An Interview With Chad Silverstein
AI will disintermediate search. If you’re counting on page clicks and page views, the traffic won’t just turn to Google. It will increasingly be going to answer engines and, eventually, more personal assistive AI. And what’s fascinating about answer engines like Perplexity or ChatGPT is people talk to them, sharing far more personal details. You don’t just get keywords like you do with search. You get their real intention. Is your content and data strategy optimized for that?
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 Emily Wengert.
Emily Wengert, Managing Director, Global Head of AI Strategy at Huge, leads design and strategy around the future of AI-powered experiences and innovation. She was named one of Insider’s “Top 15 Advertising Executives Leading the Industry’s Charge Into Generative AI” and her work with facial recognition, eye tracking, robotics, AR, VR and computer vision contributed to her being selected for Adweek’s “Creative 100” list in 2019.
As a 16-year veteran at design & technology agency Huge, Emily has worked with leading brands including Target, NBCU, P&G, Google, American Express, and Gucci, integrating human-centered applications of emerging tech to drive growth.
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?
I started my career in digital as a UX designer and embraced all the waves of innovation from desktop to mobile to responsive web. My favorite part has always been the frontier work — figuring out answers where best practices didn’t exist yet. So when web design started to become more of an optimization game, I evolved into experiential work, designing popup retail with heavy technological innovation. That’s where I got my first real taste of AI. My team and I were putting computer vision, facial recognition, emotive UI, eye tracking and robotic experiences into retail stores throughout Europe, US and Asia. It was still deterministic AI, though, so a lot of that work was done through repetitive training and brute force. Once generative AI emerged as a viable technology, I found I fell in love with digital experience design all over again. No one has figured that out yet, and it’s once again a frontier space.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
I’ve had two realizations about AI that might be counter-intuitive for someone leading work in this area. They both happened about a year or so ago.
The first was that I completely stopped having AI write for me. I used to ask it little things, like writing job descriptions or helping me think of a word, which I still do sometimes. But I used to feel guilty that when it came to writing, I was only ever halfheartedly trying to use AI. Until I realized one very important thing: writing is how I think. I’m analyzing and processing while I’m writing. If I were to give that up, I’d just be dumber, full stop. And that helps no one. When I stopped worrying about the output (the words) and started embracing the journey (the writing and therefore the thinking), then I became a lot more satisfied in my relationship with AI tools in my life. Boundaries with AI is a good thing!
Similarly, I had to learn a boundary in another sense too. I used to wake up and avidly read for 2–3 hours every morning, trying to catch up and see what I’d missed in the field since the day before. It was exciting but also exhausting. I look back and think of it like Usain Bolt’s treadmill, set to a speed I couldn’t really run. I then came to an important realization: no one can know it all. This field is so large already, and growing every day, that my attempt to know every drop of news happening in it was actually a distraction from the kind of longer-term strategies I needed to work through with my clients. I still have to keep my ear to the ground every day, but I’ve given up tracking some things. I care less about what the fastest, latest, best model is, for example, since it changes all the time and depends on use case. I can just research that in the moment when I’m implementing something. Whew! What a relief it was to ignore.
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?
The first is curiosity. I’m genuinely curious about almost anything, and that sense of wonder and desire to learn has served me over and over again. When I switched to focusing on experiential physical-digital work, I had to learn a whole new vocabulary. It was humbling at times to be seasoned in my career but then not know basic terms for things — like the time an architect partner told me we’d have to “fur” out the wall. The only thing I could picture was a fuzzy 1970s shag carpet on the wall (not what he meant, turns out). I just swallowed my ego and asked for a definition. This skill continues to help me. Because AI is an infinite topic, with so much to learn, I’ve long shed the burden of needing to be the smartest person in the room. I’d rather be the most curious.
The second is discomfort. To embrace AI, it requires a tolerance for a fair amount of uncertainty. Even when first working with deterministic AI, it still felt unpredictable. Now with more generative tooling, that only increases. Personally, I crave a little discomfort. When things get too easy, I start to feel a little bored. It’s that pioneering side of me that always wants to figure something new out. Do I have stress dreams about wrangling AI to do what I’m trying to get it to do in the thick of a project? 100%. But that’s also when I’m most excited.
The third trait has to be collaboration. This is especially true with my technology colleagues, but goes all the way to data, design, legal, compliance, content, the list goes on. AI work requires so many contributors, not just at Huge but on our client’s side too. Prior to the gen AI boom, I worked largely with Chief Marketing Officers or Chief Digital Officers, Now CTOs or CIOs are at the table almost every time. It requires trust to bring a group together as well as a desire to build a common language around shared goals.
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?
There’s so many stories to tell, but one of my favorites has to be creating OLI for the Olympic Games in Paris last summer. NBCU reached out to Huge with the challenge that 7,000 hours of content can be hard for fans to sift through. Could we use generative AI to let them get exactly where they want to go to precisely what they want to watch? Of course, the technology is perfect for that problem. It allowed fans to type in anything they enjoyed watching — an athlete, country, sport, specific competition — and get back precisely what they needed to know, including dates, times, channel or a deep link into streaming.
This product came with some complex challenges. Generative AI tooling for the enterprise was still emergent at the time. But safety was paramount for a brand like NBCU. We also needed 100% accuracy using a technology known for hallucinations, no less. So we carefully applied generative AI only where we needed it — namely, to understand what people were asking to watch as well as to craft the conversational chatter that would be returned to them. The programming itself was a classic lookup in a database to avoid any risk that a date or time could be made up.
Few people have built generative AI systems for end consumers at the scale and level of attention that the Olympics receive. But this was even harder. Data updated every 15 minutes, straight from the producers in Paris, unlocking a new data stream that hadn’t been fully available to consumers before. It was like building a generative system on a waterbed and we did it! NBCU worked tirelessly with my team and I. It was a great partnership.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
There are two misconceptions that I combat all the time: one technical and one strategic. The technical one is that you have to just put up with hallucinations since it hasn’t been solved yet. This either scares some businesses away or makes them tolerate risk they probably shouldn’t be embracing. As we showed in the OLI work, really fantastic solutions often come from integrating the brilliance of a generative capability with classic if-this-then-that solutions. The key is knowing when something absolutely must come from a source and when it’s ok to generalize or summarize.
The second misconception that makes me nervous when I hear it is businesses that think they can hang back and wait on generative AI. They often have other priorities because they’re still catching up in other areas of digital. I don’t believe every brand needs to upend their whole business today (though some businesses like SAAS should probably be thinking quite seriously about it). However, the superpower of generative AI, both as a technology inside the organization and in its external offering, means that those planning to be fast followers risk becoming instant laggards. An organization needs to begin experimenting now.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
I know we need questions like this to guide prioritization inside businesses, but imagine asking someone that same question about the internet. We now know the internet is both where you do business, how you do business, and basically the vehicle for absolutely anything to get done. Imagine picking only one of those to do. It’d be a terrible strategy. The same is true for AI. AI can augment how you produce assets or content, how you code, analyze business intelligence, communicate your value to the world, vet partners or vendors, or serve your audiences. The impact goes beyond efficiency to supporting better quality, customer lifetime value, reduced churn, better employee satisfaction, and the list goes on.
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.
1.At Huge, we’re particularly focused on how businesses can use AI capabilities to power what we call Intelligent Experiences (or IX). This is now the backbone of all the work we do and how we help brands prepare for the future. Within IX, there are a lot of ways business problems get solved.
The first lane I think about is Intelligent Marketing. Whether in the B2B or B2C space, marketing has been one of the first areas where businesses are embracing more generative AI. This falls into 2 critical areas: content workflows and marketing management.
Content workflow covers a lot of things. We’ve helped clients use AI to go from a brief to drafting a web landing page in mere days, saving time and benefiting from insight around which modules perform best for different audiences. For another client, we’ve worked on brand compliant asset creation where their IP is fully protected but the assets can be extended and modified almost as fast as they can get conceived for a campaign. This lets the business not just be done sooner, but also consider more variations and adjust their marketing more on the fly based on audience response.
That leads to the second part of Intelligent Marketing: management, where there’s a lot of evolution in the tech that opens the door for better customization and 1:1 messaging. For one client, we found that CRM response rates went up when a human + AI combo worked on the email campaign, which was segmented for different audiences.
2. A second major transformation opportunity is in Intelligent Products & Services. We’re seeing a lot of clients connect generative AI to their contact center to help staff respond. Quite a few have upgraded their chatbots to toss out those pre-set, unintelligent content flows and give more natural language and dynamic answers directly to customers. We believe this can be taken even further, to connect the data signals across all channels into an end-to-end services capability that reduces churn, increases loyalty, and drives adoption. For a retail banking client, we’ve recently examined their business to show how an overarching overhaul of how their services are presented would improve audience engagement in international markets.
3. A third important area we think about is Intelligent Software. The SAAS industry is under threat as these gen AI capabilities unlock deeper analysis, summarization and better business intelligence to augment decision making. The exciting part is that software providers today can rethink how their offering incorporates AI, integrating with bespoke data sets to create entirely new value. It’s critical for SAAS providers to not just treat AI as a garnish on the side (the let’s-add-a-chatbot strategy) but rather imbue intelligent capability throughout.
4. Another big area we’re focused on is what we call Intelligent Twin. This serves all of the areas already mentioned above. Think of this like a digital replica of your audience types. By having a digital twin, business leaders can evaluate through simulation how their audiences might react to a new campaign or a new feature. Twins can help synthesize data points from all over your business to uncover opportunity areas. And they can be used over time to better inform future business decisions.
5. Finally, and this is actually a big area of thinking for us, there’s Intelligent Commerce. Commerce, of course, has services and software as well, but includes how we rethink loyalty and building trust in a new business environment where personal AI assistants facilitate research, decision making, even direct purchasing on behalf of someone.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
The first step is to find a general model that you like (think Claude, OpenAI, or Gemini) and sign up for a paid subscription. Most of the time, paying for the service helps ensure your business data is safer (though definitely still read those terms of service). The great thing about a general model is that it can do so many general things to improve how employees spend their time. The hardest part is ensuring you and your team use and benefit from these tools. Create moments in company-wide meetings or team weeklies to share ways you’ve used these tools to help inspire each other.
If you’re open to advancing past that, look for places where you do repeating tasks and could upgrade to an agent. Some of this is built into existing tools you might already use, like SalesForce. Everybody (Microsoft, Google, Amazon, etc) is trying to get into the agent game. Don’t worry about using all of them, but start thinking in terms of what agents might be right for your org. Agents are like automations but better because they can integrate with other software and include generative steps in the workflow. Imagine an agent that helps set up a new hire in all your systems. Or an agent that analyzes an inbound lead, researches background, and vets its value to the business to pursue. When implemented thoughtfully, agents can truly save time or lead to higher value uses of time.
And the third area to explore is a bit of a trend at the moment, but I believe “vibe coding” (made popular by Andrej Karpathy, formerly a leader of OpenAI) is important for any startup to be exploring. While many talk about this as great for side hustles, I do believe in the next 12 months, we’ll start to see small businesses and even enterprises embrace it to gain simple solutions in their operations when the tool or capability might not exist yet. At the moment, vibe coding is risky if you don’t understand code yourself. However, the tools that let you do it (Lovable, Cursor, Replit, among many others) are important to understand in a business looking to move fast.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
Remember that inaction is a choice. It’s the choice to lag behind. Instead, don’t be afraid to start small. For the hesitant, businesses usually start within their walls with their own employees. That carries less risk in many ways and lets a business more easily learn and evolve from experiments. Many companies have tried to build proof of concepts. Some work but many fail. Two things to caution there: it’s only a failure if you don’t learn from it. And most failures in my experience happen because no one thought to talk to the end user and serve their needs. Even in the rush to experiment, it’s worth it to stop and do some research. Stay centered on your audience and you’ll see far better returns for your efforts.
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?
What a question. I have so many thoughts here, however I’d focus on three trends that matter most:
- AI will disintermediate search. If you’re counting on page clicks and page views, the traffic won’t just turn to Google. It will increasingly be going to answer engines and, eventually, more personal assistive AI. And what’s fascinating about answer engines like Perplexity or ChatGPT is people talk to them, sharing far more personal details. You don’t just get keywords like you do with search. You get their real intention. Is your content and data strategy optimized for that?
- AI will disintermediate attention and replace it with intention. If you’re counting on eyeballs on ads, you’ll have to place those ads where people still hang out (great entertainment) and not just typical webpages. That’s because truly agentic AI, as it assists consumers and employees, will do things on someone’s behalf based on their broader intention or goal, replacing the Attention Economy with the Intention Economy. I recently wrote an article about this with a colleague because we think this can shift entire services that businesses offer. If you transform your business to seek out and serve rich intentions (whether through a first-party or third-party experience), you have a way to survive in 5–10 years.
- AI will disintermediate people’s sense of self worth. This isn’t one I’m excited about, but is important to recognize. People like to talk about new jobs emerging from the old jobs that get taken away by AI. None of us bemoan the loss of entire rooms filled with typists from the 1900s, and there are likely current roles that disappear. However, whether it’s job loss or just a shifting skill needed within a particular role because AI has filled in some of the pieces, the future will be unsettling, the pace of change breathtaking. Will it be AGI or not? I’m not going to hazard a guess, but I do believe businesses that safeguard and celebrate people’s self-worth will be more valued than ever, both by employees and by customers.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
I truly believe AI has the power to bring businesses and the world around it even closer together. This impacts customer service, loyalty, and acquisition. In customer service, I dream of a future when your unique issue can be remedied in a humane way in real time. In loyalty, I dream of the loyalty program for one, where a business can see you for who you are and what you need — something still cost prohibitive and legally impossible today. For acquisition, I can even imagine that businesses and communities could work together toward a common good.
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. 🙂
AI can be abused by businesses, individuals or governments. Will we build on the seeds of intention that someone already has or will we be planting those seeds and thereby manipulating them? I think we need to recognize human vulnerabilities and create safeguards against it. There are so many wonderful sides to what AI can do (big things like identifying optimal cancer drugs for an individual. Or small, everyday things like summarizing and prioritizing your messages across all 16 platforms trying to tell you something). However, unless we explore regulations and the right to not be manipulated, the bad could outweigh the good.
How can our readers further follow you online?
Thanks for asking. https://www.linkedin.com/in/emilywengert/
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.
Emily Wengert of Huge 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.