HITT – The AI Opportunity You Cannot Quote

Okay. So today’s title of the webinar is AI opportunity you cannot quote. So let me kinda go ahead and start with a really simple scenario that comes up a lot. Just imagine that one of your customers calls you and says, hey.

We need some help with AI. That’s great. What do you quote then? Because they call you and they say, we need a, let’s say, new contact center platform.

Platform. We know what to do. We typically ask how many agents, what are you using today, what channels, what integrations do you need, what are you trying to fix. Fantastic.

We can start building an opportunity.

If they need something like UCaaS or connectivity or cloud or even security, mobility, like, whatever it is, we have trained ourselves to recognize these opportunities quickly. But now customers are like, our CEO says we need an AI strategy, or we have fifteen different AI projects happening. Nobody knows which ones we should actually be investing in. Shadow AI is a big problem. We’ve got multiple departments using multiple licenses. We don’t know how much we’re consuming. What is going on?

Or the very popular one these days, which is we want to build an AI agent that can do all these different things.

And everybody in the company is using just something different, and the security team is completely losing their minds. Or, okay, here’s my personal favorite.

We bought Copilot. Now what do we do? And, like, in this scenario, what exactly are you quoting? And that’s what I’m gonna talk about today because I think there’s a massive AI opportunity sitting sitting in front of you that we are still really learning how to recognize.

And the interesting part is that it may not have a skew like we are traditionally used to selling. So this is basically the business we’ve all grown up in. We find the business need. We find the product.

We find the supplier. We get a quote, and we close the deal. Obviously, I am way oversimplifying this. Right?

No deal is this easy. There’s discovery and solution design and everything else that happens in between all these different pieces. But at the core, a lot of our industry has been built around identifying a technology requirement and then finding the right technology to solve just that. And we’ve gotten really, really good at it.

Like, you guys are all rock stars. Now you hear the words five hundred seat contact center, and immediately your brain starts going through all of these different suppliers. You hear, say, Microsoft environment, remote workforce, security concerns, and you’re already thinking about where that opportunity could potentially go. Well, AI does not always work like that.

Sometimes there isn’t a prob, there isn’t a problem. There isn’t a requirement. Right? There there’s not a particular, I should say, product requirement just yet.

There is just an issue. There might be just a desired outcome. Maybe there’s just a a general problem. And I think that’s where some of us are accidentally walking past these really good opportunities because what we’re waiting for is for the customer to tell us what they want to buy.

And the majority of the time with artificial intelligence, they don’t know. And so in a lot of cases, that’s exactly why they need all of you.

So think about how quickly this conversation has changed. I mean, it wasn’t too long ago. Right? Customers were asking us, hey.

What is all this ChatGPT stuff? What is generative AI? And then it became, well, what should we buy? Everybody wanted Copilot.

Everybody wanted ChatGPT Enterprise. Everybody wanted some kind of chatbot. And then it became, okay. Well, what can we actually build with this?

And now I think we’re entering a much more interesting phase where customers are starting to ask questions like, how do we actually make it work? It’s not in a demo. It’s not in a poo a POC or proof of concept. It’s not because somebody just showed the CEO something really cool at a conference.

It’s more around how do we actually make AI work across the organization? How do we connect it to our data? How do we secure it, govern it, integrate it into workflows? How do we actually get employees to use it?

And how do we measure whether it’s, well, doing anything? And that is such a different conversation.

Right? Access to AI is not difficult anymore, and it used to be. Now you can get access to really, really incredibly powerful AI in about thirty seconds or less depending on your Internet speed. But operationalizing it is the hardest part.

And operationalizing AI creates a whole different category of opportunity for all of us. Now this is how I want you to start thinking about AI opportunities. Okay? The product is the easy part to see.

It’s above the water. There’s the Copilot license. There’s any conversational AI platform out there. There’s an agent platform, the LLM, OpenAI, Anthropic.

There’s the application itself. That’s where our eyes naturally go because that’s what we’re used to selling. But if you look underneath it, before that product ever delivers a

business outcome, somebody may have to figure out the strategy. Somebody has to deal with the data.

Somebody has to integrate it. Somebody has to think about security. So think governance, development, automation, all of these different things. And then even after you’re done with all of that, someone has to get the organization to actually adopt the darn thing.

That’s the iceberg. And here’s the mistake I do not want all of us to make.

We are fighting over the thing above the waterline when there’s an entire services opportunity sitting underneath it. And sometimes getting involved underneath the waterline is actually what leads you to a sale. So when I say AI services on here, you look beyond the product. When I say AI services, I do not mean one specific thing. There’s actually an entire ecosystem developing very quickly around this. And at Partner Summit, if you joined my early bird session, I actually broke this into six different buckets. The first one is strategy and advisory.

This is simply what should we do? What is our AI road map? Are we ready? Which use cases actually matter most to us, and where should we start investments first?

Then there’s a generative AI piece. How do we actually implement and customize all these different tools we’re using? How do we ground them in company knowledge, and how do we build applications around them?

Then there’s AI agents and automation. By far, this is the most popular. Think agentic, agentic, agentic. It’s things like how do we build something that doesn’t just answer a question, but now can actually take action, thus making it agentic. Then you’ve got machine learning and analytics in this next piece. It’s how do we use all of this data that we have or data we’re gathering to predict something or to identify something or make a better decision?

And then there’s the governance and security piece. Do not forget this one, folks. This is the the one that’s becoming a really big deal.

What are employees putting into these tools? What models are we using? Where is the data going?

What is AI actually allowed to access? What policies do we need? Do we how do we manage risk in this environment? Right?

And, finally, there’s the adoption enablement piece. So I talked a little bit earlier about getting people to use the thing. Well, here’s the thing. You can spend a fortune on artificial intelligence and then get absolutely nothing out of it if nobody changes the way that they work with it.

So here’s what I want you to remember about this slide. Every one of these questions can actually become a paid engagement, and many of them can eventually lead to additional technology opportunities.

Now here’s the problem. Your customer is probably not going to call you and say, hey. I’d like to purchase an AI readiness assessment, please. In fact, I have yet to hear that.

They’re not going to say, hey. Can you find me a provider that specializes in AI governance framework development? Probably not. Because that’s just not how customers talk.

They’re going to say things like, we are all over the place with AI.

Our CEO wants an AI strategy. We want to build an agent. Security is freaking out about all this AI stuff. A big one, we bought Copilot, and nobody is using it.

Our data’s a mess. Our systems don’t talk to each other. Now they might sound like complaints, but they’re actually not. If you think about it, they’re actually opportunities.

And the skill we need to develop is translating customer language into what I like to call opportunity language.

When someone tells you, hey. Our data is a mess. Do not just nod and move on even if you might agree with them. Okay?

Pull the thread. Why does this matter now? What are you trying to accomplish that your data is preventing you from accomplishing? What systems are involved?

Who owns it? What’s the business impact? Because the customer is very often telling you exactly what they need. They’re just not giving you the name of the solution that’s going to help them with this particular issue.

So I’ve done some homework for you, but here are some sentences, seven in particular, that should make your ears perk up. So I want you to start listening for some of these sentences. It’s things like, we don’t know where to start. Well, my ears immediately go to, boom, strategy, readiness, road map.

We have too many AI ideas. Great problem to have. Now we’re going into use case prioritization, road map, business case development. Another one is our data is not ready.

Hey. Let’s head into the road data cloud and architecture. Chad, muck, and fuss all day.

Another one is we want to build an AI agent.

Folks, do not immediately start sending them conversational AI demos. That is, like, the worst thing you could possibly do. This is potentially things like development, integration, automation before we even figured out what the platform is that’s involved.

Another one, security is freaking out or security in the same sentence as AI. This is not a reason to stop the AI conversation. I know a lot of us get a little worried when it comes to the security piece. But security, folks, is where it’s at right now.

This is an AI security risk management and compliance opportunity. Let me give you a quick story here. A very true story. A couple months ago, I was talking to a CEO, and I said, how far along are you in your AI journey?

What have you accomplished so far? He goes, well, I’m really proud. We’ve come up with our governance policy. I go, wow.

You did that on your own. That’s amazing.

Who did it for you? Or tell me more about that. He said, well, I had my executive assistant create it in flawed last night, and here it is. I thought, oh, boy.

Right? So, this is happening, folks. They’re trusting AI to write the rules for AI. So, when you think security is not of concern, it absolutely should be a concern.

The next one is we bought Copilot. Nobody’s using it, or we feel like we’re not using it to its fullest extent. This is the adoption play, the enablement play. Yes.

We have services in here that quite literally go out to a customer on-site and train your clients’ users on Copilot. This is change management. Maybe it’s workflow redesign. The last one here is our systems don’t talk.

This is purely an integration automation, say process optimization, any Asian kind of play here.

But if you notice, there’s something very interesting about every single one of these. Not one of these customers is asking for a product, but every single customer just gave you an indication of where to go with the conversation. So this is the new muscle that I really want us to build. Alright?

So let’s take this one because it comes up all the time for the sake of just a simple example. Lots of clients go, hey. We wanna build an AI customer service agent. It’s very straightforward.

Let’s deflect a lot of those interactions from humans and see what interactions from humans and see what AI can handle so that humans can focus on the more complex stuff. The easy response here is great. I know some conversational AI companies. Let me just get you in front of three or five of them or twenty of them now, and let’s get you a demo.

Folks, don’t do that yet. Do not do that. Ask the questions. It goes so much deeper than this.

Ask things like, hey. What does the agent need to know?

In other words, what does this AI customer service agent need to know? Right now, we’re starting to talk about knowledge and data. Great. What does it need to connect to? CRM, ERP? Is it billing, scheduling, order management? Now we have the integration conversation.

And then the question of what is it actually allowed to do? Can it do things like cancel an order? Can it issue a refund? Can it get free money?

Can it give discounts? Can it give promos? Can it schedule an appointment? Can it change something in an account?

Right. Now we have automation built in.

Another question, how does it know who the customer is? Great. Now we have the identity conversation. Okay. What information can this customer service agent access? Security.

What is it allowed to say? Who decides what it’s allowed to say? Governance. And where is all of this going to run?

Cloud. And then what happens when AI cannot solve the problem? Well, now we are right back into the CX conversation. So the customer gave us one sentence, which was we want to build an AI customer service agent.

But I count at least eight different potential conversations sitting in just that one sentence. And that is why I keep pushing everybody to think horizontally about AI. It’s a horizontal sale, folks. It’s no longer in the silo of one department.

It’s very quickly interconnecting all of them. And the fact of the matter is the AI opportunity is very rarely just artificial intelligence, if that. So stop asking. What AI are you looking for?

This is the one question I would love for us to stop asking. What AI are you looking for? Yes. I have heard it before, believe it or not, because you’re putting the burden on the customer to design the solution.

And, frankly, if they already know exactly what they need, exactly what platform they want, and exactly how they want it implemented, we are super late to the game. Instead, try asking things like, what are you trying to change? Are you trying to change revenue? Are you trying to lower cost?

Are you trying to change the customer employee experiences? Right? Where is work unnecessarily manual? Where are your people getting frustrated or your customers getting frustrated?

What’s consuming everyone’s time?

Where or what can you not scale your operations? Here’s a great one. How about what AI experiments have you already tried? Because you want to figure out where on that AI maturity scale your customer is at.

If you haven’t checked out our tech trends report, folks, check it out. And I’m gonna ask the marketing team to pop the, link to the brand new tech trends report in the chat to check it out. But there’s an AI maturity scale on there where you can place your customer based on where you place them. It’s gonna tell you kinda what to do next.

Right? What is actually stopping your customer from moving into production if they happen to be in an AI opportunity or initiative now? And those questions uncover opportunities because our job is not to figure out which AI logo belongs on the slide. Our job is to understand what work actually needs to be done.

And, yes, Mikey b called it out in the chat. Yes. Mikey b is here, so flooding with flooding with questions. But are they in the trough of disillusionment?

Absolutely. For those of you who have seen the, the Gartner graph that I’ve shown many times is absolutely they they might be in this trough of disillusionment where customers do not know where to go at this point, and that’s where AI initiatives ultimately go to die.

So I know what some of you are thinking right now.

I don’t know how to do any of this. Great. Let’s start there. You do not need to code.

You do not need to build models. You do not need to design databases. You do not need to train an LLM. You don’t need to write an API, folks.

This is not your job.

I mean, if it is, like, that’s awesome. Go do that. But from a tech adviser standpoint and selling AI, your job is to recognize the opportunity, and then discover what’s actually happening, and then connect the customer with the right expertise. And ultimately, what you are doing is you’re helping orchestrate the solution.

You do not have to know how to build the AI, But you have to know that there is a project or there is an initiative, and that’s a completely, completely different skill set. And this is why I think AI creates such a massive opportunity for us. Like, look at the model on the left. So before customer needs something, you find the product, you facilitate the transaction, and there’s nothing wrong with that.

It’s a very we kind of built, right, this very successful motion in this industry. But look what happens when we move upstream. The customer does not start with a product. They actually start with a business problem.

And now you as the adviser, you are sitting in the middle of a much larger conversation. Maybe part of the answer is strategy, or part of it might be technology. Part of it might be services that I talked about earlier.

And maybe there are three suppliers involved. Maybe there’s a services partner as well as a platform provider. Quite possibly, if not every single time, security gets pulled in. Cloud gets pulled in.

Gee. Let’s talk about the infrastructure for a second. What’s gonna support all of this? Right?

Shout out to my my guy, Graeme Scott. Right? And and then maybe CX gets pulled in into all of it, but all of it is being orchestrated around one thing, and that is the business outcome. That’s a much more valuable position for you to occupy than simply being the person that someone calls when they’re ready for a quote.

You have to start digging deeper. And this is the big kind of commercial takeaway. Right? The earlier you get into these conversations, the bigger the opportunity becomes.

And if you show up when the customer says, hey. We’ve selected this product. Can you get us pricing? Well, your universe is actually pretty small, and you’re at the end.

But if you’re there when leadership is asking things like, how should we use AI to transform customer service? Oh, this is fantastic. Now you can influence strategy that can lead to services, that can expose security requirements, that can expose infrastructure requirements, which also influences the platform they go with, which ultimately determines the product.

And that’s why AI services matter even if professional services is not historically what you have focused on.

Folks, this gets you upstream. Upstream gives you influence. Get in earlier. Find more. Sell more.

So I’m gonna give everybody homework. It’s really, really simple. Pick five customers. Don’t pick five AI customers. Just pick five good customers. You know, the ones that, like, you can try stuff on and it’s no big deal.

Right? I do this to my family all the time. I’ll go to family dinner. I have a a brother-in-law who has his own company, and I do a little bit of ad hoc role play without him realizing it.

Poke around, ask him questions about AI, and try some of these theories and strategies on him to see if they work. He’s a business owner, pretty deep in tech, wants to go down the rabbit hole of AI. And, of course, I go right down there with him and try to guide him and figure out where his brain’s going. And by the way, completely validating on everything we’ve talked about today.

So pick five good AI pick five good, five good customers. Ask them one question.

What is the biggest thing preventing your organization from getting more value from AI today? And then be quiet.

It’s gonna get awkward. It might get awkward. In fact, I’m probably sure it’s gonna be awkward. Just be quiet and see what they say.

Let them answer. Do me a favor. Do not ask this in an email. Do not ask them an email.

Call them live or meet them for lunch or whatever it is. But let them answer. Because I would bet the answer is not we just need another AI license for x y z. You’re going to start hearing some crazy stuff like strategy or maybe even something about data or I’m worried about the security of this stuff.

Or does it integrate we have an integration problem, or how do I govern something like artificial intelligence, or my people don’t wanna adopt it, or we tried it a few times with automation. You’re gonna start to hear organizational problems. You’re gonna start hearing business problems. And somewhere inside that answer is probably an opportunity.

Five customers. One question. Go try it.

So I’m gonna leave you with this. We have spent our careers learning how to recognize technology opportunities. AI now is asking us to recognize something significantly bigger, and a customer might have a real problem. They might have, executive sponsorship. They might have budget. They might have urgency. They may desperately need our help, and they still have absolutely no idea what product or service they need.

That is not a bad lead, folks. It is not a bad lead. This is potentially the best lead you can get in this time of artificial intelligence right now. Because we get the opportunity to be there before the architecture is decided.

I have yet to come across more than maybe ten in outside of the Fortune five hundred who really have an idea of what architecture looks like and what AI use really looks like today. Because a lot of them are still very much in that experimentation phase, and not all of them have a team of AI engineers or AI PhD leaders or what have you. Right? So the next time a customer tells you that they need help with AI, do not immediately ask them what they want to buy or what they’re interested in. Ask them what they are trying to accomplish because the next great AI deal may not have a skew.

Right? So, I’m looking through the chat here. Mikey b is all over it in true Mikey b fashion. Folks, don’t forget about the tech trends report. I’m gonna pop it in again right here.

But, yes, folks, you can sell AI services now, outside of just traditional platforms and the licenses that you, you’re used to selling. And if you’re curious as to what those are, reach out to your friendly solution engineering team. Myself, Mikey B are here. Of course, my peers, Chad Muckenfest, Sumera Riaz, as well as Graeme Scott. So