Scaling AI for Real-World Implementation and Impact
Recordings will be available exclusively to ILP members. To learn more about becoming a member, click here.
Review our 2025 AI Conference summary and examine the year’s key developments.
Join MIT faculty, senior executives, and industry innovators for a forward‑looking forum exploring how artificial intelligence is redefining industrial operations and enabling enterprise‑scale transformation. This gathering will spotlight cutting‑edge Enterprise AI strategies, practical frameworks, and breakthrough applications across mission‑critical domains such as real-time digital twins, automated defect detection, and intelligent product lifecycle management.
Participants will gain an inside view into how MIT researchers and corporate leaders are collaborating to build scalable, reliable, and interpretable AI systems that elevate operational excellence and strengthen competitive advantage. Expect deep insights, real‑world case studies, and a clear vision of how AI is reshaping the future of industrial performance.
Visiting MIT: https://www.mit.edu/visitmit/ Where to Stay: https://institute-events.mit.edu/visit/where-to-stay Registration Questions: ocrevents@mit.edu
Associate Director, MIT.nano Director of Technical Operations, Center for Clinical and Translational Research Co-Director, MIT Advanced Manufacturing and Design Program
Dr. Brian Anthony is a leading expert in the design of intelligent, or smart, instruments and methodologies for monitoring, measuring, and controlling complex physical systems. His interdisciplinary work spans mechanical, electrical, and optical engineering, seamlessly integrated with computer science and optimization, to deliver innovative solutions across manufacturing, healthcare, and other industries.
At the core of Dr. Anthony’s research is computational instrumentation—the development of advanced tools and techniques to observe and manage intricate systems, particularly in manufacturing and medical diagnostics. His contributions include pioneering measurement and imaging technologies that enhance precision and performance in both industrial and clinical settings.
With over 30 years of experience, Dr. Anthony combines deep academic insight with practical industry expertise in technology innovation, product development, and entrepreneurship. He has successfully guided market-driven solutions from concept to commercialization, especially at the intersection of information technology and advanced manufacturing. His achievements include receiving an Emmy Award from the Academy of Television Arts and Sciences for technical innovation in broadcast engineering.
In the classroom, Dr. Anthony is dedicated to teaching the modeling and analysis of large-scale systems to support decision-making in domains such as manufacturing, medicine, and entertainment. He also leads efforts in developing optimization algorithms and software tools for system design and evaluation.
Dr. Anthony’s dual roles in academia and industry position him as a bridge between cutting-edge research and real-world application, driving impactful technologies that shape the future of engineering and innovation.
Manufacturing is shifting from isolated automation to AI-enabled, increasingly autonomous operations, where human expertise remains essential. The core challenge today is not inventing new algorithms but scaling appropriate AI from pilot projects to repeatable deployment across production lines, sites, and supply chains.
Digital twins (continuously updated virtual representations of materials, machines, and processes, informed by physical context and data) are one component at the center of this transition. When digital twins are integrated with machine learning and real-time control, AI moves beyond descriptive analytics to closed-loop decision support and optimization. Models can detect drift, anticipate failures, recommend corrective actions, and in some cases execute adjustments within defined safety and quality boundaries. Manufacturers are already demonstrating how these approaches reduce downtime, cut scrap and energy use, shorten qualification cycles, and deliver measurable operational gains.
Human expertise remains fundamental. Scalable AI systems must encode domain knowledge, align with operator workflows, and provide transparent, context-aware recommendations—so people can supervise, intervene, and continuously improve performance. In practice, impact comes from the technology and the implementation stack: data pipelines and governance, appropriate AI tools, cybersecurity and safety guardrails, and change management that sustains performance over time.
Through case studies, we will show how manufacturers are moving from proofs-of-concept to repeatable deployment patterns that translate industrial AI into real-world outcomes, at scale, with trust, and with people at the center.
Professor, MIT Computer Science & Artificial Intelligence Laboratory Member, Broad Institute of MIT and Harvard
Manolis Kellis is a professor of computer science at MIT, a member of the Broad Institute of MIT and Harvard, a principal investigator of the Computer Science and Artificial Intelligence Lab at MIT, and head of the MIT Computational Biology Group (compbio.mit.edu). His research includes disease circuitry, genetics, genomics, epigenomics, coding genes, non-coding RNAs, regulatory genomics, and comparative genomics, applied to Alzheimer's Disease, Obesity, Schizophrenia, Cardiac Disorders, Cancer, and Immune Disorders, and multiple other disorders. He has led several large-scale genomics projects, including the Roadmap Epigenomics, ENCODE, and Genotype-Tissue Expression (GTEx) projects, as well as comparative genomics projects in mammals, flies, and yeasts. He received the US Presidential Early Career Award in Science and Engineering (PECASE) by US President Barack Obama, the Mendel Medal for Outstanding Achievements in Science, the NIH Director’s Transformative Research Award, the Boston Patent Law Association award, the NSF CAREER award, the Alfred P. Sloan Fellowship, the Technology Review TR35 recognition, the AIT Niki Award, and the Sprowls award for the best Ph.D. thesis in computer science at MIT. He has authored over 325 journal publications cited 200,000 times. He has obtained more than 20 multi-year grants from the NIH, and his trainees hold faculty positions at Stanford, Harvard, CMU, McGill, Johns Hopkins, UCLA, and other top universities. He lived in Greece and France before moving to the US, and he studied and conducted research at MIT, the Xerox Palo Alto Research Center, and the Cold Spring Harbor Lab.
Most enterprise AI initiatives stall not because of model quality, but because organizations lack the cognitive architecture needed to connect data, reasoning, and action at scale. In this talk, Manolis Kellis reframes AI deployment as an organizational design problem rather than a technology problem, drawing on lessons from his MIT Sloan executive course Deploying AI in the Corporation and real-world enterprise deployments across healthcare, life sciences, manufacturing, and knowledge work.
The talk introduces cognitive cartography, a framework for representing enterprise knowledge, decisions, and workflows as structured, machine-interpretable maps, and shows how embeddings, knowledge graphs, and agentic workflows can be combined into deployable systems that actually change how organizations operate. Rather than focusing on isolated use cases, Kellis presents a principled approach for designing agentic enterprises: systems that sense, reason, act, and learn through explicit feedback loops and governance.
Through concrete examples, the session illustrates how common business functions, strategy, operations, finance, and R&D can be translated into AI-native architectures that move beyond experimentation toward durable impact. Attendees will leave with a clear mental model for why many pilots fail, what successful enterprise AI systems have in common, and how to design organizational intelligence that is explainable, adaptive, and aligned with business outcomes.
This talk is intended for executives responsible for scaling AI across the enterprise who are seeking not just adoption, but sustained competitive advantage.
Senior Lecturer, MIT Sloan School of Management Founder, Global Opportunity Forum, MIT Office of Open Learning
Dr. George Westerman is a Senior Lecturer and Principal Research Scientist at the MIT Sloan School of Management. He helps executives understand how to help their companies thrive in a world of fast-moving technological change. As a pioneering research on digital transformation, he co-authored the award winning book Leading Digital: Turning Technology into Business Transformation. Another book, The Real Business of IT: How CIOs Create and Communicate Value, serves as the basis for the MIT Sloan CIO Leadership Award program, which he co-chairs. His recent research on workforce learning, innovation culture, and AI transformation provides tangible insights to lead successful AI transformation.
George is a digital strategy advisor to the US Library of Congress, Board of Directors member for workforce non-profit WorkCred, and advisor to executives in numerous large corporations. Prior to earning a doctorate in innovation strategy from Harvard Business School, he gained more than a decade of experience in product development and technology leadership roles.
The conversation about artificial intelligence is rapidly changing from awareness and innovation to value and scale. As companies try to scale their pilot tests to a real enterprise environment, they find that much more is needed than just a good model. Our research has identified six key questions that leaders should ask - and answer - to truly AI- enable their organizations. Each corresponds to an essential action or capability. In this session we will examine the key questions and how you can start to answer them in your business.
CoFounder and CEO, DataCebo Principal Research Scientist, MIT Laboratory for Information and Decision Systems
Kalyan Veeramachaneni is a principal research scientist at the MIT Schwarzman College of Computing. In 2015, he founded MIT’s Data-to-AI Lab (part of MIT’s LIDS), where he directs a team focused on Big Data + Human Interactions + Impactful Domains. The algorithms, system,s and open-source software developed by the MIT Data-to-AI (DAI) Lab are deployed for applications in the financial, healthcare, educational, security and energy sectors.
Kalyan’s latest AI-focused start-up, DataCebo, which he co-founded in 2021, is the commercial spin-off of the MIT DAI Lab’s The Synthetic Data Vault (SDV) and is backed by Mark Gorenberg (Zetta Venture Partners) and Salil Deshpande (Uncorrelated Ventures).
His two previous AI start-ups have both been acquired. Feature Labs, a data science automation company, was acquired by Alteryx (NYSE:AYX) in 2019. PatternEx, an AI cybersecurity company, was acquired by Corelight in 2020.
In 2017, Kalyan was named as one of the 100 most creative people in business by Fast Company.
As enterprises race to deploy AI at scale, many discover that algorithmic innovation is not the limiting factor—data is. In Setting Your Data Strategy for Scaling AI Implementation in Enterprise, Kalyan Veeramachaneni explores how organizations can build the data foundations necessary to move from isolated AI pilots to repeatable, enterprise‑wide impact. Drawing on real‑world lessons from industry collaborations and advances emerging from MIT’s Data‑to‑AI Lab, the talk outlines why traditional data collection and governance models fall short in high‑velocity AI environments and what a modern, scalable data strategy must include.
Veeramachaneni will examine how enterprises can architect data pipelines that support continuous model development, ensure data quality at scale, and unlock the full potential of generative and predictive AI. He will highlight practical approaches for overcoming common bottlenecks—such as fragmented data assets, limited access for model builders, and compliance constraints—while demonstrating how emerging techniques like synthetic data can expand model training capabilities responsibly.
Participants will leave with a strategic framework for aligning data readiness with AI ambitions, enabling faster deployment cycles, more robust model performance, and organization‑wide adoption. The session is designed for leaders seeking actionable guidance on transforming data from a constraint into a competitive advantage as they scale AI across their enterprise.
Irina Gaziyeva comes to Corporate Relations from the Mechanical Engineering Department at MIT where she worked 10 years as Administrative Assistant where she has supported four senior faculty members and their research groups (20-25 graduate students). Since 2018, Irina has acted as program coordinator, teaming-up with the program manager and program faculty lead for the MechE Alliance program. She has facilitated 45+ virtual seminars, workshops, and mentoring events in this informal role. Irina has also actively connected members of the MechE community to support student career development, mentorship, and networking opportunities with MIT alumni and industry. Before MIT, Irina held positions as Administrative Assistant and Member Representative at Brookline Dental and Tufts Health plan, respectively. Irina has also been a Community Organizer in Worcester, MA.
Irina earned her B.A., Management (with Innovation & Entrepreneurship track) at Clark University in Worcester, and her M.S., Program and Project Management from Brandeis University in Waltham. She has received many awards at MIT for outstanding service, and she has extensive community volunteer work to her credit.
Dr. Stewart Jamieson is Head of Technology at Themis AI, where he leads the development of Capsa, a platform for measuring and managing risk in production AI systems. He oversees the company’s technical strategy across uncertainty quantification, anomaly detection, and decision safeguards, and is helping expand Capsa with a growing catalog of pre-wrapped models with built-in uncertainty quantification, alongside automated model onboarding and training workflows.
In this role, Stewart works closely with enterprise customers to deploy AI that is reliable, transparent, and governable in high-stakes environments. His work spans the full AI lifecycle—from model validation and risk calibration to real-time monitoring and continuous improvement—enabling organizations to build safeguards around automated decisions and operate with greater confidence. Stewart earned his Ph.D. in Autonomous Systems from MIT, where his research focused on Bayesian modeling and risk-aware decision-making under uncertainty. He regularly presents to technical and executive audiences on trustworthy AI, production deployment, and AI governance.
Katie Trauth Taylor is CEO and Cofounder of Narratize AI, a product intelligence and innovation platform empowering teams to bring products and discoveries to market faster, smarter, and with greater impact by eliminating inefficiencies, aligning teams, and preserving institutional knowledge. Katie is a growth-focused entrepreneur executive with 10+ years experience inspiring teams to design and deliver magnetic products, memorable experiences, and groundbreaking impacts. She has led strategic innovation narratives and served as a senior content strategist within fast-growth tech startups and the Fortune 500, including Boeing, NASA, Hershey, Sunoco, AAA, IFF, Dupont, Edgewell, Cincinnati Children's, Argonne National Lab, Crossover Health, Parsley Health, Omada, Physera, US Dept of Veterans Affairs, Millennium Challenge Corporation, World Food Forum, and the United Nations. She believes that everyone can be an innovator--when empowered to share their bold ideas.
Bernardo Aceituno is Co-Founder and President of Stack AI, the Enterprise AI Transformation Platform enabling organizations to orchestrate AI agents that understand data and take action. He combines deep technical expertise in machine learning with proven operational leadership.
Originally from Caracas, Venezuela, Bernardo’s passion for AI began through hands-on robotics projects at Universidad Simón Bolívar. He later earned his SM and PhD at MIT, where he conducted research in robotic manipulation at the MCube Lab, developing mathematical frameworks that enable robots to reason under uncertainty. He also interned at Meta’s Facebook AI Research, advancing machine learning systems for autonomous robotic manipulation.
Before founding Stack AI, Bernardo co-founded Grupo Aktio, scaling a mobile payments and cryptocurrency POS platform across Latin America. He led a 20+ person B2B sales team, supported over 5,000 active users, and managed more than 200 contractors. A former leader in MIT’s graduate engineering community, Bernardo now builds AI tools for the very teams he once was part of—because, as he says, “We are the market.”
Dr. Rong is a Director of Corporate Relations at MIT. He currently supervises a group of ILP program directors who promote and manage the interactions and relationships between the research at MIT and companies worldwide to help them stay abreast of the latest developments in technology and business practices.
Previously, Dr. Rong founded IKA, LLC. He has led corporate development and product innovation and provided strategic advice to companies in corporate strategy, IT leadership, digital transformation, AI, enterprise content management, and customer relationships. He held senior roles in Harte-Hanks and Vignette Corporation. He held an EU postdoctoral research fellowship at the University of Edinburgh in Scotland where he started global collaborative research.
Dr. Rong is on the board of multiple organizations, including the MIT Sloan Alumni Association of Boston from 2009 to 2012. He chaired MIT Sloan CIO Symposium from 2009-2011. He is a senior expert invited by international organizations.
Dr. Rong holds an M.B.A. in global and innovation leadership from the MIT Sloan School of Management and a Ph.D in numerical computing from the University of Guelph in Canada.
President, AUO Display Plus America
Simon Nip serves as President of AUO Display Plus America, guiding the company’s strategic vision since its founding. He is instrumental in shaping the organization’s strategy, focusing on innovation, ecosystem partnerships, and growth driven by technology collaboration and acquisitions.
Simon’s career bridges high-tech leadership and financial services, giving him a unique perspective on business building and scaling. Since 2010, he has held senior executive roles at leading technology companies in Boston. Prior to entering the tech sector, Simon established his career in investment banking and asset management with major international firms. He later leveraged this experience as an entrepreneur, successfully starting and incubating multiple ventures across international markets.
Simon holds an MBA with High Honors from the University of Chicago Booth School of Business.
Co-Founder and CTO, Maven AGI
Sami Shalabi is the Co-Founder and CTO of Maven AGI, where he is building enterprise-grade AI agents for customer experience. With more than three decades of leadership at Google, IBM, Lotus, and three startups, two of which he exited, Sami has built, scaled, and led high-performing organizations from zero to hundreds. At Google, he led, built and launched a from the ground rewrite of Google News and grew it into one of the world’s largest personalized products. Sami has created products spanning generative AI, news, healthcare, entertainment, social, and SaaS, and holds more than 55 patents. An MIT alumnus, inventor, mentor, and angel investor, he received MIT’s Young Professional Award and was named an Inc. Magazine 2025 Business Leader of the Year honoree.
Co-Founder and CEO, DataCebo Principal Research Scientist, MIT Laboratory for Information and Decision Systems