By Elizabeth (Liz) Ngonzi

Last month, I watched Martha Stewart discuss Hint Home Intelligence, the AI-powered home management company she co-founded, during the Fast Company Innovation Festival in New York. I've followed her work for many years, so I was particularly interested in hearing how she became involved in an AI venture. What I hadn't expected was to learn that she'd been thinking about developing a digital home organizer for approximately 40 years.

During the panel, Martha described how she'd approached software engineers and investors with the idea decades ago, only to be told that the technology wasn't sufficiently developed to handle what she envisioned. She went on to build an extraordinary business spanning publishing, television, consumer products, and other ventures, but apparently never abandoned the original concept.

Her experience illustrates something I've been discussing with leaders and institutions as we consider how AI is changing the value of human expertise. We spend considerable time talking about which jobs or functions AI might replace, but much less time examining the knowledge, relationships, and intellectual property that people and organizations have accumulated over decades, and how technology might allow us to apply those assets in entirely new ways.

I think this is particularly relevant for seasoned professionals who may have spent 20, 30, or even 40 years developing expertise in their respective fields. Rather than assuming that the arrival of AI diminishes the value of that experience, perhaps we should be examining how the technology can help us make better use of what we've learned.

That's what I mean when I speak about Regenerative Intelligence: using AI to help activate and extend human knowledge and capabilities, while creating opportunities for that knowledge to continue evolving and generating value.

Martha's experience also offers an interesting example of what I've been calling the Human Moat, the combination of expertise, judgment, relationships, and earned trust that distinguishes what an individual or institution brings to the table.

During the panel, which also featured Hint co-founder and CEO Yih-Han Ma and investor and advisor Brian Kelly, also known as The Points Guy, Martha explained that she first imagined creating a home organizer around the time she was developing Martha Stewart Living.

If you've ever managed a home, you know how complicated that can become. There are appliances to maintain, insurance policies to understand, renovations to document, service providers to coordinate, and any number of unexpected problems that arise. Martha saw an opportunity to help people organize all of that information and make better decisions.

She approached people in the technology industry, including engineers and venture capitalists, but the response was that what she envisioned was too complex for the computing capabilities available at the time.

I found that fascinating because we sometimes assume that innovation begins when a new technology arrives. But in this case, the vision existed long before the technology could support it.

Meanwhile, Martha spent the intervening decades developing an extraordinary body of intellectual property. According to the panel introduction, she's authored 102 books, in addition to producing extensive content across magazines, television, and digital platforms. Much of that work relates directly to the practical aspects of managing a home, from cooking and gardening to renovations and maintenance.

Today, Hint is attempting to bring aspects of that expertise together with AI and property-specific information to help homeowners manage their homes more effectively. During the discussion, Ma explained that the platform draws on approximately 1,300 data points about a property, combining that information with broader expertise to identify maintenance needs, help homeowners understand insurance coverage, and provide recommendations.

The company launched with $10 million in seed funding, which certainly demonstrates investor interest, although its longer-term commercial performance remains to be seen.

What interests me is the way an idea that couldn't be executed four decades ago has become possible through a combination of technological advancement, accumulated expertise, and collaboration.

And that brings me to something I've been thinking about in relation to the value of experience. How much knowledge exists within our organizations, professional networks, and personal archives that we haven't been able to fully use because we lacked the tools or partnerships to make it accessible?

As I listened to Martha speak, I was reminded of her 2005 book, The Martha Rules: 10 Essentials for Achieving Success as You Start, Build, or Manage a Business.

The book reflects lessons from her experience building businesses, including the importance of pursuing work that genuinely interests you, understanding your market, maintaining high standards, and surrounding yourself with capable people.

I think it's worth revisiting those principles in the context of AI because the technology is making it easier to produce all kinds of things, whether that's written content, software, marketing materials, or new products. But the ability to produce something doesn't automatically mean that anyone needs it, that it solves the right problem, or that people will trust it. One example from the panel illustrates this beautifully:

Ma described an early version of Hint's website that featured an image of an especially luxurious home. Martha challenged the choice because she felt it didn't represent the customers the company was trying to reach.

Think about that for a moment! Here is someone whose brand has long been associated with beautiful homes and aspirational living, yet she recognized that the image might alienate the very people who could benefit from the product.

The company wasn't developing Hint exclusively for wealthy homeowners with multiple properties. Ma described wanting to support people who had worked hard to purchase their first homes and were now confronting the unexpected costs and complexities of maintaining them.

Martha's intervention wasn't about whether the photograph looked good. It was about whether the company's positioning reflected the needs and experiences of its intended customers.

That's the kind of judgment that comes from understanding a market, building relationships with audiences, and developing an instinct for what will resonate. It's also why I believe we need to be careful about treating technical capabilities as substitutes for the experience that informs good decisions.

The fundamentals Martha wrote about two decades ago haven't become irrelevant simply because we're now working with AI. If anything, the increased speed at which organizations can produce and distribute content and products makes judgment, quality, and credibility even more important.

One of my favorite moments came toward the end of the panel when the moderator asked Martha whether working with a technology company had made her interested in learning to code.

Martha said that coding interested her, but she didn't need to do it herself. "They're coding for us," she explained.

I loved that response because it speaks to something I encounter frequently in my work with leaders and organizations.

There's sometimes an assumption that to participate meaningfully in AI, you need to become a technical expert. Of course, leaders should understand the capabilities and limitations of the technology, particularly when they're making decisions about how it's deployed. But that doesn't mean everyone needs to become a software engineer or develop sophisticated technical skills.

What matters is understanding the problem you're trying to solve, knowing what good outcomes look like, and being able to collaborate with people who bring complementary capabilities.

What's particularly interesting about Martha's involvement is that she's not simply lending her name to the company.

Ma described her as someone who continually asks questions, brings ideas to the team, follows developments in technology, and challenges their thinking. He also emphasized that she remains a student despite everything she's accomplished.

I think that's an important characteristic of effective leadership, particularly now when the technology is changing so quickly.

You don't need to know everything, but you do need to remain curious and willing to learn. And you should recognize that your experience can contribute something valuable to the development of technology, even if you're not the person writing the code.

This is where the concept of a Human Moat becomes particularly relevant. Your value isn't simply the information you've accumulated. It's also your ability to interpret that information, recognize patterns, understand context, ask better questions, and apply your judgment to situations that may be unfamiliar to others.

There's another aspect of Hint's origin story that I particularly enjoyed, and it relates directly to my 1+1+AI=10® framework.

Martha and Brian Kelly described how the venture came together during an Easter gathering at her home. Brian was there with his children, along with Kyle Rush, who would become Hint's co-founder and CTO.

As they were talking, Martha shared her longstanding interest in creating a home management platform. Brian immediately recognized the problem because he'd experienced the challenges of maintaining his own property, including dealing with contractors, managing costs, and trying to make informed decisions.

It turned out that Kyle and his colleagues were already developing technology in this area. Through those relationships, Martha's decades-old vision connected with a team that had relevant technical and commercial experience.

I thought this was a wonderful illustration of the role that human relationships play in innovation.

My 1+1+AI=10® framework is based on the principle that when we bring together individual expertise, complementary human capabilities, and AI, we can create opportunities and outcomes that wouldn't be possible through any one of those elements working independently.

In Martha's case, she brought decades of domain expertise, intellectual property, an established brand, and a deep understanding of what it takes to manage a home.

The broader team brought experience in consumer services, technology development, investment, and understanding the challenges homeowners face. Ma, for example, described spending years working with businesses that served millions of homeowners and hearing repeatedly about their frustrations with unexpected expenses, confusing decisions, and service providers they didn't always trust.

AI provides the ability to process property-specific information, connect it with relevant expertise, and deliver recommendations to homeowners in ways that would have been difficult to accomplish through traditional one-to-one services.

The potential result is a platform that makes specialized knowledge more accessible while creating a new business opportunity.

I emphasize potential because a promising concept and a well-funded launch don't necessarily guarantee a successful business. Hint will still need to demonstrate that it provides reliable advice, solves problems customers genuinely value, and can sustain an effective commercial model.

Nevertheless, the way the team came together illustrates why I believe innovation shouldn't be viewed exclusively through a technological lens.

It was a conversation among people with different experiences, interests, and relationships that created the opportunity to bring these capabilities together.

And interestingly, Martha hadn't abandoned her vision just because she couldn't accomplish it when she first conceived it.

The broader implications of this venture go well beyond home management.

As AI models become more capable and widely available, organizations are going to have to think carefully about what distinguishes their products and services from those of their competitors.

Access to a sophisticated AI model is certainly valuable, but if competitors can access similar technology, that alone may not provide a sustainable advantage.

That's where domain expertise, proprietary knowledge, established relationships, and institutional experience become particularly important.

I've seen different versions of this through my own work.

For example, while serving on the board of the Court of Master Sommeliers Americas, I became involved in exploring how AI-powered live translation could support the organization's educational and examination activities in Japan and Taiwan.

The challenge wasn't that participants lacked specialized knowledge about wine. It was that language differences could interfere with their ability to demonstrate that knowledge.

Working with Wordly AI, we explored how live translation could help reduce those barriers, allowing greater attention to be placed on the expertise participants were actually expected to demonstrate.

I've also explored this in developing my own digital twin, Liz Ngonzi GPT ∞, which draws on my professional knowledge, publications, and frameworks to create additional ways for people to engage with my work.

Neither example is identical to Martha's business model, but both illustrate how AI can help make specialized expertise accessible in ways that would previously have required more time, resources, or direct human involvement.

There's an important distinction here, however. Digitizing knowledge doesn't automatically preserve its meaning or guarantee its accuracy.

If an AI system is drawing on professional expertise, we need to consider the quality of its sources, how that information is interpreted, whether it remains current, and how people can evaluate the recommendations it produces.

In my own work, I emphasize source-grounded approaches precisely because expertise loses much of its value when it becomes disconnected from the context and standards that make it credible.

And that brings me back to Martha!

Her contribution to Hint isn't limited to the knowledge she's already documented. She's actively participating in the company's development, questioning decisions, and helping the team consider how its recommendations and user experience should evolve.

That continued involvement is significant because it demonstrates that experienced professionals need not simply hand over their knowledge to technology. They can participate in shaping how the technology uses that knowledge and how it serves others.

One of the other aspects of the panel that caught my attention was the discussion about trust.

Ma described hearing from homeowners who felt that many of the companies they encountered were primarily interested in selling them something. They struggled to determine whose recommendations were genuinely in their interests.

That experience helped shape the team's ambition to build a business that would operate on behalf of homeowners.

During the discussion, Ma emphasized that the homeowner's data belongs to the homeowner and that the company has a responsibility to protect it rather than sell it or use it for unrelated purposes.

I appreciated hearing that commitment because a platform of this kind may handle highly personal information, including property records, insurance policies, maintenance histories, and other details about people's homes.

But as someone who works in responsible AI governance, I also recognize that commitments need to be translated into operational practices.

It matters how data is collected, stored, accessed, and protected. It matters whether users understand how recommendations are generated, particularly when those recommendations concern insurance coverage, repairs, or significant financial decisions. And it matters whether the company's eventual revenue model reinforces its commitment to operating in the homeowner's interest.

These are not separate considerations from business success. They're part of what will determine whether customers continue trusting the platform.

Martha and Brian bring established relationships with audiences who have followed their work for years, which may help Hint attract attention and initial interest. But maintaining that trust will ultimately depend on the company's ability to deliver on its promises.

That is another dimension of the Human Moat. Trust can take decades to build, yet it must continue to be earned through the decisions an organization makes and the experiences it provides.

I think there's a useful exercise here for anyone who's spent years building a career, organization, or body of specialized knowledge.

Consider the information and expertise you've accumulated. It might include proprietary methodologies, research, client experiences, educational content, operational practices, professional relationships, or insights that have never been formally documented.

Some of those assets may have been developed for a particular purpose at a particular moment. Others may represent ideas you've wanted to pursue but haven't had the time, resources, or capabilities to realize.

Now consider what might be possible if you approached them through the lens of Regenerative Intelligence.

I'd begin by looking at the problems your experience has prepared you to understand particularly well. Martha's original idea wasn't driven by an interest in creating software for its own sake. She had identified a complex, recurring problem that she knew intimately.

I'd then examine which aspects of your expertise could be documented, organized, or made more accessible through technology, while being realistic about what still requires human judgment and direct engagement.

The next consideration is partnerships. You may have extensive domain expertise but lack the technical capabilities to develop a particular application. Someone else may understand the technology but lack your insight into the market or the problem. Bringing those perspectives together may create opportunities neither party could pursue independently.

I would also pay close attention to intellectual property, data stewardship, and governance. If you're going to build new services or products around your expertise, you need to understand what you own, what you're permitted to use, how information will be protected, and how the resulting systems will be evaluated.

Finally, I'd think about how you intend to measure value. Depending on your goals, that might involve generating new revenue, reducing costs, extending your advisory reach, improving decisions, increasing access to specialized knowledge, or enabling colleagues and future generations to build on what you've learned.

There isn't one model that will work for everyone.

Some professionals may develop new businesses. Others may strengthen the organizations they already lead, create new educational resources, or find ways to preserve institutional knowledge that might otherwise be lost when experienced colleagues retire or move on.

What matters is recognizing that accumulated expertise can be an active resource for future innovation rather than something that belongs exclusively to the past.

I began this article with Martha's 40-year-old idea because I think there's something important about the fact that she continued carrying it with her, even while developing so many other businesses and projects.

When the technology and the right collaborators became available, she was able to participate in bringing that idea to life.

And I particularly appreciated hearing Ma describe her continuing curiosity and willingness to learn. Despite everything she's accomplished, she's still engaging with new ideas, asking questions, and contributing to a venture in an unfamiliar technological environment.

I recognize that not everyone has access to Martha Stewart's resources, reputation, or professional network. Those advantages matter, and it would be unrealistic to suggest that every seasoned professional can replicate her experience.

But the broader lesson is still relevant.

Throughout our careers, we accumulate much more than job titles and professional accomplishments. We develop judgment, relationships, specialized knowledge, ways of solving problems, and an understanding of our respective fields that may be difficult to capture in a résumé or professional biography.

Some of that expertise may be immediately useful in an AI-enabled environment. Other aspects may require us to rethink how it's organized, shared, or applied.

For me, Regenerative Intelligence is about exploring those possibilities while remaining attentive to the human relationships, judgment, and responsibility that give knowledge its value.

Martha's experience illustrates that we don't necessarily have to choose between the expertise we've spent decades developing and the new capabilities emerging through AI. There's an opportunity to bring them together, learn from the process, and create something that builds on both.

I find that a much more interesting proposition than simply asking whether AI will make experienced professionals less relevant.

Elizabeth (Liz) Ngonzi, is a human-centered AI strategist and executive educator, Adjunct Assistant Professor at NYU's Center for Global Affairs, and Board Member and Founding Chair of the Ethics & Responsible AI Committee at the American Society for Artificial Intelligence. Through the LizNgonzi.ai Ecosystem, she works with leaders and institutions on human-centered AI strategy, governance, workforce transformation, and organizational growth.