An Interview With Chad Silverstein
Turn Volumes of Data into Action. AI’s ability to surface insight from massive data volumes is one of its most powerful applications. McKinsey’s generative AI tool, “Lilli,” is a standout example. Named for Lillian Dombrowski, the firm’s first professional woman hire in 1945, Lilli acts as a researcher and knowledge engine — drawing from more than 100,000 curated internal and external sources to accelerate client service and firm-wide learning.
In today’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Marva Bailer.
With 100,000 views, a TEDx talk on accessible AI and inclusive innovation introduces Marva Bailer’s mission to make technology work for everyone. She’s a board director, national media contributor, and former executive at IBM, Splunk, and Amazon, with deep expertise in data, cybersecurity, and AI. Marva serves on nonprofit and private company boards, bringing a human lens to innovation, helping leaders translate complexity into action and building cultures that thrive in disruption.
Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?
My journey into AI started long before the term was in every headline. My father’s first job was as a librarian at Enoch Pratt Library in Baltimore. I grew up surrounded by research, questions, and a deep respect for how information shapes understanding. I was fascinated by debates and speeches — I watched every U.S. presidential debate I could find on microfiche, that old-school machine in the library that felt like time travel. (Yes, I was on the winning debate team).
Throughout my career, I’ve worked across major tech waves from mainframes and networks to cloud computing. I’ve always been more interested in the outcome than the infrastructure. Conversations with customers and partners focused on curiosity and what is next. What is possible? Why not? In the early 2000s, the phrase “data is the new oil” stuck with me. I became focused on how data, whether structured or unstructured, collected from systems or sensors, could lead conversations that start with “what if.”
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
One of the most memorable moments in my AI journey was being part of the early commercial go-to-market team for IBM Watson after its Jeopardy! win captivated the world. While I wasn’t on the research team that built the system, I had the privilege of helping shape how Watson would show up in the real world, bringing it to life for enterprise customers across the U.S.
One of the first commercial use cases involved digitizing and analyzing medical journals to support more informed clinical decision-making. Shortly after, we expanded into weather data and forecasting, helping companies in retail, travel, and transportation make smarter operational decisions. I remember the stories from Armonk, IBM’s headquarters, about the origin of Watson. Legend has it the idea was sparked in a bar, when a few developers saw everyone glued to the screen during a Jeopardy! championship and thought, what if a machine could play and win?
Today, those early ideas live on through WatsonX and other AI advancements, now supercharged by modern compute power and generative models. It’s remarkable to see how far the vision has come and how quickly it’s accelerating.
You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
Thank you, the three that stand out are perspective, the ability to bring people along, and deep listening have shaped how I lead. For me, success is rooted in mutual value — built through clarity, shared purpose, and meaningful results.
1. Perspective — One of the most valuable traits in the AI space is perspective — the ability to reframe challenges and view decisions through the lens of the customer. I recently did a news segment on a headline about Shopify’s CEO Tobias Lütke, who was being criticized for his bold AI directive. The real message was, “Think like your customer.” He was asking teams to rethink resourcing and workflows, encouraging leaders to show why their goals couldn’t be achieved using AI before requesting new headcount. This reflects a modern evolution of zero-based budgeting, where the focus shifts from plans made 12 to 18 months ago to what’s truly needed now. It’s a prompt to reassess investments, skill sets, and priorities — with AI as the accelerator. As I shared on NewsNation, “He is asking them to think about AI as the accelerator — using the data they have and the creativity they bring to unlock what is next. This is about quality work for employees and delighting the customer.” That ability to reframe with clarity and focus creates lasting trust with both teams and customers.
2. Bringing People Along — Another essential trait is the ability to bring people along with the transformation. At the beginning of the enterprise data era, data was largely owned and governed department by department. Before “data lakes” became the norm, teams like marketing, sales, manufacturing, and operations often managed their information in silos. Coca-Cola was one of the first customers I worked with who saw the potential of connecting it all. They had data from machines, distribution, advertising, social media, sales, and manufacturing — and they realized the real power came from aligning teams around shared insight. Collaboration replaced control. The value wasn’t in having more data; it was in creating trust across teams to act on it. That shift created measurable success — and it came from inclusion, not hierarchy.
3. Listening — The third trait is listening. In high-growth environments, there’s often a rush to lead with a solution before fully understanding the challenge. At Splunk, I worked closely with leaders driving fast-paced change. I remember a meeting with a major healthcare organization where the executive began outlining a key challenge. Before he could finish, someone on our side jumped in to shift the conversation back to the product. That moment reinforced a lasting lesson: listening is not passive. It builds trust, surfaces what matters, and makes room for better outcomes. Some of the most meaningful applications of AI I’ve seen didn’t start with a pitch — they started with a question.
Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?
One of the earliest and most powerful conversations I helped shape around AI and business challenges came from a surprising place: The Beatles.
When news broke that AI had been used to help complete the previously unfinished Beatles song Now and Then, it sparked global discussion. I shared my perspective on Fox News and MSN, helping reframe the moment as more than a creative novelty. This was a real-world example of how AI is shifting the conversation around intellectual property, authenticity, and legacy — and how technology can connect across generations.
The business challenge behind the headlines was clear. Studios, streaming platforms, and artists are now navigating an increasingly complex landscape. What rights are needed? Who holds creative control? How do we manage voice, likeness, and style when they can be replicated with stunning precision?
The Beatles story helped make those questions tangible. AI didn’t generate the song. It enabled engineers to isolate John Lennon’s original vocals from a decades-old demo, making it possible for Paul McCartney and Ringo Starr to complete something deeply human musical idea that had waited years for the right technology to catch up.
This story made the discussion more relatable for multiple generations. People could see how AI could preserve history while opening space for future creativity. It’s these kinds of moments that spark trust and curiosity, helping us move the conversation from hype to real-world impact.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
One of the most common misconceptions I encounter is the assumption that personal use of AI tools equates to enterprise readiness. Using ChatGPT to summarize a document or draft a note helps build familiarity; it doesn’t reflect what it takes to operationalize AI across a business.
It reminds me of how we adopted mobile phones. At first, they were personal tools for calls and texts. Over time, they became the infrastructure for entire industries — logistics, transportation, finance, media. Enterprise value came from rethinking workflows, systems, and customer engagement. The same is true with AI. Real business impact comes from intentional design, governance, and integration, not surface-level experimentation.
Another misconception is that AI is either completely free or that its compute demands make it impractical at scale. In 2006, mathematician Clive Humby coined the phrase “data is the new oil.” In 2013, then-IBM CEO Ginni Rometty expanded on that idea, calling data the next natural resource. Today, AI is powered by decades of collected data and by real-time inputs from systems, sensors, recordings, and unstructured sources — some of it governed, much of it not.
Unlocking its full value requires supercomputing power and scalable infrastructure, especially for emerging use cases like computational fluid dynamics and generative modeling. These are dynamic, self-learning systems that need both power and precision.
AI lowers the barrier to insights while raising the expectations for leadership. When businesses understand that balance, transformation becomes possible.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
AI’s greatest business impact today is increasing capacity — making it possible for companies to serve more people, more effectively, with fewer barriers. Nowhere is this clearer than in the area of accessibility.
When businesses invest in accessibility technologies powered by AI — captioning, speech-to-text, adaptive interfaces, real-time translation — they expand participation. They reach untapped talent. They open up new markets. These are not niche features. They are core enablers of productivity, inclusion, and performance.
McKinsey reports that if we remove barriers to work and life for people with disabilities, we could add $400 billion to global GDP. And according to BCG, 80% of people will experience a temporary or permanent disability during their careers. Accessibility is not a niche use case — it’s a shared reality.
As Microsoft CEO Satya Nadella said, “Accessibility is a human right.” Businesses that understand this don’t just build better workplaces. They build smarter products, stronger teams, and deeper customer trust. AI gives us the ability to design for everyone, from the start. That’s not only the right thing to do — it’s the future of business success.
Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.
Yes, Artificial intelligence is no longer a future concept — it’s a present-day business imperative. According to FactSet, 241 S&P 500 companies mentioned “AI” during their Q4 2024 earnings calls, the highest number in the past 10 years. Companies are sharing early learnings and signaling how integral these technologies are becoming to strategy, operations, and customer experience.
Here are five ways AI helps businesses solve complex challenges today:
(And create opportunities)
1. Turn Volumes of Data into Action
AI’s ability to surface insight from massive data volumes is one of its most powerful applications. McKinsey’s generative AI tool, “Lilli,” is a standout example. Named for Lillian Dombrowski, the firm’s first professional woman hire in 1945, Lilli acts as a researcher and knowledge engine — drawing from more than 100,000 curated internal and external sources to accelerate client service and firm-wide learning.
Other organizations are building similar platforms. JPMorgan Chase developed its own AI assistant to improve deal structuring and accelerate financial analysis. Deloitte has launched internal tools that empower teams to mine years of project documentation, providing instant context and recommended actions. These tools turn knowledge into impact — and help teams respond faster, smarter, and with greater consistency.
2. Strengthen Governance and Data Transparency
Strong AI governance is no longer optional. I’ve seen organizations move quickly to adopt generative tools, only to realize later that employees were entering sensitive data into systems without understanding where it would be stored or how it might be used. That presents risk across security, compliance, and competitive intelligence.
A clear example is the banking and healthcare sectors, where data privacy is heavily regulated. Some institutions have responded by building private, domain-specific AI models and implementing enterprise-wide policies on usage, training data, and audit trails. This shift toward transparency helps businesses control risk while still enabling innovation. Governance creates guardrails that protect value, build trust, and allow AI to scale responsibly.
3. Reimagine Experiences Through Multimodal Creation
AI now powers multimodal experiences — text, video, sound, motion — all personalized and delivered in real time. One example is ClevrCast, a Belgium-based company that enables simultaneous audio translation across languages with 99.9% accuracy. During live-streamed events, participants can hear and read content in their preferred language instantly. This allows businesses to engage global audiences inclusively and efficiently.
In the entertainment sector, AI-enabled avatars of ABBA, Elvis, and KISS are creating immersive concert experiences. These performances use deep learning models, motion capture, and proprietary audio data to deliver new audience experiences anchored in authenticity. When done with the proper IP frameworks, AI creates new ways to connect — continuing legacy without compromising identity.
4. Accelerate Iteration and Innovation
One of AI’s most immediate benefits is speed to insight. Whether testing product ideas, exploring design options, or launching campaigns, AI shortens the gap between vision and execution. A/B testing, content prototyping, even voiceover and UX generation can now happen in minutes. This enables businesses to test, learn, and adapt in real time, especially valuable in fast-moving industries like retail, CPG, and media. “Fail fast” in action.
5. Deepen Customer Engagement Through Personalization at Scale
A standout example is Coca-Cola’s interactive Award-Winning Holiday 2024 “Snow Globe” campaign. Users were invited to generate personalized snow globe animations using prompts — selecting themes, names, messages, and visual elements. AI curated each globe in real time using assets from Coca-Cola’s brand catalog, making every experience unique. Visitors spent an average of eight to ten minutes on the website — an exceptional level of engagement for branded content.
The campaign was launched quickly with trusted partners and showed how AI can personalize at scale while staying true to brand identity. It’s a clear example of how meaningful engagement comes from combining creativity, trust, and well-applied technology.
AI is evolving quickly, and the businesses seeing the most value are those applying it with intention. Whether it’s unlocking institutional knowledge, designing new experiences, or building trust through governance, the path forward is about clarity, creativity, and accountability. These early applications serve as proof points and signals of how AI is reshaping what business looks like — and what customers now expect.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
I see this as a strong opportunity for small businesses and startups to leverage of AI through SaaS — Software as a Service. Instead of building custom infrastructure, companies can subscribe to platforms that already embed AI capabilities. This lowers the barrier to entry and gives smaller teams access to advanced tools without needing internal data science or engineering resources.
Many teams already have access and may not realize it. AI is built into CRM platforms like HubSpot, design tools like Canva, email and marketing platforms like Mailchimp, and modern payroll systems. These embedded features help with automation, content generation, customer targeting, and predictive insights and functions that used to take much longer to execute.
What strengthens the value of these platforms is how vendors are investing in user groups, workshops, and communities to support adoption. Vendors are offering tutorials, playbooks, and peer use cases to help small teams get started and stay current. When you engage in these ecosystems, you scale faster through shared learning.
The most effective approach is to choose tools that are aligned to your industry or customer base. That ensures the embedded AI is relevant and allows teams to test, learn, and adapt quickly. Small businesses don’t need to build from scratch. They need to recognize the systems already in place — and use them more strategically.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
Start by staying curious. AI adoption is not a single decision, it’s a continuous learning process. Leaders may not have all the answers yet, but they can take ownership of how they observe, explore, and engage. That learning responsibility goes beyond any one organization or industry. We need to jointly own, accelerate, and invest in understanding how these technologies are shaping our future — together.
This month alone, I’ve participated in dozens of in-person and virtual events in Georgia — ranging from the Technology Association of Georgia and Women in Technology to Atlanta AI Week, NADC, and the Digital Healthcare Transformation Summit, which was hosted at Kennesaw State University. Georgia Tech offered a focused session on marketing and AI, while Georgia State provided a business and marketing perspective through its executive program. I also joined a live tabletop session hosted by KPMG and Jones Day in partnership with NACD, designed to help board directors explore cybersecurity and AI through legal and accounting lenses. Additionally, I attended virtual webinars hosted by Deloitte, CI&T, and Gartner, each offering timely insights into industry-specific AI strategy and adoption.
These are spaces where strategy, technology, and leadership come together. Start small — sign up, observe, and explore what’s already available. Learning is happening all around us, essential for making informed, confident decisions at the board and executive level.
Wherever you are in your career, seeking out learning signals growth. For me, this next phase is focused on board service — showing up, staying informed, and engaging with what’s next. One of my favorite practices is inviting someone to join me — a mentee, a colleague, or someone I’ve recently met. It’s a meaningful way to learn together, and a powerful way to invest in others.
There are also accessible ways to start. YouTube, LinkedIn Learning, and Udemy all provide AI-focused courses, and many are free, some offer certification. For those at the executive or board level, institutions like Harvard, Stanford, and Wharton are now offering programs designed specifically for decision-makers. I completed Harvard’s Competing in the Age of AI Executive Education program, which created space for peer learning across industries and roles. That kind of shared context is essential.
Rather than focusing on fear, ask: What’s the opportunity? What could we enable if we understood this better? What would it mean to lead with more clarity?
Leadership in AI begins with awareness and a commitment to continue learning beyond our own walls.
In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?
I’m optimistic about where this is heading, and grateful to be part of the work. Over the next decade, AI will reshape how we approach creativity, how quickly we move from idea to execution, and how we scale access in ways that were previously out of reach.
One trend I’m watching closely is the generational shift. According to a Qualcomm survey, in ten years, the majority of the workforce will be digital natives. These are individuals who grew up with mobile, on-demand, and voice-driven technology integrated into everyday life. Their expectations for personalization, speed, and adaptability will shape hiring, product design, customer experience, and boardroom priorities.
I’m encouraged by the way institutions are rethinking how data is used. In areas like cancer research and chronic disease, we’re seeing new models where healthcare systems, research labs, and technology companies are creating secure, collaborative platforms to exchange data. These emerging models unlock scale that was previously limited and open new pathways for impact.
Multimodal AI is another area gaining traction. We are moving beyond structured text and numbers to include real-time video, audio, environmental sensors, and spatial data. This enables smarter diagnostics, more adaptive interfaces, and more accessible products for people working in diverse settings and conditions. The innovation lies in the variety of data sources now being integrated and applied with greater intelligence.
In the next phase, AI will shape how we design, govern, and respond to complexity — with an expectation of speed, transparency, and inclusivity.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
AI changes how people engage. When applied with intention, it can create more inclusive and responsive experiences — for customers interacting with a brand, employees navigating their work, and communities accessing essential services. It gives organizations a way to respond with greater awareness and care.
It starts with listening. AI systems that reflect real needs are shaped by observation — how people interact, what they avoid, where they spend time. That feedback loop creates space for design choices that surprise and delight while staying useful and relevant.
There’s a deeper opportunity: shared experiences. When companies use AI to co-create — inviting customers into the process or building tools that adapt to individual use — it builds loyalty through relevance. People remember what made them feel understood. Those moments stay with them.
When done well, AI becomes a way to strengthen relationships. It reflects care through design, and helps turn insight into meaningful connection.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. 🙂
If I could start a movement, it would focus on building with intention — a mindset shift that asks creators, leaders, and institutions to center people at the beginning.
We often talk about innovation in terms of scale and speed. But the most meaningful outcomes happen when we pause to ask: Who is this for? What friction can we remove? What experience can we reimagine?
A movement grounded in intention would bring together builders, researchers, and everyday users to shape AI that includes more voices from the start. It would encourage collaboration across industries, sectors, and communities — especially those who haven’t always been at the table.
AI has the power to accelerate change. Intention ensures it accelerates the right things.
That’s why I speak about AI on national news, write on platforms like Authority and LinkedIn, and continue to share ideas through TEDx. The focus remains on how we choose to show up in the conversation — and how we shape technology to serve people.
How can our readers further follow you online?
TEDx UIC: https://youtu.be/nCbivyuPC-c?si=Z11KclcRHqxuscON
This was great. Thank you so much for the time you spent sharing with us.
About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.
Marva Bailer of Qualaix On How Artificial Intelligence Can Solve Business Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.