Becker Private Equity & Business Podcast
Understanding the Generative AI Landscape & How to Use AI to Improve Decision Making & Operations Now 3-6-25
Thu, 06 Mar 2025
In this webinar turned podcast, Dr. Darryl Williams, CEO of Partsol, joins Scott Becker to discuss the evolution of AI from national security to global supply chains, how true AI removes error for 100% accuracy, and the industries that demand precision.
Chapter 1: What is the focus of today's discussion?
This is Scott Becker with the Becker Private Equity and Business Podcast. We're thrilled today to have Dr. Darrell Williams, scientist, founder of Partnership Solutions, PartSol, joining us today on the webinar to talk about generative AI and decision-making and a lot more. Dr. Williams, thank you so much for joining us.
Let me ask you to take a moment to introduce yourself and tell us about yourself and the history of PartSol People don't realize you were sort of in the AI world before AI became a household word. Talk to us a little bit about the founding of Parcel, the history of it, and yourself.
Thank you for having me here today. The history of this really goes back to my years when I was in the military. I was an officer in the Air Force. And in the national security arena, we are inundated by global flows of data. And from those global flows of data, it's critical that... in security that there is no strategic surprise.
Chapter 2: How did Dr. Williams start in the AI field?
You don't wake up someday and find out that what you should have known actually comes to pass and you didn't know it. And so I was left in the mid-1990s, I was left with the problem of how does one discover in global flows of data information that is deemed to be undiscoverable. And so back then, I created these algorithms that, in essence, deconstruct and map supply chains of everything.
And from that, you're then able to discover things. And so it worked quite well, enabled predictive analytics, worked prior to 9-11, after 9-11. It was used extensively to stop terror attacks, discover terror attacks before they would occur. It discovered scores upon scores of them.
And then when I retired in 2007, I was asked by the government but also the private sector to continue this line of work. but it was very bespoke. The technology was just starting to emerge. And so doing four tasks per month, but then when COVID hit and all the global supply chains unraveled at the same time, we appear to be the only ones left standing.
So at that point there, I was asked to scale. And one of the first lessons I learned about AI, AI doesn't take bad algorithms and make them better. You have to have great algorithms. And with that, AI then enables you to scale. And so using AI, true AI, not Everybody now says they're AI, even though they can't spell it. It's true AI, true machine learning.
Chapter 3: What are the key lessons learned about AI?
We are able to go from four tasks per month to thousands per minute, probably per second. And from there, the whole world is open because we're the only ones that can do it with complete accuracy. And so it's a fun ride. I've learned a lot of lessons through the entrepreneurial process, but the horizon looks very good.
Thank you very, very much. And talk about that. You mentioned something a moment ago about sort of going from poor tests to X amount of tasks, but you also talked about the algorithms, the human behind the algorithms. Could you talk about that for a moment?
Because there's so much discussion now, whether it's with Elon Musk's Grok, with the other open eye solutions, the search solutions, is the algorithms behind those that drive a certain algorithm way of outcomes versus others. Talk about the effort or the secret or the thoughts in developing those algorithms that then feed into what you're doing.
So, you know, we don't have enough time on this podcast, but let me give you at least the bird's eye view is that back when I first started in the computer field, there was that phrase garbage in, garbage out. And the idea was is that you have programming that is accurate so that your results can be accurate.
And so with all the other generative AI processes out there, they are designed to take data and global flows of data. I mean, think of what Elon Musk is doing in Memphis, and Google is spending hundreds of billions of dollars per year, and their algorithms are fantastic, but ultimately, it is garbage in, garbage out. That's why they are constrained to that 88% accuracy at best.
From the very beginning, since I started the national security realm, I was compelled to first create algorithms that that filtered error out of data. So since I was compelled to do that, from the very beginning, the algorithms just don't do what everyone else's do. They first filter error, bias, nuances out of data so that the data that is running through the algorithms is 100% accurate.
And that way, instead of having garbage in, garbage out, it is... accuracy in and accuracy out. And so that is where I am different, our company's different from anywhere else, is that we not only have to look at the algorithms, but we also have to consider the data. Everyone else is just looking at, is making the assumption that the data is what it is. We hope for the best.
I took the area that hope is not a course of action, so let's filter the error out and let's make accuracy a key.
Thank you very, very much. Dr. Williams, where are you most focused today in the three or four verticals that Parcel focuses on?
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Chapter 4: What industries require 100% accuracy?
Yeah, this is really for the business owners in that imagine yourself, you're back in the 1850s and you're driving across the United States in a Conestoga wagon and someone comes up to you and shows you the schematic of an F-35 jet fighters. You are not going to believe it's possible for something to fly.
And so when we come to a bank that is taking five weeks, some of the banks take almost six months to do a know your customer of a high net worth foreign individual. And we claim to do it in 20 minutes. there is a disbelief. In fact, on the early marketing that we have done, we actually added time to our system just so someone would believe it.
So ultimately, no one believes that something like this can actually occur. And so I, as a business owner, I had to make a decision that Um, every single large organization I go to, I have to do something for free. I have to do it. I have to take a problem that keeps them awake at night and I have to solve it one time.
And so 2024, we must've done over a million dollars worth of pro bono work just to convince an individual at what we're saying is actual truth.
Let me ask you a question along those lines. So whoever you're working with, whoever CTO is working with, they ought to test with the party, they ought to get reference from the party, you know, have you do some runs with them. Let me ask you this question.
The intelligence of your consumer, the customer, whether the bank or otherwise, as all of us get a little bit more familiar with AI, like I feel like All of our education is going at a pretty good speed ramp because all of a sudden we're seeing in our Google searches when we ask a question, a whole different level of detail and data than we were getting years ago.
We had to search through and scroll through all the different search results to figure out what was relevant. Now it's summarizing it for us immediately. Are your consumers... learning a lot and coming up to speed at a decent pace, or are people still just overwhelmed in the workplace as to where to figure out where the solutions go and how to use the solutions?
Well, I tell you what, that actually fits in with that second to last question about what I'm hearing, is that they are getting more savvy with AR, with AI. The problem that I'm running and the problem I'm hearing is frustration. Even, I mean, when you type something in Google, you're going to get a Google AI that's going to tell you something.
And for 99% of the businesses and the individuals in the world, that's good enough. But with law firms, banks, national security, and health sciences, there is an overwhelming sense of frustration that they are being forced to settle for something that is there.
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Chapter 5: How does AI intersect with quantum computing?
We had to take a different sales approach. The typical sales approach is go into the middle market and work your way up until you find a decision maker. It did not work. We tried it. It did not work. It failed miserably because we found that every layer that we went up didn't believe what they were seeing, and it became crazy.
So we found that if we went out and we hired influencers in certain verticals, They could go directly to the CEO. They're vouching for us to the CEO. And when you sit down CEO to CEO with an influencer right next to you and you ask a simple question, what keeps you awake at night?
I guarantee you every CEO in the world has something that keeps them awake at night, but they cannot articulate it because it shows vulnerability. It's going to affect the shares. But when you sit down and say, what keeps you awake at night? And then solve that for them. You have a devotee for the rest of your existence. That's the only way you can sell something like this at this point.
Thank you. And talk about what are you most focused on and excited about this year? You've had this tremendous rollout. You've got the Absolute Truth and AI company at ATI. What are you most focused on and excited about currently, Dr. Williams?
I have two things. One is the AI stem cells, which is fabulous. The other one is the supply chain as a whole. Georgetown just came out with a great – a great document where they did a test and they found that less than 2% of every manufacturing company in existence, less than 2% knows deeper than two levels of their supply chain.
And so since we go far, far deeper than two levels, we are finding that
whole world is open and so um as a proof of concept we are looking at commodities because we have the ability now of looking at multiple commodities multiple levels of depth from each commodity and predicting activity in that commodity 12 months in advance we know there's a way of making money with that but we don't have a lick of understanding of what it is we're doing
So that's just some of the stuff that I put on my chief scientist hat. We just start delving into neat areas that we can use AI.
It's fantastic. I'm going to ask you a question from the audience that, again, dovetails really well with one of the questions we were going to look at later on. So this comes from an audience member. For an innovative company eager to integrate AI agents into daily operations, what's the most common misconception that we keep in mind, that we should keep in mind?
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Chapter 6: What challenges do businesses face with AI today?
So what are common misconceptions and what should we be thinking about? Almost like what advice would you give to clients starting to implement AI tools?
Yeah, the... Primary misconception is that AI is the solution to any problem you may have. There is this thinking that, because I see it numerous times with business pitch decks, is that if they have an issue, if they just sprinkle a little bit of AI on that, that first of all, what that'll do, that'll give them about six months
of uh leeway from their shareholders because it looks like they're doing something and then the other one is that it's going to solve something and really you've got to get you have to go back to the basics of business what you know your whole SWOT analysis what is your strengths what are your weaknesses you have to know what your problem is before you can apply AI to it AI never solves anything.
AI just makes a solution faster, broader, and deeper. So if you don't have a solution, you're throwing a Band-Aid on a gaping chest wound. It just doesn't work. So I see these people that have... But this is so important.
I've seen so many of the revenue cycle companies in AI... They were solving a real problem. People were working with these huge bullpens of revenue cycle workers trying to get revenue cycle claims processed. And they couldn't even hire people fast enough to do it. They couldn't pay people well to do it. They were really trying to solve a problem.
Like some houses have 2,000 people trying to follow up on claims.
and ai and ai solutions led them to have 1200 people doing it and 800 of them replaced by the machine so the 1200 really work on the more important stuff but they were really trying to solve a problem because they just they couldn't even get their hands around how to keep staffing this the way it was being done so i think your point on what are we trying to solve you know i've got one organization 35 full-time writers
I don't want to replace them with AI, but I'd like them to spend more time talking to their communities, really engage with the communities. So could we get some of the work done through computers and then have the journalists doing more and more talking directly with people in the community? Like that's a real problem that you want to solve.
Whereas if you're just throwing AI to business and haven't figured out the problem you're trying to solve, what I hear from you is that that's kind of a useless approach. You better figure out what you're trying to do and what your problems are.
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Chapter 7: How does PartSol plan to advance in healthcare technology?
And talk about this. People talk about the free AI platforms, like open AI parts. So you guys don't have like a free platform, do you? That's not how you work.
No, not at all. It's no, we don't.
A fair answer. Talk for a second about where do you see, you've talked about the verticals. Can you give us an example of specific use cases you see with clients that, that might be the most prevalent use cases in the next couple of years?
Next couple of years. Well, I mean, the family office and the investment and the banking isn't going to change over the next couple of years. So the ability to know your customer faster, because right now an individual that has a tremendous amount of money does not want to wait five weeks. And so if they have to wait five weeks, they go somewhere else. So that's going to change.
I mean, so that won't change. But where I see things changing is if they can – Well, we have, but if someone else can get to the point where they can remove error from the data streams, I think what you're going to see with an IoT capability like autonomous vehicles, autonomous taxis, space, and everything else is going to just explode. Because right now, if you're in a car...
and you're driving in Los Angeles or wherever else that has multiple 5G towers with IoT, and your error ellipses are down in the millimeters, you're fine. But if you want to take that same car and go across the United States, your errors might take you somewhere else.
And so if they can solve the errors, which we've already done, I believe you're going to see the whole autonomous sciences just blossom.
Yeah, and that's going to be fascinating to watch. You're always seeing more and more people in the Waymos, the automated autonomous taxis and so forth. And that's a new thing, but you could see how quickly that can grow as the technology gets better and better because it replaces, unfortunately or fortunately, the human element in it, unfortunately, because it replaces a lot of unskilled jobs.
Fortunately, because you get better driving and safer driving and less errors. Fascinating. Talk for a second, Dr. Williams. A few weeks ago, a month ago, all of us heard for the first time about DeepSeek. Just give us a moment on DeepSeek and what that means and why that was important.
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Chapter 8: What is the future of AI in supply chain management?
Yeah, I won't say anything about that. But how much is invested? The United States has invested tremendous amounts in AI. I mean, you even saw with CNN and Fox this morning about the AI fighter jets, which are going to be hitting production. But once again, though, they are AI fighter jets.
but they're operating on an operating system that has 88% accuracy, which means that the human will never be out of the loop. And so there's a lot of investment going on right now, but it's still garbage in, garbage out. So we'll see if we can change that paradigm.
It reminds me of all the golf clubs now being AI golf clubs. But God, I know that they're not making me a better golfer, even though I'd like to, because it's still me designing the algorithm of the stroke. Dr. Williams, I want to thank you for joining us so much. I want to thank our audience for joining us today. And I can't tell you how much we appreciate all your questions and all the inquiries.
Thank you very, very much. Thank you. My pleasure.
Thank you.
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