Planning for Q4: Budgeting Your 2026 AI Spend

If you’re like a lot of leaders I know, you’re already thinking about your annual budget for 2026. It always gets here faster than we think. So how should you be budgeting for AI in the year ahead?

Is it even possible to know what to spend, given how quickly the market is evolving? And did you have a line item in there for a little platform called Knownwell? Just kidding.

Actually, not really. Hi, I’m Courtney Baker and this is AI Knowhow from Knownwell, helping you reimagine your business in the AI era.

As always, I’m joined by Knownwell CEO David DeWolf, Chief Product and Technology Officer Mohan Rao, and Nordlight CEO Pete Buer.

We also have a discussion with Alex Kelleher of Quantum Rise on why you should be thinking about product, not projects, when it comes to your AI mindset.

But first, Pete Buer joins us for a brand new segment we’re calling What’s Your Password and Why Is It 123456. Seriously, seriously.

Pete, I’m almost afraid to ask, but have you seen that McDonald’s apparently used 123456 as a corporate password and exposed probably mine and your data along with the 64 million people listening to this podcast?

What’s the big takeaway here and where does AI fit in?

First, pardon me Courtney, while I go change my password to my McDonald’s account. Isn’t it funny with the blizzard of technological advance swirling around us, that these Darwinian fails are still a part of our everyday reality?

Context, McDonald’s AI hiring chat bot, it’s called McHire of course, was configured with the default administrator credentials of 123456 for username and password.

In about the time it takes to choke down a big Mac, millions and millions of job seekers in dozens of countries had their personal information exposed. Implications? Well, back to sort of where we started.

Even with the most sophisticated technology imaginable at our fingertips, cybersecurity is as much, if not more, still about the shortcomings of the human at some level.

Maybe we, as a takeaway, let this be a wake up call to the folks listening on the importance of discipline around teaching and reviewing basic cybersecurity practices company-wide.

This challenge of cybersecurity, of course, has blown wide open with the potential for shadow AI all through the business and what is still currently a fairly ungoverned use of different AI tools with different datasets from one end of the company to

the other. Getting this stuff wrong has never been more costly. McDonald’s can’t be loving it right now. Think of all the legal HR tech work that has to go into cleaning up this mess, PR work.

No matter how good of a job they do, McDonald’s will have, by virtue of this slip, lost trust in the marketplace.

The reality that we’re reminded of is you can destroy a brand that you’ve spent 70 years perfecting with one freaking mistake and horrible headline.

The good news is to the theme that we’ve been discussing, we always discuss throughout time, there’s some great AI-powered cybersecurity solutions out there available to help you avoid these problems, had them off at the pass.

It seems to me you can’t spend enough time, energy, or money researching your options and making sure you have the absolutely best defense for today’s risks.

Pete, thank you as always.

Thank you, Courtney.

You know what everybody says they hate, but I think they kind of secretly love, besides McDonald’s. It’s budgeting. Oh no, you don’t love it, do you?

But we’re gonna go there. We’re talking about how we’re going to spend money, people. What could be more fun than that?

I sat down recently with David and Mohan to get their perspective on how to think about budgeting for AI in 2026. David, Mohan, welcome back. It’s been a moment.

We had a little summer break and now we’re getting back to it. That also means we are nearly to 2026 and AI budgets are finally becoming real line items. It’s happening, but here’s the problem.

Most executives I talk to are either throwing money at tools without any kind of plan or freezing spend altogether because they don’t know where to start. There’s no history for this. There’s no trend line.

So today, let’s break it down. How should leaders think about budgeting for AI in 2026? And what’s a smart allocation strategy?

The first thing that comes to mind is the Shopify CEO’s email, right?

So it’s kind of looking at all your head count and saying, what can AI do for you? Right? So just sort of looking at more holistically and not looking at just tools and such, really looking at the organization comprehensively.

I think 2026 also for many companies is going to be an year where you go from initiatives to building capabilities using AI. So I think that’s a transition that needs to happen.

There are always with budget, there are rules of thumb around 70, 20, 10. 70 is your core capability, 20 is your domain tools and 10% you take some smart bets.

Those sorts of rubrics that you can always use, that’s always a good place to start, but then you got to look at your own business and improvise on that.

Yeah. I’m going to give a little bit of an answer you’re probably going to be mad at. It’s not a fair answer.

But I think the wrong question is about AI budgets. I mean, I just think, haven’t we been talking forever about how we need to solve actual business problems, right? And yes, the way we’re going to do that, the how is with AI.

But I think where companies make mistakes is where they go do AI for AI’s sake. And so, yes, we need to be investing. I love Mohan’s point about developing capabilities.

But I would be looking at my business and I would be saying, what are the investments the business needs?

And then I’d be stepping back from that, looking at my strengths, my opportunities, my weaknesses, my threats, all of those types of very traditional things. And then I would say, okay, now, which of these can I solve in an innovative way using AI?

And then I would be applying AI to very real business problems. And if you take that approach, I don’t know if you end up with an allocation for AI. Ultimately, yes, that’s where some of the money is going to go.

But you’re going to end up with business-oriented results. And what are we hearing now? We’re hearing now more and more companies are actually caring about ROI, right?

And I would say of all the CEOs that I talked to, I’m hearing more and more in the last couple of months, really since, I don’t know, I would say May of 25 here.

Companies are really starting to look at headcount because ROI hasn’t come through in other ways. And so they are looking at it as a replacement.

That’s just a sign of companies really craving, not just experimentation, not just an AI initiative for its own sake, but actual business payoff.

Yeah, I think that’s so interesting because you’re really saying, hey, it all starts higher than this, which absolutely makes sense.

But I think there is, especially as it gets broken down into different departments where the rubber meets the road and you’ve got to put something down.

And what you don’t want to do is be left behind, or have this opportunity to pursue this thing that’s perfect. But crap, I don’t have any money in the budget. No one wants to get there in 2026.

And it is a lot to keep up with, a lot to research, to really feel confident going into next year that you have thought this through well. So your advice is, hey, make sure you don’t skip the foundational part of budgeting.

Make sure that you’ve got your ducks in a row and that you’re really solved.

Solve real problems. Don’t solve the phantom AI problem that isn’t really your problem, right?

Yeah, just solve real problems, which kind of translates, you know, you’re talking about budgeting, right?

It’s a lot of bigger companies, they start around this time, you know, as the NFL training camps are starting, that’s when they start doing budgets, right? So you’ve got to really tie budget to KPIs, right?

It’s nice to think of use cases and all of this that we’ve talked about, those are important, but ultimately, it has to be tied to KPIs.

If you’re in FinTech, it’s about reduction in handling time for a loan application or decisioning accuracy or whatever it is.

These are the real KPIs that you’ve got to deal with, and you’ve got to tie the AI spend to making sure that the right KPIs are moving in the right direction. So that’s going to be the art, I think, in 2026.

I’ll also say something that’s a little controversial because we’ve always advocated to start small, but that was so last year.

You’ve got to start looking at data and your infrastructure, and we’re talking about not all businesses, information businesses, primarily here. You’ve got to start thinking of this as a capex spend.

So if you invest in good infrastructure, good data, you’re going to get a lot out of AI in terms of the bank for the buck. There’s always little tools that you can do for individual productivity.

But from moving the company fundamentally perspective, you’ve got to think of this as capex expenditures. It doesn’t mean you have to build everything. You can always buy.

There are fabulous tools out there that are coming out. But one way or the other, you’ve got to start thinking of this as in the category of capex expenditures so that you can get a bigger ROI at the end.

So I talked a minute ago about, obviously, there’s the things you do as an executive team, as a CEO together to set the foundation, and then you get these departmental budgets.

I’m curious if it might be helpful for people listening to get y’all’s thoughts on, I dropped a y’all there, to get the two of your, I can’t, what do you say if you don’t say y’all?

I say y’all all the time, what’s wrong with that?

I want to get y’all’s thoughts on which department you think should have a larger cut of allocation towards AI spend than others.

I think I’d probably start tackling this by saying which departments are the most ripe for adoption and for disruption. You know, I think marketing in a lot of ways is very, very ripe for disruption in an investment.

And we’re hearing about mature use cases and payoffs and ROI in a lot of different ways. And I would be thinking pretty big in marketing, for sure. I think IT departments obviously are more tech enabled than others.

But, you know, those investments may have been made in 2025, probably should have, right? If your software engineering teams are not using Windsurf and are not leveraging tools, you’re really behind.

So you should have a big catch up there, but it may not be new to you, right? But if you look across the trend of those two in marketing and in technology, what you step back and you begin to see is it’s about knowledge work, right?

And so then as soon as you realize, hey, this is about knowledge work, you can start to say, wow, there’s pretty big investments that we could be making to take a lot of rudimentary work out of our finance and accounting.

And if I’m a professional services organization, some of the work that I’m doing around client deliverables, what could I do there, right? So it trickles down from there.

But I would be looking at the biggest investments, probably in marketing and technology.

Do you two think this is a harder year to budget for versus, let’s say, 2019?

What happened in 2019?

Nothing. That’s why I said it. Nothing.

Nothing happened in 2019. I could look at my budget from the year before, add 10% and say, hey, we’re good. Here you go.

I mean, obviously that’s not the way anybody budgets, but it kind of is also how people budget. You kind of take the history of what you’ve done, what you’ve spent. There’s some areas, but you can’t ask for 100% increase.

So you try to figure out, okay, what are the levers I’m going to try to pull? And you submit the budget. Do you think we’re in a harder place to budget than we’ve been in the last few years?

I think so, because we’ve now gone from curiosity to real strategic advantages with using AI.

So it’s no longer the realm of pilots. Pilots are like, it was so last year, like we said, or two years ago. So these are now real production systems where we are trying to move.

And again, we’re talking about specific types of companies, whether it’s professional services, or financial services, or education, information-oriented businesses. So I think your question is right on.

It’s much harder because you’ve got to now justify the auto eye on the spend. I think the majority of the expenses will go into building core capabilities with data pipelines, and buying foundational models, and that’s where it will go.

But the question is, what do you do with that? Then it becomes the domain-specific workflows. So however you cut it, you can allocate it all to domain-specific workflows.

That’s where I think majority will go if you take the infrastructure into account as well. This could be, as David was saying, in marketing, sales automation, customer success, underwriting, whatever are your major functions in the company.

I think that’s where the majority of the budget will go. There’ll be a very small budget on some of the bets that you can take with autonomous workflows and synthetic data and those sorts of things.

But that’s still going to be the minority 5-10 percent.

I totally agree with Mohan there. I want to add one thing though that I think flips the script a little bit. I also think the ROI from AI is there to be able to have monumental impact.

So if I was looking to make big investments that I couldn’t afford in AI, and knowing that it’s this inflection year, I’d be looking for quick return that pays for itself in this year to fund more.

And I think in that regard, the availability of the return very quickly, it makes it easier to budget, because in most years where you’re just looking at last year and nothing big has happened, there aren’t a lot of pennies to squeeze out, right?

And I think there’s opportunity to realize return very quickly with AI that we can’t lose sight of.

Do you feel like some executives, some CEOs are going to kind of force maybe what the Shopify CEO said in his message and just be like, there is no personnel increase, figure out how to do it with AI. And so the budget shifts in a way that it hasn’t.

You just don’t get any personnel and you’ve got to figure out, okay, how am I going to fund AI without knowing exactly how I’m going to replace what I normally would have done with people?

I definitely think there will be that. I also think the other interesting dynamic is I think there will be a lot of increased expectations on profitability.

And people will say, we’re getting all these savings, why isn’t it hitting the bottom line, right? It’s not like you can just recoup it and spend it on something else, right? So I think those two dynamics are going to be big dynamics this year.

Yeah, very interesting.

David, Mohan, thank you as always for everybody listening. Hopefully this gave you a head start if you are already budgeting or about to start budgeting as you think about how to win in 2026.

Happy budgeting.

We talk a lot on this show about how you can use AI in your professional services business. Do you want the playbook for scaling and growing your service company in this AI era?

Good news, you can download our brand new white paper for AI powered strategies for scaling professional services. Grab it now at knownwell.com/scalingwhitepaper. Alex Kelleher is the CEO of Quantum Rise and the former global CMO of Deloitte Digital.

He’s also a founder with several successful exits to his name. Companies he’s founded include Magnetic, which was acquired by Deloitte, and TouchClarity acquired by Adobe and Omniture.

Alex, welcome to the program. We’re so pleased to have you.

Very pleased to be here. Thanks for having me. Look forward to the chat.

I was hoping to start with a little bit of background, if I may, to help set some context for the rest of the conversation.

Could you tell us about Quantum Rise and your role and where AI fits in?

Yeah, sure. So, Quantum Rise is a year old. We’re growing fast.

We’re based in Chicago. My role is founder and CEO. In just as a very quick background, spent a life building startups in the machine learning and data space.

And then a little while at larger companies, like Deloitte, you know, seeing the world of kind of professional services and consulting at scale. Quantum Rise is at its simplest an AI consultancy.

What that means is we help people understand what the potential is and then help them get that potential out of the available technology.

As you’ll learn here shortly, our research team does a fair amount of work stalking you in the background. And Kayla was so kind as to provide some additional information as well.

One of the things that we attach to your website is this notion of products, not projects.

Right.

And rather than steal any of the story, can I just tee you up with the term and have you tell us kind of why it matters and how it factors into the work?

Sure. Like all good terms, it has to start with the same letter. So that’s, of course.

How else would we remember it?

Occasionally, we squeeze PowerPoint in there too.

That’s a lot more peas.

It’s our profit as well.

Profit, yeah. It’s the royal flush. The truth is in a world that moves this fast, the only way to kind of stay true and keep on top of it is to be agile.

Of course, people who develop product quite a while ago, figured out agile was the way to go, right? It’s delivering in short sprints, if you like.

Delivering something tangible, testing that, and then changing the plan that you have going forward, and then keeping a really good handle on the performance, KPIs, measurements, whatever it is, so that you ensure you’re doing the right thing.

And so what that avoids is that you spend months or even years sometimes, and then kind of lift your head up and realize that you’ve reached the wrong solution.

And that, you know, we see that all the time, everywhere, of course, it isn’t necessarily an easy path, because I think human beings just feel very comfortable setting an outcome, written statement of work, putting a bow in it, did we get there or

not. It’s easier to consume, I guess, and plan for. But really, success comes out of loosening a little bit the goal and the timing and just being more agile and developing as you go.

And that’s, it’s more like you would build a product than deliver a project. And that’s really what we try and do with our clients.

And projects really are the heritage of professional services. Are buyers ready to buy product approaches over project?

I think more and more ready. And, you know, setting the guardrails and everybody being clear about how you measure outcomes is a really important part of it. And everybody believing that in those outcomes, right?

Because it’s kind of easy to pretend that it was worth doing. So spending a lot of time at the beginning saying, okay, why are we doing this? What’s it worth?

You know, to the dollar and cent. How do we all feel comfortable that it drove that return? It made that cost saving or drove more revenue, whatever it might be.

And then let’s go after that and keep measuring how we’re doing and keep iterating, keep pivoting.

And then in the world of technology, you know, because more and more new technology is released, let’s adopt the latest thing as it comes out, if it makes sense, and include that in how we do it, rather than trying to predict what’s going to come out

in the future. And that’s, so I think we’re getting there. You know, not always, not everywhere, but we will get there.

As you know, our listening audience is heavily made up of professional services leaders, so I suspect there’s learning for the group here.

Could you give us an example kind of how you took the product approach rather than the project approach, and the end result for a client, a real concrete outcome, was better than it would have been had you gone the old fashioned route?

Yeah, sure.

I can’t reveal names in the examples necessarily, but I’ll give you an example of a essentially a media company that we’re working with, that manages advertising across channels, and this world has been around and has been being optimized for a very

long time. How do I, and the challenge is at its simplest, how do I show the right ad to the right person at the right time? There’s a very complex problem.

The good news is there’s lots of money behind it, lots of tech that exists to utilize, and a lot of the latest generative AI and related technologies kind of pointed at that space.

It’s about content creation, it’s about adapting and generating lots of new content. The approach of this client has become, and again, there’s an initial period of trust that you have to get through, which is typically when you do the project.

So the client trusts you, delivers some outcomes, you move on. Now moving into a way of working that is very much more product-like. So we sort of trust that we’re heading for the right outcomes.

In the world of media, those outcomes are quite measurable. Have you reached the outright audience? Did they watch your ad?

Did they respond to it? Do they buy more of your product? Those are quite measurable things, which makes this a little easier in this industry.

But the problems are still very complex, and the technology that was just released at the beginning of this course is probably relevant to that problem. So it’s great.

Now we have the trust of the client, we can hop onto that technology, we can test it. We can see if it improves or doesn’t do anything, or maybe it makes worse the solutions that we’re building.

Whether we’re developing software for them, which we actually are in some instances, or we’re just doing the services implementation piece, we change our approach to how we solve that problem for this client all the time.

That would have been a very uncomfortable thing. Within a project like a waterfall structure, right? But now, the question is, are we driving better results than we did last month?

It’s very pure, right? Obviously, there’s a budget constraints, there always are, people have to budget for next year, so you’re not just going crazy around it. It is within guardrails.

Then in certain situations with this clients and others, we’re putting our own skin in the game and saying, listen, we’re in a good position to say that we think we can continue this improvement, and we’ll be motivated by that improvement rather than

you paying for our time. That’s a big shift for professional services, obviously. Again, in the media industry, people are more used to giving you a share of revenue, affiliate schemes, and all the performance-based marketing.

But I think other businesses will move into this model soon too. Then all our interests are completely aligned, and our interest is let’s drive the greatest conversion rate for this campaign. That’s all we care about.

We’re going to charge that way. The client’s totally aligned, because we’ve driven it for them. This is our purest version of our model, and I guess I’m excited to be working with that client and in that industry.

It’s a smart way to approach the work.

You’ve got a couple of years with Deloitte Digital and a CXO role, CMO, I guess. And you’ve founded and sold multiple technology companies.

Are there lessons from those, frankly, fabulous learning experiences that have shaped how you go about running your business now?

How long have you got?

Five, as long as you want.

Yeah, obviously, lots and lots of lessons. I think everything we’ve talked about so far is born of that.

I think there are lessons around building a company and culture and all sorts of things and then there are lessons around how you provide the best value to clients and go on that journey with them.

It is an outside party to their company, and it’s where you actually are operating as part of their company, and that’s really the interesting professional services thing. I mean, if you do it well, they just feel like you’re part of the company.

How you get that mix has lots of elements, again, from culture to delivery models, to building trust and openness and all those things. I think that probably when I was younger, I would have tended towards, don’t worry, this is a black box.

We’ll keep it a little secret how we’re doing this. We can’t share it. I now understand that being an open box is much better for everybody, including clients.

Wins you that trust, gets them on the same page as you in terms of understanding what’s going on, which means that then you can do much better things than presenting a black box.

Lots of lessons really about how you drive results for clients, I think, predictably. And then, yeah, we could talk for hours about sort of start-up building.

I think you hit the big one. You have on your site some posts recently that look at the notion of why AI is a reckoning for traditional consulting. Can you share the premise?

What’s the threat? And what do smart consulting firms do to respond?

Yeah, I think the threat is probably obvious to everybody in the industry in the sense that a lot of the work that we do is smart processing of a lot of information, facts, data into advice and ultimately delivery.

A lot of the elements of that can now be done pretty well by machines. I’m sort of cheating my words carefully because it’s easy to oversell the capabilities. Easy to understate the risks and the issues.

I spend my life obviously, and have spent my life extolling the virtues of it, but the hype pendulum has swung wildly in the other direction.

But if a client says, here’s the history of the company and loss of data and in a certain sector, what do you think we should be doing? How will AI impact it? What are the top priorities?

I can get a lot of that starter work done in interpretation and gathering done by machines today, obviously. And I can even to some extent generate the PowerPoint deck that presents that.

And if we draw a line from here into the future, at what point does that 80 or 90 percent of what we used to do? So clearly, we need to rethink some of the effort that we put into the outcomes we drive.

And I think increasingly, the focus for the humans is to enable it to, for us to do things quicker, for us to absorb larger amounts of information more reliably, to focus on the kind of strategic thinking bit.

There are therefore lots of things you have to think about. So how do I do my daily job? How do I build a team?

Now that some of that sort of back office research work is going away, potentially to some extent, how do I rethink the roles of the team?

All the way through to the delivery piece, which we’ve talked a little bit about, which is what are we actually here to do and how do we align our interests with clients?

My model cannot be anymore charging by hour, which has always struck me as a misaligned priority with clients. I need more hours, you’d rather you pay for less. In that, obviously opens the door to different pricing models and so on.

So those need to be thought through, clients need to be educated and get comfortable with paying that way.

If the tools that we’re talking about increasingly put into the hands of capable people, the ability to do a lot of the work that consultants would have done in the past, does that make the decision around consulting purely one of resourcing?

Like I’d rather have someone else do this work than have my people spend their time on it? Or is there something else that still definitively distinguishes the role of consulting?

I don’t think that most companies should do what they’re really expecting us to do in-house. With the exception maybe of very, very large, very scale companies rather than the top of the list.

In a think, the speed of change in the market makes that an even more pressing need for external vendors or whatever you want to call us. So that’s great news for us, I think. How we deliver what we deliver will change.

The output probably won’t change that much and hopefully, we’ll get better and quicker and more efficient. We can spend less time building PowerPoint decks and more time actually doing stuff. We can deliver more incredible solutions.

But I think that as human beings, the natural tendency is to be somewhat inward-facing to your company.

You are restricted with that experience set, bringing in a partner from our world who has experience across more than just me, is always going to be valuable, whatever that looks like. And that basic premise, I think, will continue to be true.

And we can afford to, not that we do today, we can afford to just employ people to stay on top of every model release. You got a client should never do that because it doesn’t scale across one client, whereas it does across multiple.

Alex, it’s been a pleasure. Thank you so much for spending time with us.

It’s a lot, Pete. Cheers.

Thanks as always for listening and watching. Don’t forget to give us a review on your favorite podcast player of choice. At the end of every episode, we like to ask one of our AI friends to weigh in on the topic at hand.

Hey, Perplexity. Today, we’re talking about setting AI budgets for 2026. I really like your deep research tool.

Can you make any recommendations for us?

Thanks for the compliment. For 2026 AI budgets, I’d recommend prioritizing investments in explainable AI, robust data security, and scalable user-friendly research tools, so your organization stays both innovative and accountable.

Also, consider budgeting for continuous AI literacy training to empower your team to use these technologies effectively.

Now, you’re in the know. Thanks as always for listening. We’ll see you next week with more AI applications, discussions, and experts.