The AI Daily Brief: Artificial Intelligence News and Analysis - A Framework for Choosing Winning AI Use Cases [Agent Readiness Part 3]
Episode Date: November 9, 2025In the third and final episode of our Agent Readiness series, NLW and Nufar Gaspar dive into how to identify, prioritize, and measure AI use cases inside your company. They break down a practical fram...ework for evaluating opportunities, balancing growth and efficiency initiatives, and managing your AI portfolio like an investment strategy. Plus, they explain why company knowledge agents often deliver outsized ROI and why 2026 will be the “show me the money” year for AI transformation.Series Episode 1: How to Build an AI-Ready Culture: A Practical GuideSeries Episode 2: Why Data is the Biggest Barrier to AI Readiness (And What to Do About It)Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. https://www.kpmg.us/AIpodcastsRovo - Unleash the potential of your team with AI-powered Search, Chat and Agents - https://rovo.com/AssemblyAI - The best way to build Voice AI apps - https://www.assemblyai.com/briefBlitzy.com - Go to https://blitzy.com/ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/The 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/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai
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Welcome back to the AI Daily Brief.
Today we have part three of our agent readiness series featuring new Far Gaspar,
and we are digging into use cases.
The AI Daily Brief is a daily podcast and video about the most important news and discussions
in AI.
All right, quick announcements before we dive in.
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Thanks to all of you who have contributed now hundreds of use cases.
If you would be willing to take one or two minutes to do the same,
it is at ROIurvey.a.i.
In the first episode, we talked about the cultural dimensions of agent readiness
with Newfar introducing her change framework,
In part two, we talked about data and technical readiness, where we have found at Superintelligent
that data issues are by far the biggest blocker for agent adoption across all the different
challenges that enterprises face.
Today, we are talking about what makes for a good use case and where to invest your resources
in time.
With this, you're going to get a little bit of an interworking in the way that we do use case
recommendations, and it should provide, once again, a nice, actionable framework for
how you can think about which use cases might benefit your organization most.
All right, Newfar, welcome back. Part three of three. This is technically we're calling this
use cases, but I think this is like to make it practical, like go do interesting things kind of
section of the conversation. I'll let you kick it off and frame it for us. Right. Yeah, that's a good
take. Like you said, three out of three, final part of the agent readiness series. And we did
save the best for last. And that is the use case readiness. And we don't just refer to the,
are there enough use cases in production? But rather we were talking about whether,
there are enough opportunities in the company,
and often these are not opportunities
that the company is able to articulate,
but rather ones that were able to identify for them.
It can be a question on whether the business process
in the company could be augmented or replaced by agents
and whether they have the right mindset
in order to do proper use case discovery and execution.
So that's what agent readiness from a use case perspective is.
And in terms of the process,
And because we are all such big fans of frameworks, I wanted to cover this topic following the following steps.
So we will start with identify and then select, manage, and track our use cases.
And I want to give you enough practical tools, not only to identify the next agent use case,
but also to manage it almost like an investment portfolio, such that you can continuously improve your use case readiness.
So let's start with the identify phase.
And first, I want to talk about the sources of ideas for agents.
In many cases, what I'm hearing is that, like a CEO or a company leader will go into a room
and they will say, we need to build an agent, let's build an agent.
And this is probably one of the worst way to source a use case idea because you'll probably
build a wrong agent.
Good source for you to build these agent use cases will be probably to get it from either
bottoms up or mid-level up because they often are the ones that know best what is feasible
and also they are the one that needs to be bought in in order to execute. The question still remains,
what are these good use cases? And what I wanted to do here is to provide you with some hints
for what good use cases are and sometimes what good use cases aren't. And I want you to look first
for use cases that involves a very complex and highly changing decision.
making. For example, to resolve a customer issue will require different action each time there
is a new customer issue being raised. On the flip side, and that's my very aggressive opinion,
that in any situation where you can describe a fixed process or a decision tree with limited
amount of branches, you shouldn't build an agent. You should just go for the simpler technology
because the agent will probably not be worth it. The next thing that I want you to consider are places
where humans are in the loop, but they are being the bottlenecks.
This is often where you will find a gold mine of a life for your use case
because you need a professional judgment and you just don't have enough professionals.
Think, for example, legal contracts review.
Another place where you should look into are places where you need to have 24-7 human response.
It can be for employee support, for customer support, and so on.
And also, I want you to think about cases where you want to achieve a high level of personalization.
Think, for example, where you want to issue a highly personalized email, not just one where all the text is the same and you say, hi, Nufar, this is not the one.
I'm talking about cases where you will issue a personalized outreach at the right time with the right offer and with the right text to get me hooked into your product.
Next thing that I want you to consider is that I want you to only focus on use cases where you do have some tolerance for errors.
For example, with one of the companies that I work with, payroll said, we want an agent to replace.
some of the processes for calculating the employee's salaries.
And after I stopped catching my breath from how intimidated I was with this notion,
I said, guys, you don't do an agent here.
You don't even include AI in this process.
You do a simple automation because with salary, you have to get it 100% or 200% right.
So agent will not be the right way to go about this process.
And until now, we didn't talk about kind of the elephant in the room,
but because around agents there is so much discussion and sometimes fear around job loss,
a good place to start will be to focus on the use cases that employees wants to offload.
Those will be the repetitive or the tedious type of works.
You should focus there and then you will create a better momentum for more sensitive use cases.
And also in cases where the only way to understand how a job is being done is to say,
hey, Sarah, can you explain how the job is being done?
because Sarah is the only one who knows, this is not a good place for an agent.
We need to have a process that is well-defined and well-documented for an agent to be able to interject.
And lastly, we want to have a use case that is extremely measurable, not just to measure the ROI,
but because these agents are goal-driven entities, and if you cannot measure whether you are closer
or not to your goal, you cannot implement an agent.
So if you use all of these hints, one thing that you can do at your company in order to initiate
the creation of many, many such ideas is to have an ideation sprint.
And this will help you harvest more agent ideas in the company.
And what you do there, typically you will teach the employees using slides like that or
others what agents are and aren't and what they can and cannot do.
Then you harvest many, many, many ideas across the entire company.
And then with this very large inventory, you do a very crude prioritization of your
use case to keep it simple.
And here I want you to be very glad.
and nip in the bad any use case that you can execute in a different manner, automation or
otherwise.
All right?
So that's the first step.
With so many ideas, the next step will be to select and plan an agent roadmap.
The selection should always be driven by the holy tree, the feasibility, the investment, and
the value.
And this means that you will need to score each of our use cases accordingly.
And if you've heard the previous episode where we talked about intentional opportunism,
you by now know that I'm very, very much into having low-hanging fruits and focusing on them first.
And these should be use cases with high feasibility and low investment,
or ones that are highly critical and start with them.
And then you will create the momentum of learning and doing in order to benefit future higher-stakes use cases.
So let's make it even more concrete and talk about,
the list of use cases that almost all companies should consider.
And these are ones that we often find ourselves uncover or recommend companies when we audit
them for agent readiness.
And I think none of them will surprise you.
But the first one will be the FAQ or the policy bots.
These are either internal or external.
And I'm not talking like old-fashioned bots that only know to answer from a predefined set
of questions, but rather agents that are able to answer complex questions with many nuances
in the information sources.
The other thing that everyone needs and are asking for
will be the company knowledge retrieval.
I think this comes across all interviews.
Everyone needs good access to their data.
And even though many companies are already utilizing
the Microsoft and the Glein and other solutions,
often these are not enough
and they need to create an additional layer
or looking for additional ways to get access
to their very specific data
that is fragmented across systems and so on.
The next one will be operations.
Workflow Automation.
So those will be drudge work at the team level,
like automated status reporting,
and many other things that people spend unnecessary brainpower and time to do
and agents can take from their plate.
And lastly, I'm calling them like market watchers.
These are everything related to keeping a close eye on your competition,
on your regulation, or everything that you need to know
in order to do your business well, and you never have enough time.
So these are the top most prevalent use cases, but we do find ourselves recommending many other
use cases as part of the readiness audits.
And I don't believe that anything on this list will surprise you.
And of course, it starts with the top two most common use cases.
And those will be vertical use cases around customer support and software engineering.
They come across very often.
But we also see in many cases content generation for marketing or other purposes, as well as many
sales-related USK sales, they come across very, very often in the audits and with many
companies that we've been working with. And this is also something that I mentioned in previous
session. Agents can often help with things related to contract and regulations and other
things and with the process of cleaning your data, which is where there is a lot of unlock with
the data, access issues and many things that we mentioned before. And lastly, there are many
industry or even company-specific, often ones that will create opportunities for growth in the
company that come across as highly relevant in some of these audits. So this is a very rich
selection of use cases. And what I want to encourage everyone to do is basically to manage the inventory
and the choices of which agents and which use cases you want to pursue, like you would manage
an investment portfolio, as I said at the beginning. And I want first to have you balance
between two main elements, and those will be the efficiency or the cost-focused use cases,
and also those that are more focused on the growth.
And when I talk efficiency use cases, I'm talking about all the do the work with the fuel
resources type of use cases.
Well, growth use cases in my book are everything that basically has an impact on the top line.
And I want you to balance the two.
Often we're seeing companies highly biased towards the first one of efficiency.
and not thinking enough about the growth opportunities,
and often the biggest value is on the right hand side of the growth.
So make sure that you pay a closer attention to those as well.
And then to continue on balancing your agent portfolio,
I want you to look at this proposal,
and I hear paraphrasing on the 1970s Boston Consulting Work,
gross share metrics.
It's a kind of a classical.
And I want you to look at the identified use cases
in the lens of the complexity,
versus the value.
And of course, where there is
high complexity and low value,
and often we see
such use cases, just don't go
at all. There are, of
course, the low-hanging fruits or low-hangers.
These are an awesome place to start.
And over time, though,
those should become the thing
that people self-serve.
So these should be catered by
agent-building platforms
or other capabilities
rather than having a company focus on.
And eventually, you should aim
to have a handful of what I refer to as moonshots.
These are kind of the high-risk, high-reward use cases.
Often, by the way, they correspond to either a radical shift in how you do the work or there are
a gross use case.
So that's often where the moonshot is.
And they should be led and executed by a professional and centralized AI team rather than
just the best effort in the business units.
and they require a significant investment and a specialized knowledge.
So these are not for the faint of heart.
And lastly, most of your area of focus should be in this high or highish value and decent complexity
because this is where there is enough value, but still you will be able to get agents out
the door.
And by the way, for companies that are getting low readiness scores from us in the audit,
we will never offer ideas for stuff that are more on the moonshot.
We will always focus them either on the low hangers or the focus areas,
and over time we'll encourage them to go after bolder, bigger things.
So there are a few other dimensions that you should probably consider.
One is to balance between vertical versus horizontal agents.
Don't just do one or the other, and also build versus buy.
So keep that in mind as you balance your portfolio
and make sure that you have diverse and well-balanced,
portfolio of all the complexity combinations as well as vertical, horizontal, and build versus.
Okay, so this portfolio has to be centrally managed and periodically updated in order to support
the constant learning and the growth.
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If you're talking about growth, let's focus on this one on the tracking.
And the topic of measurement is one that is very important for us.
I know that I completely disregard the build phase, not for lack of importance,
but since it's like an entire episode.
So I'm skipping over the build and moving on to the track side of things.
And I want you to continuously monitor everything that you put in production,
and that means everything.
And you shouldn't assume that just because you had a very good result in the pilot,
it will be so in production.
We often see that once something is deployed,
the hands of actual users, not just alpha or testers,
things go haywire and yield completely different value than expected.
And we've seen that more companies that are being overwhelmed by what they put in production
rather than being pleasantly surprised,
the performance and quality and cost,
and especially if they didn't measure enough in a pilot
and won't measure enough in production.
So that's why it's so critical for us.
To make it very concrete,
I provided you here with a formula of how you measure the agent ROI.
So it's going to be like a return versus investment.
So return should be measured as the benefits from the agent,
and you should make sure to include the usage and the impact,
and you should continuously measure.
It's not easy.
And whenever there is a judgment call, I encourage you to be conservative rather than anything else so you can get a realistic view of what your agent actually accomplishes.
And you should discount the uncertainty of the benefits.
So in many cases, I'm seeing companies not accounting for the cost and the impact of the errors.
If your agent causes you to have to have a refund of something, then that's a cost of managing your agent.
And it needs to be discounted from your return, basically.
and you should subtract it from the investment.
An investment will include the resources,
and it should be all the resources,
what it costs you to build or to buy,
what it costs you to use,
and what it costs you to maintain.
And often we disregard these resources
that will get the agent to be sustainable in production.
So it will be the owner, the users' time,
and of course the tool and model use.
And here, especially when you do a projection,
takes sufficiently large buffers,
because in most cases,
we completely underestimate the amount.
of investment we will need to do. And of course, you need to take into consideration the cost of
your resources. And you can assume cost variability because we are seeing that models cost going
down. However, with agents, because they become more sophisticated, often they will require more
tokens and thereby balancing things out. So just reevaluate periodically. But there are many hidden
costs and you should try to account for them. So that's the most technical slide. And with that,
I want to summarize this session and give you a concrete to-do list.
So first, I want you to identify only agent relevant and worthy use cases, not vibes.
And then I want you to select the ones that have enough ROI and enough visibility
and manage them as if you are managing your investment portfolio,
such that you have a diverse, well-balanced portfolio.
And then rigorously track the impact and adapt over time.
So you don't just get it right in theory, but you get it right in practice.
Awesome. So first of all, I have to mention this. For the first two parts of this series,
when we were talking about the culture and leadership changes that were required to really do this well,
and the data and technical readiness, we can help in ways like this, you know, by providing information,
best practices, comparative things that we're seeing. But our ability to help, and this is,
I'm speaking in terms of super intelligent now, is limited. But with this one, this is exactly what super intelligent
does is help create systems for figuring out what use cases you should pursue. And it follows a lot of
this thinking, especially this sort of bottoms up idea of actually discovering what's going to be
useful from the ground level, from the sort of actual work level perspectives of people. So if you are
interested and need help with this, that is my shilfer super intelligent for this episode. This is what we do
pretty precisely. A couple of other things that I wanted to double click on. The first one is really small,
but I think I talk about it a lot because it's surprising to people.
In that portfolio balance, the low hanging, but sort of like low nudging into high value,
it would appear low value, but I actually think it's higher value than people think
is really that company knowledge retrieval, internal information sharing type of use case.
We see this so frequently as a gateway drug for people.
And what's interesting about it, and I think what pushes it from a low value to high value
or load medium value to more high value is that the value is actually,
double. The first part of the value is, of course, actually getting people the information they need
what you've set out to do with that agent or that AI. But the other part is it is like a light bulb
moment for a lot of folks when they use those tools. That sort of starts their own individual
thinking about how AI can be useful for them, right? When they have a problem that gets solved
much more quickly than it otherwise would have, as opposed to, for example, just like my email is
a little bit better than it was before because I use chat GPT or something like that. I think it really is
we very often see that being a surprisingly high value starting use case.
Yeah, it's the magic of context, basically.
Yeah.
Okay.
So another thing that I want to mention is on the ROI front.
You know, I think it's easy to talk about ROI.
It's much harder to figure out how to do it.
This is something that we're spending a lot of time thinking about better systems for
helping people with.
I think even having some framework like you provided at least gets people thinking more
comprehensively about it.
It is not as simple as performance analytics from traditional SaaS software, unfortunately.
It is not the same as just, you know, what percentage of people use the tool?
You have to add on some layer of exploration and insight to really understand how it all came together.
However, it is absolutely the case that broad expectations of when ROI is going to show up are being pulled forward fairly dramatically right now.
So KPMG recently came out with their annual C.
CEO study. And among these CEOs they serve it, it's something like 1,300 CEOs, all $500
million companies are bigger. Something like 65% of them thought that it would take three to five
years to realize ROI from their AI efforts. So this was just last year. Only about 20% said
one to three years. This year, they just released these results. It's now 69%, I think 67 or 69%
say that it's going to be one to three years. And 19% said that.
six months to one year in terms of how fast they think they're going to actually see R.O.I.
Right. The investment has paid for itself. Moreover, I just recently saw, actually just before we
were recording, that Morgan Stanley has said publicly that they believe that the cost that they
have put into AI has now been made back in terms of value, so that they are actually R.O.I.
positive. R.O.I is something that has been lurking as a thing that would be sort of for some time in the
future. I think that even with all of this happening, it's still complex to understand what
ROI is, but you're absolutely going to see way more companies showing up and saying, no,
we actually have figured out systems that we're comfortable with and confident in and are seeing
that happen now. This is not some future far off thing, I think. Yeah. I agree. I think 2026 is going to be
in many cases they show me the money year for the companies that have been doing the usage metrics and
the let's play with the tools and encourage usage type.
type of situations. However, in many cases, ROI measurement is very involving because they need to do like
A-B testing and especially on the efficiency use cases. Often it's very, very hard to quantify exactly.
That's why I said, like take buffers, take very aggressive, let's call it, discounts on the AI impact.
So there will not be a continuous discussion of, is it really the ROI or is it not the ROI? It's just because people are doing the work better or can you attribute it to the AI?
So I'm having multiple such discussions with some companies, and I believe it's going to be even more complex going into next year because the tools are going to be, from one hand, more powerful, but are going to be also table stakes.
So it will become, do you measure the ROI of having an Excel sheet, or is it like something that will be a proven incremental value?
But even with all that said and done, I believe that you have to measure because when you don't measure, it's just a matter of vibes, and it's not the way to make business decisions, not when it's year two or three or three.
for of adoption of such a technology.
I was with a group of CIOs, I guess a couple months ago now in Vegas for a big event
that I was keynoting.
And there was a breakout session after that I hosted to discuss things.
And one of the things that they all felt, there was a broad agreement and discussion about
was that traditional kind of ROI frameworks did not work for this.
And it was interesting.
Basically, they wanted to figure out how to measure ROI.
but their intuitive sense was that these things were obviously having very powerful impacts
that could be improved, but they were not willing to throw them out with the bathwater
because all old metrics didn't fit. And so they were all looking for new systems that could
better fit these tools. So I think that this is going to be a big journey this year. But boy,
I'm very encouraged to have leadership go into that, not saying does it fit sort of like old
heuristics, but how do we design new heuristics that actually match what we're doing so we can
tell if we're improving, how well it's working, how well system A versus system B does, and ask that
sort of questions rather than just kind of bundle it into thumbs up, thumbs down, should we or
shouldn't we? I think there's such an assumption that, you know, these things are happening to your
point that so much of this is table stakes that they have to figure out how to go beyond that sort
of layer one analysis. Speaking of which, I think the last point that I want to hone in on is this
efficiency versus growth thing. Obviously, this is a huge sort of bully pulpit kind of thing for me that
I talk about a lot as well. And my point has never been that you should.
shouldn't do the efficiency thing. It's just that that really is going to be table stakes.
It is, you're not going to get gold stars for having 50% more marketing content output when
everyone has 50% more marketing content output. That's just the way that it's going to be.
You know, I had a conversation with a professional services firm who had just gotten out of a meeting
with their biggest client and the biggest client told them to their face that they expected them
next year in 2026 to do the exact same amount of work for 50% of the price. And it was now their job to go
figure out how to do it. You're not being rewarded for being clever about using AI. It's just
what you have to do now. And that's why it's, you can't just do the efficiency thing because
then you're just going to be at the exact same relative position to everyone else. You have to kind of
think broadly about where new competitive opportunities lie. And that's really the sort of blue ocean
space that is opportunity. I think that the hard lesson is you kind of just got to do it all.
There's not an either or here. It's just about doing everything. That's why I like your sort of
portfolio approach idea that makes do everything, which is my.
invocation maybe a little bit more manageable for people. Do everything in a smart and balanced way. And by the way,
in most cases, the big bucks are hidden in the goals. Think, for example, on the long tail use cases,
where AI has a huge unlock there of multiple upsells and stuff that you never ever had the bandwidth
to do. And all of a sudden, you can start deploying agents and you have so many millions hidden
just in things that you never prioritize because for humans, it never made business sense to go after.
Yeah. Super helpful. Again, I'm excited to see how this hits and where people want us to go deeper. But thank you so much for this three-part series. For those of you who are listening, please use Spotify comments, YouTube, email whatever to let us know what you think about this and what you want to hear next. And Nefar, thanks again. And see you in Slack.