Pop culture has often depicted robots as cold, metallic, and menacing, built for domination, not compassion. But at Georgia Tech, the future of robotics is softer, smarter, and designed to help.

“When people think of robots, they usually imagine something like The Terminator or RoboCop: big, rigid, and made of metal,” said Hong Yeo, the G.P. “Bud” Peterson and Valerie H. Peterson Professor in the George W. Woodruff School of Mechanical Engineering. “But what we’re developing is the opposite. These artificial muscles are soft, flexible, and responsive — more like human tissue than machine.”

Yeo’s latest study, published in Materials Horizons, explores AI-powered muscles made from lifelike materials paired with intelligent control systems. The technology learns from the body and adapts in real time, creating motion that feels natural, responsive, and safe enough to support recovery.
 

Muscles That Think, Materials That Feel

Traditional robotics relies on steel, wires, and motors, but rarely captures the nuances of human motion. Yeo’s research takes a different approach. He uses hierarchically structured fibers, which are flexible materials built in layers, much like muscle and tendon. They can sense, adapt, and even “remember” how they’ve moved before.

Yeo trains machine learning algorithms to adjust those pliable materials in real time with the right amount of force or flexibility for each task.

“These muscles don’t only respond to commands,” Yeo said. “They learn from experience. They can adapt and self-correct, which makes motion smoother and more natural.”

The result of that research is deeply human. For someone recovering from a stroke or limb loss, each deliberate movement rebuilds not just strength — it rebuilds confidence, independence, and a sense of self.

 

A Glove That Gives Freedom Back

One of the first real-world applications is a prosthetic glove powered by artificial muscles (published in ACS Nano, 2025), a device that behaves more like a helping hand than a mechanical tool. Traditional prosthetics rely on rigid motors and preset motions, but Yeo’s design mirrors the natural give-and-take of real muscle.

Inside the glove, thin layers of stretchable fibers and sensors contract, twist, and flex in sync with the wearer’s intent. The glove can fine-tune grip strength, reduce tremors, and respond instantly to the user’s movements, bringing dexterity back to everyday life.

That kind of precision matters most in the smallest tasks: fastening a button, lifting a glass, holding a child’s hand.

“These aren’t just movements,” Yeo said. “They’re freedoms.”

For Yeo, the idea of restoring freedom through movement has driven his research from the very beginning.
 

A Mission Rooted in Loss

Yeo's work is deeply personal. His path to biomedical engineering began with loss — the sudden death of his father while Yeo was still in college. That moment reshaped his sense of purpose, redirecting his focus from machines that move to technologies that heal.

“Initially, I was thinking about designing cars,” he said. “But after my father’s death, I kind of woke up. Maybe I could do something that helps save someone’s life.”

That purpose continues to guide his lab’s work today, building technologies that help people recover what they’ve lost.

Achieving that vision, however, means tackling some of engineering’s toughest challenges.
 

Soft Machines, Hard Problems

Creating lifelike muscles isn’t easy. They need to be soft but strong, responsive but safe. And they must avoid triggering the body’s immune system. That means building materials that can survive inside the body — and learn to belong there.

“We always think about not only function, but adaptability,” Yeo said. “If it’s going to be part of someone’s body, it has to work with them, not against them.”

His team calibrates these synthetic fibers like precision instruments — tested, adjusted, and re-tuned until they operate in sync with the body’s natural movements. Over time, they develop a kind of “muscle memory,” adapting fluidly to changing conditions. That dynamic adaptability, Yeo explained, is what separates a machine from a prosthetic that truly feels alive.
 

From Collaboration to Innovation

Solving problems this complex requires more than one discipline. It takes an entire ecosystem of collaboration. Yeo’s lab brings together experts in mechanical engineering, materials science, medicine, and computer science to design smarter, safer devices.

“You can’t solve this kind of problem in isolation,” he said. “We need all of it — polymers, artificial intelligence, biomechanics — working together.”

That collaborative model is supported by the National Science Foundation (NSF), the National Institutes of Health, and Georgia Tech’s Institute for Matter and Systems. In 2023, Yeo received a $3 million NSF grant to train the next generation of engineers building smart medical technology.

His team now works closely with healthcare providers and industry partners to bring these devices out of the lab and into patients’ lives.


The Future You Can Feel

The future of robotics, according to Yeo, won’t be defined by power or complexity but by feel.

“If it feels foreign, people won’t use it,” he said. “But if it feels like part of you, that’s when it can truly change lives.”

It’s the opposite of The Terminator, where machines replace us. Yeo is designing these machines to help us reclaim ourselves.

 

By Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute | Supply Chain Advisor | Former Executive at Frito-Lay, AJC International, and Coca-Cola

The Moment That Changed How I Listen 

When I chaired the National Product Supply Group at Coca-Cola, one of our most respected board members was Jeff Edwards. Jeff had decades of experience and commanded respect without ever seeking attention. In a four-hour meeting, Jeff might speak two or three times—never more. But when he did, everyone stopped to listen.

What made Jeff so impactful wasn’t the number of words he used—it was the care behind them. He listened intently, gathered information, built context, and added value only when his perspective would move the conversation forward. His real skill was not speaking—it was listening with purpose.

That experience stayed with me, especially because earlier in my own career, I had a very different experience. While working at AJC International, I attended a leadership program at the Center for Creative Leadership. Early in the program, a cohort of about twenty of us sat in a facilitated discussion. What we didn’t know was that we were being filmed.

Later that day, each of us reviewed our videos one-on-one with an instructor. Watching myself was humbling. I saw a young professional trying too hard to prove himself—talking far too much, jumping in before others, and dominating the conversation. It was uncomfortable to watch, but invaluable. It forced me to face how insecurity can manifest as over-talking and how much more powerful restraint and self-awareness can be. I’ve been on a "less is more" journey ever since.

Why Communication Is a Supply Chain Differentiator 

We often talk about supply chain as end-to-end, but that phrase means something deeper than process visibility—it implies constant collaboration. Supply chain professionals must connect with suppliers, customers, and internal stakeholders across every function. 

That means communication is the connective tissue of our profession.

• Upstream and downstream, we are translators—interpreting complex data, system logic, and network realities for people who make decisions.
• Inside organizations, we act as bridges between technical teams and commercial leaders.
• Across tiers, we negotiate, influence, and build trust with partners who don’t see what we see every day.

Even as automation expands, supply chains remain messy, human, and physical. Systems can handle the routine, but edge cases, disruptions, and exceptions still rely on judgment—and judgment relies on communication. The ability to see, listen, and convey context in real time is what keeps operations resilient when variability strikes.

In our earlier SCL articles, we wrote that skills that survive AI are the ones that emphasize human discernment—and that critical thinking is about interpreting and questioning rather than accepting data at face value. Communication is where these two intersect. It is how human understanding flows across the supply chain network.

When Communication Breaks Down

I once worked with a technically gifted colleague—let’s call him Forrest—who had deep analytical capability but struggled to speak up in group settings. His insights were sharp, but his inability to communicate them left him isolated. Eventually, he left the organization. It was a tough reminder that technical strength without communication is unrealized potential.

In a global supply chain, it’s not enough to know the answer. You have to make others understand why it’s the answer—and what to do with it. Communication is how insight becomes action. 

The Many Dimensions of Communication

We tend to equate communication with speaking, but it’s much broader. Great communicators master four dimensions:

1. Speaking – Conveying information clearly, concisely, and confidently.
2. Writing – Capturing ideas and decisions in a way that travels across teams and time zones.
3. Listening – Absorbing context before contributing, and letting others be heard.
4. Observing – Seeing what others miss and using that insight to guide action.

The fourth one—observing—is often overlooked.

Recently, while reading with my granddaughter, she picked out a children’s book titled Bud Finds Her Gift. It’s about discovering one's special ability, and Bud's gift turned out to be observation—simply noticing things others missed. Watching her read that story reminded me how powerful observation really is.

I thought of my former colleague, Tim Harville, with whom I worked at Coregistics. Tim often walked the warehouse with new supervisors, teaching them to "see the operation"—to notice what looks good, what's out of place, and where waste or opportunity hides in plain sight. His goal wasn't to test them—it was to train their eyes. Observation, in that sense, is a key communication skill. You can't describe, explain, or improve what you haven't first seen clearly. 

Can Communication Be Taught? Absolutely.

I’ve seen it done.

At Frito-Lay, we invested in communication training for new managers—everything from eliminating filler words to using purposeful body language and structuring messages with intent. At Coca-Cola, Toastmasters chapters gave leaders a safe space to practice public speaking, storytelling, and feedback.

And beyond formal training, there's practice in the everyday moments—taking notes in meetings, volunteering to summarize a discussion, representing a project team, or offering to speak at a class or event. Every repetition builds comfort and clarity.

My own Center for Creative Leadership experience was the beginning of that practice for me. Decades later, I still catch myself needing to slow down, listen, and wait for the right moment. The lesson never stops.

Painting the Picture: When It Works and When It’s Missing

When communication works, credibility follows. Jeff Edwards didn’t have to compete for airtime; his credibility made his words count. When it's missing, even talented people like Forrest can struggle to influence or grow.

Both extremes teach the same lesson: communication isn't about more or less—it's about meaning. It's knowing when to speak, what to say, and how to connect it to the needs of others. 

Practical Ways to Build Communication Strength

• Listen to learn. Take notes, paraphrase what you've heard, and confirm understanding
• Translate technical into practical. Explain what data means for the business, not just what it shows.
• Observe before you act. Practice "seeing" your operation or process with fresh eyes.
• Simplify your writing. Clarity beats cleverness every time.
• Seek feedback. Ask trusted peers to tell you how your communication lands.
• Prepare with intent. Know your audience, outcome, and key message before you speak. 

Reflection Questions

• Where in my current role does communication make or break outcomes?
• When was the last time I adjusted how I communicate to fit my audience?
• Do I listen more than I speak—and what might I learn if I did?
• How can I model communication that builds understanding rather than winning airtime? 

Closing Thought

Technical skills and analytics may earn you a seat at the table, but communication determines whether your ideas move the organization forward.

In a world of AI, automation, and constant change, the ability to listen, observe, and translate context into action remains our most human—and most valuable—differentiator.

Building on more than a year of successful collaboration, Dolby Labs has extended its investment in Georgia Tech’s College of Computing for a second year, donating $600,000 to support cutting-edge research.

Dolby and the College each have laboratories in the Coda building, which promotes collaboration at various levels. The audiovisual technology company supported seven research projects last year, spanning computing systems and AI modeling. The partnership also includes events such as this month’s co-hosted student seminar.

“This partnership has reinforced the importance of taking an interdisciplinary approach to our research,” said Vivek Sarkar, Dean of Computing, who worked in industry for two decades before returning to academia.

“I’d like to see us go even deeper in finding ways to combine faculty from different schools and different research areas to work with one partner.”

[VIDEO: GT Computing Dean Discusses Dolby Deal Details with Senior VP]

Yalong Yang, an assistant professor at Georgia Tech’s School of Interactive Computing, is one of the researchers who received Dolby support last year. He and his lab have been working on creating interactive, immersive versions of stories from the New York Times. 

“We’re particularly interested in the engagement side,” Yang said. “That’s what Dolby’s business is about.” Yang and his collaborators have been showing the immersive stories to test subjects while collecting data on heart rate and eye movement.

These collaborations have resulted in several published papers. The code developed is released as open source, enabling anyone to use it. Meanwhile, Dolby scientists can tailor the code for their own needs. 

“We deliberately look for ambitious, farther-looking projects," said Shriram Revankar, senior vice president of Dolby’s Advanced Technology Group.

[RELATED: Dean's Session Spotlights Industry Role in Preparing Students for Workforce Success]

"These are the risks that academia can take and do well in, because they have constant access to new students and other faculty."

At its core, the partnership is about developing relationships among faculty, students, and Dolby, according to Humphrey Shi.

"The students get experience in solving real-world problems for an international corporation, and Dolby’s researchers expand their knowledge through connecting with Tech faculty," said Shi, an associate professor in interactive computing whose research has also been supported by Dolby.

The Center for 21st Century Universities (C21U) is excited to announce that Sanghyun Jang will join Georgia Tech as a C21U visiting research scholar starting on October 20, 2025. He comes from South Korea, where he served as director of the education data center at the Korea Education and Research Information Service (KERIS). For one year, he will be based in Atlanta, Georgia, collaborating with C21U faculty and researchers to develop AI-based learning systems and leverage educational data to improve student outcomes.

“We are pleased to welcome Sanghyun Jang to C21U as our visiting scholar. His leadership and expertise in Korea’s national digital and AI education initiatives offer an invaluable global perspective to our goal of promoting innovation in lifelong learning. His visit will assist us in exploring new models of AI-enabled education that link K–12, higher education, and lifelong learners worldwide,” said C21U Executive Director Stephen Harmon.

Sanghyun has extensive experience leading national education data initiatives in South Korea and collaborating with international organizations like UNESCO and the World Bank. This aligns well with C21U’s mission to promote personalized, data-driven teaching and learning at scale. His expertise highlights our commitment to global collaboration and leadership in AI-powered education.
“I deeply appreciate the opportunity to join C21U and collaborate with Georgia Tech’s outstanding researchers. Their innovative work in AI and lifelong learning provides a strong foundation for meaningful international collaboration and innovation. I am excited that this visiting scholar experience will help build a global network for AI and education research by connecting KERIS, Korean universities, and Georgia Tech,” said Sanghyun Jang.

C21U’s team looks forward to sharing updates on Sanghyun’s work throughout the year. Stay tuned for upcoming events and research highlights. 

Sanghyun Jang holds a doctorate in computer engineering from Dongguk University.
 

The Center for 21st Century Universities (C21U) has announced the inaugural cohort of Bill Kent Family Foundation AI in Higher Education Faculty Fellows for 2025–26. This C21U-led fellowship program supports faculty projects that explore innovative, ethical, and impactful uses of artificial intelligence in teaching and learning. 

The fellows are Professor Flavio Fenton from the College of Sciences, Joy Arulraj from the College of Computing, Patrick Danahy from the College of Design, and Professor and Associate Chair of the School of Electrical and Computer Engineering Ying Zhang, from the College of Engineering. Each fellow will lead a project that advances AI’s role in higher education.

“We deeply appreciate the generosity of the Dr. Bill Kent family in establishing this first philanthropic gift to our new College. Their generous support will allow us to encourage practical applications of AI and foster an appreciation for its ethical use,” said William Gaudelli, inaugural dean of the Georgia Tech College of Lifetime Learning. “This Fellowship will ensure we grow and learn about its use thoughtfully, developing highly innovative and engaging pedagogical experiences for all life’s stages.”

Arulraj’s TokenSmith: Fast, Local, Citable LLM Tutoring introduces a privacy-conscious AI tutoring system for database courses that provides verifiable, course-aligned answers. Fenton’s AI as a Learning Assistant develops AI-enabled instructional modules for physics, neuroscience, and scientific writing to improve conceptual understanding and promote ethical AI use. Danahy’s AI-Enabled Design Ideation and Robotic 3D Printing with Open-Source Platforms integrates AI-driven design and robotic fabrication into architecture education while addressing ethics and sustainability. Zhang’s AI-Enabled Personalized Engineering Education expands personalized learning in large engineering courses through AI tutoring frameworks and integrates AI literacy into the curriculum.

“The Bill Kent Family Fellowship gives our faculty the resources and flexibility to experiment with AI in ways that directly benefit students and inform the future of higher education,” said Stephen Harmon, executive director of C21U.

The fellowship received 21 applications from all seven Georgia Tech colleges, reflecting the educational AI subject-matter experts for their units and the Institute as a whole. Fellows will develop and implement their projects during the 2025–26 academic year and share outcomes through C21U Learning Labs and other campus events.

The Bill Kent Family Foundation partnered with C21U to establish this fellowship and support faculty innovation at Georgia Tech. Through this program, the Foundation invests in projects that explore responsible and impactful uses of artificial intelligence in teaching and learning. By funding this initiative, the Foundation aims to empower educators to develop scalable instructional models, promote ethical AI practices, and prepare students for a future shaped by emerging technologies.

Instructors creating online courses have long faced a tradeoff: use text-based materials that are easy to update, or invest in engaging but time-consuming video formats. As a result, learners often get either flexibility or immersion, but rarely both.

“In a field that moves as fast as artificial intelligence, it’s important to be able to update material frequently,” says David Joyner, executive director of online education in the College of Computing. “That’s usually a problem because re-recording means going back into the studio and trying to make the new content fit in with the old.”

Joyner’s latest massive open online course (MOOC), Foundations of Generative AI, uses artificial intelligence to solve that challenge. Images for the course are created using Sora and DALL·E 3, while early drafts of quizzes were generated by GPT-5. The course also uses Grady, an AI autograder that provides feedback on open-ended essays.

The most striking innovation is DAI-vid (pronounced day-eye-vid), a video avatar of Joyner that leads the instruction. To create it, Joyner uploaded a five-minute clip of himself to the generative AI platform HeyGen, along with course scripts and other inputs. The result is a lifelike digital instructor who can let Joyner update his lessons far more easily.

“With AI, we can just modify the text and have the updated video pop right out,” Joyner says. “It takes minutes at my desk instead of an hour in the studio.”

This approach allows Joyner to keep course materials current and produce new videos entirely on his own. “It’s strange, but in a lot of ways this course feels more like it’s mine than the ones where I’m on camera,” he says. “Because AI lets me handle every part of production myself, the finished product feels like my complete work.”

Joyner sees this experiment as an example of AI’s potential to enhance human talent rather than replace it. “Give me AI and I can do five times more than I could alone,” he says. “But give it to our professional video producers, and they will still far outpace me, because expertise matters most. AI just amplifies it.”

Foundations of Generative AI is now available on edX, and the same material is also part of the OMSCS course CS7637: Knowledge-Based AI.

Students in machine learning and linear algebra courses this semester are learning from one of Georgia Tech’s most celebrated instructors.

Raphaël Pestourie has earned back-to-back selections to the Institute’s Course Instructor Opinion Survey (CIOS) honor roll, placing him among the top-ranked teachers for Fall 2024 and Spring 2025.

By returning to the classroom this semester to teach two more courses, Pestourie continues to leverage proven experience to mentor the next generation of researchers in his field.

“Students played a very important part in the survey process, and I thank them for making the classes great,” said Pestourie, an assistant professor in the School of Computational Science and Engineering (CSE).

“I'm incredibly grateful that students shared their feedback so that I could go the extra mile to not only apply my expertise to teach in ways that I think work, but transform my instruction to reach students in the most impactful way I can.”

CIOS honor rolls recognize instructors for outstanding teaching and educational impact, based on student feedback provided through end-of-course surveys. 

Student praise of Pestourie’s CSE 8803: Scientific Machine Learning class placed him on the Fall 2024 CIOS honor roll. He earned selection to the Spring 2025 honor roll for his instruction of CX 4230: Computer Simulation. 

CSE 8803 is a graduate-level, special topics class that Pestourie created around his field of expertise. Scientific machine learning involves merging two traditionally distinct fields: scientific computing and machine learning.

In scientific computing, researchers build and use models based on established physical laws. Machine learning differs in that it employs data-driven models to find patterns without prior assumptions. Combining the two fields opens new ways to analyze data and solve challenging problems in science and engineering.

Pestourie organized student-focused scientific machine learning symposiums in Fall 2023 and 2024. CSE 8803 students work on projects throughout the course and present their work at these symposiums. Pestourie will use the same approach this semester. 

Compared to CSE 8803, CX 4230 is an undergraduate course that teaches students how to create computer models of complex systems. A complex system has many interacting entities that influence each other’s behaviors and patterns. Disease spread in a human network is one example of a complex system. 

CX 4230 is a required course for computer science students studying the Modeling & Simulation thread. It is also an elective course in the Scientific and Engineering Computing minor.  

“I see 8803 as my educational baby. Being acknowledged for it with a CIOS honor roll felt great,” Pestourie said. 

“In a way, I'm prouder of CX 4230 because it was a large, undergraduate regular offering that I was teaching for the first time. The honor roll selection came almost as a surprise.”

To be eligible for the honor roll recognition, instructors must have a minimum CIOS response rate of 70%. Composite scores for three CIOS items are then used to rank instructors. Those items are:

• Instructor’s respect and concern for students
• Instructor’s level of enthusiasm about the course
• Instructor’s ability to stimulate interest in the subject matter

Georgia Tech’s Center for Teaching and Learning (CTL) and the Office of Academic Effectiveness present the CIOS Honor Rolls. CTL recognizes honor roll recipients at its Celebrating Teaching Day events, held annually in March.

CTL offers the Class of 1969 Teaching Fellowship, in which Pestourie participated in the 2024-2025 cohort. The program aims to broaden perspectives with insight into evidence-based best practices and exposure to new and innovative teaching methods.

The fellowship offers one-on-one consultations with a teaching and learning specialist. Cohorts meet weekly in the fall semester and monthly in the spring semester for instruction seminars. 

The fellowship facilitates peer observations where instructors visit other classrooms, exchange feedback, and learn effective techniques to try in their own classes.

“I'm very grateful for the Class of 1969 fellowship program and to Karen Franklin, who coordinates it,” Pestourie said. “The honor roll is not just a one-person award. Support from the Institute and other people in the program made it happen.”

Like in Fall 2023 and 2024, Pestourie is teaching CSE 8803: Scientific Machine Learning again this semester. Additionally, he teaches CSE 8801: Linear Algebra, Probability, and Statistics.

Linear algebra and applied probability are among the fundamental subjects in modern data science. Like his scientific machine learning class, Pestourie created CSE 8801. This semester marks the second time Pestourie is teaching the course since Fall 2024.

Pestourie designed CSE 8801 as a refresher course for newer graduate students. This addresses a point of need to help students get off to a good start at Georgia Tech. By offering guidance early in their graduate careers, Pestourie’s work in the classroom also aims to cultivate future collaborators and serve his academic community.

“I see teaching as our one shot at making a good first impression as a research field and a community,” he said. 

“I see my work as a teacher as training my future colleagues, and I see it as my duty to our community to do my best in attracting the best talent toward our research field.”

Two Georgia Tech Ph.D. students created a student-run, faculty-graded, fully-accredited course that links math, engineering and machine learning.

Andrew Rosemberg, with assistance from Michael Klamkin, both student researchers with the U.S. National Science Foundation AI Research Institute for Advances in Optimization (AI4OPT), designed the course to bridge gaps they saw in existing classrooms.

“While Georgia Tech offers excellent courses on optimization, control, and learning, we found no single class that connected all these fields in a cohesive way,” Rosemberg said. “In our research, it was clear these topics are deeply interconnected.”

Problem-driven learning

The course starts with fundamental problems and works backward to the methods required to solve them. Rosemberg said this approach was intentional. He said that courses often center around methods in isolation rather than showing how the methods contribute to the larger context. This keeps the course focused on problem-driven discovery.

The class also serves as a way for Rosemberg and Klamkin to strengthen their own teaching and mentoring skills.

Goals and structure

The primary goal of the course is to help students build a clear understanding of how mathematical programming, classical optimal control, and machine learning techniques such as reinforcement learning connect to one another. Students are also working to produce a structured book by the end of the semester.

“The hope is that this resource will not only solidify our own learning but also serve as a guide for other students who want to approach these problems in the future,” Rosemberg said.

Responsibilities are distributed across participants, with each student delivering lectures, reviewing peers’ work, and contributing to collective discussions. Rosemberg and Klamkin provide additional support where needed, while faculty mentor and director of AI4OPT, Pascal Van Hentenryck, ensures the class stays aligned with broader academic objectives.

Student ownership and collaboration

Rosemberg noted that the student-led model gives students a deeper sense of ownership, making them responsible for their own learning, and having a stronger impact. This model allows students to determine what to learn and why, which promotes critical thinking.

The course uses GitHub as its primary workflow platform. Rosemberg said adds transparency and prepares students for real-world research practices.

“GitHub functions much like university systems such as Canvas or Piazza. It also has the added benefit of making all contributions visible to the world,” Rosemberg explained. “This helps students take pride and ownership of their work, while also introducing them to Git, an essential tool for software development and modern STEM research.”

Emerging insights and challenges

Students have begun aligning their research with course themes, including shaping qualifying exam topics around the intersections of operations research, optimal control and reinforcement learning. Rosemberg said exploring the comparative strengths of these fields side by side has been one of the most rewarding outcomes.

Balancing independence with guidance has proven to be the greatest challenge. He said they have been evolving alongside the students in real time and have learned to emphasize mutual responsibility to promote the collective progress of the class.

Looking ahead

Rosemberg said future iterations of the course may place more emphasis on setting expectations early, given the effort required to deliver a lecture in this format.

His advice for others who may want to replicate the model is to focus on building a committed core team.

“Start with a small, motivated group,” Rosemberg said. “Like a startup, success depends less on the structure and more on the dedication of the people involved.”

By Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute | Supply Chain Advisor | Former Executive at Frito-Lay, AJC International, and Coca-Cola

Introduction

This year has felt like a lifetime in the Generative AI (GenAI) world. Tools, capabilities, and best practices are shifting monthly, sometimes weekly. For supply chain professionals, the message is clear: ongoing development is not optional. Like lean, analytics, or S&OP in prior decades, GenAI proficiency is quickly becoming a differentiator. The question is not if you’ll integrate GenAI into your workflow, but how quickly and effectively. 

The Evolution of GenAI in 2025

When we look back to January, it’s striking how much progress has been made in less than a year. Early in 2025, the conversation centered on agentic AI and larger models. GPT-5 and Claude 4 improved reasoning and context windows, while OpenAI introduced ChatGPT Agent in preview, able to carry out bounded multi-step tasks like retrieving files, browsing the web, and drafting structured outputs. In supply chain, this translated into early experiments with automating shipment steps or running contract reviews in a single query — tasks that were pilot-level at best in January.

By mid-year, multimodal capabilities and enterprise copilots began shifting from concept to daily use. Users could combine text, image, and voice inputs to detect defects or summarize complex documents, and copilots became embedded inside SAP, Oracle, Microsoft, and Google platforms. For the first time, GenAI wasn’t just a tool "off to the side" but something integrated directly into the systems supply chain professionals rely on.

In the second half of the year, new capabilities started layering on: memory, specialized small models, and synthetic data with digital twins. Memory allowed copilots to recall context from prior chats or S&OP cycles, reducing rework. Domain-tuned models made GenAI lighter, cheaper, and faster for logistics, procurement, and planning tasks. And digital twin integration allowed organizations to stress-test networks under disruption scenarios, from weather to labor shortages.

Enterprises also moved closer to operations with AI at the edge, using IoT data for predictive maintenance or real-time routing. At the same time, guardrails and compliance became a central topic, with more organizations creating clear "green/yellow/red" tiers for safe use. And in Q4, collaboration AI and hybrid architectures came to the forefront — copilots that can negotiate contracts in multiple languages, and architectures that blend closed and open-source models to balance sovereignty, cost, and security.

For mainstream individual users, the picture is simpler but still powerful. Anyone with ChatGPT Plus or Copilot today can take advantage of:

• Memory and custom instructions to save preferences and formats across sessions.
• Project-only memory (rolling out) to organize work by context.
• Agent previews like Operator to see how automation might work on bounded tasks.
• Connectors and file uploads to bring internal data into conversations. 

For leaders, the focus is on policy, safe pilots, and scaling. They are:

• Sponsoring agent experiments in low-risk domains (like supplier alerts).
• Embedding copilots in enterprise systems for daily planning and reporting.
• Formalizing AI use policies so employees know what’s encouraged, conditional, and off-limits.

The net result: what started in January as experimentation has, by October, become a layered landscape. Individual users now have practical tools to reclaim time, while leaders are piloting more ambitious integrations and building the governance to make adoption sustainable.

1. Action Planning is Critical

The pace of change makes a one-and-done training activity insufficient. Think of GenAI skills like fitness: it requires steady reps over time. Professionals who set quarterly development goals — experimenting with new tools, building prompt libraries, testing workflows — will not only stay current but pull ahead.

💡 Try This Quarter:

• Build a custom prompt library for routine tasks (e.g., supplier follow-ups, KPI summaries).
• Test one open-source tool such as LangChain or Haystack.
• Use AI to summarize two recent meetings and validate output with your notes. 

2. Prompt Maturity is the New Literacy

I’ve personally learned the most about prompting by asking ChatGPT to critique my style against a 12-step framework. The feedback gave me a process improvement plan I still use today. Prompt maturity isn’t abstract — it’s a measurable, improvable skill.

💡 Applied step: Rewrite one work prompt per week by climbing the ladder. 

3. Unlocking Personal Productivity

One of the fastest returns from GenAI comes from personal productivity. In our short courses this year, I’ve seen learners gain comfort and lower stress as they practice more with the tools. Many reclaimed time by using GenAI for emails, presentations, meeting notes, and data prep.

While the list of GenAI time-saving strategies is broad, some uses are already mainstream and validated by thousands of professionals. The table below organizes these strategies into categories, provides guidance on how to accomplish them, and highlights common watch-outs to ensure they deliver value without risk.

💡 Try this week: Track one workflow where AI saved time and estimate the hours reclaimed.

4. Critical Thinking: Ironically More Important than Ever

We wrote about critical thinking and added it to our curriculum after studies raised concerns about overreliance on AI. The smarter the tools become, the more important it is to validate their outputs.

💡 Applied step: Take one AI output this week and run it through the checklist — you’ll see both strengths and blind spots.

5. Advocating for Strategy and Guardrails

We’ve seen firsthand how AI policies can evolve. One major retailer shifted in less than a year from a rigid “only data scientists experiment” model to encouraging all employees to try safe versions of multiple LLMs. This shift shows why professionals should advocate for strategy and guardrails that evolve with the technology.

💡 Ask your manager: Which of our daily tasks fall into green, yellow, and red today? 

6. Agents: Early but Essential

Many industry partners are actively testing agents. Our software partners are hitting singles and doubles now, with bigger “home run” opportunities still developing. Agents aren’t fully reliable yet, but they are advancing quickly and will increasingly appear in ERP, TMS, and WMS platforms. 

In practice, most organizations today sit between Level 1 (Exploratory) and Level 2 (Task-Specific Agents), with early pilots pushing into Level 3 (Augmented Workflows). Tech-forward enterprises — particularly in retail, e-commerce, and global manufacturing — are building domain-specific agents for forecasting, procurement support, and transportation planning, often embedded inside ERP or planning platforms. These companies are experimenting with multi-agent coordination but keep humans firmly in the loop. By contrast, mainstream companies are still largely in the exploratory stage: individuals using general copilots for drafting documents or ad hoc analysis, without enterprise integration, security controls, or governance. The gap is widening — forward-leaning firms are developing playbooks for orchestrated workflows, while many organizations are just beginning to set policies and figure out where AI fits safely into their operations.

Looking ahead, Level 4 (Collaborative Automation) is where the near-term breakthroughs will happen. In the next 3–5 years, we can expect multi-agent orchestration to become a practical tool for managing recurring disruptions — think transportation rerouting during weather events or automated supplier alerts when delivery milestones are missed. Early adoption will occur in large, tech-forward enterprises with strong governance and secure infrastructure. Level 5 (Autonomous Resilience) remains aspirational: while the vision of end-to-end supply chain automation is compelling, regulatory hurdles, trust, and explainability challenges mean human oversight will remain essential. The more realistic trajectory is that enterprises will selectively automate narrow disruption scenarios while maintaining tight human control, with broader autonomy coming only as governance, standards, and trust mechanisms mature.

💡 Applied step: Identify one repetitive process in your work that could be a candidate for an agent. 

7. Human in the Loop: Non-Negotiable

Competition has improved model quality this year — but hallucinations and memory issues remain. That’s why “human in the loop” is not just a principle; it’s operational reality. AI is still an assistant, not a replacement.

💡 Applied step: Write down one checkpoint you always apply before sharing AI outputs.

Conclusion

These observations — from teaching courses, updating curriculum, and watching partners experiment — motivated this article. GenAI is evolving at extraordinary speed, and our profession must evolve with it. Build your plan, refine your prompts, reclaim time, apply critical thinking, advocate for strategy, explore agents, and always keep the human in the loop. Those who do will thrive in 2026 and beyond.

Saad Bhamla of Georgia Tech’s School of Chemical and Biomolecular Engineering (ChBE) is a member of a global cohort of eight scientists and engineers who were named Schmidt Polymaths. They will each receive up to $2.5 million over five years to pursue research in new disciplines or using new methodologies, Schmidt Sciences announced today.

As Schmidt Polymaths, the researchers pursue new approaches compared to previous work. The new cohort of polymaths will answer questions like how to expand access to healthcare with low-cost technologies, what happens to our chromosomes when we age and how to create more accurate computer simulations of climate. 

Bhamla, associate professor in ChBE@GT, is the first Schmidt Polymath from Georgia Tech. He will develop low-cost technologies to tackle planetary-scale challenges, including AI-enabled point-of-care diagnostics in low-resource environments, and he will also engineer autonomous morphing machines that adapt, evolve and learn like living systems.

The eight selected scientists represent the fifth cohort of the highly selective Schmidt Polymaths program. Awardees must have been tenured—or achieved similar status—within the previous three years. Previous cohorts have used the award to design new sensor devices, perform experiments at atomic resolutions, analyze trees of life with faster and more efficient algorithms, discover new mathematical formulas assisted by AI, and more. 

Drawn from universities worldwide and selected through a competitive application process, Schmidt Polymaths are required to demonstrate past ability and future potential to pursue early-stage, novel research that would otherwise be challenging to fund—even without the current dramatic declines in U.S. funding for science. 

“Our world is one deeply interconnected system---but to study it more deeply, we’ve divided it into increasingly narrow categories,” said Wendy Schmidt, who co-founded Schmidt Sciences with her husband Eric. “Schmidt Polymaths see the bigger picture, pursue answers beyond boundaries and expand the edges of what’s possible.  Their work can help steer  us all toward a healthier  future, for people and the planet.”

About Schmidt Sciences

Schmidt Sciences is a nonprofit organization founded in 2024 by Eric and Wendy Schmidt that works to accelerate scientific knowledge and breakthroughs with the most promising, advanced tools to support a thriving planet. The organization prioritizes research in areas poised for impact including AI and advanced computing, astrophysics, biosciences, climate, and space—as well as supporting researchers in a variety of disciplines through its science systems program.

RELATED: Forbes featured Bhamla in the article: Saad Bhamla Is A Polymath