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“The first billion-dollar company with just one person in it will exist within five years—and I believe it’ll happen even sooner.”
– Phillip Swan
[01:13]- What is the AI Solutions Group?
Phillip explains the group’s mission to integrate AI responsibly while addressing ethical and compliance challenges.
[02:04]- The Mad Rush Towards AI
Phillip discusses the tectonic shifts in AI development and how companies are racing to adapt.
[05:00]- The Turing Test and AI’s Evolution
Phillip provides a brief history of AI, from Alan Turing’s concepts to today’s breakthroughs in generative AI.
[09:19]- Shadow AI and Corporate Risks
Insights into the dangers of unregulated AI use and how it could lead to leaks of sensitive corporate data.
[13:30]- AI Will Not Replace Humans (but will augment them)
Phillip emphasizes that AI will enhance human roles rather than replace them entirely, highlighting the need for human-AI collaboration.
[21:43]- What Future Are You Solving For?
A thought-provoking discussion on how businesses should align their AI strategies with long-term goals.
[29:44]- The 7% Rule for Change Management
Phillip shares a fascinating insight: achieving critical mass in organizational change only requires 7% of the workforce.
[37:06]- The One-Person Billion-Dollar Company
Phillip shares a bold prediction about the rise of billion-dollar companies with minimal personnel, driven by AI.
[39:18]- AI’s Disruption of CRM
How AI is poised to turn customer relationship management systems like Salesforce on their heads.
[44:50]- Benefits of AI in Manufacturing
A compelling case study on how AI enhances safety, reduces shrinkage, and improves operational efficiency in manufacturing.
[52:28]- The Most Challenging Project
Phillip reflects on introducing voice-over-IP technology and the lessons he learned about solving customer pain points.
[56:04]- Avoiding Unintended Consequences of AI
Phillip emphasizes the importance of understanding the downstream effects of AI solutions on customers.
[01:00:12]- Books That Influenced Phillip Swan
Phillip recommends impactful books like Good to Great, Crucial Conversations, and Extreme Ownership.
[01:03:47]- How to Get in Touch with Phillip Swan
Phillip invites listeners to connect with him on LinkedIn and subscribe to his newsletter for more insights.
In this episode of The Chat, host Terry speaks with Phillip Swan, Chief Product Officer at AI Solutions Group, about the transformative impact of AI on industries and business strategies. Phillip shares insights on using AI to solve “migraine-level” business problems, emphasizing responsible AI adoption in areas like safety, operational efficiency, and customer-centric innovation. They discuss AI’s rapid evolution, the future of AGI, and the disruptive potential for industries like CRM, while highlighting the importance of ethical considerations, change management, and simplicity in implementation. Packed with practical advice and visionary predictions, this episode is a must-listen for leaders navigating the AI revolution.
00:00- Terry:
Welcome to “The Chat”, where we take you behind the inbound scenes of some of the world’s fastest growing companies. For the past nine years as Chatmetrics, we’ve had an exclusive inside view of how these industry leaders are revolutionizing inbound marketing and sales across diverse sectors. Each week we bring you conversations with top minds in marketing and sales, sharing insider secrets and strategies that drive inbound success.
00:39- Terry:
Join us on “The Chat”, your front row seat to the future of inbound.
Hi Phillip,
thanks for joining me today on the chat.
Phillip Swan is the chief product and go to market at the AI solution group, uh, which the name in itself conjures up all sorts of thoughts and questions.
Um, welcome, welcome, Phillip.
01:09- Phillip:
Thanks a lot, Terry. Pleasure to be here.
01:13- Terry:
Look, I thought we’d just jump straight into it today. There’s a couple of background questions that I want to ask you. But the AI solution group, I mean, it conjures so much at the moment with everything that’s going on. You and I had a conversation a little while ago, um, and you gave me a heads up about a potential release.
Uh, and that release actually happened last week. It looked like with strawberry, uh, by open AI now, oh one. The AI Solution Group. What is it? What do you guys do?
01:42- Phillip:
What do we do? We help us. We’re both, uh, uh, services and offers companies. So we offer both software as a service. Well, it’s actually AI as a service, as well as. Offers to help fortune 500 companies reimagined their business with responsible AI and what is responsible. I mean, it means it’s good. It’s ethical and it’s safe.
And we talk about AI being good for people and AI being good for the planet. And as you and I have spoken a few times that, uh, you know, it’s just, there’s a mad rush right now is tectonic. The plate shifting type activities that are going on right now. Nothing is the same. And I mean, I literally get up in the morning thinking, okay, what do I have to relearn today?
02:30- Terry:
Well, uh, it’s interesting. You should say that one of the, uh, large Fortune 500 companies, and the name just slips my memory at the moment, announced, uh, earlier this week or late last week that they were dropping Salesforce as a CRM and were about to build their own completely entirely in house.
02:50- Phillip:
That’s right. They can, you can do that today. That’s exactly correct. You do that with agents. So Salesforce, I’ve heard the rumor. Now this is rumor. I don’t know whether it’s actually true or not. It’s halting all development to basically redo their whole. Platform with AI. I don’t know if that’s even possible for them to do that because they’ve got so many, um, artifacts and, you know, from all the acquisitions that they’ve made.
It’s, it’s, it’s a challenge for CRM customers. There’s no question about it.
03:19- Terry:
Well, at the front end of the market, and that’s where we operate as well. Um, people like qualified, they’ve got Piper, they’re AI SDR effectively. Um, they’re, they’re, they’re calling, coining it, uh, Salesforce did late last week, come out and announce, uh, their own SDRs, AI SDRs, um, you know, I’ve got a bit of a, I’ve had some experience, um, with some of the more recent ones.
I’ve got a bit of a mixed position in terms of, I think that AI SDRs are the definition of that is not necessarily what people think it is on the surface. It’s not doing away with people entirely. You know, there’s got to be a human component in there. But look, you know, we’ll come back to that. Um, we’ll come
04:03- Phillip:
Yeah, cause that’s a hot topic. The SDR topic is definitely a hot topic, Terry. We should definitely cover this.
04:08- Terry:
Yeah, we’ll look, we will. And look, maybe now is the time to do it then. I mean, in, you spoke about, you said SAS, but it’s AI as a service. What’s the main things that you’re focusing on at the moment? Or you’re seeing the main concerns in the organizations that you’re working with?
04:26- Phillip:
Well, the biggest thing is what the hell do we actually do with AI? That’s really the first one. And how, because what happened, what’s happened with the AI is not something new. Okay. It’s, it’s almost a hundred year old problem. It started off, you could arguably say. All right, it’s a, 75 year old problem. If you date back to 1950, I actually think it goes back to 1936 when Alan Turing wrote his first paper on the whole concept of what ultimately became microprocessing and artificial intelligence.
And in 1950 he created this thing called the Turing Test, which was a very simple test that you put a human in the middle, and if the human can not tell whether they’re talking to a computer or a human, the computer has reached intelligence.
05:14- Phillip:
And the f the first time the term artificial intelligence was used and coined was in 1954 in the, in, um, um, in, in a Dartmouth, in the us uh, conference that was going on there with darpa. So. It’s an old problem. What is new is the vast quantities of data. What is new is the compute power, even though it is still choked to a large degree because of just being able to get access to silicon is near on impossible unless you’re one of the large hyperscalers. So for companies, what they’re struggling with because they’ve tried it in the past and it’s failed. Is what part of the business can you start dipping your toes into?
So really start getting down to what’s the hype? What’s the doom? So we we start customers off with an AI reality check. What is really possible and what’s not and getting people level set as to what’s the hype? What’s the doom? You know, why is the doom not possible?
And why, how can I introduce it without an AI into my enterprise without letting all of my corporate secrets get out into the marketplace of having, um, having my own instance of open AI or anthropic or whatever LLMs you’re using. What’s happening today is that the companies, because AI grew up recently in the modern era with remote process automation.
06:45- Phillip:
So it started off with web scraping, then with RPA. So you had companies like UiPath and those who have done extremely well with that remote process automation. And it’s, um, it’s really about focusing in on the use cases, right? What are the use cases and what’s come through with generative AI. Is the ability to now really radically reduce your research time. And you know, what started in 2019 with GT two then GPT-3, and now GPT-4, and you’ve mentioned four oh and 4 0 1, which is the strawberry release that the, um, that they were just starting to announce, um, and bring out to limited, uh, personnel. That preview, 401 preview is what is not GPT 5. GPT 5 is being trained by 401 preview. And that will come out sometime in October. And from what I have seen, because we have access to, uh, OpenAI insiders that others don’t, is it makes 401 preview look stupid. And as you and I both know, it’s anything but stupid. Uh, and what GPT 5 is the precursor to is this thing that is out in the wild called Orion.
And what Orion effectively is, whether they call it this or not, is AGI, which is Artificial General Intelligence, which is where, where it becomes as, you know, acts and behaves as intelligent as a human does, if not more so. Uh, which starts getting into the whole Skynet thing and the whole, well, what happens now and the, so what the, the way the industry is rapidly evolving is everything’s going to be an agent. So a simple task could have 11 agents in it. Right to get it done and running and running those in parallel. So what we are focused in on our company is really about how do you introduce those agents into the market responsibly? So you’re not running into the Skynet situation. You’re not running into ethics issues or compliance issues or regulatory issues, especially if you’re a bank insurance company. You name the, you know, the regulated industries across the world and they have problems, right? And, And, how do I introduce risk? And it becomes a change management problem, Terry, because how do I introduce AI responsibly into my organization? I don’t have this thing going on called shadow AI. And what shadow AI is, is just what I just described.
09:19- Phillip:
You’ve got company people within companies who are putting these prompts out there With data that is proprietary to the company. So their proprietary data is now out in the wild because they’ve trained us. And the perfect example of this was you can go online today and show me the source code to Samsung’s latest operating system, and it will do it because somebody at Samsung put the source code on there because it said to have me write the source code, right? So there’s You’ve got to get into the responsibility side of things because all of a sudden now company secrets are getting out into the world and you have to, you have to reign that in and you’ve got to reign the whole organization in. So it really is a change management problem, if that makes sense.
10:03- Phillip:
Uh,
10:05- Terry:
does, and that I would like to talk to a little bit more in detail. But tell me this, are you concerned? You’re writing amongst it probably more than most, and you have certainly visibility and foresight more than most, are you concerned about what a AGI might bring?
10:22- Phillip:
yes, in a short word, yes. Uh, but do I think that OpenAI and others are working to try and do it responsibly? Darn right they are. Um, they’re taking this extremely seriously. Um, uh, and so there will be issues with it of that. I am certain if you have companies like us who are paying very, very close attention to all the, the global regulatory and compliance, Uh, side of things, you’ll be okay as long as you’re partnered with a company like us that will do this because it is a quagmire.
You’ve got laws changing literally daily across the globe right now. In the US, each state is coming out with its own laws. So there’s, right now there are over, there are over 300 pieces of legislation out there on AI compliance and, uh, hasn’t even, Going through the old review process. Yeah, it’s It’s just not Terry.
11:23- Terry:
Well, that was my next question. You know, you say, you know, with an organization like yours, that’s keeping an eye on all these regulatory requirements, what regulatory requirements? It’s still, it’s still a big washing. Look, I know there’s a lot there at the moment, but there’s
11:35- Phillip:
oh, yeah,
11:36- Terry:
uh, you know, a washing machine of things that we don’t even know about yet.
That’s going to be coming out with
11:41- Phillip:
absolutely, right
I mean the biggest part is on data right data sovereignty data security data privacy privacy of consumers privacy of minors safety of minors safety of people who are At risk, whether it’s people who have, you know, whether they have mental health challenges or other challenges in life, right? These are all the You step into this and you’re rapidly up to your neck in all of this quagmire and this mud and you go like, well, how in the heck do I actually get to my destination?
12:16- Terry:
Mm.
12:16- Phillip:
the way we get our clients to the destination, Terry, is really quite simple. We ask them one question. What future are you solving for? And it stops them dead in the tracks because they’re thinking about tomorrow. Right? Think about the future you want to suffer. What is that nirvana? What is that panacea? What is that business outcome that you want to see? And then start working back from it. And I don’t know if any of your audience has ever watched The Martian, but Matt Damon said something very pressing at the end of the movie where he said, It’s, you do the math. This is all about doing the math. Right? And eventually, you get home. You do enough things right, you will get home. That I guarantee. So there’s going to be immense amount of change and disruption happening over the next three months, four months, five months, and six months as AGI becomes real. And then what becomes real in 2026 is Real autonomous robots, right?
So people are not going to get replaced by AI want to be very clear. So I’m not worried about that.
13:30- Terry:
Nor am I. And you know, we, we are a people business first and foremost. And it was one of the things when this first started and I started to look at the capability of chatbots and think, well, you know, is this the end of our business? Or do we have to do a full hard pivot to Aoi? The reality is no. Look, I don’t think it’s ever going to completely take people out of the equation.
You know, there’s a whole host of, you know, really for this conversation, but there’s a whole host of places where, you know, people matter in the conversation.
But as far, as far as even with things like the agents, um, I’m sure that I will envisage a day where there will be certain procurement slash acquisition processes where you will have AI agents scouring through your organization and looking at all of the bottlenecks and areas where there’s efficiency, um, uh, opportunities.
And they will go through the marketplace and they will scour all of the available products. And then they will find all of the AI, um, sales agents, so to speak. And you will have a, Effectively a transaction that gets to the point of two AI agents talking to each other to say, Hey, these are the problems I’ve got to solve here.
This is the best way to solve them. And then that will be presented to a human potentially to make a decision on. I don’t envisage anywhere yet where it’s going to get to that point beyond that.
14:57- Phillip:
You’re absolutely right. So what we say is you automate with human inspection points. So it goes back to the point. AI will not replace humans, but you will get replaced by a human who’s using AI That I guarantee you. Um, and that’s the part you, your audience needs to get to is getting comfortable with AI and knowing how to make yourself efficient. I’ve been going at this for, you know, for over 30 years on AI. So this is not a new problem to me. What is new to me now is. is. how I let go of all my own biases and that has been a trick and let me tell you it’s not come easy but I’ve gotten self aware enough to know that feeling where I feel like Before you say no, shut up and just let it, let it, then let it roll and see where it goes to because, and that’s the trick that I did. It’s just like, just let it go. That’s all I say to myself. Let it go. And it’s been freaking amazing what’s happened as a result because it’s just opened up my perspectives. And as a result, it’s opened up my team’s perspectives, has opened up our entire company’s perspectives about how do we deliver amazing outcomes for our customers?
And that’s all we’re in business for. We’re only in business to, and yes, the money will come as a result of all of that, but we are focused. We are customer obsessed, client obsessed.
16:22- Terry:
And I remember your and I first conversation several, several years ago now. And that was one of the things that you and I were so aligned on was the value of absolutely making sure that everything we do effectively is for the benefit of the customer. So that was a, a, a very joint, uh, a joint value.
16:42- Phillip:
You’ve got to be deliberate. You really have to be deliberate when it comes to AI. And what I do is, is that I literally. I go into Vizio. I mean, I’m a Microsoft user, right? So I go into Vizio and I literally draw out the process. And then when I draw out the process, I work to optimize it. And when I’ve got to optimize my thing, I give it to my engineers.
Go and build me this, right? Um, I’m talking about just agents, right? These are the things. So we actually put proper product requirement documents together. We do proper business narratives. We do proper summaries. We do proper process flows. So, I mean, we are really buttoned up when it comes to product delivery and what we’re going to deliver to customers.
17:22- Phillip:
And that’s what we do. You know, and the point is good discipline pays off over, you know, you may miss things, but you’ve got it in writing and just write stuff down. Don’t, don’t expect the AI to go and make this shit up for you. Excuse my French. It’s, um, because it won’t, it will make up nonsense. And you have this thing called hallucination and it’s a very real problem in AI. What hallucination is, is that it makes data, you know, that is representing to you. It makes it look like it’s the truth.
17:51- Terry:
We almost giving you the answers that you’re looking for.
17:54- Phillip:
Yeah, so it’s almost like propaganda, but it’s not almost. It is propaganda of sorts, right? So, um, you know, so you have to be really, really careful. And here’s a hint to your audience. Put three words in front of every single prompt that you do. Can you guess what they are? Terry, do not
and that, and those three words in front of every prompt will reduce hallucination by about 90 to 95%. There’s a little trick for you right
18:25- Terry:
All right. Okay. That’s good to know. You know, it’s very interesting. My own experiences with early on, I was very disappointed, um, particularly around using it for prompting for some content that we were doing at the time. And I realized quickly that the power of, what we have currently. Is in the prompting it’s, it’s this, you know, I think there’ll be a whole new, um, industry come up as, as prompt engineers,
18:55- Phillip:
already is. So do you know what? If the average prompt engineer in the United States, you know how much money they make in the United States, a prompt engineer, 900, 000 a
year, and that’s on average. An AI engineer out of school will earn about 350, 000 in the United States. Um, and an AI engineer with experience will earn over a million bucks a year. It’s software engineers within 12 months. If you’re not already an AI engineer, you better be thinking about your career real hard because we are literally building products in 10 minutes. That’s, that’s literally, and these are not simple products. You know, 10 minutes of expensive compute time to build those products.
That’s what we’re talking about. And the quality is good. It’s not excellent. It’s good. It’s good enough to get started. And it’s good enough to then have an engineer to go in and optimize it. Uh, but we literally reduced 10 months’ worth of development into 10 minutes, quite literally.
19:58- Terry:
I’ve played around with that a little myself with a few simple little apps and I’ve been shocked, been blown away. It’s been an area that I have virtually zero, not virtually, I have zero coding skills.
My business partner is an engineer, uh, he’s a developer and, and quite full stack. He’s built everything in our business from the VoIP system through to, you know, the software dashboards and websites that we use, et cetera.
20:22- Terry:
So a very, very capable guy. Um, and so I’ve never had the need or desire to get involved in coding at all. But I built a small app, uh, got the code built for a small app out of an AI, sent it over to him and he asked me who did it. It was quite shocked. It was quite shocked when he found that I’d done it because we actually it was, it was good enough to be working.
So, um, you know, if I can do it. And I’m a Neanderthal when it comes to anything coding. Um, yeah, but, uh, but look, one of the things, as I said before, that the prompting side of things I’ve become quite skilled at, it’s been something that’s been a focus because I can see the benefit of these agents and using more and more, I mean, you know, I’ve done a lot of play around with the, um, GPTs, the chat GPTs, the personal GPTs to make your own, um, It’s fascinating how different a personality they can have,
depending on the little amount of training and teaching you do yourself.
You give it yourself and the information you give things even like, um, brand tone and, and content. Um, even the way it answers where it looks for information as opposed to, uh, just having a chat with standard GPT.
It’s, it’s been fascinating for me. A great learning. Um, so what future are you solving for?
21:43- Phillip:
The one that we’re solving for quite honestly is to, um, change, change the world. I mean, it’s literally that’s it’s literally that broad. Um, and when we say we’re changing the world, it’s like, how do you make this a better place? Right? All of us are a bunch of older people in the company for the most part, um, and we’ve gone through multiple tectonic shifts in, um, you know, in our space for me, that was mobile, it was voice over IP, then the internet, and now artificial intelligence, right?
So, you know, one thing that we all have in common in our company is that we’re all platform people. So we are very much into building a platform and getting that built out and. actually adopted globally. We’re experts at doing this. So the future we’re solving for is to give companies, developers, and, um, and partners, the ability, you know, a safe platform in which they can develop and deploy these agents without thinking that they’re going to go off the reservation and then put, you know, into, Sky net and gosh knows what you’re blowing up the planets.
Um, bad for people. Um, people are getting harmed, all of that stuff that we, this is the platform that we will be releasing later this year. Uh, so we’re literally, you know, within two months away, if, if that from the releasing this into the wild. And so I’m talking about something that is, um, really important for people to note is that this. Um, you’re not going to get to safety ethics or, um, or goodness by thinking you can do it by yourself or by hiring a bunch of attorneys because it just isn’t going to happen. You think you can do this on your own. Nobody can. And I literally, and I’m not saying this is an arrogant thing, things are moving so quickly. With such amounts of volume, no one person, no one company can be experts in this.
What we’re doing is, is that we’re providing the framework and the infrastructure in which you are going to be able to safely build and deploy these agents and maintain those agents in a way that it’s not going to start breeding, um, disinformation or, um, harmful to individuals or companies or organizations, society, um, you know, where we’re starting to get. Schwarzenegger coming across in the future being, you know, zaps in to go and kill the robots, right? So now that’s not gonna happen, right? It’s not gonna happen. Not when you’ve got, not, it’s not just our company, there are other companies that are, have got a lot of Um, expertise and compliance and regulatory.
Also, they’re going as we are the only company that we know that are doing it in a complete holistic fashion that drives the value to the end customer without getting overbooked, overburdened with all the regulatory compliance, safety, um, uh, aspects of running an AI solution or building AI solutions.
24:56- Terry:
So can you give me an example of, of what an AI solution is for, for your clients? So what, what does that look like, say from, from end to end?
25:05- Phillip:
I’ll give you a very specific one that we’re actually building right now. I won’t tell you who the company is, uh, for obvious reasons. Uh, they actually have three use cases. They actually have multiple use cases. Um, but the, um, the biggest use case that they have is actually employee safety. Uh, they are a manufacturing company, so they’re operating large equipment that if people get. You know, appendages, arms, legs, fingers, feet, whatever, stuck in, they’re going to die. Um, and so they’ve been working through those issues, they’ve gone through a significant change management to actually get theirs improving significantly. But it’s still not there. They want, um, an alert system, um, Uh, they also want for product shrinkage or leakage, right?
So they want to make sure that people, when they’re bending down, are not putting products into their shoes, their pants, their pockets, et cetera. Right? Um, same thing with safety that they’re not putting their arms or fingers near equipment that’s moving at, you know, multiple revolutions, you know, thousands of revolutions a second, kind of minute type of thing, right?
Where you die. Right. Um,
26:18- Terry:
Mm-Hmm.
26:19- Phillip:
and so, you know, this is an example of a good project that’s good for people and also good for the planet, right? Because one, it saves them incredible amount of, um, if you see a raw product that actually goes into these machines to finish out the end product and gives them more efficiencies in terms of, you know, You know, of how they turn around, um, different jobs on the same machinery, right?
26:43- Phillip:
So, and then the other part is, it’s, it gives them the ability to be more proactive around safety as opposed to reactive when an incident happens.
26:57- Terry:
I love that example. I love that example. I think, you know, you’ve mentioned change management a lot of times through this conversation. What’s your thoughts as far as change management is concerned and how big a challenge is that compared to the adoption of the, of the new processes and the new technology?
27:15- Terry:
Mhm.
27:17- Phillip:
Terry, right? So change management processes, right? I think they’re, you know, for change management, you’ve got, you know, you’ve got to start at the leadership, right? And really understand what, you know, what are the business objectives again, back to that? What future are you solving for? And then what do you need to do to really focus on what jobs have to be done to actually make this, you know, make this become true and getting people on board in the simple. So it’s, you know, it can be as simple as on the job training can be simple as cards that they wear on their, on the, you know, on their security passes to get in the building.
It can be, Um, any, any number of activities that will help them and reinforce this. So it’s, but here’s the trick. The trick is 7 percent of the organizationals population. So 7 percent of your company is all you need before change. Actually, Kerry has reaches critical mass, so you want to focus on that 7 percent of the organization that is going to bring, you know, the 93 percent of the organization along with it, because you will have critical mass by that 7 percent and you will see things fly quickly. Does that answer kind of what you’re looking at?
28:34- Terry:
Yeah, it does. It does. Look, change management, something I’ve been quite involved in a couple of merger and merger and acquisitions and then and then full integrations of those businesses through those acquisition process. So everything from, you know, technology operations, full sites, business integrations.
So the human side of it, I think too often is misunderstood and it’s underestimated and it’s usually, you know, I find in most change management, um, um, pieces it’s underinvested in
29:05- Phillip:
Oh, totally. It’s under invested. And, you know, if you don’t, if you don’t run a change management exercise, your ability to integrate. AI into your organization, whether you’re a two person company or a 2000 person company or a 20, 000 person company will fail because you know, you’ve got, you’ve got to get the people understanding what it is and showing them the path to get to this future destination.
Because if there’s one thing I’ve learned in my career, you can’t just go to the future destination, expect people to understand where you’ve come to. You’ve got to show them the path to get there.
29:44- Terry:
And there’s a huge difference between three terms that I hear thrown around often. There’s integration, implementation, and change management. There are three very, very different things. You have a new process, a new technology that you want to have implemented into the business. You can have an implementation plan, but that doesn’t deal with the people.
Moving them from where they are today, their values, beliefs, fears, and loves, wants, desires, and career ambitions, and all of those things, that all of a sudden they’re in a new paradigm and a new world and a new way of doing things. You know, I think that it was interesting. You haven’t heard that mentioned before about the 7%.
Um, but that’s rings true, but it won’t get the other 93. There’ll be probably, it’ll get the other, uh, 80s, 88 and, and then there’ll be 5 percent that will drop out of the process on the way through. They just don’t want
30:40- Terry:
to be part of the new paradigm.
30:41- Phillip:
part of it. That’s exciting. You always have that, right? And the other, they’ll probably leave the company at that point, right? Because if they’re not part of the program, right.
But 7 percent does get you to critical mass. You can look that up.
30:55- Terry:
Excellent. Tell me the as far as the responsible development of I and integration into businesses. You talked about the way that you’re doing it. Do you see an environment where there’s organizations feigning to do what you do? but not necessarily taking the same precautions that you’re taking. Um, you know, there’s a lot to go through my mind in terms of, you know, we operate a lot with the smaller business, smaller medium business areas where there’s a lot of fast moving, move to the next.
Bright, shiny thing. Um, you know, there’s a couple of organizations that I’ve seen do things with AI selling a product, but once you dig in it, cause I’m obviously concerned about security for our organization,
you dig in a little bit deeper and you think, well, I’m not going to put any of our data anywhere near this thing.
31:44- Terry:
So what does that future look like? I think there’s,
31:49- Phillip:
So the future looks like you’re going to have effectively bespoke. And if you’re, if you’re a company, you’re going to have bespoke instances of whatever AGI looks like in six months. And. I can’t tell you what it’s going to look like because I just simply don’t know, right? I don’t, and I don’t think even OpenAI knows, right, yet, right?
Because they’re busy building it right now. And, and so it’s, um, so, so take the unknowns out of the question. What can you control? Well, what I can control is What we’re going to take to build an AI solution, right? And figuring out what the use cases are, that’s really important. And then, is it important enough to actually drive back?
Now, Microsoft, for as large of a company that they are, for Azure AI, there are 8 use cases that drive 80 percent of their revenue. It’s not that many, and the reason why it’s only 8 is because all the rest are just nonsense right now. And what’s going to drive the outcome for the business and one of the first big ones is it’s like getting your getting your company data in a secure instance.
You bring up security. Well, the hyperscalers like Microsoft. They have security pretty well in hand, right? Is it perfect? No. Are they continually getting beat up? Yes. Do they have highly competent people solving the problem? Heck yes. Right? So they’re putting big brains on this problem, this problem continually day after day.
And this is the key thing. You never let your guard down. And the key thing with AI is that you’ve got to put the guardrails on. up. So it’s like going to 10 pin bowling, like we do in the States with your kids. You put the guardrails up so that when you roll the ball, they roll the ball. It doesn’t go down, you know, um, down the trash line.
33:43- Phillip:
Right? So it’s, it’s by putting those guardrails up that are hard enough, but are wide enough that will give them room to flex, but also generate the end result that you’re looking for, which is truth, right? Safety. Right. Um, it’s been tested, right? So you’ve gone through testing and it is, and that you actually have this traceability or transparency about how it’s made decisions, and that is harder than you think, right?
And, and so it’s, it’s the level of transparency, the level of observability, the level of control that you have, because. You’re going to have bots create, not bots, you’re going to have agents creating agents. That’s a given, right? It’s, um, so the question now is how do you architect when the agents are creating those agents that the same genetics flow down into these other agents around safety, goodness, and safety?
Ethics, compliance, privacy, security, you know, and it’s not just about security and privacy of your data at rest or a data in motion. It’s actually, and this is what companies miss all the time. We see this regularly, you know, more times than I care to mention that they’re not dealing with secure security and privacy of the data when it’s in compute. They claim they are, but they’re not. So, which means that bad actors can find your data if they break it, which makes the overall security maturity model and footprint that your company operates by. You gotta get your game up there big time because if you’re not careful, things will run amok. Um, and your brand value will be decimated as a result, and we’re very, very careful with what we do and how we do it.
35:47- Phillip:
I’m not going to disclose how we do it, but we have some pretty significant tricks up our sleeves because there are three of us, including myself, who have lived most of their career, careers in security and privacy. So, um, we’ve architected a way. That it gives confidence in security, privacy, and if data does leak, we can’t figure out what it is, right?
So we just assume data is leaking. So we’ve done other things to protect that data,
36:17- Terry:
Sure, sure, sure. Yeah, it does. It does. It does. Look, you mentioned, um, manufacturing. Do you think that there’s particular industries, uh, that are most ripe for an AI driven transformation? Valuation.
36:31- Phillip:
to banking to manufacturing to telecommunications, um, I don’t think there’s going to be a vertical that or a business that cannot take advantage of this. And I’m going to lay this one down. I believe what Sam Altman said a couple of weeks ago that the first billion dollar company with one person in it will exist, will exist within five years.
I actually think it will happen way sooner than five years. Think about that for a second. single company with a single person with a billion dollars of revenue.
37:06- Terry:
Yeah. Unbelievable.
37:07- Phillip:
no, no, no, no, no. I’m not talking valuation. I’m talking revenue, man.
I’m talking revenue,
37:13- Terry:
dollar revenue
37:14- Phillip:
billion dollar revenue with one person in it.
37:18- Terry:
that is mind blowing.
37:20- Phillip:
It is mind blowing. and I know, and I know how it’s going to happen.
Having built 5 billion businesses, multi billion dollar businesses in my career, I know how it’s done. Right. There’s, it’s actually not that hard once you know how to do it, it’s really not that hard. Yeah. Yeah. Yep.
37:37- Terry:
you and I should have a chat after this. Maybe it’d be a two person, billion dollar business. Phillip, one of the things that you’re known for, beyond your expertise with security and, and, um, product development or product ownership is the, you’re known as the go to market unleashed guy.
37:55- Phillip:
Yep.
37:57- Terry:
Tell me, go to market is such a hot topic at the moment.
It’s almost like there’s been this new, uh, and when I say new, I’m probably talking over the last two years, I’ve noticed it more than it, than before. Um, this phrase about go to market, it’s a movement. There’s go to market teams now. I mean, to me. Go to market has existed from the beginning of time when,
38:20- Phillip:
Exactly.
38:21- Terry:
was sold, you know, when you’ve got a bit of gray in your beard, you start to be able to reflect on things and think, well, there’s not too much that’s actually new here.
I mean, AI is obviously very new and it’s like you mentioned a few other, you know, technology changes that we’ve been through, like mobile phones and so forth. But how do you think, how do you see AI changing, um, the go to market,
38:43- Terry:
uh, process and flows? I mean, I’ve got ideas on that. Obviously that’s, we’re sitting very much in that space as well, but for you in particular, I’m very interested to
38:52- Phillip:
personally, and you hit on it earlier. Personally, I think CRM is going to be completely and utterly turned upside down on its head, right? Um, I think if you’re a Salesforce, a HubSpot, a Copper or any of those guys, Um, If you don’t have your AI strategy in play now, you’re going to be relegated by a competitor in four months who is, and you will literally be relegated.
I’m actually worried for Microsoft, uh, and some of the, some of the stuff that’s coming out, um, the, but that’s a whole separate topic. So where, um, where I see it changing and go to market. We, we touched on the SDR function earlier. Um, so go to market to me as a whole process. And like you said, it’s existed since the age of time from, you know, initial marketing of your product.
And I don’t care if you’re selling a nap because back several 100 years ago to, you know, selling, you know, selling the best ERP solution out there in the market that configures in 30 seconds. And there’s the best thing. And since sliced bread and solves your problem and makes you money. If you can find that tool, let me know, because I’ve been searching my whole life for that tool, but, um, the, uh, but the point is, is that, you know, the go to market from pre awareness, and this is another mistake that companies make, they think, they talk about go to market being like from awareness all the way through to purchase and maybe even activation. I call BS on that. The right is actually even bigger than that. It starts off with pre awareness. What does that mean? It means that somebody is pre aware of your brand, has a kind of idea of what you do, but it’s just, you’re out there. They see you. So there’s familiarity so that when they do have the problem that you solve, that they’re already aware, you’ve got a leg up already to do it.
40:47- Phillip:
So I start talking about pre awareness and it’s pre awareness might be as simple as this jacket. Right with my logo on it, or it might be a simple in the supermarket world of driving down the street and seeing four different supermarkets. Now I’ll go to this one for that one, that one for the other, and this one for the for something else, right?
You know, instinctively where to do that. People know this. People talk about go to market and this whole concept where I go like, okay, you’re just trying to make shit up now. Right. And it’s, it really isn’t right. But one thing and one thing only in my mind, winning a customer for life and solving not a pain point for that customer, but a fricking migraine. Because if you’re not out solving a migraine for that customer, why are you even wasting their time? Right? You know it. You’ve had migraines in your life. I’ve had migraines in my life. We will pay good money to go and see a doctor to solve the migraine, right? And
get the best medication out there for that migraine. Because we
41:49- Phillip:
don’t want, we don’t, you know, for all the reasons we know. It’s the same thing with this, with the problem. So, identify migraine level pain points. And I do mean migraine level. Right. We’re the CEO, the CFO, the chief customer officer, the chief product officer, a multi billion dollar corporations are worried. They’re worried about one thing and one thing only. Now that will be different for every company. It could be revenue. It could be investment. It could be any number of things. But it’s a migraine for them that if they don’t solve, their stock price is going to get impacted negatively. Right. Is that level of an impact? And those are the problems we got. So we don’t like to solve nice level. You’ve got a bunch of the other people out there doing that, right? We will, it’s as easy to build a billion dollar company is a 1, 000, 000 company. I hate to blow everybody’s gasket on that one, but it’s true. And so it’s just a question of what size of problem are you solving? And is it big enough? And is the is the customer base big enough to come and buy from you to solve this? And if you answer those questions right very quickly, don’t waste any more time and go in and actually start designing something. Talk to customers, get the voice of the customer, really understand and interview multiple customers. Is this a real pain point? And then dig and keep digging until you’ve got no more questions to answer. Right. A good example is this. You know, I worked on two products this weekend. I was probably between Tuesday and Sunday. I probably between me and one of my business partners. We spent 80 hours on building us not with AI, but by hand.
43:30- Phillip:
Why? Because we needed to. We can’t trust AI to build out products. That’s the lazy person’s way of talk of building out product. And if you hear the term MVP minimum viable product and you throw that in my face, I will rip it up in front of you. Quite literally, I am that draconian because MVP equals laziness in my mind.
Now, this will be highly controversial with your audience. I’m sure,
43:54- Terry:
It will be. It will be.
43:56- Phillip:
It’s about building viable product, point, full stop, exclamation mark, italics, bold, underline. There’s nothing minimum about it. Minimum is laziness, folks. It’s pure laziness. If you’re going to solve a problem, solve it well, viably for the customer. And that means I’m getting into every part of their business to make sure you’ve not missed anything. And it’s when you get it right, magic happens, but it’s a lot of work to get it right. A lot of work.
44:27- Terry:
Absolutely. Absolutely, there is. And we’re feeling that pain ourselves at the moment, but that’s another, uh, another conversation.
So in, in terms of the project that you mentioned with regard to the manufacturer, um, bringing AI into an organization, what are the, what sort of benefits are we looking at? I guess it’s probably a bit like asking how long is a piece of string. It depends on how inefficient there are at the moment.
Um, but in most places that I imagine there’s, um, there’s opportunities for improvement everywhere when you get to the stage of being able to have such a, a minute attention to detail on it. But, um, so we say Cost Cost benefits.
45:03- Phillip:
So first of all, they’ve got to get around the first, the number one fear, which is privacy and security, right? If I use AI, is my company information going to be sitting out there for others to use? We’ve got to address that elephant in the room upfront. Um, if you don’t, it’s going to be a nagging doubt for everybody and your conversations won’t go far.
So you have to, first of all, have a good answer, how their information is going to. Uh, be private and that and that is as simple as creating your own private instance of an opening of, um, you know, like Microsoft and open AI have, uh, have a working solution together, right? And using that could be as simple as that, then, um, For the manufacturing company, for them, the first efficiency is about operational and cost efficiencies, as well as safety.
45:53- Phillip:
Safety is actually the real problem that they want to solve. Um, but they’re getting comfortable with AI with the first two, because they won’t disrupt safety, right? So if they get comfortable with their first two use cases, which they are, Leading into the safety problem, right? Which is the bigger problem for them, right? It’s all about brand value. It’s all about brand reputation and people and being good for their employees and getting their employees safe and having a culture of safety at their manufacturing production facilities.
Because there are multiple.
46:32- Terry:
And, and, you know, safety, I know, if it’s spoken in isolation, generally, the processes and controls you put in place to keep people, people safe will generally improve the overall efficiencies of the business to a large extent and bring other benefits that they may not necessarily see to start with.
46:50- Phillip:
Correct. The other benefit that they do see, and they have seen from the beginning, is avoiding, uh, because they have significant product, uh, shrinkage or leakage, right? So that’s, you know, people on the manufacturing floor take, you know, stealing products, right? happens in every manufacturing facility.
Um, so the question is how bad of it? It’s a pretty bad problem for them.
Um, and so this will also solve, hopefully solve for that problem as well.
47:15- Terry:
Okay. is, do you see, um, do you, are you working on any projects at the moment that, uh, broader than that across, um, an organization? Are they predominantly looking at focusing on one particular migraine to solve at the moment and then looking to expand beyond that? Is there any, any organizations that’s gone, you know what?
We just need this everywhere. We need it in our marketing. We need it in our sales. We need it in our production or. Product.
47:44- Phillip:
don’t, I don’t think there’s an organization out there that’s not asking that question,
right? Is that how many organizations it’s just like you and I have had this conversation as well in the past, but customer centricity, right? Everybody claims to be customer centric. Well, you and I call BS on that too, because if your whole organization isn’t customer centric, then you’re not customer centric.
And I mean, I say the whole organization, it’s not just having sales and marketing aligned, right? Fine. That’s. Product engineering, you know, every person in the company is a VP of first customer impressions, right? Everybody from the person at the front desk to the CEO, everybody,
48:26- Terry:
100%.
48:27- Phillip:
right? And companies forget this. Right? And, and so you’ve got to have your whole company bought into integrating AI into their workflows, right? In a responsible manner, that without releasing proprietary information, the shadow AI problem that we talked about. And so it starts with the C suite, it starts with the CEO, it actually starts with the board, right? And having the board aware, first and foremost, what it is. What does AI actually mean for their business and how can it actually turn the business around so that it’s actually integrated in versus a bolt on? Because right now AI is being bolted on with chat GPT, Anthropic, with Pi, with Perplexity, you name the LLMs out there as well.
You know, they’re just bolt ons right now. You know, so you, it was like in the old days of cloud, right, where people were saying, okay, we’re just going to move from on prem into the cloud, right? It was a lift and shift, right? And that didn’t, that never worked, right? And then you start to hear about companies that are born in the cloud, right?
So they’ve never been, right? And so, you know, We are born in responsible AI, our company, right? There’s no artifacts, right? We are all enterprise solution people, right? But this, this is what we do, right? We, you know, we understand how to architect, right? I love a whiteboard, right? It’s just like whiteboards are my friend.
I have so much great content that gets put up onto whiteboards. You have no idea. Because I just love drawing that stuff up, because when you can visualize it, you can now create it. And, you know, and don’t get shy about writing documents as well, right, to define things, because we’re losing the art of writing.
Don’t lose it, because if you lose it, how do you know what to build? As a product guy, if I can’t write it down, I can’t hand this to engineers and say, go and build this. Right?
50:36- Terry:
So, where did I see, now that you mention that about writing, I think it was in the Strawberry Release, that Strawberry has been trained or the, the owners of these LLMs will only train on information available on the internet before 2023 because that’s when I was introduced. And then now you’ve got this copious quantity of AI generated, uh, content on the internet.
So it’s not necessarily learning, it’s learning more of a beige and mixed content of AI and human versus before 23, where it was all human based supposedly. So, um, That, that to me, I think is pretty interesting. What does that mean then in, you know, 20 years time from now, they’re still using pre 2023, the old, you know, human Bible.
51:30- Phillip:
So here’s why I honestly think, I think, I think after the end of December, LLMs are dead
51:37- Terry:
Hmm. Hmm.
51:39- Phillip:
because AGI will replace LLMs.
51:42- Terry:
Okay. Well, that’s a big call. I’m going to write that one down. End of December.
51:49- Phillip:
I think the LLMs are, I mean, they’re not, they won’t be dead for a while, but they’re, what will come out will effectively transform what
51:58- Terry:
Supersede. Yeah. Yeah, absolutely.
52:00- Phillip:
is to perceive them. Exactly. Perfect word.
You’re at the start of your day. I’m at the end of my day. So I’m starting to struggle words.
52:07- Terry:
That’s all right. Look, I won’t keep you too much longer. There is, um, one question that I would like to ask you and I’m expecting to be surprised by the answer. You know, you’ve had a background, you’ve got a background with some pretty considerable accomplishments in it across a range of, of, um, Uh, and businesses, very, very large organizations.
Um, what, what’s been the most challenging project, um, that you’ve worked on and, and and what was the biggest learning that you, that you, that you got from that on your way through?
52:38- Phillip:
Wow, that is a really great question. Um, I think the first, uh, that is a really great question. Um,
52:49- Terry:
Well, while you’re thinking about that, look, I no, I’ve got an answer.
52:52- Phillip:
I’ve an answer.
52:53- Terry:
I want to mention, though, for the, for the listeners that, you know, you’ve worked in companies like Microsoft, T Mobile, Texas Instruments. I mean, they’re, you know, they’ve been on the cutting edge of, uh, you know, of technology over the years. And that’s where you, that’s the, you know, the playground that you’ve operated in.
Um, so that’s, I was super so
53:11- Phillip:
I think the most challenging project I ever worked on was, uh, as introducing voice over IP into the market. And I was back in the late nineties. Um, and at the time it wasn’t a technology, there was a technological challenge that we had solved because we had brilliant engineers, um, uh, which Uh, which is actually how my company ended up getting acquired by TI.
We deliberately engineered it to be acquired by TI. And it was. We built a multi billion dollar business. out of nothing for TI. Um, and what the biggest challenge that we had was at the time ITU, the International Telecommunication Union, was out suing everybody. Actually it was more Lucent at the time from AT& T, but we’re out suing everybody and their uncle for Uh, to avoid voice over IP becoming a thing.
And we, the most interesting problem there was, is that we solved that indemnification problem in one fell swoop. And here’s the thing, simplicity always sells. And this is what I’m a big believer in, right? Which is why I’m a radical simplification guy. This is how you build. You know, you built, you know, this is how I unleashed go to market. And so the simplification was we went out and acquired a company, a company that was going bankrupt. We bought all of their IP in one fell swoop for a big, a big check. It was a big check. It was 11 million.
54:43- Terry:
Mm hmm.
54:43- Phillip:
And that million dollars allowed us to immediately enter into what were called mutually assured destruction agreements with Lucent so that we can indemnify every single one of our clients and ultimately Texas Instruments to indemnify all of its clients with all the patents that we had acquired and we had all of the agreements in place. That was the most challenging project for me to solve. Um, back in the day and solving that problem
55:17- Terry:
And in solving that, what, what’s the biggest learning that you’ve carried on then with your career since then
55:23- Phillip:
In life is like solve the customer’s migraine. Don’t give them another one. And the other one that we were going to give them, if we didn’t do that, was that they had to go and pay all these additional fees to loosen the new bridge and God knows who else writes to go and, um, um, put out there is really, really do your homework in every aspect of the customer journey, even after the, bought your product and used your product. What. what unintended consequence has happened as a result of using your product, and go and solve that for them too. That’s my advice, because they don’t want them going like, okay, I just solved one migraine, now I’ve got five more.
56:04- Terry:
Pretty good advice. Get to know your customers, customer first. I mean, it’s a,
56:09- Phillip:
It’s Grok Your Customer. I call it Grok Your Customer, which comes out of 1960 novel by Robert Heinlein, The Stranger in a Strange Land. You may have read it.
56:19- Terry:
I haven’t read that one.
56:20- Phillip:
But what grok means is that you marriage with your client and you literally assimilate and strap them onto your back that you understand them as well as you know, your own fingernails, right?
It’s, uh, um, you got to know your customer as well, if not better than they do. That’s the secret in all of this.
56:39- Terry:
Look in, in, uh, in closing up, is there anything else you’d like to add? Is there something that I haven’t asked Phillip or haven’t touched on?
56:46- Phillip:
How do you reach me?
56:48- Terry:
I’ll get to that. I’ll get to that. The last, the last question I had for you really was that what, what piece, if you, if there was somebody now that was just starting out in their.
57:01- Terry:
I journey, a junior coming through in sitting in an organization, a junior leader, potentially, um, sitting in an organization, um, starting their AI journey. What what piece of advice would you give them?
57:16- Phillip:
Ask yourself the same question as we ask our customers. What future am I wanting to solve for myself? Right. be for myself. And if becoming expert in becoming an AI engineer or a prompt engineer or, or some other career path, you start working back from there and really understanding out there, there is so much available. to read now on the internet and not all of it’s new. Thankfully, right? There’s a lot of legacy stuff out there that’s worth reading as well. Um, the one thing I would, I would just note to everybody, humans still like to buy from humans. They may not like to talk to humans in the sales process, but at some point they do need to talk to a human and they know they do.
Their decisions already typically have already been made. They can be swayed really Like, you know, you don’t force a relationship with the, with the customer people buy from people they like, right? People it’s. It’s all about the emotion, right? So people buy with the heart and think with their head, right?
So it’s after we buy something, our brain suddenly kicks in, Oh God, I shouldn’t have done
58:25- Terry:
They buy an emotion and justify with logic. That’s how it works, isn’t it? Yeah,
58:29- Phillip:
that’s exactly
it. But
58:30- Terry:
works.
58:31- Phillip:
appeal to the heart, always appeal to the heart always.
58:35- Terry:
what do you do in your downtime? What do you like to do on weekends?
58:39- Phillip:
Uh, well, aside from right now, which is working seven days a week, which
58:43- Terry:
It’s a zero, zero downtime.
58:45- Phillip:
But my downtime is, uh, I go on a hike every single day. I live in the mountains and I hike every single day. It’s where I get my clarity of thought. That’s where I get my exercise. It’s where I get, so you’ve got to get your own space.
Some people do reading for me, it’s hiking. Um, I’ve got four things that are important in my life. It’s my family, my friends. You know, my hiking and solving complex business problems and when, you know, when I can take the complexity and turn it into something that’s simple. I know that I’ve done it and that’s not easy folks.
59:19- Terry:
It’s not.
59:20- Phillip:
It’s not easy, right? Even for somebody like me has got decades worth of experience on this. It’s like, you know, When the answer hits you, you go like, oh my God. It’s easy. Right? But until you get there, it is hard and you’ve just gotta keep working it and working it and working it until you know it’s easy.
59:36- Terry:
When do you easy? When do you know it’s easy? How, how do you
59:39- Phillip:
your gut tell you Your gut will tell you.
Your gut will tell you. It’s like your, you’ll have you your eureka moment, right, where you’ll be sitting in the bathtub and going, Eureka, I found it
right. Absolutely.
it. You just know it.
59:55- Terry:
Have there been any books that have had a huge impact on your career over the years? Anything you’d like to recommend for the
01:00:02- Phillip:
Yes. So there are several, but I think the two that I want to think about one is an older book by a guy by the name of Jim, Jim Collins. Um, that was called good to great.
That’s a classic of an oldie. I actually had him train my sales team at Microsoft. It was a brilliant sessions that we have, um, where he talks about getting the right people on the bus and, uh, really about, um, Understanding and solving the customer problem. Um, Critical Conversations is another one, and Extreme Ownerships. Those are the three books that I’m, um, are kind of bibles to me.
01:00:40- Terry:
Um, critical, crucial, crucial conversations.
01:00:43- Phillip:
Crucial Conversations, I’m sorry, Crucial Conversations, not Critical, you’re
01:00:46- Terry:
I just want to get it right so I can pass it on as well. Excellent. I like, I really enjoyed, um, good to great, you know, get everybody, the right people on the bus, the wrong people off the bus and everybody swimming in the right direction up the river.
01:00:58- Phillip:
we talk about it, but it really works. It really works.
01:01:02- Terry:
It does work. I mean, uh, I. Went through a period in my career where after my qualifications, I started to move into management roles without necessarily getting any. Well, from a very young age, I’d moved into management roles without really getting any specific management training or, or, um, or qualifications as such.
And when I started to get some qualifications, some training, and then you start to read some of these books, the, my philosophy of, of keeping it simple. Um, the good to great book in a nutshell is write people on, run people off, and then everybody’s swimming in the right direction. Um, you know, there are other simple little books that when you can distill the whole message of the book down into a framework and go, okay, that’s what this is about, then everything just starts to chunk together.
01:01:52- Phillip:
And you realize that what you’re doing and, and how you’re doing it. Does actually make a difference and it does work. And you’re right.
This stuff does work, you know, whether you like it or not. And there’s a million different ways to say it and learn about it. And everybody learns differently. Um, that if you do what you learn in these books, uh, and in obviously from the, from the great leaders and trainers of the world, um, if you do it, then it makes a huge difference. The last thing I would just like to add. Just more thing. If you have an idea and you think it will work, test it, test it. You’ve got to pressure test your ideas before you take it to market. This is another key thing. If you don’t pressure test it, the market will pressure test it for you, and the chances are it will blow up.
01:02:32- Phillip:
So you, you really want to pressure test every aspect of your business process in the application before you actually take it out because you’ll save yourselves Incredible angst and hurt during that process.
01:02:47- Terry:
And again, that comes down to talking to customers or potential customers. Doesn’t it really understanding whether what the idea that you’ve got is solving a migraine or not.
01:02:56- Phillip:
Yeah, it was their migraine. And are you solving it in a way that’s compelling to them, right? Because there’s one thing, okay, I’ve identified the migraine. But as we know, which is why the two products have taken so much work over this week, is I had to drill into every single aspect of the customer journey to make sure we weren’t creating more migraines.
01:03:14- Terry:
Absolutely. Absolutely. Phillip, how can people get in touch with you? What’s the best, best way?
01:03:21- Phillip:
Best ways on LinkedIn, funnily enough, and uh, my profile is Phillip with two L’s P H I L L I P dash S W A N, like the bird,
01:03:32- Terry:
And I’ll have all the links the, in the podcast as well.
01:03:35- Phillip:
perfect, and uh, DM me. The first thing you’ll see is my weekly newsletter. Sign up for that. It’s free. You’ll, you’ll get, it’s just content I handwrite, um, and uh, hand, you know, curate.
And so, Uh, people are finding it useful, built up quite a nice audience for it.
01:03:51- Terry:
I’m a subscriber and can highly recommend it. I enjoy, enjoy reading it every week. Yeah.
01:03:57- Phillip:
and then DM me if you’ve got questions and we’ll take the conversations there that’s it. I’m pretty easy to get a hold of.
01:04:03- Terry:
Excellent. Well, Phillip, thank you. It’s been an absolute pleasure and, and, uh, a very interesting educational experience for me, this conversation today. I appreciate your time.
01:04:12- Phillip:
Likewise, Terry, always a pleasure to talk to you.
01:04:15- Terry:
Talk again soon. Bye for now.
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