer use cases and major transformation programs [12:50] Using Learning Arc to support ERP rollouts and onboarding [13:54] Building trust with L&D teams [14:14] How AI can turn months of content work into minutes [15:30] What learning in the flow of work means inside Learning Arc [16:34] Using screen context to surface relevant learning content [17:00] AI literacy training and responsible AI tool access [18:15] Reimagining the learning portal experience [19:50] Giving learners flexible formats like audio, video, and text [21:00] Why different employees need different learning experiences [21:44] Why human oversight remains central to AI generated learning [23:21] How authors control, review, and refine AI created content [24:30] Preventing hallucinations and grounding AI in approved source material [26:19] Balancing personalization and scalability [28:53] What Learning Arc signals about the future of enterprise software [30:45] How AI may change product, development, and software team roles [31:30] Training employees on tools that change constantly [33:14] Why change is central to WalkMe’s mission [35:56] Final thoughts on Learning Arc and the future of workplace training

Robert Klamser on Using AI to Make Chapter 11 Bankruptcy Easier to Understand

Robert Klamser is helping make one of the most complex corners of the legal system easier to navigate. As the leader of Stretto’s AI focused innovation efforts, Robert works on technology that supports professionals, creditors, vendors, employees, courts, and other stakeholders involved in bankruptcy cases. Stretto recently won an AI Excellence Award for Conductor, its AI powered platform built to make Chapter 11 information more accessible, understandable, and useful. In this episode, Russ and Robert explore why bankruptcy is such a document heavy, high stakes, and often confusing process. Robert explains how large Chapter 11 cases can involve thousands of pages of filings, multiple jurisdictions, local rules, federal bankruptcy code, and millions of affected stakeholders who may have never encountered bankruptcy before. They dive into Stretto Conductor and how it helps users ask plain language questions about complex bankruptcy documents. Robert shares how the platform is designed to understand filings in the context of the specific case, the related docket history, the bankruptcy code, and the local rules that may shape the answer. The conversation also covers why general purpose AI is not enough for this kind of work. Robert explains the importance of domain specific AI, grounded answers, citations, legal precision, multilingual access, and making sure the platform provides information without crossing into legal advice. Along the way, Robert discusses creditor communications, call center operations, hallucination concerns, attorney trust, AI adoption in the legal industry, and why the future of legal technology will likely depend on purpose built systems that do one thing extremely well. Topics Covered: [00:01] Welcome and intro, Robert Klamser and Stretto’s AI Excellence Award win [00:17] Stretto’s role in bankruptcy support services and technology [02:11] The communication challenges inside large Chapter 11 cases [03:00] How Conductor helps vendors, creditors, and lawyers understand filings [04:34] What is broken about how information flows in bankruptcy cases [06:31] Why basic case information can be hard for stakeholders to access [07:00] How bankruptcy case websites changed access to documents [09:18] Why documents still need context from the bankruptcy code and local rules [10:54] What Stretto Conductor does differently [11:12] Teaching AI the rules, nuance, and structure of bankruptcy [13:31] Why legal AI must be grounded, intelligent, and precise [15:37] Why purpose built AI matters in bankruptcy law [17:02] Why one document rarely tells the full story in a Chapter 11 case [19:20] Naive retrieval, missing context, and reasoning errors in general AI tools [21:01] Building trust with skeptical legal professionals [21:21] Why every answer must be cited and grounded in the right source [22:38] How users are learning to trust Conductor [23:00] Why Conductor answers questions instead of drafting legal documents [24:21] How large bankruptcies create massive temporary operating structures [24:55] Supporting bankrupt entities while the business keeps operating [27:08] Making newly filed documents understandable within minutes [28:00] Multilingual access for stakeholders in global bankruptcy cases [28:54] How Conductor can reduce pressure on large call centers [31:45] Why lawyers have traditionally been slow to adopt new technology [32:27] How courts, caution, and hallucination concerns affect AI adoption [35:53] Where legal AI may be heading over the next five years [37:00] The need for clearer standards around AI use in legal work [38:25] Why clients may soon expect attorneys to use AI efficiently [39:58] Final thoughts on making bankruptcy easier to understand

Jim Napolitano on 3DLive, Virtual Twins, and the Future of Immersive Product Design

Jim Napolitano is helping brands, engineers, product teams, and enterprise leaders experience product development in a more immersive way. As North American Services Director at Dassault Systèmes 3DEXCITE, Jim works with teams using virtual twins, spatial computing, and the 3DLive application to bring complex product data into interactive 3D environments. Dassault Systèmes recently won an innovation award for 3DLive and its work with Apple Vision Pro. In this episode, Russ and Jim explore how virtual twins are changing the way companies design, simulate, manufacture, market, and improve products. Jim explains how a virtual twin becomes a data fueled version of a product, allowing teams to test, review, and understand products before they exist physically. They dive into 3DLive and how immersive technology helps engineers, executives, designers, marketers, and end users collaborate inside product data. Jim shares examples from Formula 1, automotive design, aviation, consumer packaging, manufacturing training, and even medical applications like a living heart. The conversation also covers why live data matters, how spatial computing can make product review more intuitive, and how partnerships with Apple and Nvidia are helping Dassault Systèmes bring virtual twin experiences to life in new ways. Along the way, Jim discusses immersive collaboration, faster decision making, physical prototyping, simulation, AI, training scenarios, consumer research, and why virtual twins may soon become central to how companies build and improve nearly everything. Topics Covered: [00:01] Welcome and intro, Jim Napolitano and Dassault Systèmes’ 3DLive award win [00:39] What Dassault Systèmes does and the role of virtual twins [01:25] Connecting agency leadership, brand work, and immersive technology [02:41] What a virtual twin is and how it differs from an agent [04:02] Spatial computing, sense computing, and intuitive product interaction [05:14] Using Formula 1 to show simulation data in immersive 3D [06:30] Exploring vehicle components inside a virtual twin [07:32] How 3DLive supports collaboration across different locations [09:00] Making immersive tools useful for non technical users [10:00] Using 3DLive for consumer packaging research [11:07] Replacing expensive physical iteration with immersive product review [11:50] Aviation use cases and reconfiguring passenger cabin spaces [13:20] What surprised Dassault Systèmes during real customer deployments [14:00] Collaboration with Apple on Apple Vision Pro experiences [15:26] Knowing when a virtual twin is good enough for decision making [16:13] Using existing product data instead of creating one off experiences [17:10] Training technicians with virtual equipment and troubleshooting scenarios [18:22] Why live data matters in product development [20:05] Dassault Systèmes’ partnerships with Apple and Nvidia [21:25] Common principles across automotive, aerospace, and medical use cases [22:00] The living heart example and virtual twin applications in medicine [24:28] How immersive tools and AI can shorten product development cycles [25:17] How AI may support engineering, science, and strategic decision making [26:27] Final thoughts on the future of 3DLive and virtual twin technology

Alexey Sheremetyev on Turning Your Home Into an Editable Digital Twin

Alexey Sheremetyev is helping homeowners, designers, real estate professionals, builders, and contractors reimagine how physical spaces become digital. As Co-Founder and CPO of Planner 5D, Alexey helped build a platform that allows users to scan rooms with a phone camera and turn them into editable 3D home plans. Planner 5D recently won an AI Excellence Award for its Home Scanner technology. In this episode, Russ and Alexey explore how Planner 5D grew from a personal renovation problem into a platform used by more than 100 million people around the world. Alexey shares how his background in web design and user experience shaped the product, and why the goal was always to make home planning simple enough for consumers but powerful enough for professionals. They dive into Home Scanner, Planner 5D’s AI powered feature that uses computer vision to turn real rooms into editable digital twins. Alexey explains how the technology can recognize room layouts, measurements, furniture, colors, textures, flooring, and objects without requiring expensive hardware or specialized training. The conversation also covers why editable 3D plans matter more than static renderings, how AI helps handle messy real world spaces, and why Planner 5D’s years of user generated floor plans and designs have become one of its most valuable assets. Along the way, Alexey discusses renovation planning, real estate workflows, professional design collaboration, smart home possibilities, home maintenance, property history, and his vision for Planner 5D becoming a persistent digital memory for the home itself. Topics Covered: [00:01] Welcome and intro, Alexey Sheremetyev and Planner 5D’s AI Excellence Award win [00:29] What Planner 5D does for homeowners and professionals [00:37] Creating digital twins and editable 3D home plans [01:27] Turning room design into a consumer grade 3D experience [01:56] How Alexey’s own apartment renovation inspired Planner 5D [03:53] Why traditional room planning can be frustrating and inaccurate [04:18] How a design background shaped the Home Scanner experience [05:00] Using AI to automate manual measurements and room recreation [05:59] Why Home Scanner is technically harder than it looks [06:17] Planner 5D’s advantage after 15 years and more than 100 million users [07:35] Solving spatial reconstruction through software instead of costly hardware [07:56] Using computer vision instead of relying only on LiDAR [10:12] What Planner 5D learned from real users scanning real spaces [11:00] How real estate professionals use digital twins in their workflows [12:14] Building for both consumers and professional users [13:20] How builders and contractors use Planner 5D as a presale tool [14:27] Why editable 3D plans create a different user experience than static images [15:50] How Planner 5D checks scan accuracy for renovation planning [16:18] AI validation, human review, user feedback, and correction tools [18:51] Handling furniture, bad lighting, unusual room shapes, and messy spaces [19:30] Training AI with a large base of floor plans, designs, and user data [22:00] Using Planner 5D as a filing cabinet and memory system for the home [24:10] Why spatial reconstruction may go beyond home design [25:57] Planner 5D as the next stage of the smart home [27:46] Why the digital twin could travel with the house, not the owner [28:00] Smart home integrations, appliances, and connected home systems [29:30] Helping homeowners understand and maintain older properties [30:00] Using AI to recommend repairs and improvements that may increase home value [32:00] Why user expectations for instant answers and context are changing [33:59] Final thoughts on the future of Planner 5D and generative AI