204: The Surprising Connection Between Data Foundations and AI's Value Ceiling

Joshua Burkhow: Well, that was a fun conversation. Here's a few key takeaways that I took from my conversation with Fern. The first one is that the value ceiling is real. You know, most organizations, they're using AI, but they can't point to the business outcomes that actually improved and they can't measure the value.

That's the ceiling. And most companies are actually sitting right under it and they aren't even realizing it. The second thing. Here's a question that I can't stop thinking about.

If every company in your industry is using the same AI, trained on the same data, producing similar results, where does the competitive advantage actually come from? Well, my guest today might actually have the answer and it's the opposite of what most executives want to hear. Hello, this is Alter Everything. It's a podcast about AI analytics and the future of work.

I'm Joshua Burke out. Fern, welcome. Do I have this right? You started in oceanography.

You spent years at the Bell Labs on continuous monitoring and led research as well as at TDWI through the last three major waves. And this year you founded the AI Foundations Group. I really imagine how many times you've seen the same cycles sort of come and go. is that AI governance is not data governance with a new name.

points out that a key difference is that it has to monitor the output of models, not just the inputs. And with the agentic systems, auditing becomes a whole new problem. Even Fern pointed out that auditors are now using agents to audit agentic AI. It's a field that's still being figured out.

My guest today is Dr. Fern Halper, founder of the AI Foundations Group, of research at TDWI, and author of Data Makes the World Go Round. She spent 30 years inside of data and AI, and she has one of the clearest reads I've found on what's actually working inside of enterprises right now ⁓ what isn't. Most AI coverage fixates on what tools can do next.

Fern Halper: You it right. ⁓ Joshua Burkhow: Time flies. So ⁓ of going back over all this history and data world, ⁓ I think you could cover in droves, ⁓ actually like to start where we're at. You've this in 2026 ⁓ as an inflection point and not a sort of breakthrough year that a lot of people are talking about.

What is sort of signal that you look for that tells you ⁓ that this really an inflection point versus Now, the last one is the one that I'll probably be chewing on for quite a while. know, original thought is now becoming a scarce asset. And I don't say this tongue in cheek or being snarky, but you know, as more of us use the same tools, trained on the same data, the outputs drift towards the center of the bell curve. The answer isn't to use less AI, it's to design for augmentation, not substitution.

What agents will automate? Which model wins? Sort of want to ignore all of that today. The more interesting question sits underneath.

Most organizations have deployed AI in some form, but few can point to what's actually changed. And the reasons have very little to do with the models. So three things I want to hash out with Fern today. where companies actually get stuck.

sort of just another round of hype. Fern Halper: Right, right. I was writing about the inflection point in my blog and I said that organizations are trying to make their way towards agentic AI and that 2026 was actually going to be a year of building. So I think that that year of building is around the foundations that are needed for both generative AI that uses company data and then agentic AI, which of course uses company data also.

And Joshua Burkhow: Ask your team to critique what it gives back. Ask them to deliberately work without AI on some days. We need that thinking muscle, that thing that you have to protect. Now, before you go, if you liked today's episode, can I ask you a personal favor and share it with your friends and colleagues?

Let us know what resonated, what you want us to explore next. What is interesting to you? something that she calls the value ceiling. Second, what predictive analytics adoption curve from the last decade tells us about where agentic AI lands in this decade?

And then lastly, with the question that I opened with, in a market where everyone uses the same tools, where does that competitive edge come from? Let's get into it. Fern Halper: In my research at TDWI, see that organizations is sort of slightly more than midway through this journey, although it's obviously a continuous journey because everything is always changing. So what I'm looking for to tell me whether or not the inflection point is real is around both data readiness for AI and then the operational readiness for it.

So the data readiness, Joshua Burkhow: You can email us at podcast at alterix.com. That's a L T E R Y X. And you can subscribe to alter everything on YouTube, Spotify, Apple podcasts, or wherever you listen.

I really appreciate you listening and we'll see you next time. Fern Halper: I'm looking at data around data silos, organizations consolidating, unifying, well as unstructured data. ⁓ seeing that in ⁓ research ⁓ organizations have a significant impact from AI, they believe that the data foundation is table stakes. So there's group that has actually made progress ⁓ with AI, they obviously view that data foundation.

as tables takes because when you ask them what their early mistakes were, their early mistakes were around data silos and thinking that they could just layer AI on top of fragmented data foundations that weren't governed. And they realized that that was a problem. I do think that they still now this year have to deal with ⁓ data ⁓ they're putting more mature handling of unstructured data in place. ⁓ those are the two signals in terms of the data.

⁓ foundation and then in terms of operationalizing AI, I'm looking at whether organizations have the right skills and tools and roles in place. know, so a lot of organizations have thought about new roles like ops people and engineering roles, you know, so are they putting those in place? You know, are they also doing things like building or buying tools around agent registries or tools that's going to help to monitor agents? You know, basically are they taking steps?

to get themselves into production. So those are the types of signals that I'm looking for. Joshua Burkhow: Yeah, those I mean, those are pretty clear if you're paying attention to them, right? I think you're ⁓ a of history as as we've both been in been in this game for a little bit.

Do you sort of chuckle and and when you see sort of these patterns happen again where people are, ⁓ you know, looking at and say, hey, know, data is really important. ⁓ We sort of comment around the 2012. big data wave that came in and then 2017 when machine learning wave came in. were a lot of these where again, the data foundations were so important.

Do you see that as well? Fern Halper: ⁓ Well, I in terms of the big data wave, think data people got excited about it, but not necessarily consumers of software to extent that they are with AI. So in some way, ⁓ this is ⁓ bit different. certainly like the big data wave was about getting people excited about what they could do ⁓ with a lot data, ⁓ ⁓ and how can enrich data and...

⁓ get much better results. It was about how to deal with the data. To me, this is so much more complex than that is, you know, it's about the data, it's about the organizations, about the skills, it's about the governance. You know, everything has to be part of the system to achieve success, you know, really.

yeah, it's the wave, but this one has gotten so many people excited that in some ways it's a little bit different. It's sort of a sea change. ⁓ Wait, that's not a good, I was an oceanographer, wait a minute, that's not a good analogy. It's a tsunami as opposed to a wave, how about that?

Joshua Burkhow: Yeah. Good partner. got you. I got you.

Yeah. Yeah. Yeah. We've never heard that, marketing slogan of tsunamis, right?

so when you, when you talk to executives, going back to the of importance of the, the data foundation, do you get the sense that more and more execs are ⁓ coming to the table? and realizing that they don't have to waste cycles, you know, going down these roads when if they, really rely on, on building a good, strong data foundation. do you see that more often or is that sort of worried it's sort of falling away and they're not, they're not sort of getting the historical lessons here.

Fern Halper: Yeah, that's a really good question. think a lot of them aren't getting the historical lessons. And certainly, you know, we see executives stepping down because they don't think that they know enough about AI. ⁓ know, a lot of them didn't even think about data foundation to begin with.

They just sort of jumped right into, ⁓ you off the shelf consumerized. AI without even thinking about what was underneath and just said let's do AI whatever that means. So yeah, you know, ⁓ there are a lot that are thinking about it and it depends who you talk to ⁓ ⁓ definitely hear you on that one. Joshua Burkhow: Yeah.

Yeah. It's just, it's, ⁓ it's one of those things where you can sort of have a conversation within five minutes and understand if they're, really capturing the, the essence of the fact, like you're sort of hinting at it is that there's a lot more behind the curtain ⁓ of this, this world that we're, going into. for you, you actually have a concept that you call the value ceiling. I hope I get that right, from what I gather, it's, it's this.

point where sort of individual productivity gains from these off the shelf AI tools stop translating into organizational value? Fern Halper: Yeah, you have that right. ⁓ In the book that I just wrote, I talk about a value ceiling in the context of sort of an AI maturity journey. And certainly with generative AI, a lot of organizations, as I was saying before, they were trying to leapfrog over foundational capabilities that they otherwise would have had to put in place for AI, machine learning, predictive analytics.

So that includes the data foundation. So as they're using these off the shelf consumerized tools that are really point solutions, they're mostly getting what they think are productivity gains from it. So in this curve, I talk about quick wins in low risk use cases, right? Things like generating code to build a website or using generative AI to write content.

But then the curve flattens and they won't get to truly high value if they don't have the foundations in place. So I show ⁓ a plateau. ⁓ And that's where the ceiling comes in. The ceiling is the plateau of the curve, because they get stuck.

And once they have those foundations in place, the organization, the data, the skills, the governance, the operational know-how, then the curve can start to grow upwards again. Joshua Burkhow: Got it, got it. That makes sense. Are you able to sort of elucidate this idea for folks who maybe are new to this and ⁓ ⁓ what does the company go through when they're doing it?

Is it just a matter of going through and getting all those foundational elements in line or ⁓ have you seen this sort of come fruition? Fern Halper: You know, it's so interesting because when you have conversations with people, and they're talking about AI, almost takes 10 minutes before ⁓ you can out what they're actually talking about when it comes to AI. ⁓ Yeah. ⁓ then, know, once I sort of explained about the consumerized path ⁓ versus the company enterprise path that uses company data, you know, then we Joshua Burkhow: 100%.

⁓ I so agree with that. Yes. Fern Halper: come to a little bit of different conversation. So ⁓ ⁓ takes ⁓ while and they've heard data is the new oil or whatever, ⁓ data So they sort of get that and then they understand ⁓ what may struggling with.

⁓ Joshua Burkhow: Yeah, I, I, sort of empathetic to this ⁓ too, because you, you talk to a lot of folks, even the ones like us have been around a while that nobody an expert in, in every area of a know, governance can people live their whole careers doing governance alone, let alone having to make sure that we, get that sort of managed appropriately and implemented like if you put me in the of say a leader and I come to you say Fern, help me out. ⁓ I'm trying my best ⁓ to get out of sort of AI hype things and get going.

⁓ What are some of the first landscape pieces? Like would you, ⁓ would you go straight to data and say, hey, how's your foundation? Or what would you sort of advise from? from your point of view.

Fern Halper: Yeah, well, I always say that should start with the business need, you know, that there's no point in building that doesn't solve a business need. But if were ⁓ off the shelf and then if they're struggling, to actually take the step, like say they're trying to build an application like a chatbot ⁓ that interacts with customers and maybe had a general chatbot before. or maybe one that was using company FAQs to provide information to customers, but they want to expand it.

And to do that, they're going to need to access customer data. And then they find out that they have 10 different ways to define a customer, and those are going to provide different data about the customer. they should know that they have a problem, right? if they build a simple model and they see that outputs are garbage, that indicates that their data is flawed or biased.

So I think that you have to get to the data question pretty quickly. ⁓ what's the issue that you're to then do an honest inventory of what data you have in place to it. And ⁓ then pretty quickly you're gonna see, well, do have that in place or do I ⁓ not have it in place? Joshua Burkhow: Yeah, yeah.

Fern Halper: But even getting to that point to think, where's all of that data? What that inventory look like? ⁓ and doesn't have to be every piece of data that you have in the company, but just to solve that business need and then you can build out now and out. But it's just interesting happens with some companies.

Joshua Burkhow: Yeah, there is this sort of trade off and you hinted at this one again is the honest assessment of it. I've been so gung-ho about the results and the outcomes and, trying to get to a specified goal that I don't look as honestly and cleanly at what's really there, right? Like, Hey, do I have all the elements? Do I have 50 % of it?

Is it. a hot mess or is it good shape and can I sort of talk to that fairly I think we can't shortcut that, right? You Fern Halper: You can't, and it's just so interesting now too, because the types of applications that companies are building now, they're using unstructured text data. ⁓ you know, we're so used to dealing with the structured data and now they're using the unstructured documents.

And I was looking at the results of a survey recently and I was just floored by, the biggest use case was, basically around documents and... Joshua Burkhow: Yeah. Fern Halper: customer notes those of things. so even they had some sort of structured data infrastructure in place, organizations, a lot of organizations don't have the right unstructured repositories in So some of it's not even their fault, if you know what I mean.

Joshua Burkhow: That's right. ⁓ totally agree. anything that you find interesting about ⁓ the value ceiling and how it's sort of playing out from, ⁓ from the point which you wrote about it? Fern Halper: Yeah, I guess I've been thinking a lot about when organizations think that they're actually making progress because they have these co-pilots or whatever in place.

And I've been thinking about it sort of in terms of the illusion of progress versus actual progress. So they have a lot of activity, but they don't necessarily have measurable impact from their AI. ⁓ I just think that that's important that you always have to measure, about what the measurements are. those have put the work in with the data and other foundations are the ones that I always see ⁓ measuring in production.

⁓ And that aren't are stuck in the pilots and experimentation that plateau. And ⁓ that in terms of any sort of gaps that organizations have to fill, know, if they actually want grow, you know, like they can keep getting, I guess, productivity enhancements, but if they actually want to grow the value of what they're trying to do, they have to move past ⁓ illusion of progress to actual progress, if that makes sense. Joshua Burkhow: Yeah. No, totally.

I mean, again, talk about history lessons, right? Like the machine learning when and data science, when that all came, came about, it was the same thing. It was this idea that, we have smartest people on the planet and data scientists are, are essentially gods of the universe. and they had a lot of the hoopla and the hype, but like, were they making of real dent in ⁓ actual progress, real progress was changing the company.

⁓ And I think that the ⁓ underlying assessment that I was that ⁓ the change management still this day is the hard thing, is the thing that I would argue most companies not great at, ⁓ is ⁓ getting those things place doing the hard work and doing the foundational aspects to make these things. take hold and actually, make progress. What a concept, right? Fern Halper: Right, not to mention actual change management.

You I remember when I was at Bell Labs and we could actually customers that were going to disconnect AT &T services and were very excited about it. We had built the model, ⁓ you this was a long time ago ⁓ and &T I'm sure does ⁓ all of advanced things now because they were doing advanced things back then. But, you know, we to the people who ran the call center and said, you know, we can predict who's going to disconnect. you know, the service and they just looked at us like we had five heads and you know, why would we do that and how would we do that and how would we operationalize that?

And, you know, certainly all of the, the compute power wasn't there at the time to actually that. But yeah, but it goes to, the cultural type of that is also sort of needed. So. Joshua Burkhow: make it viable, Yeah.

Yeah. Yeah, I totally agree. we're trying to get this new incredible technology, amazing technology in place. But it's just the tip of an iceberg that has, all these things together, your data foundations, got to get ⁓ the skillsets, right?

All these pieces. ⁓ of the ones that I want shift to is ⁓ the big topic of governance. And ⁓ really interested in your thoughts and ideas around this because I talk about governance a lot. Fern Halper: Mm.

Joshua Burkhow: never really saw AI governance the same as sort of data governance. I've always sort of saw them, okay, they have the same word in them, but they're pretty different disciplines, maybe like different foundational sort ⁓ concepts here. a lot of companies treat them as sort of the same thing that seems like a problem. Fern Halper: I completely agree you.

I view that data governance and AI governance are complementary. ⁓ And certainly, there's governance for AI. But AI governance is something that's different, right? It's going to deal with the models, not the data.

So data governance ⁓ never had to with things like versioning and documenting AI models. ⁓ It didn't deal model registries. It didn't need to deal with explainability of outputs or observability. ⁓ ⁓ for looking at models and how decay.

⁓ different, you if you don't have data governance, you're not gonna have good AI, but I've seen organizations same as you, they try to treat the same, then ⁓ try to ⁓ make it to completely different groups that are dealing with But you now they're sort of coming together and saying, ⁓ Joshua Burkhow: Right. Fern Halper: you know, we all should be under the same umbrella. AI is different than data governance, but you know, there's overlap with what you have to be thinking about.

But you know, it's the same thing where people built models, but they never thought about operationalizing ⁓ them. now, they're building AI models, they're not thinking about governing them. So it's a conundrum, but ⁓ are at least getting board. with it now to actually think about how they're going to govern their AI models, which is good news.

Joshua Burkhow: Yeah, right. And I think the interesting thing is, like we've got, you know, sort of the LLM that we're dealing with, but now agentic AI is coming out quick and ⁓ you've got all these other concepts like MCP and, and, other components, those are sort of new, ⁓ grounds to figure out what AI governance looks like. Fern Halper: Yeah, it's so much more complex. mean, governing a learning model.

That's hard enough, ⁓ right? And the vendors I talked to about AI, they talk about, access controls, we provide access controls. Like somehow that solves everything. ⁓ Or at least that's the place to But I don't know that I agree with that.

You know, I think if you're going to build and govern an agentic system, ⁓ you need to actually start with what that system going to do. ⁓ So you have to map it out. You have to look at where all of the control points in that system need to be, the inputs, the outputs, what happens in the agents, how they interact with each other. And so the governance design needs to come up front with your system design.

I how many people are thinking about that? And you have to decide whether you're willing to live with the terms of the risk as you map that out. I've been actually hearing from people who are talking about that these systems to start out with should be more deterministic and perform certain tasks, you know, rather than ⁓ letting ⁓ be probabilistic and, you know, go a little bit wild. So I think that that makes sense, you in terms of AI governance.

the good news though is organizations are getting on board with it. Joshua Burkhow: Yeah. Fern Halper: they're saying, absolutely, we have to do this. that ⁓ conference ⁓ I'm at, in TDWI research that I do, ⁓ ⁓ saying, whoa, whoa, ⁓ whoa, in terms of their agentic systems, we got to put the guardrails there first.

So least they're thinking. you know, first, because I guess some organizations sort of said, let, let's just build the agents and see what happens. And, you know, now they're stuck with tens of thousands of agents and ⁓ they're maybe that wasn't a good idea. you ⁓ know, companies, different philosophies.

Joshua Burkhow: Yeah. I mean, it's really wild when you think about it because ⁓ the two that come to mind is just the cost implications, don't know I've met anyone that could tell you, Hey, ⁓ I've got 10 analysts and they cost me exactly this amount ⁓ based on whatever work going after. and use of LLMs and the cloud codes and codecs of the world and building stuff. And so there's this sort of from a cost management point of view, that's one area.

⁓ ⁓ one that I'm finding really interesting and ⁓ tied closely with AI governance is the legal aspects. And I'm curious if have any points of view on that or any hard lines on that realm, because what I'm seeing is Legal is, is as an organization is sort of having to force themselves into say, Hey guys, you got to think about this more clearly. And then there's the sort of push and pull that's happening in a lot of organizations around. yeah, just don't worry about it.

Let's just try this out. Like you said, ⁓ we're gonna, we're gonna see where the boundaries are and we'll come back to you. You know, Fern Halper: I'm a lot more organizations when I talk to them, they're legal is part of, governance team and they're trying to interpret the compliance obligations that they have, cetera. And certainly, ⁓ you you think about GDPR even, I don't think people were thinking about like AI models, but yet, you there is a provision in there that says if someone wants open a credit card and you deny them, you have to be able to explain to them why you denied them, you know, so you need to understand ⁓ what you're doing and all that needs to be governed, in some way.

And now it seems like people are ⁓ realizing, ⁓ you know, I have to able to explain that or whatever. And then you look at the EU AI Act, which is even more explicit about certain things. So legal definitely has to get involved. that's other ⁓ news.

think that the I'm talking to are bringing them in. Joshua Burkhow: ⁓ I ⁓ one question I want to shift into the next sort of theme here is Do you see with all the new things coming out from MCP to all the new technologies that are coming into AI world, do you think this is not gonna be a sort of field or settled practice for quite a while? Do you think it's gonna? Fern Halper: think it's early.

What I see a lot of organizations doing is that they're trying to the frameworks that are out there for AI governance, ⁓ and then ⁓ they some of big four they take pieces of it and then try to put it together as their own framework. they're not rushing into it, I guess I would say, because not only do they have to sort of put a framework together, they have to put an operating model ⁓ together about what that looks like in company and, ⁓ you know, dealing ⁓ with this and do we get the business involved?

like you were saying, legal, ⁓ is involved, IT, you know, there's just like ⁓ lots of people who are involved and I think it's just gonna a while. And that was just for... ⁓ you know, sort of thinking about generative AI and not even agent, like I just think that that's, haven't seen, ⁓ good frameworks out there that look at agentic ⁓ AI. Like was saying, I think, ⁓ it's case by case, it out, thinking about, it from an auditing and control perspective, cetera.

And that's just going to take a while. Joshua Burkhow: Yeah. Yeah. I mean, I, could probably foresee entire companies coming out and just focusing ⁓ on to govern agentic AI.

⁓ by itself is, is, ⁓ it me a headache just thinking about it. Like it's a, sure challenge. Fern Halper: it's interesting because the auditing field, you know, I've kept trying to keep up with it a little bit since my days at Bell Labs dealing with continuous process auditing. know, auditors are now talking about using agents to audit agentic AI systems.

that talk about your mind boggling. Yeah, so. Joshua Burkhow: I'm ⁓ to get your head wrapped around that one. Like ⁓ audits who now?

⁓ ⁓ a, ⁓ we essentially have get to a place where possible, right? Where, where ⁓ every corner of a corporation enterprise can tap into power of agent, ⁓ Gentic AI, but have mechanisms in place that make it. Fern Halper: Right, right. Joshua Burkhow: safe and reliable and, and, and, ability to go back to the regulators and say, Hey, this is how we did exactly this thing.

The credit application, right? Yeah. It's, it's fascinating. ⁓ yeah.

⁓ yeah. I can't imagine. Yeah. Fern Halper: And supply chain is going to be a big one for agentic AI.

So how are you auditing? Yeah, it's going to be very interesting. I don't think we're there yet by any stretch. So that's just my opinion.

Joshua Burkhow: ⁓ think it's, I got my started in supply chain and I've always been fascinated because it's the sort of, multi-dimensional problem at all times, you know, trying to get all these moving pieces working together. And then to your point, you add, ⁓ AI on top of that, it's, ⁓ know, it's not the sort of segmented approach that, could work, It's gotta be pretty comprehensive and complex and agentic AIs is highly complex already, right? what, ⁓ one thing that surprised Fern, ⁓ your recent writing is that ⁓ the, deepest isn't governance failure, ⁓ but it's something ⁓ sort of quieter happening to people that are using these tools every day.

I, ⁓ I did my Fern Halper: Right, right. Joshua Burkhow: My previous podcast was about sort of AI and people. you, wrote in February that in, in an AI saturated market, original thought becomes a scarce asset. Can you unpack that for me?

Can you sort of give me, give me the underlying why, why scarce? Why now? Fern Halper: Yeah, I guess, you know, we've been talking a little bit about it, but what I see happening is that many organizations are making use of AI tools, you know, the co-pilots, the assistants, the chat GPT's, the thousands of other tools that are out there in TDWI research, more than 90 % of them are making use of ⁓ off the self-service types of tools. ⁓ And, you those models were trained on a corpus of data that was the internet, ⁓ but as more organizations are using generative AI to create something and that something is let loose the world like marketing content, then the models become trained on data that's already generated and the outputs average towards the center of the distribution of the bell curve.

just becomes this homogenized thinking and everyone's just sort of at the middle of the bell curve. So to me, that's not where original thought occurs. not at the center, it occurs at the long tail. ⁓ then if that tail is smaller, ⁓ the probability of original thought decreases.

Because if everyone's just putting prompts ⁓ generative AI systems, mean, to me, where's the creativity and insights? I maybe there is some in some cases. ⁓ feel like we all become mediocre. I remember someone ⁓ a survey a couple of years ago was asking about if people were positive or negative towards AI and these were data and AI professionals ⁓ and you most of them were positive, really positive about it.

But there were a couple of people who were saying, you know, we're just ⁓ tending towards mediocracy and been thinking more and more about that. It's sort of like a bad sci fi show, you know, where generations before, wise people created technology and now. the sad looking people, ⁓ that currently here ⁓ can't fix it it breaks, you know, so ⁓ it just made me think a lot what I'm hearing, what I'm seeing and, ⁓ when you read articles that are online and whatever, you're sort of saying, everyone's saying the same thing.

It's, it's just all homogenized thinking ⁓ at this point. Joshua Burkhow: Yeah. Yeah. It's, it's really such a fascinating thing.

when try to share why I'm excited about AI is, is the creative part. ⁓ on the whole, I don't consider myself creative. what I found with AI, for me at least, is it allows me to get my messed up thoughts and messed up, not bad, but messed up, untangle them a little bit and get some thoughts, get some ideas ⁓ that might have sort of spark of creativity, a spark of sort of, hey, ⁓ I wonder if else has come to this crossroads ⁓ of these two ideas and then flushing it out. I mean, I probably spend way more tokens on that than anything is just sort of going down these, these rabbit holes of, of sort of flushing these things out.

I think AI ⁓ been ⁓ so to the realm of, Hey, it's a productivity tool. it's ⁓ go get your work done. You can automate your PowerPoints. You can automate your Excel documents.

You can automate your workflows. ⁓ Let's, know, go. And those are all. great.

I'm not sort of harping on those, but they at odds? Right? Are they ⁓ we sort of sacrificing this ability to do these productive things without the sort of original thought to be like, maybe there's a better way. You know, ⁓ maybe there's we could think through this and come up with new creative ideas that, evolve the dialogue conversation.

Fern Halper: Yeah, I guess I think that they talk about food groups or how to stay healthy, ⁓ it's a balance. maybe, you this is also a balance of things because certainly, not saying you can't get creative ideas out of AI, because if you know enough you're critical, you know, your critical thinking, your creativity is enough that you're putting the right thing into the prompt window, then you know, you may get something useful back. And certainly, you we've all been helped, ⁓ sure, you know, by the off the shelf generative AI tools that are out there.

But Joshua Burkhow: can you sort of help paint a picture of how you think this plays out in ⁓ an enterprise? Like, would you do? How would you, how would we sort of affect if we, if we were so bold to think we Fern Halper: you know, from a leadership perspective, I think that, yeah, that effective leaders need to sort of design for augmentation and not substitution, you know, that means they have to be explicit about where Joshua Burkhow: don't get your work done. Fern Halper: AI is appropriate and where it's not appropriate.

maybe AI can help accelerate your documentation Or your initial exploration, ⁓ you need to be expected define the problem, validate the assumptions, defend the results, make sure that they're correct. What I see some organizations doing is implementing practices like requiring their data analysts to explain ⁓ critique generated outputs, ⁓ like to produce a first pass answer before using AI. And I remember back when I was in graduate school, my major professor was saying, ⁓ look the data, ⁓ look the data, don't just run the analysis, so you don't want to restrict it.

You just want to ensure that AI doesn't replace thinking, you want to keep your teams ⁓ sharp. You want, embed AI or sort of embed the thinking ⁓ into the process, the logic and verifying the sources Joshua Burkhow: That's right. Yeah. Yeah, I, I totally get it.

Fern Halper: Some organizations are putting peer review processes that specifically look for AI errors or your ⁓ Joshua Burkhow: I really like because it's easy to, ⁓ automate this process, do this thing, do this thing. essentially get my work done. if we don't ⁓ augment the along with what we're doing, the drain. That's the drain of original thought, it's juxtaposed against sort of experience I've had with our CEO and I work closely with him on AI projects and he's very much ⁓ pushing gamut like hey, let's think different.

⁓ Let's do the sort of AI slop that everybody else, ⁓ what other new could we build? What other original ideas could we come up with? Fern Halper: Yeah, but when I look at companies that are transforming, ⁓ basically, ⁓ it goes to we were saying before. It's not necessarily the ones who are just using off the shelf AI tools.

⁓ It's the ones ⁓ are putting the ⁓ in place and doing the hard work. But they're reaping ⁓ the benefits ⁓ from And that's to say that someone just can't come along and come up with an idea. and say, let's create an app that does X, Y, and Z for our customers and we can make millions of dollars. Obviously, that's happening.

I'm looking at more sustained and more potential for growth. So ⁓ how I'm looking at it. Joshua Burkhow: Yeah. Yeah.

Yeah. ⁓ yeah. that that world where you sustained original thought ⁓ and a strong foundation that is reliable and audible and these things. perfect recipe for ⁓ Yeah, that's pretty foundational.

I want to get into this next piece of a transition, but it's the of idea of this predictive analytics precedent. you've written that predictive follows the same sort adoption pattern to Gentic AI is following. ⁓ Fern Halper: Yeah, you when I got to TDWI, I started asking organizations whether they were adopting, if they were implementing predictive analytics and every year, know, every quarter, whatever, I'd be asking the same question. And then I'd also ask, are you planning to do this, or you're experimenting with it now?

And if organizations continued on the same path. and actually were doing what they thought that they were going to do, you know, with predictive analytics, the adoption would have been at 80 % already, and it's more like, to 45%, So I think that in some ways, you know, maybe agentic AI is a little bit different. I can't say for certain, but, you know, if ⁓ I see 30 to 40 % of organizations responding to surveys saying they're experimenting with agentic but only about 10 to 20 % have it in it's sort of the same idea, know, like where's it going to be in the next couple years, what would adoption be like 10 years from now, it's also complex as we were talking about before.

⁓ So I think that's gonna strike some companies down. And I don't like to sound negative because I've always been interested in what the next ⁓ is. But just think that people underestimate what's involved. So ⁓ that always concerns me.

Joshua Burkhow: Yeah. Yeah. Yeah. Yeah.

Hindsight's always 2020, isn't it? But then once you start to see these things of get into the organizations and actually get put into place ⁓ and then get way down ⁓ that pathway, say five years, 10 years, you start to the difference ⁓ of what really hyped to the beginning to the mean, ⁓ big data had it, learning had it, predictive analytics had it. ⁓ I mean, I even think, you know, AI is so pervasive, right? my mother who technophobe to the ⁓ degree ⁓ that can't like I can barely get her to use her phone, but she's using an LLM.

Like that to me is wild that like it's pervasive It's being ⁓ used in every person either knows it or ⁓ Does it? It's not like machine learning or predictive analytics where you had to have some sort of quant background, some interest in it, some sort of love for data ⁓ that sort. So smaller population. Does the fact that AI expand all corners of the earth change ⁓ landscape there?

Does Right? Fern Halper: Yeah, that's what I think. I think because it's so well known at this point and everyone's using it. Like you're saying your mother, my sister, talks about, you know, ⁓ I talked chat, chat said this, said, you don't call it chat, GPT.

And she said, no, that's too long. I just call it chat, you know, so they have their own lingo, you know, ⁓ maybe agentic AI will get further everyone's talking about agents Joshua Burkhow: Yeah, yeah, yeah, yeah. Fern Halper: and ⁓ going anywhere. Yeah, maybe not ⁓ gonna take off to the degree that some people...

thought, I don't think it's going anywhere and people are understanding it more, I think, in some ways. Joshua Burkhow: Yeah, right. I got a fun one for you, Fern. So one of the things that I like to do on this podcast is do what we call a lightning round where ⁓ I'm going to of throw ⁓ few things at and get your sort of quick, immediate reaction.

nothing too elaborate or in And they're meant to be sort of ⁓ quick, instinctive answers. But no curveballs. I ⁓ don't try to you too much. good for it?

Fern Halper: I'll give it a try. Okay. Joshua Burkhow: All right, all right. So I have six of them here.

Let's see, see how they go. give me signal a company has hit the value ceiling without realizing it. What's ⁓ one thing that just, you see it and you're like, yep, you're there. Fern Halper: Okay, so when I see that they're using AI, but business outcomes aren't improving, they can't measure value.

Joshua Burkhow: Can't measure value, that's a good one. thing AI governance has to do that data governance never did. Fern Halper: I'll say monitor the outputs of models, so monitor software outputs. Joshua Burkhow: That's a good one.

Good. One habit that protects critical thinking on a team using AI every day. Fern Halper: ⁓ that's a good question. Deliberately do work without AI on some days.

How about that? Joshua Burkhow: Ooh, good one. You're going to start a riot in the streets on that one. yeah, yeah, totally.

⁓ one vendor claim about agentic AI that you do not believe in yet, or do you do not believe Fern Halper: Yeah, try to do that. ⁓ say don't use it, don't use it, don't use it. Yeah. Yeah.

⁓ don't believe that companies are putting multi-agent systems to work in a production environment in any meaningful way. I don't think that there are fully autonomous running end-to-end business processes. without human oversight, and I guess, you know, are talking about everyone, you know, you need human. Oversight, but then they also talk about you know that agents are your team members.

is another interesting cultural type of dynamic. So Joshua Burkhow: Yeah. That's right. next one is one skill that stays valuable for the next 10 years, regardless of how fast all these tools move.

Fern Halper: How about having good judgment? being able to frame ⁓ ⁓ assess trade-offs, although I guess agents are to doing that, you know, and making decisions under uncertainty. ⁓ Joshua Burkhow: Like being able ⁓ to think through things critically. All right, last one.

⁓ You've done ⁓ amazing so far. So one question every executive ask before green lighting an AI project. Fern Halper: Mm-hmm. Hmm.

Joshua Burkhow: What should be that one question? Fern Halper: How does this change what we're doing and is it worth change? Joshua Burkhow: Which probably shouldn't be much different than the other projects that they're green lighting, right? ⁓ mean, that ⁓ pretty straightforward.

Cool. so I'm going to, ⁓ we'll, a quick question ⁓ to of to round it out. And then I think we can, we can let you go but if a listener runs a data or analytics team and is, is listening to, to this podcast and they want to move. Fern Halper: Yeah, yeah, yeah, yeah.

Joshua Burkhow: beyond the sort of productivity theater into sort of real value. They want to actually, ⁓ you know, enact that change. What would you advise them to forward on? Well, ⁓ you we're not talking to the executive per se, but talking ⁓ the data analyst or the analytics team or that newly minted AI.

engineer, would you have for Fern Halper: AI, I guess I was, guess if their organization thinks that AI is just an off shelf tool, ⁓ and they want to move further and push the needle further in terms of thinking about their AI programs. then they might start with an AI team. It's a concept that is a small group. They listen to problems.

They try out new ways. to solve it. mean, ⁓ like in some ways, the center of that I was in at Bell Labs long, long, long ago, you know, was we wanted to solve business problems in new ways. you know, and they follow a framework to solve certain problems, And one thing to be sure is that you set metrics.

success metrics as you do this, because I always say that success begets success, because I've seen that over and over again, and it's really a virtuous circle. If you can do something where you can show that you delivered value and you measured it, then people start to get excited, ⁓ that worked. Look at that. Let's do the next thing.

Joshua Burkhow: Yeah. Yeah. Yeah. Yeah.

Yeah. And keep it going. Exactly. Yeah.

Well, Fern, thank you so much. I appreciate your time. I'm very grateful for your wisdom here. think this has been immensely valuable.

If people to follow up with you, follow sort of what your book and... connect with you, what would be the right place to do that? Fern Halper: Well, they can connect with me on LinkedIn. They can send me an email, go to aifoundationsgroup.

com and all of my information is there. Joshua Burkhow: Cool. Connect with you. Thank you so much.

I'll make sure that we post that in ⁓ show notes, but with that, I just want to say thank you so much for your time. I appreciate you. Fern Halper: That was fun to talk to you. Thank you.

Joshua Burkhow: Thanks, take care.