Agentic (Headless) Ecosystems

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Julia Nimchinski: fireside chat. Welcome, Jay Mcdane.

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Julia Nimchinski: Mark Stills.

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Mark Stouse, Proof Analytics: There!

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Julia Nimchinski: Little Introduction.

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Jay McBain: There we go!

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Mark Stouse, Proof Analytics: Hey! How are you?

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Jay McBain: I’m doing good.

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Mark Stouse, Proof Analytics: Awesome.

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Jay McBain: You want me to go first.st

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Julia Nimchinski: Let’s do it.

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Jay McBain: Alright. Jay Mcbain, chief analyst for Canalys canalys is Latin for channel. We think about partnerships and alliances, ecosystems and obviously thinking about agentic AI today, as all those things surround each customer.

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Julia Nimchinski: Mark.

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Mark Stouse, Proof Analytics: Hi, I’m mark stews. I’m the CEO of proof analytics. We specialize in the other. AI causal AI. So

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Mark Stouse, Proof Analytics: we are all about why things happen the way they happen? If you had changed this, how would it happen differently than it did?

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Mark Stouse, Proof Analytics: It’s navigation, really. Using AI. It’s a GPS for a business.

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Julia Nimchinski: So excited for this conversation when we were promoting this within the community received a lot of questions, and obviously having you both.

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Julia Nimchinski: as far as I chat, the craziest one

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Julia Nimchinski: was, and I will start with that.

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Mark Stouse, Proof Analytics: You’re gonna start with the craziest.

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Julia Nimchinski: What are we gonna do when the agent is gonna go out of control?

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Julia Nimchinski: J. Mark.

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Jay McBain: Well for me.

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Jay McBain: you know I’m not sure it gets out of control, but the level of permutations and combinations when you’re in kind of the partnering business.

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Jay McBain: When you start to think about, for example, the 28 moments measurable moments before a customer makes a decision as as traditionally marketing would have handed that off to sales. There might have been a partner at some point to collect the money.

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Jay McBain: Well, today there’s 28 moments where marketing and sales are both involved. There’s 7 partners on average in the deal.

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Jay McBain: and you’ve got a new millennial age buyer who doesn’t want to talk to a human.

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Jay McBain: So they’re actively going after digital moments when you start multiplying all these things together, you get into the trillions of permutations.

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Jay McBain: But in the end nothing’s linear anymore.

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Jay McBain: So allowing agents to get a little bit out of control

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Jay McBain: and and start to measure these moments and bring them back into a system that may not be human led

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Jay McBain: that can help guide that customer to a successful outcome.

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Jay McBain: And then, if that successful outcome is using your product and using those partners, you know, that’s even a bonus.

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Jay McBain: But orchestrating all these moments.

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Jay McBain: At this time relies on AI,

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Jay McBain: because no level of spreadsheet or no software we have today in the channel space, you know, can handle this

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Jay McBain: level of you know, combinations.

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Julia Nimchinski: Mark, this is a moment. But yeah, I’m really cautious. Here.

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Julia Nimchinski: let’s debug. What? What are your thoughts on Agentic AI, seeing a lot of skepticism on Linkedin wilderness.

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Mark Stouse, Proof Analytics: So if we kind of view any technology right as it evolves as

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Mark Stouse, Proof Analytics: it’s in this state, it’s now in this state, it keeps improving hopefully. Right? All this kind of stuff right?

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Mark Stouse, Proof Analytics: Right now, agentic is for a variety of reasons, little more than automation. Okay, which is

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Mark Stouse, Proof Analytics: not a terrible thing. Okay, but relative to the way it’s being sold

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Mark Stouse, Proof Analytics: and the way that people’s expectations are being cultivated.

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Mark Stouse, Proof Analytics: You know, we’re we’re kind of back in the jungle again. Right? I mean, technology has been doing this as an industry has been doing this for

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Mark Stouse, Proof Analytics: well, as long as I’ve been in it, right? And so

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Mark Stouse, Proof Analytics: I think there’s gonna be a lot of disappointed people in the immediate short term.

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Mark Stouse, Proof Analytics: right? This is kind of why we have this trough of disillusionment, you know, in this slow crawl back up to the other side. Right?

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Mark Stouse, Proof Analytics: We kind of do it to ourselves to some degree.

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Mark Stouse, Proof Analytics: I think that number one. Nobody is really focusing on the importance of data quality to this whole thing. Right? I mean, if Mckenzie’s right and

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Mark Stouse, Proof Analytics: my own experience suggests that they’re probably pretty close that only about 3 to 5% of corporate data can support.

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Mark Stouse, Proof Analytics: You know, like enterprise type AI

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Mark Stouse, Proof Analytics: scaled AI, maybe a better way of putting it.

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Mark Stouse, Proof Analytics: Then I mean, that’s a problem. I mean, that’s a really, you know, that’s that’s where the issue isn’t the the gun. Okay, the the cannon. It’s your supply chain of cannonballs. That’s the problem.

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Mark Stouse, Proof Analytics: so you’ve got that. Then, you know, after 3 or 4 decades of trying really hard to make technology deterministic.

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Mark Stouse, Proof Analytics: I think everybody’s starting to realize that that’s just not the way you know, life is rejecting that

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Mark Stouse, Proof Analytics: because life is the reality of life is highly probabilistic.

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Mark Stouse, Proof Analytics: So I think that in order to have agents that can make the right decisions

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Mark Stouse, Proof Analytics: more and more and more independently of us, which is kind of the whole idea, right?

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Mark Stouse, Proof Analytics: They need to have agency.

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Mark Stouse, Proof Analytics: and so they have to be able to maneuver. They have to be able to understand changing externalities and what they need, how they need to react. And all this kind of stuff.

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Mark Stouse, Proof Analytics: And they have they better damn well, have good data that to operate from

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Mark Stouse, Proof Analytics: in multiple levels, right? And so I would, I would just say that that’s

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Mark Stouse, Proof Analytics: that’s where things are gonna get really interesting. And then the the other. You know, there’s always the ex, the big big externality that really comes in kind of like a meteor, and just goes boom right? And in this case, on a very broad basis. That’s the Delaware revisions of fiduciary duty. Right? I mean, you’re gonna have to get

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Mark Stouse, Proof Analytics: your act together on your data, or you’re going to be sued into oblivion by shareholders that are so pissed off because you can’t support exactly this kind of AI that we’re discussing.

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Julia Nimchinski: Jay, what are you seeing in the data and the ecosystem land.

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Jay McBain: Yeah, maybe put some a little bit of history and a little bit of future to to what Mark just said. I mean, I live through as an analyst.

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Jay McBain: You know the Rpa phase when we took companies like Uipath and Blue prism and automation anywhere, and we created a 60 billion dollar Ipo around Uipath. This was the future of workflows and business processes and logic, all coming together, and kind of a drag and drop, you know, codeless world that was going to drive us forward. It stopped kind of around. What Mark said is the quality of data.

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Jay McBain: I remind people today that 83% of the world’s business data

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Jay McBain: and the promise of AI over the course of this 20 year era that we’re stepping into

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Jay McBain: relies on that data that hasn’t been trained into index and for cleanse for AI. Yet.

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Jay McBain: you know, we’re coming out of consumer poetry, music, and deep fakes

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Jay McBain: into more role based agentic AI, which is better emails, faster. Better sales, processes, invoicing, billing service tickets. You know, everybody’s personal role gets improved by 10 or 20%.

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Jay McBain: But the you know.

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Jay McBain: promise of AI, which is, you know, in the true. My co-pilot talks to your co-pilot world.

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Jay McBain: That hasn’t even started yet.

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Jay McBain: We’re we’re measuring the global system integrators. We’re measuring the fortune 500.

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Jay McBain: Every flight you’ve ever taken sits on a saber system in the, you know, that was put in place in the 19 seventies. Every banking transaction you’ve ever made sits on an Ibm mainframe put in place in the 19 seventies. Every social security request with the government, basically, most of our lives revolve around very old, unstructured, unusable data

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Jay McBain: to, you know, to this. But we get about 10 years to figure that out

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Jay McBain: agentic AI in the next 3 to 5 years, and then more physical. AI. After that.

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Jay McBain: then leads us into kind of this larger frame. But understanding decades that go by are important and understand that this isn’t a bomb that goes off.

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Jay McBain: but actually a set

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Jay McBain: rollout of of how this is going to interact. But things do change very quickly, and and and things do look very different.

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Jay McBain: Bill Gates said in 1996 that we overestimate the 1st 2 years of any new technology.

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Jay McBain: and we underestimate the 1st 10.

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Jay McBain: And I believe Agentic AI is going to fit that model perfectly.

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Mark Stouse, Proof Analytics: Yeah, I would. I would just add this little story, cause I I agree with what you’re saying.

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Mark Stouse, Proof Analytics: Right? So among other things, I’ve I’ve found myself as an expert witness in several cases regarding data quality.

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Mark Stouse, Proof Analytics: There was a shareholder lawsuit against a company in California pretty pretty big, not super big but pretty big around Crm data quality.

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Mark Stouse, Proof Analytics: which, if you’ve been in this business, as everybody on this call has for any length of time, you know that

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Mark Stouse, Proof Analytics: it’s a meme, okay of just how bad it can get.

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Mark Stouse, Proof Analytics: And so they got permission from the judge to put fraud detection software onto the Crm.

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Mark Stouse, Proof Analytics: And there was roughly 14 years worth of sales data in that Crm.

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Mark Stouse, Proof Analytics: And 2 thirds of it. Little more than 2 thirds of it came back as engineered.

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Mark Stouse, Proof Analytics: Okay? Which, of course, is no great shock. Okay to to probably anybody.

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Mark Stouse, Proof Analytics: But it actually goes to the heart of this data quality issue. And that is.

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Mark Stouse, Proof Analytics: while there are kind of

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Mark Stouse, Proof Analytics: external reasons why data can be messed up, screwed up, broken, you know, whatever right.

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Mark Stouse, Proof Analytics: The vast majority of the problem is that everybody is, for their own reasons, on a very hyper local basis, are cooking their own books

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Mark Stouse, Proof Analytics: right? Because they’re trying to protect their job. They’re trying to do this. They’re trying to do that right. And then it all aggregates and accumulates over time. And then we have this kind of situation.

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Mark Stouse, Proof Analytics: So it’s gonna be, you know, this is gonna be a a far bigger limiting effect on

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Mark Stouse, Proof Analytics: technology period going forward. Okay? Certainly. Agentic.

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Mark Stouse, Proof Analytics: Then a lot of people want to acknowledge.

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Mark Stouse, Proof Analytics: And you know it’s it’s just one of those things where it is, what it is.

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Julia Nimchinski: One of the community questions that we’re receiving here is actually to the point of data and cross functional

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Julia Nimchinski: aspects of it, Jay, that I believe you reassured in your post.

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Julia Nimchinski: So the question is, when Gtm. Teams each spin up agents, how do you avoid chaos and overlap.

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Jay McBain: Yeah, I mean, it’s the the strategy, you know. I just saw yesterday’s martek stack up to 15,367 Isvs. You know the sales tech stack, you know, over 5,000. I run my own channel tech stack above here, but

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Jay McBain: add the Cx tack stack and the product stack. On top of that there are 250,000

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Jay McBain: Isvs

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Jay McBain: on the way to a million. You know this decade now that AI is building software. We don’t even have to go and hire developers as part of building our Sas company.

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Jay McBain: The fact of the matter is, you know that the 7 layer stack of of how we solve our own jobs

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Jay McBain: and how we’ve kind of set up the processes of, you know, moving data

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Jay McBain: change pretty considerably when there’s no logging.

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Jay McBain: when each of these crud databases actually become headless.

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Jay McBain: And we’re starting to introduce them to agents

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Jay McBain: and then create these paths that aren’t linear.

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Jay McBain: but agents that can bounce across data sets when they need them.

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Jay McBain: So I’ll use an example. Salesforce does a really big sales study every year.

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Jay McBain: and they talked to 5,500 cros

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Jay McBain: in the most recent study, 89% of salespeople in the world use partners

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Jay McBain: every day. So for the 11% that aren’t like 58% plan to within a year. It kind of gets to the 96% of the entire 5.4 trillion dollar tick industry that has partners surrounding the customer

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Jay McBain: in a sales process. That kind of avoided that for a long time.

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Jay McBain: Now there’s a need agentic, and in Salesforce’s world it’ll be agent force

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Jay McBain: in Microsoft’s world. It’ll be a co-pilot. But there’s a need before you think about your account based marketing plan.

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Jay McBain: Before you think about the 28 moments the customer is gonna go through before making a decision.

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Jay McBain: You have to go tap into a hundred more databases

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Jay McBain: to understand the moments that are actually partner led moments not

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Jay McBain: product led, not sales led or marketing led moments

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Jay McBain: so to be successful in the future. You have to do that better than your competitor better than the salesperson working for your competitor is doing. And then in the end, we know that it can’t be a person doing it.

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Jay McBain: The systems have to be built to be automated

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Jay McBain: to handle that next best action with the customer without humans. So it’s not only headless, it’s humanless.

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Jay McBain: And so you ask about chaos.

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Jay McBain: What data do I introduce? What outcomes do I allow?

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Jay McBain: And how do I constantly measure and readjust.

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Jay McBain: You know how that’s how that’s working at the end. Customer. Because there’s really only one thing that takes away chaos

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Jay McBain: is customer obsession.

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Julia Nimchinski: The only moments that matter are your customers. Moments.

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Jay McBain: And for all the movement around Agentic AI. It’s gonna come down to how those moments move from one to the next.

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Jay McBain: and whether it’s adding friction to your processes, marketing sales, cx processes or helping.

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Mark Stouse, Proof Analytics: So this is actually where maybe we have a slightly different point of view, which is probably why everybody wants to hear this.

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Mark Stouse, Proof Analytics: it’s

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Mark Stouse, Proof Analytics: I think it’s gonna be the buyers use of agents that are going to be far more

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Mark Stouse, Proof Analytics: life altering right than anything that marketers or salespeople on the on the vendor side ever choose to do.

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Mark Stouse, Proof Analytics: There’s a there’s a huge shift in the balance of power

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Mark Stouse, Proof Analytics: that goes beyond kind of like the final decision that it’s always been the the buyers right

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Mark Stouse, Proof Analytics: into how they want to be dealt with, and

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Mark Stouse, Proof Analytics: I’m already seeing a lot of buyer bots

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Mark Stouse, Proof Analytics: that are doing phenomenal things. Right they are. They’re going out

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Mark Stouse, Proof Analytics: with only a problem statement. And they’re sweeping through the marketplace. They’re collecting all kinds of information. They’re stratifying the options. They are personalizing all of the vendor content internally from the buyer perspective. Right?

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Mark Stouse, Proof Analytics: Basically, it’s almost like

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Mark Stouse, Proof Analytics: they’re doing what Forrester has been talking about as a quasi reality for the last 20 years. That’s actually happening today. Right?

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Mark Stouse, Proof Analytics: I think it’s 1 of the reasons why

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Mark Stouse, Proof Analytics: we’re probably looking at 80 to 90% attrition on sales roles over the next 3 or 4 years.

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Mark Stouse, Proof Analytics: I think it’s why the state of marketing and b 2 b is going to be significantly different, because there’s going to be full parts of what have been a, you know, kind of like a part of the norm, right, whether you think of it as a stack or as an organization that are just gonna vaporize

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Mark Stouse, Proof Analytics: right? And so I think that

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Mark Stouse, Proof Analytics: one of the things about a super technology and AI is definitely falls in that category, and agents are an expression of that.

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Mark Stouse, Proof Analytics: What defines a super technology is that plays both sides of the field

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Mark Stouse, Proof Analytics: right? There’s almost like mutual assured destruction at the end of the day. And so, Julia, you know, my, take on how you deal with how you govern

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Mark Stouse, Proof Analytics: chaos. Right? Is that number one.

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Mark Stouse, Proof Analytics: One side is ultimately calling the tune, and that’s a and that’s a form of governance right there.

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Mark Stouse, Proof Analytics: So if I reject marketing automation emails

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Mark Stouse, Proof Analytics: at my server level and just say not not doing it anymore.

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Mark Stouse, Proof Analytics: The odds are over some period of time that marketers are going to stop doing that right? Because it’s no longer effective.

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Mark Stouse, Proof Analytics: I think that also, you’re looking at

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Mark Stouse, Proof Analytics: a point at which you ironically get back to Hu, humanity. Okay.

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02:49:19.660 –> 02:49:35.460
Mark Stouse, Proof Analytics: if everybody does what everybody has always done, and that is, use AI use agents to just, you know, scale into the ether right stuff that they don’t know works.

935
02:49:36.200 –> 02:49:44.670
Mark Stouse, Proof Analytics: And so this is where, having the causal reference, the causal brain on all this is going to be so indispensable.

936
02:49:45.680 –> 02:49:53.589
Jay McBain: Yeah. So I spent a lot of my career at Forrester. You know, sitting beside the person that authored the death of the salesman.

937
02:49:55.420 –> 02:50:02.610
Jay McBain: you know, if I go back 20 years at A, you know, one of these major technological events that that you mentioned

938
02:50:02.810 –> 02:50:10.020
Jay McBain: is when Google, you know, became 81% of the start of every customer journey.

939
02:50:10.350 –> 02:50:19.580
Jay McBain: Whether you’re buying a car, any considered purchase, whether you’re buying a million dollars worth of software, 81% of buyers relied on a Google search.

940
02:50:19.960 –> 02:50:30.829
Jay McBain: So you know what happened. The the ether, what you talk about, and all the things no. And whole industries were created. The 15,000 Martech and Ad. Tech tools I mentioned.

941
02:50:31.310 –> 02:50:35.839
Jay McBain: You know, half of them were engineered on how to get to the 1st 10 listings

942
02:50:36.040 –> 02:50:37.839
Jay McBain: on the front page of Google.

943
02:50:38.511 –> 02:50:43.529
Mark Stouse, Proof Analytics: Careers were formed, entire university courses and degrees were created.

944
02:50:43.830 –> 02:50:52.270
Jay McBain: And now the world just doesn’t the buyer. I totally agree with you. By the way, on the buyer, agent side 75% of the new

945
02:50:52.420 –> 02:51:04.360
Jay McBain: majority buyer as of January this year. Demographics say the majority buyers now born after 1982. So we do have a millennial led buyer. 75% of them don’t want to talk to a human.

946
02:51:04.900 –> 02:51:08.400
Jay McBain: So they’re going to build these better and better buyer bots

947
02:51:08.520 –> 02:51:13.869
Jay McBain: and and buyer agents that that work on their behalf that are so much better than a Google search.

948
02:51:14.250 –> 02:51:19.490
Jay McBain: I don’t want 10 listings that I have to go listen to podcasts and read ebooks. I mean, you go do that for me.

949
02:51:19.640 –> 02:51:22.379
Jay McBain: And you accelerate a lot of these 28 moments.

950
02:51:22.600 –> 02:51:40.230
Jay McBain: But in it’s how it’s consumed. And you talked about stratifying. You talked about reading the Internet cover to cover. I worked at Ibm when we played jeopardy and played Ken Jennings, and we only had milliseconds to read the Internet cover to cover before we had to answer. You know, Alex Trebek, in the form of a question.

951
02:51:40.720 –> 02:51:46.730
Jay McBain: You know this AI, and how it’s formed and how it’s built. There’s going to be a counter force

952
02:51:47.320 –> 02:51:50.650
Jay McBain: of how it consumes information and then how it lists.

953
02:51:50.830 –> 02:51:59.040
Jay McBain: You know the the different things. So you know, jobs that we don’t know exist yet. Careers and university degrees that don’t exist yet

954
02:51:59.400 –> 02:52:02.870
Jay McBain: are on the counter force of these fire agents.

955
02:52:03.160 –> 02:52:09.010
Jay McBain: and how companies are going to, you know, over the next 3, 5, and 10 years

956
02:52:09.510 –> 02:52:13.300
Jay McBain: going to respond to how these these agents.

957
02:52:13.300 –> 02:52:18.649
Mark Stouse, Proof Analytics: Yeah, no, I I agree with that. I just think that like one of the things that

958
02:52:19.530 –> 02:52:27.530
Mark Stouse, Proof Analytics: is so important today for today’s buyer, right is the issue of risk, right?

959
02:52:27.640 –> 02:52:35.410
Mark Stouse, Proof Analytics: None of the stuff Google, etc, etc. Right? Gave you any insight into relative risk? Right?

960
02:52:36.260 –> 02:52:42.910
Mark Stouse, Proof Analytics: And that is actually where the interaction of AI agents and

961
02:52:43.190 –> 02:52:57.460
Mark Stouse, Proof Analytics: other things right? That some of which have not even come yet. That’s where that’s gonna be so important. And it’s where also, I’m taking this perspective because I operate

962
02:52:57.750 –> 02:53:15.400
Mark Stouse, Proof Analytics: in an area of causal. AI, right? And so this is kind of like from a vendor perspective everything you’re trying to accomplish against, to achieve this particular outcome net of all this stuff in the middle that you don’t control.

963
02:53:15.910 –> 02:53:20.310
Mark Stouse, Proof Analytics: one of which is the the buyer obsession with risk.

964
02:53:20.770 –> 02:53:22.910
Mark Stouse, Proof Analytics: Right? How do you deal with that? It’s

965
02:53:23.490 –> 02:53:44.000
Mark Stouse, Proof Analytics: it’s expressed today in longer and longer buying journeys. Number of deals closed with no decision. I mean the it’s endless right? And so I think that that this is where the the companies that I’m aware of that are using buyer. Bots, for example.

966
02:53:44.380 –> 02:53:45.590
Mark Stouse, Proof Analytics: are saying.

967
02:53:46.040 –> 02:53:55.530
Mark Stouse, Proof Analytics: evaluate what all these vendors are saying out there about themselves and put it on a factual index.

968
02:53:55.860 –> 02:54:01.660
Mark Stouse, Proof Analytics: Right? Tell us who we can really at least trust the quality of their information.

969
02:54:01.790 –> 02:54:11.029
Mark Stouse, Proof Analytics: And are they saying things that actually are executable are real, are in production, right things like that.

970
02:54:11.270 –> 02:54:18.580
Mark Stouse, Proof Analytics: And so I I think that’s just in the short term. That’s where it’s really, really going. The problem is is that

971
02:54:19.100 –> 02:54:23.920
Mark Stouse, Proof Analytics: all the stuff that salesforce is doing. I’m not picking on salesforce.

972
02:54:24.170 –> 02:54:26.299
Jay McBain: Isn’t about that.

973
02:54:26.730 –> 02:54:29.770
Mark Stouse, Proof Analytics: It’s about glorified errand, boys.

974
02:54:30.250 –> 02:54:40.819
Jay McBain: Yeah, you’ve gone back to risk a a couple of times. But you know, in parallel to all of this, and over the course of the next decade. You know, today, you’ve got a 282 billion dollar cyber security industry.

975
02:54:41.090 –> 02:54:45.800
Jay McBain: And inside of that you have a 28% growth rate on compliance.

976
02:54:46.280 –> 02:54:53.070
Jay McBain: So you know, companies looking at regulatory governance, this is before, by the way, your airlines, your governments, your banks.

977
02:54:53.390 –> 02:54:58.270
Jay McBain: I’ve decided to move any of that on premises data into any of these models.

978
02:54:58.270 –> 02:54:58.700
Ken Fine: See.

979
02:54:58.700 –> 02:54:59.140
Mark Stouse, Proof Analytics: Not so.

980
02:54:59.140 –> 02:55:03.000
Jay McBain: You can understand that that compounded growth around risk

981
02:55:03.120 –> 02:55:07.029
Jay McBain: is gonna grow at the same, you know, parallel pace

982
02:55:07.200 –> 02:55:14.360
Jay McBain: that agentic AI grows over this time. So anyone that’s looking at these models is

983
02:55:14.570 –> 02:55:18.160
Jay McBain: understanding. They need a you know, a check mark in certain places.

984
02:55:18.160 –> 02:55:25.609
Mark Stouse, Proof Analytics: The only the only caveat that I would put on that is that right now, agentic is exclusively

985
02:55:25.800 –> 02:55:29.240
Mark Stouse, Proof Analytics: a correlation based machine learning based

986
02:55:29.780 –> 02:55:40.010
Mark Stouse, Proof Analytics: thing right? So when we’re talking about the difference between the a correlative answer and a causal inference answer.

987
02:55:40.130 –> 02:55:53.959
Mark Stouse, Proof Analytics: we’re talking about anywhere from 4,000 to 7,000 basis points right of spread. So we’re going to rapidly get to a point where a new brain is going to be necessary

988
02:55:54.140 –> 02:55:57.159
Mark Stouse, Proof Analytics: for the agents. I mean you just. It’s unavoidable.

989
02:55:57.480 –> 02:56:00.210
Jay McBain: Yeah. Well, the last word I think on this topic is.

990
02:56:00.400 –> 02:56:04.550
Jay McBain: you know, there there is no avoidance of risk.

991
02:56:05.307 –> 02:56:09.310
Jay McBain: There is a protection of risk, though which is the what is reasonable.

992
02:56:09.670 –> 02:56:14.980
Jay McBain: And you know, if I just go back to cyber for a second, it’s not a question of if it’s a question of when.

993
02:56:15.230 –> 02:56:27.209
Jay McBain: And so AI is going to be the same thing, it’s going to be a question of when your personal customer data or financial data shows up on Chat Gpt for the general audience to see it. It’s a question of when

994
02:56:27.400 –> 02:56:31.919
Jay McBain: did you take precautions? Did you check all the boxes

995
02:56:32.200 –> 02:56:37.889
Jay McBain: that a reasonable person would check. That’s the judge, would, you know, judge on that?

996
02:56:38.080 –> 02:56:47.490
Jay McBain: And then, if you did, with all the smartest people in the world that allows you to move on from risk and and aggressively, you know. Go down this path.

997
02:56:47.800 –> 02:56:48.780
Jay McBain: Julia.

998
02:56:49.900 –> 02:56:55.329
Julia Nimchinski: Thank you so much, such a pleasure. Our community is loving it, seeing a lot of

999
02:56:56.247 –> 02:57:06.389
Julia Nimchinski: comments and conversations. But we are at the top of the hour, and before we transition to our next session, Mark and Jay, what would be your recommendation to people

1000
02:57:06.610 –> 02:57:09.770
Julia Nimchinski: is actually considering, like, what’s the next step?

1001
02:57:11.480 –> 02:57:18.819
Julia Nimchinski: In terms of agentic ecosystems? Headless ecosystems. Where do we start? How do we transition.

1002
02:57:20.600 –> 02:57:21.690
Mark Stouse, Proof Analytics: Data straight.

1003
02:57:22.400 –> 02:57:27.840
Mark Stouse, Proof Analytics: Right? I mean, otherwise, you’re you’re starting from a Ca, a chaos position.

1004
02:57:29.940 –> 02:57:37.970
Jay McBain: Yeah. For me. It’s just get obsessed about your own job role. Obviously get obsessed about your customer and the part of that you serve

1005
02:57:38.450 –> 02:57:49.079
Jay McBain: and for your own career perspective. You know, you need to go in deeper than you know. 99% of other people in in similar parts of their career than you are.

1006
02:57:51.630 –> 02:57:52.860
Julia Nimchinski: Thank you so much again.