The ActivateCX Podcast

The Secrets to Adopting AI in Business

• Frank Rogers • Season 2 • Episode 27

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Activate AI in Your business https://activateCX.arroyo360.com/activateCXtoday

Want to know why some companies struggle with adopting contact center AI solutions? Join Erik Smith at RingCentral and Frank Rogers in demystifying AI adoption in modern businesses, as we explore the common fears and challenges faced by organizations. Subscribe to stay informed on how education and collaboration can pave the way for successful AI integration.

Eric, what's blocking companies from adopting AI? Yeah, I think there's a few. So one, I still think that there's a fear that's pervasive. And so what I try to do is help customers just understand the technology a little bit better. Sometimes that's a missing link is just some education. So they feel comfortable. I think there's a lot of big buzzwords out there. So trying to speak plainly can help. Make up a lot of ground. And then internally as well, there's some different power dynamics from the info security team and legal and the C suite everybody's got their differences of opinions. And I think anything that we can do documentation wise, education wise, as I said, to just bring more of that information to light is a great first step, but it can certainly be challenging, especially in the contact center to navigate all of those different teams that have their, their own respective fears and opinions. So there's probably some things that we would find natively inside of any organization, right? Maybe fundamentally politics or just resistance that could be generational, that could be business function oriented and that gets in the way of that. Do you think that that primarily there's no centralized place inside that organization for ownership of this technology and maybe the customer experience, period? So I think it's a wide variance, actually. So we're seeing some customers that have established something to that degree, whether it was just designating a person to become the specialist or the overseer. We have some folks that have been recently hired that have that expertise at some companies. And then on the far opposite end of that spectrum it's, it's a bit of the wild west, which, it's good for, I think, companies like mine, where we can come in and help and be a trusted partner, you know, working with people like yourself, Frank, to help everyone just evolve as we go. Because I think that evolution in most companies needs to happen soon, or they're at a competitive disadvantage. So that's a big piece of it, but we're, we're seeing a wide array of, of different setups currently. I would imagine that also to a certain degree impacts investment, because if you look at the componentry of AI, where you might have a co pilot and a Gemini on one side, where that's really your, engine, and then you may have a knowledge base or , some container of information that's going to be the source of truth. And then fundamentally an application, we'll just throw one out there, like a Cognigy, which is leveraging all of this, metering all of this, and bringing it into a channel. That's not a place for people to be investing their own monies, like in terms of hiring data scientists, like isn't really bring on board business analysts and bring people into the organization that are cross functional to try and figure out what the problems are inside the business. Isn't that where people should be spending their time? Yeah, I'd agree with that for the most part. And maybe just to add to that, somebody that comes in that can also understand competently what they're evaluating, I think that's a big piece of it is how can we bring somebody in that has some of this requisite Otherwise, I think the, the fear of the risk of purchasing something that's both expensive and unclear if it will be successful is, is higher than it's been in the long time contact center. Technology has been around for a while. People are not generally afraid about buying phone lines. And now we've introduced a whole new way of thinking about things. And it's probably magnified by the fact that every, every headline. Is either, you know, AI is here to save the world or AI is here to destroy the world. So, that's tricky, tricky landscape right now. Both are true potentially. What are leading factors to a bad experience around deploying? Yeah, that's a good question. So I think there's a few that come to mind right away. One is, is biting off more than you can chew and thinking you're going to solve every problem, problem right away. It's not uncommon that In a sales cycle and an evaluation will be going down one path and all of a sudden they'll send us 25 additional use cases. And it's like, okay, team, we can't, we can't cure all the world's ills in one, in a week. So there's some of that going on. I think there are. Lots of people that are intrigued by AI, which is great. So maybe stakeholders within your organization doing their own diligence and learning, and then they come to the table with ideas. So one thing we really work to do is, Hey, let's define some use cases that both make an impact, but also don't take a year to implement. To implement so you can start seeing some benefit now and then we can evolve from that foundation and that really comes down to choosing the right partners and technology so that we can solve both short term and long term use cases as well. And you can evolve throughout. Yeah, I think that's probably like the number one. Thing that is a challenge. I think also not understanding that AI is not a plug and play thing. So despite all of the great marketing emails out there, apologies to marketers. This is not a plug and play technology, at least not if you want it to do much, right? There's training that has to go into it. There's a lot of thoughtfulness around use case and deployment and making sure that you have the right resourcing and time. An effort dedicated to this is, is critical. I think that probably never in the history of humankind has there been such a Delta between a product and the actuality of delivering on that product than is the subject of AI. It's fundamentally, been in the keep it simple stupid mindset, to help people wrap their minds around it. But it is, Excruciatingly detailed to bring it to life. What what do you think from the standpoint of the challenges inside of a business of bringing it to life, when do people typically run into that intersection where they encounter the complexity and it causes them to stop? We have come across companies that are gun shy now because they've gone through an implementation that went poorly. And again, the cost is going to come right. Whatever technology you buy that that bill is coming every 30th of the month. And so if you're not getting the results. Yet you've taken on this potentially multi six figure investment. I think that can cause a lot of friction internally within an organization. So I think that's a bigger hurdle to overcome is, is once bitten, twice shy versus somebody that's net new and maybe cautiously optimistic, but still willing, to go forward with it. As financial benefits of AI have a tendency to accelerate a process, maybe ahead of the organization's ability to consume it, whether it be technically or ethically inside of the business, does that override some of the, technical decision making that should be a little bit slower and discerning and, override that with speed to market. So it probably depends on the company and the industry. I think a lot of these use cases where you can see significant financial return are fairly innocuous. Even if we just took something like authentication and using either some technology like voice biometrics or multi question authentication previously you've had a human doing, I mean, that can take a couple of minutes. Somebody is fumbling around with their Info to try to get it to you or can't remember their birthday. I mean, who knows, right? But if you can even just shave a minute or two off every call and your contact center is seeing 300, 000 calls a month, I mean, you just printed some free money. I mean, it's just as simple as that. Instead of spending in dollars with humans, you're spending in pennies with AI. And so I think on that end, some of those use cases. You know, fairly easy to just start really seeing substantial results with. And I think sometimes maybe companies are overthinking a little bit when we get into the more technical and I think probably the use cases that you're alluding to,, it's a fine line. I think for the most part, especially in the contact center, there's enough guard rails in place where you're probably not going to go off the rails so badly that it damages the company or the brand. Historically. And again, I think it also depends on where this is applied to. When AI first came out, I was actually working in the social media side of the world, social media and digital technology. And we saw a lot of bad mistakes with AI answering public Facebook post and said something wild. And all of a sudden it had 3 million impressions and you just sunk the brand. Right, I think we've accounted for most of those now. So we're not seeing gaffes of that nature that could do serious. Reputational harm. It's much more in the one to one space, which has less prevalence to do something like that. So, for the most part in the contact center, I'd say you can probably run a little bit faster than you're running today. Where do you see problems and expectations setting that if a person was going through the buying process for saying, moving from something that was hyper focused around Human interaction and human channels to blending in a digital channel and a self service flow, what would be some of the things, maybe warning signs that you would want to put forth to somebody and say, Hey, if you hear these four things, beware. Simplicity of use and maybe timeline to, value is something to just inspect. Or be cautious of I'll just reiterate, this is not plug and play. So if you want real results, it does require real investments, both in money and in time and effort. And so anything that sounds too good to be true, like just poke at it. Doesn't mean it's not true. Just. Really understand what you're getting into, I think is one two. I would also, I would always look to third party documentation. So there's enough companies out there, the gardeners, the foresters of the world that can help you just feel confident in your decision and also potentially provide some insights into what questions you should be asking, what the evaluation criteria should be, future outlook on where the space is going, but also some. Some information about companies and the strength of those companies. We went from having, I don't know, a dozen players in the conversational AI space to maybe. 1000 overnight. So it's, it's certainly a noisy, noisy marketplace right now. I don't envy the customer trying to make a decision. But I, you know, any additional diligence you can do that's outside of just salespeople like myself telling you that their product is here and it's here to save the world is, is worth your time. That's good advice. So let's, let's touch on something that you brought up just a few minutes ago, which was the aspect of human beings as part and parcel to this overall equation of applying AI. And I think that there's, two clear sides to this discussion maybe more, but we'll just work with two right now. And the first one is that there are people that object to AI being involved with handling a customer interaction where they believe that there isn't the requisite level of empathy and humanity as part of that touch. How do you, address that viewpoint from people that throw that out as a summary objection to bringing AI in the business? My view is always through the lens of customer satisfaction and customer happiness. I think whatever technology you implement needs to have, that is the first priority because otherwise you're just going to bleed customers. And then all of a sudden your revenues are down, profits are down. I mean, at the end of the day, we're all conducting business. And so if you, as you think about the longterm health of your company, That is the critical factor in my opinion. Fortunately, I think as we go into the discovery and understanding these use cases, we're trying to understand which of these maybe don't need the highest level of human touch first. Right? I think that is first and foremost what we always try to do. Like, hey, what are the use cases that this is a mundane effort? Right. Let's say, for example, I need to transfer 50 from my checking account to my savings account that that in itself is a automated process at the ATM. So why not replicate that with somebody that feels more comfortable calling into your phone system? Right? That's low hanging fruit. The other side of this now, too, is even really in the last 12 months, the text to speech engines have gotten so much more sophisticated. So if you've seen some of the demos lately, even from just maybe six months ago, there's been some major, major investments. So the quality of the voice, the human like sound to the AI is really helping to solve some of that on its own. Cause we've just seen that improvement. So you can change, it can be a regional dialect. So if you're, if you live in Texas, some of my friends in Austin, you can have a Texas sound and voice with a little twang in it. You live in Southern California, maybe that's not your bag and you choose something different for that regional office. So getting better and better all the time. So maybe if we look at it through that lens of just maintaining that customer experience, the other side is the perceived disruption from both a management perspective, as well as just people in the rank and file of a contact center or in any of the customer facing roles that their job can be displaced. And so as, as we're sitting down and looking at putting together an assessment of maybe the user stories and, and how that's going to play out in terms of an AI treatment and what that might do in terms of deflection away from a group of people now comes, well, what are those people going to do? Is that something that we are going to use as a. for reducing staff or are there other things that we're not addressing that now we have capacity to address, but it always seems to be a real challenge in terms of having management, especially management that's involved with those direct reports. It becomes very personal. How do we overcome this in the organization? Because it seems to me that it's something that should be, outside of a sales cycle for lack of a better word, more co opted with the operational side of the business and HR to really think about what does this mean to our staff? I think that credit unions, as I speak with really customers across all industry segments, Are the most concerned, right? They don't call their customers customers, they call the members and they are the most keen to keep that high touch feel there. So in the best of all worlds, we use the AI to help the company get some of the process out of the way again, like whether that's authentication, Whether it's some basic transactional stuff, because then you're actually freeing up your agent team to spend more time with those customers. You can depend less on having to make sure that we're trying to shrink average handle time because they do have time and then therefore your customer satisfaction goes up. The ability to train those people to potentially up and cross sell and get people more engaged from the customer base. I think all of those can have a net positive impact on both your top line and bottom line, which is important, right? That's what we're all trying to improve. In all companies, I hope. I've seen that definitely across the board from the standpoint you use average handle time is as a metric. I mean, it's it has consistently over time been a metric that people are trying to squish, right? You're trying to get that pushed down and tighten down. But now people are a little bit more focused on what's the quality of that conversation. And what does that mean in terms of moving the needle from a business perspective, especially if those people have responsibilities for upselling or cross selling, or there are levels of sensitivity around retention. And so that extra time actually improved the retention with that customer. Yeah. All of those things I think are a worthy pursuit. And to your point about it being a cross functional discussion. I think the companies that get that right are going to come out ahead. I think there's a temptation to say we can cut our agent team in half because of AI and we're going to save X and that in the short term we hit our quarterly number and life is good. I don't know that that's the best longterm thinking. And I also know too, I think at a macro level. People will become displaced. I don't think as I shared with you in the prep, not blissfully ignorant that this isn't going to be massive disruption. We've seen that throughout history as major technology innovations come to fruition. Right. The cotton gin, the spinning loom, like all of those things, maybe generationally, it's great, but within that generation, there's a lot of, of difficulty and struggle. And I think companies that figure out how to use their people. In a meaningful way to impact their customers while getting the advantages of this cost savings from AI. I think they win. I think reputationally, I think financially that's, the home run. Eric Smith, you're an industry icon. Thanks for being on the show. Thanks Frank. Always a pleasure to chat with you. Thanks for having me.

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Frank Rogers

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