In this episode, former Goldman Sachs partner Mike Ryan, CEO and co-founder of Bullet Point Network, joins host Jason Mudd to discuss the trust problem at the heart of AI, why trust is now the biggest barrier to enterprise AI adoption, and how you can keep human judgment in control of AI output.
Tune in to learn more!
Watch the episode here:
Meet our guest:
Our guest is Mike Ryan, the CEO and co-founder of Bullet Point Network, an AI-driven investment platform. Mike is a former Goldman Sachs partner and global co-head of equities. He also served on the Harvard endowment’s investment committee.
5 things you’ll learn during the full episode:
- Why trust, not capability, is now the biggest barrier to enterprise AI adoption
- How AI-enabled advisory support pairs speed with human judgment and quality control
- Why off-the-shelf AI isn’t built for high-stakes communication
- How AI can reduce the constant rebuild of senior communication
- The 20-60-20 strategy for avoiding AI slop
Listen to the episode here:
Quotables
- “If you can’t trust the information you’re getting, you can’t rely on it.” — Mike Ryan
- “AI is not going to take the job of most smart people, but a person using AI better than you might take your job.” — Mike Ryan
- “Just because you can produce it fast doesn’t mean you want it or want to distribute it.” — Mike Ryan
- “You don’t want AI to take you fast in the wrong direction.” — Mike Ryan
- “If you use AI the same way, you can elevate your thinking and your mindfulness about the organization and think more as a strategist.” — Jason Mudd
If you enjoyed this episode, please take a moment to share it with a colleague or friend. You may also support us through Buy Me a Coffee or by leaving us a quick podcast review.
Resources
Guest’s contact info and resources:
Additional Resources:
- Fighting to be trustworthy to AI and humans
- Reputation management for AI search: monitoring and response framework
- 7 responsible principles to guide the use of AI in public relations
If you like this episode, you're going to love this:
- The PR playbook for getting recommended by AI with Unusual
- AI and PR with Microsoft’s CCO Frank Shaw
- The future of PR in the age of AI with Agility PR
Recorded: Oct. 8, 2026
About your host Jason Mudd
Jason Mudd is a nationally recognized public relations expert featured by CNN, Entrepreneur, Forbes, NPR, The New York Times, PRWeek, and The Wall Street Journal.
Named North America’s top PR leader by the World Communication Forum, he serves as Partner of Axia Public Relations — recognized by Forbes as one of America’s Best PR Agencies.
Jason has advised some of the country’s most admired and fastest-growing companies, leading campaigns for iconic brands including American Airlines, Budweiser, Dave & Buster’s, GE, H&R Block, Hilton, HP, Miller Lite, New York Life, Pizza Hut, Southern Comfort, and Verizon.
He’s also a professional public speaker, accredited PR practitioner, published author, entrepreneur, and host of On Top of PR with Jason Mudd — a podcast ranked among the top 2.5% globally by Listen Notes and a top 100 marketing podcast on Apple Podcasts. His guests have included leaders from Disney, Microsoft, Southwest Airlines, and Wells Fargo. Learn more about Jason at https://www.axiapr.com/team/
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Transcript
Episode Transcript
00:00:00 - 00:00:20
Mike Ryan
It’s great to get things fast, and it’s great to have the powerful capability of AI behind you. But if you can’t trust the information you’re getting, you can’t rely on it. It’s not only of no use; it’s actually dangerous and harmful.
00:00:20 - 00:01:02
Jason Mudd
Hello, I’m Jason Mudd with Axia Public Relations. Welcome to a new episode of On Top of PR. Today we’re talking about the trust problem of AI and how we can solve it. My guest today is Mike Ryan, CEO and co-founder of Bullet Point Network, an AI-driven investment platform built on an AI-plus-one philosophy that combines AI with human expertise to speed investment decisions. A former Goldman Sachs partner and global co-head of equities and former member of the Harvard endowment investment committee, Mike brings more than 20 years of experience in investing, leadership and advisory work. Mike Ryan, welcome to On Top of PR.
00:01:02 - 00:01:06
Mike Ryan
Great to be with you, Jason. Thank you so much for having me and for that generous introduction.
00:01:06 - 00:01:59
Jason Mudd
Glad to have you here. I’m very interested in talking more about this trust problem that’s at the heart of AI and how we might solve it. As we talked about earlier, our audience is public relations, communication and marketing leaders, typically working for a public or private enterprise, but we also have a lot of public service, public sector and government employees listening, as well as nonprofit employees.
I think a lot of those executives are excited about how PR is helping to influence AI citations and how AI models depend on earned media coverage, news stories and third-party credibility about a brand or an organization to cite, reference, refer or recommend them. But internally, AI adoption is facing some challenges with trust. So I’m glad we’re talking about that today, and I’m excited to dive into that a little bit more with you.
00:01:59 - 00:02:41
Mike Ryan
Great. Yeah, I think it’s one of the most important issues that we have. Trust and safety are critical in any activity that we do, and AI has really changed the equations on both of those. Today we’ll probably talk about trust the most.
Ultimately, it’s great to get things fast, and it’s great to have the powerful capability of AI behind you. But if you can’t trust the information you’re getting, you can’t rely on it. It’s not only of no use; it’s actually dangerous and harmful. So what people are trying hard to do is figure out the best ways to improve the reliability and trustworthiness of the AI agents and the AI information they use.
00:02:41 - 00:02:51
Jason Mudd
I agree with you, Mike. I think that’s a great observation. Tell me, how did you get into this space? What was your prompt to transition into AI?
00:02:51 - 00:04:44
Mike Ryan
No pun intended. But yeah, I’ve always been an investor and an analyst, really. I love analyzing businesses in particular and thinking hard about what factors should really drive a decision. A lot of that in my business is some combination of stories and numbers. You have to hear the narrative and understand the vision, the anecdotes and the key factors about what’s going to make this special, unique and successful. But you also have to quantify it and understand things like revenues, growth rates, margins and valuations. That’s a set of jobs I’ve been doing for three decades, and I love doing it. It’s fascinating.
Technology has always been a big part of it, because in gathering information, technology has been an amazing advantage. We’re now at a stage, and have been for the last five years or so, where you can literally use AI to help you analyze information. And as your audience I’m sure knows, you can also use it to help produce content and actually generate information. So we have a massive transformational change that’s gone on here. You have more information than you can handle. You have a tremendous capability to filter and analyze, and now a growing capability to produce written content, video content and images.
How to do that best is something I think everybody is spending time on. Nonprofits, and I’m on the board of several, have AI on the agenda. Big public companies and private companies, from small to large, are spending a tremendous amount of time on it, and investors are fueling it with capital. I think it’s a really major trend, maybe the most important trend in our society.
00:04:44 - 00:05:05
Jason Mudd
One thing I know we wanted to talk about is this idea that the biggest barrier to enterprise AI adoption isn’t capability anymore. It’s simply trust. When we’re thinking about trust, are we talking about the trust of the company’s internal leadership and management? Is it employee trust? How would you define the challenge?
00:05:05 - 00:06:14
Mike Ryan
Absolutely. It’s a great call-out. I think the biggest impediment is not being able to trust the answers you get from AI, not being able to trust the information it gives you or produces for you. Think about it this way: We all meet people in our lives, and we have people who work for us and with us. If those people, one time in 10, say something you know to be false or absolutely wrong, you’re going to stop trusting them. You’re going to think they’re not a reasonable person to get information from to base decisions on. None of us would willingly hire or collaborate with a person who’s going to give us bad information or false answers one time in 10, or what in the world of AI are now sometimes called hallucinations. We don’t need that in our midst.
And yet we’re all using AI more and more. Frankly, we’re going to continue to do so because it’s getting better and more capable every day, and it’s incredibly fast and incredibly powerful at doing tasks. So the trust I’m mostly talking about is: Can the users of information trust the answers they’re getting from AI?
00:06:14 - 00:07:25
Jason Mudd
I like that a lot, and that’s definitely one of the challenges. Anecdotally, I play trivia about twice a week with a group of people. Some of the people we play with off and on come in, and they’re just so confident in their answers. Then when they get it wrong, you start second-guessing them, whether you mean to or not.
I think it’s the same thing. We’ve all been burned by AI with information that we either clearly knew wasn’t right or later found out wasn’t correct. Just yesterday, I was working with AI to draft a message. I’d gotten far enough along that I said, “OK, put it in my inbox as a draft, and I’ll review it later.” Then, before I could even review it, I got a reply from the recipient. I was like, “Wait, what happened here?” So I went back and checked, and it was like, “Whoops, I accidentally pressed send instead of following the command to just save it. Even though I told you I saved it, I actually sent it.”
That was a trust issue for me. It set our relationship back a little, not with the recipient, but with my preferred AI platform. I’m now starting to say, “Maybe I don’t want to give it that authority.”
00:07:25 - 00:10:06
Mike Ryan
I hear you loud and clear. Those kinds of things are happening all the time. It’s almost like a tale of two cities, or a real paradox. Some of the things AI does for us and around us just amaze us. The speed and power of it are breathtaking. Other times, it’s simply getting things wrong or doing something you didn’t want or authorize, and you’re wondering how that happened. One of the things that’s been built and trained into almost all the large language models is this tremendously solicitous tone, this very positive, reinforcing communication style. So you’ll say, “Hey, what happened here?” And it’ll say, “You’re absolutely right.” But I don’t want to be absolutely right. I want you not to do it next time, or I didn’t want you to do it last time. So we have to work on that, and we can talk about how we do that.
The way we handle it: We’re primarily dealing with two things. We’re dealing with investors who are making decisions about whether and how much to invest in a business. They’re typically reviewing many opportunities, hundreds or thousands of opportunities that come their way. They’re filtering them down to the few that deserve serious attention. They do lots of diligence and analysis on those few, and then they make final decisions with conviction on the one or two to actually advance capital to. That’s one of our main use cases.
The other one is companies. We work with the portfolio companies of those funds and some other companies. They’re usually making strategic scenario models to plan their business, to analyze and understand where their opportunities and risks are and how their results can be amplified. Then they’re communicating that. They’re making internal communication to their C-suite and executive team, they’re making board presentations, and they’re making external presentations, often to capital partners that are going to fund them in the next financing round, or to M&A partners they might want to do an M&A transaction with.
We help with those two use cases. Both of them involve a lot of information, a lot of synthesis of stories and numbers and, frankly, some amount of prediction about the future. You’re forecasting things, telling stories of opportunity and talking about where you may take your business going forward. That’s pretty hard work, especially that last part: synthesizing and forecasting the future, and then communicating it. So we’ve really worked on optimizing the trust factor and the trust layer on top of what the models can do.
00:10:06 - 00:10:08
Jason Mudd
And how are you doing that?
00:10:08 - 00:13:44
Mike Ryan
It’s a combination of technology and workflows. The main phrase that I think most analysts know all too well is “garbage in, garbage out.” If you put bad inputs in, you’re going to get bad conclusions out. It doesn’t matter how smart you are or how hard you work. So the first thing we do is create a lot of filters on the source pile, the source information the AI uses. A lot of that is now done by some pretty sophisticated database architecture we’ve developed at Bullet Point Network. But you can also do it as simply as making sure you only include or upload sources that are relevant and accurate.
We then do some pairing and prioritization, because a lot of our clients are using literally millions of pages of documents and many dense spreadsheets of numbers, and we’re combining all that. The way AI works is it has what’s called a context limit. You type in a prompt, you give it source information, or it searches the internet, and it can absorb or read only so much. That’s the context window. So we pair every prompt with the most relevant sources in that walled garden of trustworthy information, so you’re not overloading the AI with too much. You might feel good: “Hey, I’ve loaded all the information in, and AI’s giving me an answer.” But it’s not actually reading all the information; it’s sampling it. So we want to give it the most relevant things to sample from on each and every prompt. That’s step one.
Step two is we believe very strongly in transparency. Sunlight is the best disinfectant for problems. So we give the user traceable, clickable footnotes that take them not just to the document or spreadsheet where it came from, but to the exact area, the exact page and paragraph or cell and number it came from. That’s really helpful, so people can do some spot-checking. They can also look at the areas that prompted and delivered the answer to them, and they may want to read more information around that to give their own brain a little infusion. Transparent footnotes and source citations are critical: traceable, clickable, usable footnotes.
The last thing we’re big believers in, and this is maybe more unique to our business but similar, I think, to marketing experts who are very quantitative, is that numbers are really important. So we have a trusted spreadsheet be the calculation engine for any calculations your AI relies on, with the ability to control and understand the inputs, the formulas and the outputs in that spreadsheet. Then we have the AI look at both folders of documents and trusted numbers from a spreadsheet, on which we can build scenarios with our models, like upside, downside and base case, and make lots of visualizations, charts and tables. It’s great to have all that visual benefit, but only if the numbers underlying it are trustworthy.
So we’re big believers in the AI-plus-one concept. We’re the AI, Bullet Point Network, but you’re the one. You should have control over the thesis, the sources, the prompts and the flow that’s used. That’s basically how we do it. We have a combination of people and technology and a purpose-built workflow that enables you to drop in at just the right time and easily iterate, understand and collaborate, either among a team of humans, with a team of agents or with a combination of people and AI.
00:13:44 - 00:13:49
Jason Mudd
Are you describing AI-enabled advisory support? Is that what you’re talking about here?
00:13:49 - 00:14:02
Mike Ryan
Yes. In large part, it’s AI-enabled advisory support for investment advice, investment analysis, strategic finance advice for companies and strategic decision-making. We’re really talking about decision-making support.
00:14:02 - 00:14:16
Jason Mudd
OK, that makes sense to me. We were also going to talk about how off-the-shelf AI is powerful, but it isn’t built for high-stakes communication. Can you explain that to us?
00:14:16 - 00:16:58
Mike Ryan
Sure. We’re big fans of off-the-shelf AI. We have connections and APIs into all of the OpenAI models, all of the Anthropic Claude models, and the Perplexity and Gemini models. We use them all. We also have our own proprietary models that we use and train. So we believe there’s a lot of value in off-the-shelf capabilities. It’s important to recognize, though, that they’ve largely been trained on a huge volume of information that’s available on the internet. They have their own limitations because they’re trying to be all things to all people. They’re trying to give restaurant recommendations in Monaco, health care advice for your mother-in-law, information on your own financial plan, the ability to fix an HVAC system in your house and many, many other things. That’s an enormous remit.
So what you often have to do is curate, with your own domain expertise, information, training and workflows that are exclusively focused on what you’re doing. That’s something our AI can do particularly well in the world of investment analysis and strategic finance decision-making. Other domains do that very well, too, such as marketing and health care; I know of several other platforms that do it in those domains. But you want domain-expert information available, feeding and training any model you’re using. And frankly, you want to have human domain experts.
People are anxious about AI for lots of reasons, and we may or may not get into all of them here today. But one of the reasons, to speak freely and bluntly, is they worry it’s going to eliminate their job. I often say AI is not going to take the job of most smart people, but a person using AI better than you might take your job. There’s almost no better time to be a domain expert or someone with great judgment, real experience, real creativity and the ability to persuade, which I bet many of your audience members have in droves, particularly the last point, communication and persuasion.
Fundamentally, a human who is well versed in how to use AI and how to use quality control and features to make it more trustworthy, and who has domain expertise, judgment and experience, is more powerful than ever in human history. They are the person who can be 10x more productive, 10x more impactful and more.
00:16:58 - 00:17:29
Jason Mudd
I agree with you. The people who are hesitant to adopt AI, at least in the last several years, are the ones I think are really far behind. Then you see it when the light bulb comes on, and they’re like, “My goodness, this is so powerful, and I can do so many more things.” It’s fun to watch that transformation happen. I know one of the points you wanted to talk about was how AI can reduce the constant rebuild of senior communication. What does that mean exactly?
00:17:29 - 00:19:05
Mike Ryan
Well, I grew up doing this work, building analysis and presenting it to my team, my boss, various investment committees, management committees, commitments committees and the like. Frankly, when new information came in, it seemed inevitably to come in in the evening, not at the start of the day. Then you’d have to update all your materials, your spreadsheet model and your slides. You’d have all kinds of work to do. Technology in general, and AI in particular, can really cut through that in a dramatic fashion. We can set up capabilities, agents and workflows so the updating process is trivial. It’s done with ease and speed.
What that does is enable the producer of the work, me 20 years ago, to use their brain for real judgments and real discussion and debate with others, not the rote, mechanical updating of words, numbers, names, paragraphs, charts and tables. That’s a really important thing. Now, at the end of the day, you’re always going to read it and quality-control it before you turn it over to your CEO, your capital-providing LPs, your investment committee or whomever your boss is. So I think having an AI-plus-one mentality in general is a good one. When you’re handing it over to important people, you want to go into that bright-eyed and clear-minded, not exhausted from having just done a lot of mundane, rote work.
00:19:05 - 00:19:50
Jason Mudd
Exactly. I feel the same way many times about how we offer our services to clients as a PR firm. We’re trying to make it so the head of communication within the organization isn’t down in the weeds of the day-to-day tactical blocking and tackling of their role. That way, they can be more readily available as a trusted adviser to the leadership team and think strategically, instead of worrying about getting back to their office and knocking out a deliverable or some other tactical work that needs to be completed. I think if you use AI the same way, you can elevate your thinking and your mindfulness about the organization and think more as a strategist than worry about the day-to-day activities.
00:19:50 - 00:20:21
Mike Ryan
I couldn’t agree with you more. As I said, I think it’s going to create a more extreme barbell than we’ve seen before between the exceptional, high-quality thinkers and everyone else. The gap is widening, because people who are really able to elevate and give critical insight, judgment and persuasion are going to separate themselves. Everybody can basically do the things that cover the basics in an automated way, often with AI.
00:20:21 - 00:20:30
Jason Mudd
Mike, if somebody’s interested in learning more about you and your company or connecting with you, what’s the best way for them to do that?
00:20:30 - 00:21:54
Mike Ryan
Sure. Lots of people connect with me on LinkedIn, Mike Ryan, Bullet Point Network, and our website is bpnai.com. We have lots of information on our website, so you can connect with me over LinkedIn or check us out on the website.
All of our companies and many of our funds actually use PR because they know they have to get their message out. They’re all trying to optimize what the search engines say about them and now, increasingly, what ChatGPT, Claude and the AI engines say about them. So they have a lot of focus on how they get their message out. The one thing I’d say I’ve found from the best firms we work with at doing that, which is in your neck of the woods more so than mine, is it’s pretty much now become about content, about producing interesting, thoughtful content. People are much more likely to find that, and the AI engines are much more likely to read and absorb that. That helps build your authority and your brand in a way that direct advertising, just talking about your features and your product, doesn’t really do.
The AIs are really figuring that out. That’s why some people are getting boosted to the top of AEO and getting AI to recognize them. It’s because the AI is reading your content, your thought leadership and your authority in whatever area you operate.
00:21:54 - 00:22:15
Jason Mudd
That’s exactly right. That’s what we’re finding as well. Our clients that have hired us to create earned media coverage for them are seeing that they’re getting more leads from AI referrals and references than they have in the past, and certainly more than they’re getting nowadays from search engines, it looks like. So if people can stay on top of that, I think it can be very valuable for their organization.
00:22:15 - 00:23:27
Mike Ryan
Absolutely. There’s a tricky concept and a new term that’s finding its way into our society called AI slop. You’ve got to be careful that you’re not producing a lot of generic AI output that really isn’t insightful or deep. Just because you can produce it fast doesn’t mean you want it or want to distribute it. You want to make sure you have something meaningful to say.
One little trick we recommend people use is some version of a 20-60-20 strategy, where you, a smart human, do the first 20%. You come up with the ideas, you start the process, you get the ball in play. Then AI does 60% and deepens it, advances it and gives you critical material that supports your direction. Then you are the last 20%, and you put the conclusion together with your own nuance and insight, wrapping it in. People who let AI start usually end up with AI slop. When a human provides that first cut, lets AI take the big middle and then puts the final finishing touch on it, that seems to be the right recipe for most of our users.
00:23:27 - 00:23:58
Jason Mudd
I completely agree with you, Mike. In fact, we’ve talked about that exact same concept a little bit on this show. For me, the source was the idea that’s how Steve Jobs used to lead his teams: give them the first 10% of the input, work closely with them to identify it, let them do the next 80% of the work, and then check in at the last 10% to make sure it was in line with what you were hoping and expecting. But whether it’s 80% or 20%, I think it all works out just fine. You’re right.
00:23:58 - 00:24:43
Mike Ryan
If you’re Steve Jobs, it’s probably a 10-80-10 thing, and maybe that’s generous. But for most of us with our own individual projects, it’s probably worth our time to give it the first 20, which is a little bit of 10 on the direction and a little bit of 10 on the important things in my thesis or on my mind. When you get the AIs going, you’ve got to be real careful.
When I was a kid, we didn’t take a ton of family vacations, but they mostly followed a pattern of my dad sitting in the front seat with my mom and the kids in the back. Somewhere along the way, he would occasionally say, “Good news is we’re making great time. The bad news is we’re going in the wrong direction,” because there wasn’t Google Maps and so forth. So you don’t want AI to take you fast in the wrong direction.
00:24:43 - 00:27:03
Jason Mudd
I like that a lot. Mike, thanks for being here and sharing your insights with our audience today. I really appreciate that. You’ve made it clear they can connect with you on LinkedIn. We’ll put a link to your LinkedIn profile as well as your company website in the episode notes to make it easier on our audience. You’ll be able to find this episode and its episode notes at ontopofpr.com/mike-ryan.
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With that, this is Jason Mudd signing off from Axia Public Relations, hoping something great happens to you today. Be well.
Topics: On Top of PR, artificial intelligence
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