By Dr. Gleb Tsipursky

The world of design and manufacturing is being quietly but powerfully transformed by a new wave of artificial intelligence. Helping lead this revolution is Paul Powers, the founder and CEO of Physna, a company whose name is derived from “physical DNA.” In a recent interview, Powers offered a glimpse into how Gen AI is being used to decode the complexities of 3-D modeling, creating tools that not only understand the physical world but help shape its digital twin with precision.

Unlocking the Language of 3-D Geometry

Physna doesn’t just interpret 3-D models—it translates them into code. This translation forms the basis of a novel approach where geometry is no longer locked in static visualizations but becomes a searchable, comparable, and analyzable data form. Powers describes this as building a “bridge between what’s physical and digital,” allowing machines to understand shapes and structures as intuitively as they parse text.

At the core of Physna’s value proposition is its ability to extract more meaningful data from 3-D models than traditional systems. By transforming these models into a format readable by AI, the company has developed capabilities far beyond surface-level geometry. Their tools can decipher internal structures, assess proper scale and measurements, and ultimately enable both analytical and generative AI applications.

From Insight to Innovation: The Lifecycle of an AI-Driven Project

Physna’s projects follow a sequence that begins with ingestion and analysis of a company’s 3-D model data. “Instead of starting with a giant training dataset to make something generalized,” Powers explains, “we focus on data specific to a company or industry.” This specificity is crucial. A part designed for aerospace functions vastly differently than one built for automotive applications, even if they might appear similar on the surface.

The early stages of a project are analytical. By understanding the relationships between a part’s geometry and its metadata—such as how it’s manufactured or how it performs in the field—Physna provides immediate benefits. Companies can deduplicate parts, identify alternative suppliers, and streamline design processes by leveraging existing knowledge. This alone can significantly reduce costs and engineering cycle times.

Only after these foundational insights are established does generative AI come into play. Here, Gen AI doesn’t attempt to create entire planes or vehicles from scratch. Instead, it is used to optimize individual components—say, a more wear-resistant fuel valve under a specific weight threshold. Powers emphasizes that reliable generative outputs stem from deep analytical understanding. “You need to understand what works and what doesn’t based on past data before you can responsibly generate new designs.”

Enhancing, Not Replacing, Human Ingenuity

The specter of job displacement often shadows AI discussions. But in Powers’ experience, engineers and designers inside companies aren’t threatened by these tools. “They’re thinking, ‘You’re making my job way easier,’” he notes. Physna helps these professionals move away from redundant tasks like redesigning already existing parts and toward higher-value activities such as innovation and optimization.

The fear of obsolescence, he argues, is largely external. Inside organizations, the reaction tends to be pragmatic. If a tool prevents engineers from inadvertently reinventing the wheel, it’s not eliminating jobs—it’s enhancing human capability. “You’re making them able to make faster, more accurate, and better decisions more rapidly,” he says.

Breaking the Learning Curve with Intuitive Design

One of the clearest insights from Powers is his view on adoption. “If we have to spend too long training somebody, we’ve messed up,” he says. His goal is to make Physna’s platform so intuitive that even highly technical tasks become accessible without extensive onboarding. This is achieved by designing interfaces that mirror natural human interactions—point, click, and increasingly, converse.

He draws parallels with ChatGPT, noting how the lack of a traditional menu system allows users to engage in a more natural, conversational flow. While complex design tasks still require precision, the goal is to move toward an interface where users can communicate intentions directly and get results without elaborate instructions.

Trust, however, remains the biggest barrier. Not blind trust, but a willingness to “dare to try something new.” Powers sees this psychological leap as more significant than any technical hurdle. Once users see the productivity gains, resistance fades.

The Future of Gen AI in Design

Looking ahead, Powers anticipates a future where AI systems become both more specialized and more generalized. It sounds paradoxical, but what he means is that domain-specific tools like Physna will offer generalized, intuitive interfaces. Users won’t need to understand the inner workings of 3-D modeling or AI to get meaningful results—they’ll simply describe what they need, and the system will do the heavy lifting in the background.

He also sees deeper personalization on the horizon. Gen AI tools, like language models today, will become increasingly attuned to individual users. Preferences, habits, and workflows will be learned and adapted to, allowing the AI to anticipate needs instead of just responding to commands.

And the physical-digital divide will continue to shrink. Physna is already experimenting with integrations involving augmented reality, providing real-time interactions with 3-D models. In the near future, your glasses might not only display a model but also predict what you’re about to ask about it—and take action accordingly.

Designing the Future, One Model at a Time

Paul Powers and his team at Physna aren’t just digitizing design—they’re reimagining it. By blending the strengths of analytical and generative AI, they’re creating a platform that empowers engineers to innovate faster, smarter, and with more context than ever before. In this world, design becomes less about drawing from scratch and more about evolving with insight.

As Gen AI continues to evolve, so too will its role in shaping the physical world. With leaders like Powers at the helm, the promise of AI in design feels less like a distant horizon and more like a rapidly unfolding reality.

About the Author

Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps SME leaders in professional and financial services transform AI hype into real-world results. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

By Dr. Gleb Tsipursky

In today’s rapidly evolving technological landscape, generative artificial intelligence (Gen AI) has emerged as a transformative force, reshaping industries and redefining operational paradigms. It offers unprecedented opportunities for organizations to enhance efficiency, innovation, and competitiveness. For organizational leaders, the imperative is clear: to harness the full potential of Gen AI, a meticulously crafted learning program is essential. Such a program must address the diverse needs of the workforce, ensuring that employees are not only knowledgeable but also proficient in applying Gen AI tools within their specific roles. As Gen AI adoption accelerates, training programs must prioritize relevance, accessibility, and equity to ensure maximum impact.

Tailoring Learning Content to Specific Roles to Use Gen AI

A universal training approach often falls short in addressing the unique requirements of different roles within an organization. To maximize the impact of Gen AI, it’s crucial to develop role-specific learning modules that align with the distinct responsibilities and expertise levels of employees. This targeted strategy ensures that each team member acquires relevant knowledge and skills pertinent to their function, driving both efficiency and innovation.

For instance, in a law firm setting, attorneys might focus on leveraging Gen AI for legal research, contract analysis, and drafting legal documents, while paralegals could concentrate on automating routine administrative tasks. Similarly, in the healthcare sector, clinicians might utilize Gen AI for diagnostic insights, while administrative staff employ it for scheduling and patient communications. Personalized and role-based learning has traditionally been challenging to implement at scale, but Gen AI offers solutions that enable organizations to overcome these barriers. By tailoring training to specific job functions, organizations foster greater engagement, ensuring employees see direct applications of their learning.

Balancing Theory with Practical Application to use Gen AI

While understanding the theoretical foundations of Gen AI is important, the true value emerges from practical application. Incorporating hands-on exercises and real-world case studies into training programs bridges the gap between abstract concepts and day-to-day practice. This experiential learning approach enables employees to apply Gen AI tools effectively, fostering both confidence and competence.

An effective Gen AI learning program must be accessible and inclusive, catering to employees with varying technical backgrounds and learning preferences. Offering training materials in multiple formats—such as video tutorials, written guides, and interactive simulations—ensures that all employees can engage with the content effectively. For example, some employees may prefer step-by-step video demonstrations, while others might favor detailed textual instructions.

Beyond delivery methods, inclusivity extends to addressing potential biases in training materials and the Gen AI tools themselves. Research from Harvard Business Review underscores that AI systems can inadvertently perpetuate bias if not carefully monitored. Organizations should conduct regular audits of their training content and AI algorithms to promote fairness and equity. Partnering with external experts or adopting frameworks like the Responsible AI approach from Microsoft can help organizations maintain high standards of inclusivity and ethics.

Inclusivity also means providing additional support for employees who may face barriers to learning, such as offering extra training sessions or creating peer mentoring programs. By prioritizing inclusivity, organizations foster a learning environment where every employee feels empowered to develop their Gen AI skills, enhancing organizational capability and morale.

Case Study: A Law Firm’s Gen AI Transformation

A regional law firm recognized the strategic importance of Gen AI in maintaining its competitive edge. To standardize knowledge and leverage AI’s potential, the firm asked me to help them launch a comprehensive learning program tailored to its diverse workforce. Attorneys focused on applications like legal research, drafting, and risk analysis, while paralegals and administrative staff concentrated on automating scheduling, data management, and communications.

The program’s cornerstone was its emphasis on practical application. Employees participated in workshops where they applied AI tools to real-world scenarios, such as automating the review of case documents or drafting responses to legal inquiries. Training materials were made accessible in various formats—video, interactive modules, and written guides—ensuring all staff could engage effectively.

Within six months, the firm observed significant improvements:

• Adoption rates of Gen AI tools surged from under 20% to over 85%.
• Attorneys reported a 30% increase in efficiency for document drafting and analysis.
• Administrative staff reduced time spent on routine tasks by 20%, freeing them to focus on higher-value activities.

These results underscore the transformative potential of a well-designed Gen AI learning program, demonstrating measurable benefits across multiple dimensions of organizational performance.

Overcoming Common Challenges

Despite its promise, implementing a Gen AI learning program is not without challenges. One common obstacle is resistance to change, particularly among employees unfamiliar with AI technologies. Likewise, many employees suffer from automation anxiety, the fear that their skills may become obsolete. To address this, leaders should emphasize the tangible benefits of Gen AI through clear communication and success stories of upskilling from within the organization.

Another challenge is maintaining engagement throughout the training process. Gen AI learning programs should incorporate gamification elements, such as quizzes and progress tracking, to sustain interest.

Lastly, organizations must allocate sufficient resources—both financial and human—to develop and sustain their training initiatives. Securing leadership buy-in is essential to ensure that Gen AI training receives the necessary support and aligns with broader organizational goals, while managing risks.

Conclusion

Designing an effective Gen AI learning program is a strategic imperative for organizational leaders in the era of artificial intelligence. By tailoring content to specific roles, balancing theoretical knowledge with practical application, and ensuring accessibility and inclusivity, organizations can empower their workforce to harness the full potential of Gen AI. The success of these initiatives lies not only in enhancing individual capabilities but also in driving broader organizational transformation. As demonstrated by real-world case studies, a thoughtfully designed learning program can unlock significant efficiency gains, foster innovation, and position organizations as leaders in their industries.

About the Author

Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps SME leaders in professional and financial services transform AI hype into real-world results. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.