David Mainiero Podcast Transcript
David Mainiero joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, Home of The Digital Executive Podcast.
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Welcome to The Digital Executive. Today’s guest is David Mainiero. David Mainiero drives the enterprise AI strategy as Chief AI officer and leads AI Digital Labs, the innovative transformation practice helping client partners unlock the power of artificial intelligence.
He has started and scaled tech enabled businesses across several verticals, including Ingenious Prep, the category leader in university admission consulting. Most recently at Factor, he spearheaded AI enablement for Fortune 100 legal departments and launched the Sense Collective, a pioneering community for enterprise AI adoption.
He earned his Juris Doctorate from Harvard Law School and a BA from Dartmouth College.
Well, good afternoon, David. Welcome to the show.
David Mainiero: Thank you. Great to be here.
Brian Thomas: Absolutely, my friend. I appreciate it. Jumping on for a podcast early in the day is great. David, I’m gonna jump right into your first question.
You’ve built and scaled tech enabled companies across multiple industries. How has the entrepreneurial background shaped your approach as Chief AI Officer, especially when designing enterprise wide AI strategies?
David Mainiero: It’s an interesting question. I don’t know if this is kind of. By design, but certainly in retrospect, it feels; it feels by design.
I started out in, in law school, I started an educational consulting company, and the focus of that was really giving people, granted it was geared toward a particular objective, getting into college or graduate school, but it was geared toward, the idea that there was no shortcut to success.
There were, best practices, there were things you could do to kind of pursue your own unique path. There were resources that could accelerate that, but really the idea was that there was no shortcut and you had to kind of do things the right way. For a lot of people, that answer’s frustrating, right?
That it’s gonna take time, that there’s no silver bullet and no magic solution or, or panacea to kind of what your end goal might be. And today, that end goal is kind of AI empowerment and AI literacy both on an individual level. And on an organizational level. So, I think that a lot of my focus on education at least in, in that industry and then ultimately in the legal world it shifted to an industry which has sort of resisted the previous waves of technological innovation.
Lawyers love to say it depends to everything, which means that they resisted the Web 2.0 dashboards or. Dropdown menus that would force a lawyer to check. A box, between three different options and say, I don’t know, it’s in the gray area between those. And throughout both of those experiences working at a startup, a from you kind of bootstrap startup from a, from, person one to working with Fortune 500 companies, like Meta CrowdStrike and,
Bank of New York and various industry leaders and seeing what their kind of AI adoption looked like in the earliest days. It’s given me a good understanding of kind of the commonalities between. Smaller mid-size enterprises and some of the biggest enterprises in the world and how to take that into account and the kind of commonality of the human experience throughout all that, right?
We’re still all the same people that work at these types of companies, no matter the size, granted. Maybe smaller companies have, or try to gear their hiring towards people who are a little bit more move fast and break stuff versus kind of managerial focused. And even that doesn’t always hold.
But I would say that overall, aside from that kind of size and sector based experience. All the core tenets, if you were to read any book about entrepreneurship or any bio from a founder, they all talk about things like failing fast and learning from what happened in that period.
And, and being able to adapt and having a thick skin and really being able to understand. What your next move is be able to pivot, pivot quickly. And so doing things the right way over time, towards a particular mission or goal, helps you build a company in an entrepreneurial sense.
But also, there’s this demand, particularly these days with, how much venture money there is out there and people seeing these very. Aggressive valuations for pre-revenue companies. There’s this pressure to achieve results now, and that’s something that’s omnipresent in business, that you need results now.
So really designing enterprise strategies that focus on a. The a kind of a two track solution that you have to do it the right way and slowly and bring your people along, but you also need results now. And finding an operating model and kind of investment of resources that allows that. I, I think that’s the key.
Brian Thomas: Thank you, David. I appreciate that. Love the backstory. Obviously, your first educational company outta law school or during law school was the genesis of your entrepreneurial journey, which is great. You talked about AI literacy and an empowerment at the individual and the organizational level, which you’re.
Is really important and your experience lends itself. You know, working at startups, large Fortune five hundreds lend itself towards your knowledge and experience. But the thing that I really highlighted is designing enterprise strategies for organizations. For results now, right? People want that.
Now, especially in business, obviously. Mm-hmm. To stay in business is, you’ve gotta, you’ve gotta get those margins quickly. So I appreciate that David. Many organizations experiment with ai, but struggle to move beyond pilots. What distinguishes companies that successfully operationalize AI at scale from those that get stuck in experimentation mode?
David Mainiero: It’s interesting because. If you think of the way that a lot of these mandates start, it’s the CEO or a board or someone at the top saying, we need AI now. And the rest of the team saying, oh, okay, well, well what, what kind of AI do we need? What do we want to use it for? And there’s this, this just impulse, this itch to do something with ai because people recognize its transformational capabilities, but they don’t know exactly how to apply it to their business. And the reality is that to fully apply it to your business is a pretty wholesale change. Of course there are some, low hanging fruit, quick incremental wins.
You can rack up early on. But once people get past those, kind of low hanging fruit use cases, I, I see a very common path of it. It’s not that people are trying to do a check the box exercise, it’s just that this is side of desk for people and not everyone wakes up saying, oh, okay, well, I’m really busy.
I can’t get my work done that I have on my plate today. As is a lot of priorities, let me completely overhaul our organization for AI transformation. So the. What people end up doing is they nominate someone to be sort of a transformation lead. They set up steering committees or use case discussions with a particular cadence, and they have big Google docs or, lists in in Excel of.
Of use cases and then nothing happens from there. Maybe a few eager beavers start to, try to build, become user builders and build kind of customized micro tools. But organizationally not much happens. And then you see them end up in what I call a pilot purgatory of, a few people trying to unlock budget for, upgraded AI subscriptions to existing.
SaaS tools and then they don’t really have an objective with that. There’s not a what do you hope to achieve from this technology pilot? It’s just kind of trying things, and I don’t mean to disparage kind of dabbling and trying things. I think it’s great, but I think it needs to have a pretty set timeframe and a clear set of objectives that, you can have a kind of go or go go or no go decision from that.
I would say first of all, the one the companies that successfully operationalize AI at scale are ones that can imbue it into a particular business process, regardless of whether that process is, this is a lot easier. For instance, at a Fortune 500 company, for instance, a legal department that has thousands and thousands of non-disclosure agreements, they negotiate in a year.
It’s a lot easier to kind of. Take what has maybe traditionally been a business process outsourcing decision that was, would send those agreements to be handled by maybe an offshore managed service for which the processes, the playbook had to be reduced to writing already. And there’s already a comfort level with kind of someone else doing it.
And the transition from that to having AI agents do that work that’s a little bit easier. And because there are thousands of those agreements, it’s easier to kind of make an impact at scale there for a lot of small and medium size enterprises, it’s not necessarily. They don’t have that kind of volume such that a kind of quick efficiency win makes a huge difference.
So I think at the small and mid-size enterprise level, what I see from those who are successful is not those thinking about ai, first and foremost, to cut or to promote efficiency, but those looking to use AI to win. How can we deliver something that was previously either really impractical or impossible to one of our clients to help kind of delight them and deliver a better service?
Brian Thomas: Thank you. I appreciate that. Insights are great. You talked about, people do recognize ai. As a, a tool or, or a platform technology that can be transformational. And of course they wanna jump in and take advantage of that, but sometimes it’s just they don’t know what they don’t know.
And people don’t know where to start. They try pilots, maybe they subscribe to an ai a particular tool or SaaS tool. But as you stated, and I think this is important, projects need clear set of objectives and timeframes in order to be successful. It, it takes some clear goal setting, obviously, and sometimes budget and stakeholders to be involved to make something successful.
So I appreciate that. And David, AI Digital Labs focuses on unlocking, unlocking AI powered transformation through strategy, experimentation, and implementation. What kinds of use cases are gaining the fastest traction right now across enterprises you work with?
David Mainiero: That’s an interesting question. I would say, our business at AI Digital is as kind of a full stack force multiplier for agencies and advertisers, which means we touch on a lot of the work that our clients do themselves, and we help them, achieve certain scale and and help them, augment their own work product and deliver better results in, in that manner. And so we have kind of a firsthand look through the work we do at, at the work that they do. I would say we’ve made. Really impressive strides. Both. We have a, an a tech platform called Elevate, which is our AI intelligence platform.
And we use this as the shared surface of our work with clients, and we use it specifically to deliver things that they are interested in. So, we’ve found, we’ve made great strides in competitor research you know, audience intelligence. Other types of research and strategy building different personas using synthetic focus groups.
Instead of going out and paying a focus group, you could simulate one with AI and help to kind of get a candid reaction from five or six different simulated personalities. Reflected across, five or six different ideal customer personas or, or buyer personas. So a lot of really interesting research and packaging work.
In terms of devising strategy we have a lot of raw data at AI digital that we’ve collected over almost a decade. And thankfully we’ve been organizing and labeling that data from all of our campaigns and. We also have our Elevate Intelligence platform reasoning over 10 or 15 different. Kind of gated data sources that you would normally pay for separate subscriptions for, that we pay for commercial licenses for.
And so, it’s much more than what you would achieve through a chat PT or a Gemini. Through the kind of consumer facing app, you’re actually able to reason over not only our historical campaign data, but all of these different gated. Private curated data sources which gives a really great advantage.
And then once we have kind of those high level insights, we’re able to distill them down into smaller agents that our, our clients are able to, achieve a particular objective or task with with, without having to kind of have the imagination of entering the chat bot platform and, having the world be their oyster.
That’s available too. But a lot of times that can be intimidating for folks. And so we try to serve up the insights in a curated and useful fashion that is respectful of folks time and the effort that goes into these big campaigns.
Brian Thomas: Thank you. I appreciate that. You know, your company does a lot. It’s the full Stack agency, as you mentioned, provides various services at scale for these enterprises.
And you talked about some of the services you provide. These are just a few that I highlighted here. Audience intelligence, research strategy, persona simulations. You’ve got a bunch of AI data that’s available to you and your customers. And then you talked about elevate intelligence platform.
Obviously given your customers kind of that, all-in-one access to a lot of the tools today. So, I appreciate that. David, the last question I have for you today. As we look ahead, what skill structures and leadership models will define the next generation of AI first enterprises, and how will the role of the Chief AI officer evolve over the next decade?
David Mainiero: It’s a great question. I wish I knew exactly how my role would evolve over the next decade. I have some predictions. I think chief AI Officer means different things at different companies. I think ultimately, and. Maybe seven to 10 years, maybe sooner. This type of position won’t exist because you know it well, maybe it will, but you know, I kind of, I always try to analogize to humans and so, we have chief people, officers or, heads of HR to help deal with our human resources.
We may have a role in the organization that maybe reports into the CTO or maybe. Is maintained separately as Chief AI officer that manages the infrastructure and context and tasks managed by AI agents. So, I don’t see this necessarily as a very technical role. I’m not a coder myself.
There are companies at which Chief AI officer, meta notably their Chief AI officers, uh, Alexander Wang from scale ai originally. He’s very technical and part of their super intelligence team and their research lab. So, there will be kind of different paths that folks follow, but. I wanna highlight something in your question.
You asked which skills structures and I think leadership models will define the next generation. And I think leadership models right now are the, is the critical element of that because proper leadership, the way you talk about ai, when people are out there, yelling at the top of their lungs about promoting AI efficiency.
Employees get nervous about that. They instantly internalize that as, oh, efficiency, that means I’m gonna lose my job. And so the way people talk about AI is very important. The way people are incentivized to build, to share, to celebrate AI successes on their team. That’s where we’re gonna be able to harness the collective wisdom of a great group like we have at AI Digital, which is.
Almost 450 strong right now. It’s not gonna come from, brilliant ideas from someone in a position like mine. Um. Someone in a position like mine is there to steward the organization and encourage and empower them to build these skills, to invest in hands-on interactive resources particularly AI training and coaching agents to help them build a certain literacy and comfort level.
And then just to build the organizational muscle to. Keep becoming user builders and empower everyone that they can do this too. And this is not within the realm of it, or some outside coders or, or, or anyone exclusively that really everyone should participate in this. And it’s a critical part of, I think, their employee value proposition at any company.
No one knows exactly what the future of work will look like, and I think it’s a great benefit to be investing so deeply in our employees. Career skills that are portable for the rest of their lives.
Brian Thomas: Thank you. I appreciate that. You talked a little bit about the prediction of chief AI Officer.
Obviously right now it means a lot of different things in different organizations around the world, but that will change some we don’t know how much, but you did talk about how at the end, the center of it really is people. And that’s why we have evolving positions and different things, which we’re absolutely gonna have with the evolvement of, evolution really of AI here. But leadership is important. You talked about that leadership models and proper leadership is key now and in the future. How we communicate ai, how’s its messaged removing that anxiety from employees, for example, telling the why behind it. And I think empowerment, which I took away is very important as well.
We wanna empower everybody within an organization to embrace learning and, and growing their. Particular skillset. So I appreciate that. And David, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
David Mainiero: Yeah. Thanks so much for having me, Brian. This is a great conversation. Always happy to talk about how organizations can make AI actually work for them particularly given that it’s at the tip of everyone’s tongue these days.
Brian Thomas: Bye for now.
David Mainiero Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
