The AI Daily Brief: Artificial Intelligence News and Analysis - The 7 Biggest Mistakes Companies Are Making with AI and Agent Adoption

Episode Date: April 16, 2025

Companies keep repeating these costly mistakes when adopting AI and agents. Nufar Gaspar, an AI adoption strategist formerly at Intel, breaks down seven critical errors organizations make, including unrealistic expectations, unclear strategies, poor data access, and mismanaged communication.Get Ad Free AI Daily Brief: ⁠⁠⁠⁠⁠⁠⁠⁠https://patreon.com/AIDailyBrief⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/ai⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about how KPMG can help you drive value with our AI solutions.Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlw⁠⁠⁠⁠⁠Plumb - The Automation Platform for AI Experts - ⁠⁠⁠⁠⁠https://useplumb.com/nlw⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Subscribe to the newsletter: https://aidailybrief.beehiiv.com/Join our Discord: https://bit.ly/aibreakdown

Today on the AI Daily Brief, we are joined by Newfar Gaspar to talk about seven common mistakes in AI in agent adoption. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Hello, friends. As you know, it's Spring Break Week, and we are doing a series of interviews with really interesting guests. And today we are joined once again by Newfar Gaspar. Newfar has led AI efforts at Intel. She consults with numerous companies of all sizes, including some of the big,

biggest in the world on their AI adoption strategy. And she works with me at Super Intelligent on our agent readiness audits. In this episode, we're going to explore some common mistakes that we're seeing when it comes to AI and agent adoption with some recommendations for how to avoid them. All right, Newfar. Welcome back to the AI Daily Brief. How's it going? Good.

Great to be here. Yeah. So we have a lot more to talk about because we've had a chance to do a lot more stuff since last year we're here. We've been doing these agent readiness audits in the super context where we're learning directly from companies, you know, what they're doing with AI and agents, what their challenges are, what's working well, what's working what's not. You've also been working independently of that with a number of companies. So we've sort of a big, big interesting base of material, I think, to pull from today. Right.

And we've been seeing companies of all sizes, right, from like a handful of people all the way to the biggest companies out there. Yeah. I think that that's one of the things that we'll talk about and it will be interesting to sort of keep in mind is how different or similar these challenges are kind of across across different size of company as well as across things like industry vertical. But so what we wanted to talk about today is the sort of challenges or common mistakes that companies are making, basically things that are holding them back from where they could be and where they want to be and potentially some specific real world strategies to actually help them. So we've got seven of those mistakes. What we're going to do is we'll go through them kind of one by one. I'll sort of set up and have you explain a little bit about it. And then we'll talk through each one.

Is there anything else that we should have in mind, though, as background before we dive into each of these in terms of just setting the stage for where companies are, broadly speaking? Yeah. So I think there are two things that also come across a lot when you do your weekly long grades and other more opinionated stuff is that some people are starting to get a little bit frustrated with the big gap between what the media like yourself is promising that we should be able to do with AI, we as in companies, versus what actually happens on the ground. So there is like a very big gap there. And while what we're going to discuss here is not based on any very

detailed survey of thousands of participants, it's very fresh and anecdotally comes from many different people and sources. So that's why I feel very comfortable with what we're sharing. I'm seeing, recently, I'm seeing different responses of leaders and managers to the current gap between AI promise and deliverable, basically. So some of the managers are starting to get a little bit more, perhaps, I don't know if skeptic is the right word, but a little bit more careful with how they're willing to invest in AI. Even in one of the largest companies out there, I'm hearing that they're not willing to introduce

new tools as easily as they used to do like one or two years ago, and they're much more worry about the ROI and any guarantees about the new tools and agents and AI, whatever, that will be introduced. So that's one, like a persona of behavior that we're seeing. The other are people that are still very excited, but they're expecting agents to be like the silver bullets. And in some cases, even do like a leapfrog beyond having basically nothing now and expecting agents to help them bridge the gap. In some cases, it might help them bridge the gaps. In other, we all know that agents are not silver bullets and we will discuss some strategies and things that they can do in order to get much more from agents. And in general, we're still seeing mistakes

across the board and we believe that there is so much more that we can do from what is being done now in order to really get the value and not stay in this frustrated period of promise versus deliverable. Yeah. One thing that I'm noticing as well that I think is putting sort of or is about to put even more pressure on this is there has been obviously a big increase in general global market volatility and instability over the last few weeks. And already I'm seeing it translate to new urgency and pressure around can AI and agents specifically actually deliver and more pressure to try to figure that out. The more tense companies are about cost, the more that they are hoping that these things can be sort of great saviors. And the challenges take on even a bigger

urgency. You know, AI has never really been a field where people could take three or six months off and just see how it develops. But I think that that's even less so now. And so even as perhaps some of that skepticism or frustration is increasing, I don't see leadership taking their foot off the gas pedal at all. So, you know, it's an interesting time. But let's dive into some of these common mistakes and see how much we can kind of help people move through them. So the first that you shared is this idea of a single direction approach to how agent and AI innovation should happen. So what do you mean by single direction? So I've seen two things. One is organizations kind of expecting that AI will only happen in a very organic way, kind of a grassroots, their internal

employees will be psyched about AI. They will bring ideas. Perhaps they will work about their innovation in the nighttime and in the daytime will continue to do the regular job. And through that approach, everything will happen and all the promise will be fulfilled. And from what we've seen, it's not really the case. Like even if those grassroots and these employees are very, very talented bringing great ideas, the best that they get by doing that as a side gig is to have a very good demo or a very good pilot. But then comes the time that you have to productize. And then they say, but that's not my role to support whatever high capability that I will create. This incredible rag system will require some maintenance,

someone will need to open a support ticket and close them and so on. And they're saying that's not my job. So maybe someone else will take it to production. And then we're seeing many very great initiatives kind of hold in that phase. And in other cases, employees don't even do much beyond perhaps using the tools for their day-to-day job because they're just too busy, to get themselves less busy, they just don't have the bandwidth allocation from their managers. So that's one direction where it only will get you thus far.

In the other direction, we're sometimes seeing organizations where the managers are so psyched that they're kind of taking a top-down approach to AI adoption. As an example, one CEO was saying, hey, we're going to be an AI-first company, and we're going to do everything with AI, and we're going to only hire agents from now on and so on. And then you interview as part of our audits, the employees, and you realize that there is a very clear undercurrent of fear of job loss, that there is an undercurrent of like distrust in AI.

So it's very clear when you read those two responses that that's not going to yield a good enough result. And even in cases where managers are more invested, creating plans and ideation and stuff like that, if they're not working in tandem with their employees, it just won't hold because it's top down. And often they overlook some of what really bothers employees, how they're actually using, what are the day-to-day challenges. And they're only seeing from a very like bird's eye view, everything looks very clear.

And that's not necessarily the case once you go into the details of the agent implementation and the use cases themselves. Yeah, it's interesting. I mean, it sounds like even though we're talking about there being sort of this challenge from two directions, like people are either missing sort of the support for bottoms up they're missing the need for bottoms up, you know, involvement with top-down decision-making. They're both fundamentally questions of leadership, right? Leadership deficits, just in very different ways. Either leadership that hasn't sort of helped people figure out how to take their experiments

and turn them into something broader and connected them with sort of the resources to design a full system around them, or leadership that is sort of visionary but not practical and hasn't got buy-in. I mean, is that a fair kind of characterization? The second one, perhaps I will like caveat a little bit that often they do have the buy-in, but they believe that they know enough to make the decision for the organization. And then they're not the ones that are going to go and execute and the one that's going to go and use. So they're missing critical details and thereby it will not succeed. Yeah.

I think that this is definitely something that is emergent from these audits, because we do these audits both on sort of a leadership level where it's, you know, a handful of leaders across the organization, but we also often get down to the individual contributor or employee level as well. And a common thing is managers, even who were individual contributors themselves a few years ago, there is a certain atrophy of what the day-to-day actual job looks like, how processes have changed, right? We live in a very, very dynamic work environment where even outside of AI, the way that you get things done, the context in which you do things is evolving so quickly that there can be, you know, a meaningful gap between managers' understanding of how work happens

and employees who are doing the work's understanding of how work happens, which when you're trying to make decisions around, you know, work augmentation or task replacement, those can be pretty critical differences in perspective. Yeah. Yeah, I agree. And when you look from a very, very bird-eye view, I always laugh like it's when you fly high enough, all the lines seem straight. And if you are too senior, that always happens to it. And it's not their fault. It's just the granularity in which they can operate. So again, the risk of being reductive every time, because I think it's, you know, it's very challenging to paint with a broad brush, the prescription. But it does feel like there are a couple common sort of ways to counteract this, or perhaps maybe even better way to

look at it is organizations who aren't struggling with this particular challenge. What is it, what is it that they're doing that makes them to not succumb to this particular set of issues? Yeah, so it will probably not surprise you and the audience that we're talking about the two-direction approach to kind of merge the top down and the bottom up because we have to have the leadership, like you said, it is a leadership challenge first and foremost, but we also have to have the experts and the employees that will be the super users and users of the system in the loop early on, whether it's to eradicate their fear, to make sure that

they're on board and they understand that it's going to empower them or augment them or whatever word you're going to use, you will probably say in some case replace them, but they still have to be involved in order to define the system properly. But also managers need to be involved in the details so that when it comes at the time to allocate the resources, the funds, the bandwidth of employees to build a system right or to pay for a vendor, they understand the complexity and why perhaps something costs as much as it costs or it takes. as much time as people are quoting. So it needs to be kind of a bidirectional thing.

And even if it's a grassroots, I think it's very important for leaders to say, okay, this is a direction that we're encouraging innovation versus this is something that was already handled elsewhere or not a strategic direction that we want to go to. So perhaps not to stifle innovation, because that's not what we want to do, but also not to make it so open-headed

that people are basically wasting companies' resources. because they have an idea and it's not going to go anywhere. Yeah, I think that it's worth noting to, I see agents as exacerbating rather than helping this problem. I think that agents are inherently more replacing than augmenting, at least in terms of how people think about them currently. You know, agent, the ROI that companies are looking for from agents is, can they do a thing more cheaply, efficiently, more quickly than are people doing?

it. Now, what that doesn't say is how companies are going to choose to use those new efficiency gains. Are they going to just slash headcount or are they going to reinvest people's time that's now freed up in further growth? Each company has to make those decisions. I think that making those decisions and articulating those things is going to be absolutely integral to solving some of the challenges of employee concern. Basically, I think that agents are ratcheting up the pressure to get this balance more right than it is currently. Maybe let's move to number two, characterize it as a haphazard approach to AI adoption. So it feels kind of related to number one, but speak more to what the common mistake here is.

So here often it's, yeah, it's a little bit of a continuation, but often what you will see is like a best effort. Let's let people. And again, I don't care now about the top down or bottoms up, but let's let people or organizations do whatever they have in mind. Everyone can work on AI and should work on AI because it's the most critical. technological change in our generation and so on. But then when you ask everyone to work about AI, it's literally like asking no one to work on AI because it's very chaotic. We see a lot of duplicated efforts.

You know, even when we interviewed companies that are not very large, and you ask people that are basically peers, what's permissive to use, what's already been done, they don't know. And let alone when you talk about large organizations, there is a huge difference in what they know. And that creates a lot of frustration, a lot of waste in these companies. And in many cases, that's not something that will get you to good results. Another funny anecdote is that in a company where literally they had zero or near zero AI usage,

we were asking them, like, who is responsible for AI adoption? And are you happy with that? And because they didn't know, they said something like, well, the CEO is responsible. And because the CEO is responsible, of course I'm happy with the AI policy. because like what else can they say? So if we're talking about one title is very little governance, very little policy, in many cases not articulated, like you said before, even if in some cases you sit down and have one pager that articulates what are your do's and dons,

and what is your vision with their adoption, even something as simple as that that takes an average CEO or leader, and not too many hours to craft will create huge difference versus companies that are just rock and rolling on whatever out there. So, yeah, so let's go into the prescription a little bit. Like, what's the template for how to get this right from what we're seeing right now? Because obviously, you've got to imagine that this is fairly evolutionary and the best way to manage this is, you know, could be different from different organizations.

But what are common best practices that we're starting to see? So one thing that I'm seeing repeatedly, and I don't know if it's a popular opinion, but what we're seeing is that in companies where there are dedicated AI teams, and it can be a team that only does AI internally or people that have enough bandwidth, and enough bandwidth is more than 10% weekly, which many companies are trying to allocate to their employees, and that's not enough, but some kind of a dedicated group and a council that oversees AI adoption across the company.

It can be cross-functional. It can be a team that is dotted to the CTO, chief AI offers, or a chief information officer, it doesn't matter as long as there is a very clear team that is the owner of AI. Typically, this is where you see much more mature results, better results. So that's one thing that is very important. The other is having a clear AI policy, and it should be proportional to the size, the age, and the risk of the company. So, of course, the mega corporates, they have their lawyers and they have information security officers and everyone on board to define AI policy

and they have to get it right because it's way too risky. But even for them, they often can overdo or underdo that. And even if you're a very small startup, just getting started, have someone in charge and this person or these persons

should define what is the company policy and they should and can revise it over time, but at least have something that the employees can refer to and can work off from, even if what they want to do is, you with the policy. So yeah, I mean, it's super interesting. My sense, too, is once again, that agents are complicating rather than simplifying this in the sense that when the decisions around AI were things like, you know, are we going to use Gemini or co-pilot across the whole organization? There's a natural

centralization there. Also, when you're first exploring, getting a committee together to do that exploring, a lot of organizations sort of defaulted to that fairly centralized approach. Agents introduced, a sort of a re-bulcanization of software where the platforms that are building agents that are good for the marketing group might be different than those that are good for the sales organization, might be different than those who are thinking about customer success and might be different than those who are thinking about core product. And they also might all have different constraints when it comes to regulatory and compliance. And so, you know, I think that we're certainly noticing a reassertion of decision-making from line of business leads, functional heads,

like that. And I think that, you know, perhaps counterintuitively, it sort of increases rather than decreases the need for some coordination apparatus because I don't think that you're going to be able to make all your sort of agent buying decisions or agent hiring decisions centrally, right? Because you just have, you're too far from the actual surface area of the business that those agents are going to interact with. But they have to be coherent with one another. They have to all plug into the same set of policies. They probably all have to have a similar relationship with data. And and so that the haphazard approach becomes even more tricky when there is a natural impulse for many different parts of the organization to run in different directions because they're trying to just make the right decision for whatever it is that they're doing. Yes, and they will also, like everything that was defined up to date, might not hold with regards to information security

and other governance elements. So let's not open this Pandora Box, but over there there is also a world of risks that they even if you feel like you have a very good AI policy to date, you should probably revisit that and make sure that it's agent ready. Okay, number three, unrealistic expectations. I think many organizations will relate to this, but talk a little bit more about the types of unrealistic expectations that we're seeing.

Yeah, so there are many unrealistic expectations that we're seeing from different people. Some of them goes back to what we talked about in the intro, that media creates such a hype that everything is seamless and ready and the technology is already AGI and so on. So then we're seeing different things. For example, there is a huge gap and a huge time lag between how long it takes you to create a demo and what AI enabled and in recent times it becomes even more and more prominent with all the lovable and the V0 and all the other tools for prototyping.

It's so easy to create a working demo in a few days. but even with the best of tools to date, to bring something, an agent or an AI capability to production, an enterprise grade, with everything that it entails, access to data, good quality over time, monitoring, and so on and so on. In some cases, it can take even 10x more time than how long it took you to work on the demo,

or sometimes even more than that. And that's both employees and managers, neglect to understand that there is so much investment in putting something in production, And that's even before I'm accounting for the support costs that are quite high as well. So that's one type of gap between expectations and reality. The other thing is that while people acknowledge that GPT is not perfect and they know that the eye is not perfect, when they are contemplating an agent or something that is much more autonomous,

they expect it to be near perfect. In many cases, much better than what a human will be able to accomplish. So they expect the AI to be way, way, way better than the human. And this is not where the technology currently at. We know that there will be a lot of probabilistic effects and that even the best of capabilities that we will put out there, there will have some faults. So as of now, the best known methods are to have some guardrails

and to guard against those probable mistakes. But just as an example, the other day I was talking to a company about a potential agent and I was asking them, like if it has a single mistake each week, will that be enough, assuming that I'm stating the obvious and they winced so clearly that even a single mistake is intolerable, this is not something that the current technology can work with. Yeah, it is absolutely the case. No, the surveys and research is finding over and over and over again, as well as anecdotal

evidence, that people's threshold for how much they're willing to let AI be wrong or how much, you know, incorrectness they're willing to bear from AI is much, much lower than the human equivalent for whatever set of reasons, right? It's especially because since customer success is such a common use case where, you know, AI and agents are already production ready, they're deployed, we're seeing evidence, people's expectations are absolutely that the computer should be more right than the people. Right. Otherwise, they want to leave it for the people. To the people. Okay. So two additional things that, of course, there are many more,

but just wanted to highlight a few. So there is an expectation and perhaps we should be at fault for that that creating an agent is very easy. And while some agents are easy to create, it's still a technology that is new, that if you want to deploy it for complex flows and complex business cases, there is a lot of preparation work. You need to have your technological stack ready.

You need to have the right skills in place and the data and so on. And we even saw in one of the interviews that CTTO was saying something like, I want to introduce a new agent each week. And that's perfectly fine. And we commend them for their enthusiasm. But in reality, it's probably not going to be something feasible even for the best of companies. Because there is still a lot of effort for introducing agents. And maybe later this year or early next year, we will be at the state that even smaller companies get introduced a new agent each week.

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and the ability to protect your prompts and workflow IP. Make your life easier, your clients happier, and your business thrive with plum. Sign up today at useplum.com. That's Plum with a B forward slash NLW. All right, AI Daily Brief listeners, today I'm excited to tell you about the disruption incubator. One of the things that our team sees all the time is a lot of frustration from enterprises. There's a fatigue around small incremental solutions, a concern around not thinking big enough, tons of bureaucratic challenges, of course, inside big companies. And frankly, we just hear all the time from CEOs, CTOs, other types of leaders that they

want to ship some groundbreaking AI agent or product or feature. In many cases, they even have a pretty well-thought-out vision for what this could be, that their teams are just not in an environment conducive to that type of ambition. Well, it turns out our friends at Fractional have experienced the exact same thing. Fractional are the top AI engineers specializing in transformative AI product development, and to answer this particular challenge, they have, with perhaps a little bit of help from Superintelligent, set up what they're calling the disruption incubator for exactly this type of situation. The idea of the disruption incubator is to give a small group of your most talented people an overly ambitious mandate,

something that might have taken one to two years within their current construct. send them to San Francisco to work with the team at Fractional, and within two to three months ship something that would have previously been impossible. The idea here is that you are not just building some powerful new agent or AI feature, but you're actually investing in your AI leadership at the same time. If this is something interesting to you, send us a note at agent at besupor.a.I with the word disruption in the title, and we will get right back to you with more information. Again, that's agent at besupor.a.i with disruption in the subject line. Yeah, I think that this is, it's really interesting because on the one hand, there is a, I think a sense, people go through this disillusionment period a lot when they, when they dig in and they find out that it's more complex, that the actual sort of capabilities that are available today are a little bit different than what they're imagining. I think part of it is exacerbated by the fact that people are actually correctly imagining. When they think about what agents can be, when they sort of hear these terms, they're not incorrect.

in terms of where things are going to be. They're directionally sort of grocking, I think, what the, what the capabilities are. And I think that for whatever reason, agents are easier for people to conceptualize than the sort of co-pilot assistant type of AI, because it's a complete package, right? It's a thing that does this work. It really is, I think, in some ways, a more, it's more aligned with how people have historically thought about artificial intelligence as an entire sort of embodied package. But because there's this sort of disillusionment period, it is a natural and common reaction to basically sort of turn away from the hype and sort of, you know, just reevaluate, slow down. And I actually think that perhaps the biggest risk

for companies right now is taking the wrong lessons from their understanding or their recognition that agent capabilities are not as far as they are. I think a lot of companies are going to throw their strategy from, you know, fifth gear into slow to neutral and try to wait for things to get better. And by the time that agent capabilities come back online in a bigger way, they will all of a sudden, where they were once positioned to be ahead, they're now going to be behind. I'm seeing this a lot. I think that people are, you know, there's, there's a counterreaction. The thread boys are now instead of threading about how amazing agents are. They're also threading about how much hype there is. And it's creating an excuse for companies to slow down. And I think that that's going to really,

really bite them. So, you know, my sense is that it is essential to have a proper, you know, as much as possible, sort of proper expectations around what agents can do, a kind of clear-eyed assessment of the challenges of the specific capabilities they have. And by the way, we're doing a whole additional episode about what agents actually can do right now, which use cases are ready for prime time, but also to continue to build towards the future where agent capabilities seem to be doubling, basically every seven months according to one set of research or maybe even faster than that. So it's fascinating because it's, I think that maybe the lesson that some people would take around unrealistic expectations is in practice exactly the opposite lesson that they should be

taking. Yeah, I don't think that they should slow down. I just think that they should, first of all, invest enough resources. And second, invest enough. And that's actually a good segue to the last thing that I had in mind with regards to the asset expectations. And that is that people are kind of expecting that it's a turnkey. Like once I have a new tool, a new agent or something like that, then automatically all the value will kind of, I don't know, someone will send you some money in the mail or something with regards to your AI adoption and that's not the case. In many cases we're saying, and that's true both for AI and for agents that it takes some time, it takes some training, whether it's the employees who are using

the tools or whether it's fine-tuning the actual system to be much better for the organization. And you need to invest the time. So whether it's because you're afraid of agents that you're kind of halting, that's not good or in the like single employee case, often we're seeing people like going into a tool, playing with that once or twice, and then they say, oh, it's not good, and walking away. And you have to invest the time and sometimes significant amount of time, a few hours, the minimum, to be able to get from the tool, the full potential. Once you're willing to pay these tax, then all of a sudden you're getting all the goodness from whatever high capability that you put in place. So that's through both in the macro, like you're talking about, in bringing agent on board,

you will not stay behind and you will learn from the process of starting to implement your first agents. And then you will be very much ready to go once agents are so much better and you are able to really introduce one agent every week in your team. And also in the micro level, as an employee, as a manager, that I'm able to get the most out of it. So my day to day, my team's day today is doing so much better. My only last note here before we move on to number four is that I would like to say definitively as the CEO of Superintelligent, if you are a company who wants to deploy one agent per week, we will help you get there. It may take more resources than you think, but we will get you set up. All right, let's move on to number four. Poor data access, the

Achilles heel of AI. Talk to us about this one. Yeah. So there's nothing new there, but the more you go into the weeds with company about actual use cases, the more you realize time and again that everyone needs access to their own internal data. Their own internal data is the only thing that will, in many cases, differentiate between successful and unsuccessful adoption. And still, for most especially large and legacy companies, their internal documents lies in dozens different places, in many cases, not even in a databases,

but resides on people computer. And one company, for example, said, like, hey, we had a great idea for an AI product. It had a rag. It had an agent. But then we worked for about a quarter of a year just on collecting the documents that will go into the system. The actual system took us, like, I don't know, a week or two.

And the reality is that an organization is just not ready for agents, for AI with their data. and even if they put the best rag system in place often, it still will not suffice unless they do concentrated efforts of bringing the data. And even in the agent cases, in many cases, they need to describe something that does not sit in any document, the way people currently do their job.

I always tell companies, like, if the only way for you to describe the business process is to go talk to Bob, that means that it's not agent-ready, because only Bob knows how to do this business process. So in many cases, the first thing that we need to do is to sit down with Bob and have Bob describe the entire business process, document everything, bring all relevant documents into the process. And only then you can really contemplate bringing on board the agents or the AI capability. So it sounds like the answer to this one is just recognizing it as a problem and taking it seriously, like taking it as its own sort of full consideration outside the deployment or implementation of any one particular agent.

Yeah, you have to have a good rag in place, no matter if you build by or anything in between, but you have to have one that is agent ready. And you have to arrange your data and your knowledge. And it's a very tedious effort, but the incentive is so high that it's going to be worthwhile your effort in doing so. And you better do it now because once you want to deploy an agent every week, you better have your data in place already. One of the things that we harp on all the time is, and again, going back to this idea of sort of misplaced expectations, a great way to use the time between how good agents are now and how good you know they're going to be in 12 months is to build out this sort of infrastructure that makes you ready for them when they are ready in 12 months. So I think it's an important point.

Number five, wrong considerations, thinking incorrectly or at least in some potentially distracting ways around how to choose tools and vendors. What are the types of mistakes that we're seeing here? So we're seeing kind of a bandwidth of multiple mistakes. Some organizations that are very tech savvy often will try to build everything in-house, often even building capabilities that the best of companies and startups have already have a much better product, just because either they didn't do their due diligence in understanding what's out there or they are very much in the mindset of not invented here, so they rather create everything from scratch. And often that creates a very suboptimal results and a complete waste of time.

Another type of maybe the other end of the spectrum is companies, we're seeing companies that are basically buying everything that the major vendors will promise them, following every marketing deck and everything that is being told. And then they are often very frustrated the other way around because it's costing them many, many thousands of dollars and their employees or the overall business outcome is not as expected. So that's the other end of the spectrum. Some other common mistakes that we're seeing is getting into the loop of many,

many different pilots and structuring them in a very linear, fashion. So we go into a pilot with a single vendor for a quarter of a year, only to realize that the employees that were part of the pilot were unhappy, and only then we start working on the other pilot and a year in, and we have zero tools of AI that we bought into the broad population, and employees are very frustrated because they feel very much left behind. And lastly, I've seen companies that have yet to even approve a single AI tool. to date, two years in, or even more than two years in. So that's another type of approach that is going to get them not very far. Yeah. On this last piece, there are still some fascinating

misperceptions or real legacy concerns that you see, you know, continue to be intransigent. I think Ethan Malek tweeted about this, I don't know, a week ago or so, that he's just constantly shocked by how many companies that he talks to are not moving on things because they're convinced that companies are going to steal their data and that that's sort of like their chief concern and they just haven't gotten over it or at least they're using it as an excuse to not buy. So, okay, with those being the common mistakes, what are the ways to avoid doing that or not doing that as the case may be? Yeah, so it's a bit of an art form here because you have to be very careful about when you build,

when you buy and when you basically hire someone to do. the work for you. And it's always, as always, should be in proportional and in context of your company and the business case. But I believe that in most cases, it should be the right mix for this DNA for you. So in some cases, perhaps it's very good for you to build everything from scratch because you're so much ahead and no one is creating that, but first make sure that that's the case. In many other cases, especially when we're talking agents and everything is being so new, it's probably going to be best to start working with someone who has more experience, whether it's a tool or a service provider that can build it for you, and perhaps learn from

them on the go, and such that eventually will be able to build and maintain and improve those agents on your own. Another thing that you should probably do is make sure that you don't take anything face value, carefully vet any vendor, make sure that you're interviewing companies that has their agents, their capabilities in production and that they're happy and that they're good match to whatever you need. And like we talked at the beginning, perhaps if you have a very clear policy for how you introduce a new vendor or a new tool that can remove some of the decision fatigue

for the people in the loop. Because like we talked about agents, many different departments will have to make these hiring decisions by hiring, I mean hiring agents. And the more it's a structured process that gives them, the last mile for decision making, the better they can move rather than be stagnant, like the companies that are currently not doing well.

Maybe a couple more is when you work about pilots, maybe you should scope them in a very, very structured way or even kind of cascade multiple pilots very well, one after the other so that you end up with something, even if some of them are not as promising as they are meant to be. And lastly, make sure that the relevant people are in the loop. Like, don't just have top-down managers making decisions about which tools to use. Make sure that these super users are also in the loop that they are working with the vendors.

So they are making sure that whatever the company needs is actually being serviced by the tool or service provider that is being purchased. Well, that sounds like it bridges fairly directly into common mistake number six, which is a siloed approach in poor communication about AI adoption. Yeah. So to some extent, it does, but you know, you will be surprised by how often, like, different employees and different groups are completely unaware of what's happening across the company. What are the company policies?

What are the tools that are permissive and everything in between? And, you know, that's very expected in major companies. But like I said, at the beginning, we've seen that also with very small, even a of people companies happening. And in many cases, it's a very simple thing that you can do. You can basically create either one place where everything about AI is being communicated. When it's a larger organization, the one thing that I've seen yields the most ROI for companies if they create an internal AI network of champions, of enthusiasts, of the practitioners,

are the people who actually do the work. And it can be as simple as a Slack channel or something much more complex and extensive like we've done at Intel. We created an entire community of people that are talking and working about AI. We also had a very major portal that puts everything in place.

But even if you just have a Slack channel where everyone that is working on AI are in one place, you are much better. positions to succeed. And even more importantly, you need to, in my opinion, take the very positive carrot approach where everyone that does something with the eye and shares it with other is being put on basically on a pedestal for others to observe and behave more like them. Because the more, in my opinion, positive reinforcement you give the employees for sharing what

they do and basically breaking these silos, the better the overall outcome that they come company is seeing from that. So it's not very complex, but you will be surprised how many companies are doing zero sharing around the AI adoption and AI capabilities that they have accessible for their employees. It's absolutely fascinating. When we started doing the agent readiness audits, which is really a two-part process, part one is using voice agents to collect this really rich voice interviews with lots and lots of people across companies. And then second, processing it through, know, a set of proprietary knowledge bases about agent capabilities to come back with actionable recommendations. I think that probably we would have both thought, certainly I thought,

that the biggest value would be around the analysis, right? The connecting what we heard with the knowledge of where agents are. And I think my observation so far has been that there is so much value in simply unlocking and connecting the information experience and perspectives of people who might be literally sitting next to each other, you know, in a room or if, you know, if not in the same room, talking to each other every day and actually really don't know how the other people are engaging with these topics, where they matter for them, what challenges they're facing. It is shocking just how valuable it is to unlock some of that information. Just the quotes. The quotes on their own worth a gold mine. Okay. So last last

last common mistake, one that will echo things that I've been saying throughout this, assuming that companies still have time and can postpone taking concrete steps to adopt agents until the technology is more mature. Right. Yeah. So I think you mentioned that here and before, but, you know, the technology is very young. And some rational people might say, let's wait. Let's let others be the first guinea pigs and we'll pick it up once it's.

more ready. The thing is that you probably cannot afford that because like we talked about anything with AI, there is a very steep learning curve here. And if you will not invest the time now, then just jumping on board, I don't know, six to 12 months from now means that you will lose so much experimentation and learning time. And even if some of the agents are not prime time ready, there are so many agents that are. So beyond the concrete business value that, you can't yield to date, you're missing out on the opportunity to really get the infrastructure like the data that we talked about and your culture ready and your tech stack ready and having the skill set and engaging with the right vendors and your competitors in many cases will

not wait to do all of that. So from all of these reasons and of course we are very biased because we believe in agents wholeheartedly, that's why we do what we do. But assuming that there is a good chance that we're correct and that's a huge revolution even in the scope of AI. Sitting on the sideline is probably one of the poorest decisions that you can make this day and age. Yeah. I mean, look, I think it's, for me, there's a very simple framework. If the choices are doing the right thing, doing nothing, or doing something, some of which is wrong, but some of which is right, anything is better than doing nothing right now, right? It is, it is better to get shots on gold to start building the muscles to figure things out, you know, to learn and, you know,

build the reps, even if they're from failures. But I obviously think that to the extent that companies can find the right partners to spend their time on the right things, right, to understand where agent capabilities are currently. And so how much they should be investing in, you know, pilots and proof of concepts with specific agents versus some of the things we talked about before, building the communications infrastructure internally, building expectations with their teams, building out guardrails, building out security policies, data policies, getting data ready for agents, thinking about agent infrastructure, right? There's this whole set of things that need to be done to be prepared for and fully able to embrace and take advantage of

this agent revolution. Hopefully we can help people spend their time more effectively on the things that are useful and viable right now, but with an eye to the future, which is, you know, here in some places and coming very quickly and others. All right. Well, thank you so much for this super, super fun conversation. A little plug for another conversation later this week. We're next going to get into the use cases that are actually being implemented right now, places that are ready for prime time, some ways of thinking about agents. So, you know, we're about to record it at the same time, but for you guys who are listening, it'll be in a couple of days.