The Bridgecast Live: Reimagining the Customer and Employee Relationship (Part One)

Scott Kinka (00:05):

Thank you. You guys are so well-trained. I love it. Well, welcome to another episode of The Bridgecast. As you can hear, I’m holding a mic and can barely speak at this point because we’ve had a brisk couple of days together. We’re thrilled to have you here. And for those of you who are watching this episode online, welcome to another episode of the Bridge Cast. We’re excited to have you, and as we start every episode, of course, if you are not currently a follower of the bridge cast, we would love for you to follow us on Spotify, Apple Podcasts, YouTube, or your favorite podcast platform. Today, we have a really special episode. We will have two episodes generated from this live taping, and the live taping is here at Bridgepoint Tech Summit 2024. Can I hear it from our live studio audience? 

Scott Kinka (01:00):

Super excited to have you here. Over the last couple of days, we have been discussing re-imagining the future, and we’ve been doing that as a technology community full of consultants who work directly with CIOs and the suppliers who make them and the CIOs look good together. Throughout that time, AI has been a consistent theme in the conversation. So, we’ve compiled a great group to talk through that today. Some of our Bridgepointe suppliers, the executives in those groups that are focused around not only this channel, but around AI as a route to market. We did a similar scenario last year where we had two episodes around AI, but those episodes were very much what is AI, what’s going on in AI? And we’re going to tap into a little bit of that. But today, it’s really a focus on how are people engaging in this new technology and how is it real or has become real in the marketplace? So, I have an esteemed panel here. I’m going to allow each of you actually to introduce yourselves quickly, tell us a little bit about what you do, tell us a little bit about your company. For those of you who’ve been on the podcast before, I always say just give me your company’s superpower quickly as we go. So I’m going to start with you, Nick, fire away. 

Nick Slater (Dialpad) (02:17):

Absolutely. Thank you for having me. Nick Slate. I’m the Chief Revenue Officer at Dialpad. Dialpad is an AI-powered business communications platform. We own the whole stack, and we provide UCA solutions, contact center solutions, and sales intelligence solutions to small businesses all the way through Enterprise. Thanks for having me. 

Scott Kinka (02:38):

Fantastic. Thank you. Nick Melin. 

Milind Pansare (Observe.AI) (02:41):

Yeah, so my name is Milind Pansare. I’m the VP of product marketing at Observe AI. Observe AI is the AI platform for contact centers. So, we currently power over 350 plus mid to large enterprises with our AI platform. You can think of it as a way to make conversations that your agents are having with customers better after the conversation to improve the efficiency of the contact center by allowing you to automatically QA and coach all of the agents in terms of their performance. And lastly, getting those insights, the voice of the customer in your conversations and propagating those to improve business processes throughout the whole business. 

Scott Kinka (03:30):

Fantastic. Thank you. Shawn? 

Shawn O’Connell (Talkdesk) (03:33):

Yeah, good morning. I’m Shawn O’Connell. I’m responsible for the global AI business and talkdesk for those unfamiliar with Talkdesk, we are a customer experience platform powered by AI. Really, what we’re looking to do is drive a positive outcome, reduce efficiencies or inefficiencies within the call center, and, most importantly, provide strong ROI. 

Scott Kinka (03:56):

Fantastic. Appreciate that. Kristy. 

Kristy Thomas (Vonage) (03:57):

Hi, good morning. I’m Kristy Thomas. Am I a little too loud for you guys? I’m the senior vice president for indirect global channels and alliances at Vonage. For those who don’t know what we do, we provide residential phone service to your grandma. Just kidding. Is anybody listening? There we go. We’re actually a global unified communications stack. We provide global communications on our platform for all different types of user communities, the end users when it comes to communications platforms as a service contact center for service, sales and support and others and the knowledge workers in the marketplace. Thanks for having me, Scott. 

Scott Kinka (04:40):

Appreciate it. Kristy, you’ve clearly never been in front of a live audience before. 

The Evolving Role of AI in Business Strategy

Scott Kinka (04:45):

Alright, fantastic. So we collaborated together as a group on some of the discussion points here. So some of the slides were slides that we’ve used throughout the rest of the summit here from a Bridgepointe perspective, some of them have been provided by our esteem panelists. What we’re going to do is frame the conversation with sharing some third-party data and then get into a discussion on a couple of topics. After that, I’ll toss to one of them, and then it’s kind of free for all. Let’s just see where this goes. And we have time on the clock there, so we know how we’re doing. We’ve talked quite a bit throughout the last couple of days about the priorities of CIOs and how that job has changed as a strategic job. 

Scott Kinka (05:31):

Helping our CIOs have the job they want or imagine the job they want rather than the job that happens to them. Alright? And this is one of those categories. So CIOs have functional priorities. Here’s five priorities for the year. You can read them yourself, but what’s super important, and I’m just adding an animation there, is that three of the five priorities for CIOs are effectively tied up in the very same topic. Alright? We’re talking about augmenting the business with generative AI and right-sizing AI governance, which will be one of the topics that we talk about here. And then lastly, improving the customer experience exponentially. 

Scott Kinka (06:12):

You guys remember that in the keynote session yesterday? Okay. So, with all of that in mind, John shared with us in our pre-prep, the impact radar for generative AI. Now, this is a word salad on a slide, but for those of you who haven’t seen an impact radar, this is from Gartner. The closer to the center, the more effectively ready for primetime and adopt that a technology is. And you’ll notice it’s all really on the outside looking in, but the biggest ball in the closest center inside of the impact radar is effectively generative AI chatbots for all intents and purposes. So, the first question for the panel is, we’re sort of in this era of, I used this phrase yesterday, go get some, 

Kristy Thomas (Vonage) (06:59):

Go get some. 

Scott Kinka (06:59):

As it relates to AI, just like we were on cloud five years ago. And so our customers are not exactly coming at us with well-thought-out use cases, at least yet, but many of them have board-level edicts or edicts from the CEO to go get some AI right now. So, Shawn, I’ll ask this question. Let’s connect desire and consumption. Are CX and CX technologies, is that the land and expand opportunity, if you will, for companies looking to just kind of jump into the AI pond? 

Shawn O’Connell (Talkdesk) (07:33):

Yeah, I do. I feel like it’s a land-and-expand play. Many customers already have CAS platforms. They have solutions they’ve invested in. However, maybe they address 50 to 70% of the work use cases they have or the business challenges they’re trying to solve. You should think of AI as a solution that can fill those gaps, and provide value; you can expand that across the enterprise. Most importantly, customers there start to realize the value, get the value, and they start to come back with use cases that they feel are relevant to them. You become sticky, you start to protect your install base and you’re delivering value at the same time. 

Scott Kinka (08:12):

Makes sense. Anyone else want to comment on this being sort of at least the easy button start of an AI journey for customers? 

Nick Slater (Dialpad) (08:19):

Yeah, I would agree with that. I think this is kind of the 1 0 2, 1 0 1 is the very easy transcription summarization. I think we’re all used to that at this point, but this is a very fast follow 1 0 2 where you can take an established CX environment and do things like score for sentiment, give customer satisfaction, and give call quality scorecards. It’s a very approachable way to consume AI and make it really tangible for those users and customers. 

Kristy Thomas (Vonage) (08:49):

The only thing I would add to that too, because I agree in terms of how to beachhead into this type of technology via CX, is that we talk about use cases. We talk about the conceptual value that the business brings, but if you’re not representing your customers in terms of how they can technically adopt it and show you the business outcomes financially, tell the feasibility story. You’re never going to get into land and expand. There’s got to be financial benefits to it. 

Scott Kinka (09:16):

A hundred percent. And Milind, we’ll get to some of the use cases, but fire in now if you want to add anything there. 

Milind Pansare (Observe.AI) (09:20):

Yeah, I agree with what Kristy said a hundred percent. Everyone has a budget to spend on Gen AI, but I call it the field of broken dreams if they just implement some technology for some use case. So, tying it to hard ROI and KPIs that they can talk to that quarter, they have to tell their CEO, look, this quarter, this is what I did if I invested in a voice bot or if I invested in post-interaction AI. So I think being able to partner with the vendor and being able to explain exactly where and guide where you’re going to see ROI, is what’s going to make the stakeholders making that investment a little more comfortable. 

Scott Kinka (10:05):

Hold that AI, the ROI comment in a minute because that’s the toughest thing we’re all chasing right now. Let’s just talk about where we are. One in three CIOs is currently running some form of an AI pilot. And this isn’t necessarily in the CX. This could be a generative AI pilot. This means they could have copilot fire it up in their Office 365 environment. It just means they’re started in some way. We’re going to stick with the Gartner thing here. You’ve all seen the hype cycle several times for those who haven’t. The hype cycle effectively says that any technology goes through a cycle where it reaches the current height of inflated expectations, and we’ll go through a trough of disillusionment and eventually settle into what is a consistent adoption from a technology perspective where customers understand what it is and are adopting it consistently. 

Scott Kinka (10:53):

And this was ‘23; they haven’t published the ‘24 yet. They do this one at the end of the year. So that’s an interesting question. I’m going to ask you where you think we are in the hype cycle right now because we’re getting involved in a lot of AI for AI’s sake conversations. But I just want to share this. You can all take a moment. I’m not going to read it. I found this online as I was pulling the slides together, but this is what people say is the real hype cycle about generative AI in particular. So a little bit of fun there. I’ll leave it up on the slide while I’m asking this question. Where exactly are we in the hype cycle right now? I’ll toss it out to anybody. Everybody’s got a shot. 

Navigating the AI Hype Cycle and Avoiding Common Pitfalls

Nick Slater (Dialpad) (11:31):

I’ll take a first swing. So I personally think we’re in the trough. I think there’s a lot of AI fatigue right now. I constantly say that when we go into a customer, our competitor is not Kristy or Shawn or others, but it’s the hundreds of really bad AI pitches out there. We may be the sixth or seventh AI pitch that the CIO is hearing that day. And so I think our prospects and customers are getting fatigued. They’re not hearing the business outcomes; they’re not hearing the value proposition for them. A lot of this is kind of sand slipping through fingertips. And so, going back to some of the points made by my counterparts, it’s so critical to come in with a point of view, clear ROI, and then follow through on that ROI promised. 

Shawn O’Connell (Talkdesk) (12:21):

Yeah, I’d agree with that. I think the one thing, and you hit the nail on the head, right? It’s the 15 other people that come in; they’re all telling the same story. The product suite seems to do all the same things, and now it becomes a product pitch, and it’s a 45-minute call for education rather than business impact. So where you see that execution change when you bring that point of value, but more importantly, you’ve got areas they can start using, cases they can attack, and a plan on how to do it. That’s where you start to see the shift. Otherwise, to your point, I think it is just fatigue. 

Scott Kinka (12:56):

Kristy, the C-suite is dictating to go get some AI and this ROI concept has come up a couple of times already on the panel. Give us the order of operations there as a CIO as your recommendation. Go find the use case, agree on the ROI metrics and bring it back. Walk us through not getting yourself to the edge of a cliff and jumping off if you’re a CIO, just going in and getting some AI right. 

Kristy Thomas (Vonage) (13:22):

Well, and it’s coming from top down, right? So you’ve got this vast media hype around AI and the benefits of it that are everywhere. And so that is a directive that’s coming down into the CIO’s office, and it’s trying to navigate where the prioritization and initiatives are. And I think the real miss and the opportunity for you all is, is that it’s not just AI within the office of the CIO, it’s AI within the CFO, it’s AI within the COO, there’s all these different types of initiatives and point solutions that are driving bad conversations of distraction and noise. So, your value without this use case conversation is actually the value where you go in, and you do a readiness assessment for the entire organization on behalf of the CIO. You find all of these different use cases where there’s relevance to leveraging this technology. Then, you start to find the commonalities of these use cases to really drive initiatives for adoption. 

Kristy Thomas (Vonage) (14:18):

What you’re going to find don’t want to be the spoiler alert is you’re going to find the use cases that drive the adoption where they’re actually technically ready to adopt it. Where there’s the business impact is, again, back in the sales service and support organization because the reality is in those three areas, 80% of the cost is people, cost 20% is technology. So, if you can offset that a little bit to make people more beneficial in terms of their cost ratio, then you will drive the ROI around it. So that’s where the low-hanging fruit is for the use cases, but you got to lead the witness there. 

Scott Kinka (14:54):

Yeah. Milind, I’m going to come to you on this one. You have a great story there, but it’s really interesting that you just said that we just made our people X more productive by turning on. Copilot is a really hard ROI. Okay. How many more widgets did you get done in a day or did you just work less? I’m not really sure, but you’re right. Stories about people costs are interesting. As you were telling that story, I was thinking about what you guys do on your network. I’m wondering how many of your customers are actually coming to you saying we need AI. And you’re like, yeah, it’s kind of already powering the solution that you already have with us, but if you need to tell a story, walk us through that a little bit, and you have some really good ROI stories on the back office of the business. 

Milind Pansare (Observe.AI) (15:34):

Yeah, absolutely. I mean, just where we are on that hype cycle trough, I think it depends on the company you’re talking to. If you’re talking to large financial companies, many of whom we work with, they have more AI and LLM engineers than many of us do. Even as technology providers, they’re very, very savvy about where they want to go. So, the companies that behave more as technology being their differentiator are already there. They know what they’re doing. They have the use cases. So I’ll give you an example. We have a very tech-savvy healthcare company, Accolade. So, what were they looking to solve? When you are on the phone, they’re in patient services. So it’s the number you call on the back of your benefits employee benefits card. They have people who are talking about healthcare; they’re having empathetic conversations about difficult things that are going on with your family members. 

Milind Pansare (Observe.AI) (16:34):

And they had people writing notes because in a contact center, you have to do notes, and then you got to push them in the CRM. The notes were terrible, and people weren’t paying attention to the actual call. So at Accolade, they were able to automate that very simple use case right away. And so 50% of the after-call work has just been eliminated. So those might sound like massive numbers, but with generative AI, you can get good ROI on specific use cases today. And what’s happened is not that 50% savings, but the quality of the calls Accolade has with their members is so much better in every interaction. Great example. 

Scott Kinka (17:19):

And it’s a great tie-in here. This is from Foundry which publishes CIO magazine, and I look at how companies are leveraging AI and generative AI, the top use cases, number one, are chatbots and virtual assistants, right? Number two, content generation. That should be obvious. We’re seeing that. And then three, I think one and three kind of fit the conversation you were just talking about here, industry-specific applications. It’s really hard to come up with an ROI on bullet two there, but the other two can be very specific. Here’s another look at it. This is from our research partner, Infotech, top five big bets they’re recommending to CIOs around AI. And in our pre-call, we kind of went through this slide, we all talked together, and I said, I think probably 1, 2, 4 and five frankly all have a place in CX solutions really at the end of the day. So here’s a little; we will just go through this. So tell us a customer story. I have a chart up here with some examples. It’s pulled from the infotech research, but we got a really good one from you Milind right there. I’m going to start here. Nick, tell us a good customer story about how they got from 

Scott Kinka (18:31):

AI, two letters to three letters, ROI in this. 

Nick Slater (Dialpad) (18:36):

Yeah, absolutely. So I think first, it all starts with what is the definition of AI? And we’ve really boiled that down to it’s time savings and the accumulation of data and insights to make more informed decisions. And what do all companies want to do? They want to accelerate revenue. They want to protect profitability. And so if you can take those variables and tie them together based on the needs for that individual or that team that you’re selling to, that’s where the magic happens. So, we have a number of use cases. We always say you must have a clear ROI when selling our AI solution. It’s what makes it defensible, but armor proofs it. And so I take a customer like the LA Chargers, who we’re very happy to have in the Dialpad family. They have a lot of folks who are doing a lot of outbound dialing. 

Nick Slater (Dialpad) (19:27):

They’re selling tickets, they’re selling sponsorships, they’re calling their valued customers, they’re setting up big promotional tours for their team. And it’s really tough to trust that each of those individuals is saying the things you want them to say in the order you want them to say it. And also being able to capture the information coming back. So, utilizing our AI, custom playbooks, and some of the call scoring, those individuals now stick to a particular script. AI captures the response from the customer. It’s automatically imported into their CRM, which, as a revenue leader, I am constantly thumping on reps, take notes, put it in the CRM or it doesn’t exist. Now all of that is automated, and we can actually set out ROI goals for time to ramp new individuals, time to hit their first selling goal, time to hit over achievement of their plan, and how many customer calls are ending with a satisfied outcome. That gives a whole lot of data and insights to the LA Chargers so they can more effectively run their business. 

Practical Tips for Aligning AI with CIO Priorities

Scott Kinka (20:37):

Shawn, give us a story. 

Shawn O’Connell (Talkdesk) (20:38):

Yeah, I think I’m going to take one that’s more focused because often we talk about AI in a CX setting, but the reality is you’ve got experience initiatives that carry a big impact. And in the case of McAfee, they wanted to address and support over 3000 employees. And so that includes things like onboarding, new hires, offboarding benefits, and information. And when you only have a handful of internal IT staff or agents that do that, you’ve got to figure out how you automate much of that, right? And so their focus and the CIO’s focus was we want to automate 90% of those queries. And when you think about it, there will be queries you just can’t automate. There will be steps, security procedures and things that are required. When it was done, they were able to take close to 90%, not all, away, automate all of those steps tied to other technologies and other platforms, map that to disparate databases so they get the answers they needed, and they were able to take those expensive IT resources that were handling these calls and repurpose them on other projects that were far more strategic. 

Shawn O’Connell (Talkdesk) (21:49):

So you had a better customer-employee experience and a better overall outcome. People didn’t have to wait for an email to go into a queue for three or four days for something to come back, and it’s still unresolved. They could get real-time support. And I think those are strong, valuable use cases internally that drive not only ROI but also great employee satisfaction, and companies are looking at that. 

Scott Kinka (22:10):

It’s a great story. It also brings up another question, which I’m going to say to hold on because we’re going to get there, which is the concept of customer preparedness. You talked about a lot of process in there that we talked to many customers who don’t have it written down, that sort of changes thing, but we’re going to come back to that problem and tackle it as a group. Kristy, tell us a customer story. 

Kristy Thomas (Vonage) (22:27):

Yeah, sure. This is a little story about Jack and Diane. 

Scott Kinka (22:32):

A little diddy. 

Kristy Thomas (Vonage) (22:33):

A little, yeah, a bit of the story now. So, I want to take a little bit of the intermix of what you guys just told around customer experience, employee experience, and platform optimization. So, we have a customer focusing on debt resolution and credit help. And so that’s a very sensitive emotional thing for many people in the marketplace. So, just hold that in terms of the profile of their customers. This company is actually invested heavily in Salesforce across all their clouds. So they have Marketing Cloud, they have Sales Cloud, they have Service Cloud, but they’re great. They’re in the cloud as a SaaS platform but don’t talk to each other. And there’s lots of quintessential information in there. So how AI has been leveraged with this organization is it’s interwoven through those clouds and then also extended out to the customer, meaning they’ve leveraged a digital engagement to engage in the customer in a channel that they can feel friendly in. 

Kristy Thomas (Vonage) (23:35):

This is an emotional thing, and sometimes people don’t want to talk to a human yet. You have to bring them in and get them into that safe zone. And when you do get ’em into that safe zone to talk to ’em about the value that you bring as an organization to get ’em out of this situation of debt, you got to make sure that you’ve got all the rated data, real-time, to help them along the way before you lose them. And it’s a process. And so how we work with Salesforce and this customer is if then AI automation throughout these platforms, but then also making sure we’re prompting the customer service representative that’s taking this individual through a journey of being unhealthy with their financial situation to healthy, not over one phone call, but over a period of time. And so it’s a life journey, and so you’re constantly looking. The other thing is that it’s looking at data lakes to see patterns of that particular profile of an individual to find commonalities and help them faster along this journey because they’re not the only ones in the situation. So, it’s not just leveraging AI for customer experience or employee experience. Still, it’s also leveraging the platforms in the data lakes to get this information faster and do something about it if that’s the next best action. 

Scott Kinka (24:45):

It’s really an ecosystem solution at the end of the day. Right. I mean, I love that. I mean, you mentioned Salesforce, we got into data, all of those pieces. You could buy a platform that does AI things, but I think regularly we’re thinking silver bullet and we’re not like, well, how about data? Well, how about the CRM process? Well, what’s feeding into that?