Trust, Not Technology: Alice Sesay Pope on Leading AI Transformation That Customers—and Employees—Actually Believe In (#800)

Paula: Hello, and welcome to Let’s Talk Loyalty and Loyalty TV, a show for loyalty marketing professionals.

Paula: I’m Paula Thomas, the founder and CEO of Let’s Talk Loyalty and Loyalty TV, where we feature insightful conversations with loyalty professionals from the world’s leading brands.

Paula: Today’s episode is hosted by Nyeleti Su Angelinkuna, a customer loyalty strategist with a proven record of helping blue chip global brands forge deeper connections with their customers.

Nyeleti: We all know it, AI is transforming every industry.

Nyeleti: But what if the biggest challenge isn’t the technology itself, it’s trust.

Nyeleti: How do organizations adopt AI in a way that customers trust, employees embrace, and leaders can govern with confidence?

Nyeleti: My name is Nyeleti Su Angel Gunna, and on this episode of Let’s Talk Loyalty and Loyalty TV, I will be joined by Alice Sesay Pope, an AI transformation leader whose career spans organizations such as Amazon, Microsoft, and Visa.

Nyeleti: Alice, welcome to the podcast.

Nyeleti: I am so excited to have you to share your perspective on this critical topic of AI trust today.

Alice: Nyeleti, it’s a pleasure to be here with you.

Alice: I’m excited.

Nyeleti: So, to get us started, I am going to ask you a question that we ask all our guests, and our regular viewers and listeners know exactly what’s coming.

Nyeleti: The question is designed to give us a little bit of a voyage in your mind, and it is, what is your favorite non-fiction book?

Nyeleti: It could be something business related or perhaps something that has to do with personal development.

Alice: You know what?

Alice: My favorite book is not even a new book.

Alice: I would say it’s the classic.

Alice: And my favorite book is Good to Great by Jim Collins.

Alice: And I love it because it gives leaders suggestions to shift from just being good in how they lead teams to being great in how they deliver results.

Alice: But the foundation is biblical.

Alice: And the reason why that’s more important to me, and so important to me in this season, there was a time when I did not feel I could be my authentic self and let people know that I have that foundation of the Bible.

Alice: And now I’m at a point in my career, in my life, that I want to show up as my authentic self.

Alice: I want to show up as a leader with integrity.

Alice: And therefore, I had to open that book again.

Alice: And it is one of my favorites.

Nyeleti: I love it.

Nyeleti: You know, go big or go home, but it’s true.

Nyeleti: There’s no room for mediocrity.

Nyeleti: And to stand in your greatness is definitely one of the superpowers that we all have.

Nyeleti: And we owe it to ourselves and the world to go from good to great.

Alice: Yeah.

Nyeleti: And so, you know, talking about greatness, as we record this conversation, you have just been named one of the 50 women to watch for boards in North America.

Nyeleti: A big, massive congratulations on that.

Nyeleti: And I thought it’s actually a fit in place by way of introduction, you know, to ask you to tell us a little bit about yourself, your professional background and the journey that’s brought you to this point in your career.

Alice: Wonderful.

Alice: Thank you so much.

Alice: And it’s been an honor to get that award.

Alice: I was pleasantly surprised but grateful, humbled and grateful.

Alice: So a few things about me.

Alice: There are three reoccurring themes in my career.

Alice: Transformation, AI implementation, and customer experience.

Alice: Those are the three things I would say that have been consistent throughout my career as a leader leading global teams at Microsoft, and then in the financial and payments industry, companies like USAA, Capital One, and then Visa, and my last role at Amazon.

Alice: In all of those roles, I consistently wanted to elevate the overall customer experience, ensure that I could inspire employees.

Alice: I remember one of my transformation themes that I had was passion for excellence, service from the heart, show teams that they could be great in the way how they deliver results, but do it in a way that was compassionate, and that employees saw that there was a win-win for the business and as well as for them.

Alice: So that’s my reoccurring theme.

Alice: I have an engineering background and an MBA.

Alice: I have been part of the Harvard Women’s Leadership Fellow, and then recently I’ve been an author.

Nyeleti: Talking about being an author and so much to unpack from that background, you have just published, you authored the book The Trust Algorithm, How Leaders Build Trust with Generative AI.

Nyeleti: This is going to be a significant part of the conversation.

Nyeleti: So maybe you could tell us a quick synopsis of that and why you decided to write that book.

Alice: Yeah, thank you.

Alice: I wanted to write the book because I realized that we are in a trust recession.

Alice: And as leaders have the pressure to launch generative AI solutions quickly, I didn’t want them to have the pitfalls.

Alice: I wanted them to understand how to build trust, how to understand the customer base.

Alice: But as they’re doing that, create a playbook that supports their associates during this transformation, but also to launch solutions that are governed with a trustworthy governance model.

Alice: And there’s a playbook on how to implement generative AI with a clear roadmap.

Alice: And that’s why I did it.

Alice: It was really important to me as a customer advocate at heart, but also understanding that there are great benefits to generative AI, but there needs to be a clear governance and risk mitigation plan when things go wrong.

Nyeleti: You’ve written an amazing piece and most people think about AI.

Nyeleti: And I think the hardest part is technology.

Nyeleti: But you say trust.

Nyeleti: Why that?

Nyeleti: Why not the mechanism, the brand or the experience itself?

Nyeleti: Why do you center on trust?

Alice: Right.

Alice: You know, I start off the book by saying we are in a trust recession.

Alice: We have more data than we’ve ever had, but we’re not sure if customers trust that brands are always going to make decisions in their best interests.

Alice: At the same time, there is some distrust of media and the information we receive.

Alice: Because artificial intelligence has been so good, sometimes there are deep fakes and scams.

Alice: So I believe trust is important for us to continue to drive customer loyalty.

Alice: Customers need to trust that we are making the decisions that enhance their overall experience, and that will regain their loyalty for the long term.

Nyeleti: And I love that you painted on loyalty, because loyalty is really about the long term.

Nyeleti: It’s about bringing the customer on a journey.

Nyeleti: You meet them at that acquisition phase, but there’s also a way of meeting them.

Nyeleti: How do you find your customers?

Nyeleti: Where do you source for them?

Nyeleti: And what type of experiences do you put around that initial content, and how do you manage that relationship going forward?

Nyeleti: So centering again on that, AI as a trust algorithm, as you put it in the book, you also talk about people and processes and governance as a way of streamlining all of these experiences.

Nyeleti: Would you just expatriate a little bit more on that?

Alice: Yeah.

Alice: When we think of people, sometimes we forget that they’re a critical part of any sort of generative AI implementation.

Alice: So in terms of people, you have to forefront think about the customer, and you all do that so well with as you think about customer loyalty.

Alice: As you’re launching these features, is it reducing customer friction?

Alice: Are you delivering an experience that at least meets the bar of what you previously delivered?

Alice: But more importantly, we prefer situations where you’re elevating the overall customer experience.

Alice: But as we’re thinking about people, we can’t forget about our associates.

Alice: Are we making the work easier for them or harder?

Alice: Can they trust it?

Alice: Can they give us the best insights?

Alice: Because of course, in many instances, associates are interacting with customers on a regular basis.

Alice: And when our associates give us that information, can they trust that now their jobs, their roles are not at risk?

Nyeleti: Yes.

Nyeleti: And that is very good because the one part is that it’s a human factor.

Nyeleti: How does this impact human behavior in the office and among employees?

Nyeleti: Is innovation accelerating collaboration as it should, or are people holding on to the ideas?

Nyeleti: And that’s a conversation I would like to pick up later with you.

Nyeleti: But stay in on what you said about the customer experience and how AI can elevate it, not just meet a need.

Nyeleti: You know, often in organizations, we talk about use cases.

Nyeleti: And I’ve just noticed that AI becomes the default answer to every problem before it’s really fully diagnosed.

Nyeleti: So as a starting point, you know, how do we distinguish between an underlying problem that really calls for an AI solution versus what could just be a product enhancement or better analytics or, you know, just a better customer service in general?

Nyeleti: When is it authentically, you know, generative AI, for example?

Alice: You know, that’s a great point that you bring up.

Alice: And you know what, I would like to think about the Amazon experiences with their devices.

Alice: I think the device business has done a great job to think about how to leverage generative AI to enhance the customer experience.

Alice: Most people that buy Amazon devices have multiple relationships with the organization.

Alice: They have prime video, they might have music, they might have games, and they also have the shopping experience.

Alice: And generative AI can be utilized to analyze the customer’s relationship, understand their preferences, personalize that experience.

Alice: And one of the things that I love, so many times we think of the customer experience just by a chat or just by a voice.

Alice: But with the device business, you could have a problem with your device while you’re on your scribe or maybe you’re on your Kindle.

Alice: And right there, there’s a prompt that helps the customer, that gives, that gives the customer situations how to better enjoy their product.

Nyeleti: Yes.

Alice: Those are pleasant surprises that customers love.

Alice: It’s not forced and it’s proactive and it’s personalized.

Nyeleti: And it feels intuitive in that moment.

Nyeleti: And we often like to talk about surprise and delight in the loyalty business.

Nyeleti: So in that specific use case where you have a prompt from a chat bot that’s actually helping you solve the issue at hand, I wonder, are we at that place where the customer, the consumer is happy to trust an agent and they know that this is AI, it’s not a human being with information, with a conversation across different consumer platforms, or is there a sense of holding back?

Alice: That’s a good point.

Alice: When brands are transparent with the way they utilize the data, customers are more accepting of it.

Alice: Alexa is a great example.

Alice: When Alexa Plus was launched, Amazon was very transparent with the consumer base that explaining to them that they wanted to conduct more actions for the customer, and so they needed information.

Alice: So, for instance, if they want to book a restaurant reservation for you or book an Uber for you, there’s some personal information that would be needed, but they explain to you exactly how that would work.

Alice: And there were many customers that were happy with that.

Alice: And those customers that were not, it’s okay.

Alice: But you were able to establish loyalty and trust because customers had the option.

Nyeleti: And what we are finding is that customers are very willing to give data and share the information.

Nyeleti: And as a result of sharing that information, what they expect in return is that you actually do know them, and then you tailor the experience to their needs and fix the specific problem that they might have.

Nyeleti: So you make a really good point on like, do they know what this data has been used for and do you deliver on it?

Alice: And you know, that’s a really critical point, because there are instances where generative AI features fail, because organizations over promise.

Alice: Don’t over promise.

Alice: Be transparent.

Alice: And you know, sometimes a solution is in pilot phase.

Nyeleti: Yes.

Alice: And you want to experiment.

Alice: Be very transparent to the customers that, you know what, this is the beta version, or you’re interacting with this new product, this new generative AI, we want to utilize it to enhance your experience.

Alice: There may be some bugs.

Alice: Give us your feedback.

Nyeleti: Yes.

Alice: Customers appreciate that so much more.

Nyeleti: It’s actually a very interesting point that you raise, because often we talk about, you know, the holdout group and doing the testing so that, you know, we get, you know, we don’t change the entire experience.

Nyeleti: But as you say, we sample a specific base and see whether those changes make sense.

Nyeleti: And that experience where the customer is informed and is a part of that process, you know, what use cases have we seen that have really worked well and now in the market, and when has it failed?

Alice: Wow.

Alice: That’s a great question.

Alice: I believe what the instances where it fails is when we design products and solutions that are great features.

Alice: That they’re great features, but they’re features that don’t necessarily reduce customer friction.

Alice: I mean, come on, we think about loyalty.

Alice: When we make that experience simple, fast, and right for the customer, and in those instances, we surprise the customer with sometimes solutions that they may not even know that they need.

Alice: Customers are more accepting of that.

Alice: But if there are situations that we give something to the customer that perhaps they don’t want and we force it on them, or we are transparent that we’re utilizing a generative AI solution, and the customer decides they want to transfer over to a person, and that transfer to a person is difficult, yes, that is very frustrating for customers.

Nyeleti: And you mentioned something, right?

Nyeleti: We often look to the tactics, this solution, but what’s the strategy behind it?

Nyeleti: What is the job to be done?

Nyeleti: What’s the underlying problem that we are solving for?

Nyeleti: And you talk about that in the book.

Nyeleti: You know, you talk about, you know, start with diagnosing the problem.

Nyeleti: What are some of the process maps that leaders can take in identifying out of a myriad of things to solve for?

Nyeleti: What do we go with first and for what reasons?

Alice: Yeah, I would say go with those instances where there’s the most customer friction.

Nyeleti: Yes.

Alice: That it will elevate the customer experience, it reduces customer effort, and have the right metrics so that you can diagnose the before and after effect of your solution.

Alice: But remember, we talked about people in the beginning of this conversation.

Alice: So there, I’ve also seen use cases that improve loyalty where we give associates generative AI solutions that better help them support customers.

Alice: A great example is in the financial services.

Alice: So at Capital One and also at USAA, USAA is great at that.

Alice: In where now we have generative AI solutions when an associate is trying to solve a regulatory issue or an escalated issue.

Alice: So all of a sudden a customer calls, they’re upset.

Alice: They feel that perhaps we have, we’re not being fair to them and the associate isn’t quite sure what to do.

Alice: But there’s a generative AI tool on their screen that gives them some guidelines of how to de-escalate the customer.

Alice: It empowers them of how to make requests and scenarios, options for the customer that may be outside of the regular policy, but it’s needed because it’s a moment that matters, and solving a customer issue in that moment that matters drives the loyalty.

Nyeleti: A hundred percent.

Nyeleti: I love that example because it just shows how AI and human can actually work together intelligently.

Nyeleti: That’s a partnership, right?

Nyeleti: So the AI helps the human, the associate to de-escalate the customer by giving them prompts, in the moment, and then the customer at the end is having a better experience.

Nyeleti: So I really love that example.

Nyeleti: And talking about experiences that elevate the customer, if the customer is going to trust these experiences, there has to be something behind them.

Nyeleti: So what does good governance look like?

Nyeleti: And accountability as well as mitigating risk when we are deploying these solutions?

Nyeleti: I love that.

Alice: You’re already thinking of governance.

Alice: I love it.

Alice: One of the things is, I think before we were using a lot of generative AI solutions, governance was something that was created between the legal and the compliance teams.

Alice: But generative AI solutions and gentic solutions changed so quickly.

Alice: One, because with a gentic, they can take an action on behalf of a customer, and generative AI solutions can teach itself.

Alice: Yeah.

Alice: So I believe the great governance models are not designed after the fact.

Alice: They’re designed with the engineers and the operators and the marketing and the brand leaders all together in the beginning of the design phase.

Alice: It’s tested, it’s monitored, but there’s a name assigned to it.

Alice: So there’s clear accountability.

Alice: If something goes wrong, who is accountable to turn that feature off?

Alice: Also, how do we detect a possible risk?

Alice: The detection model has to be robust enough.

Alice: We have to believe it and we have to make it believable for our customers.

Nyeleti: I like the idea of thinking ahead of what could possibly go wrong.

Nyeleti: But could you perhaps maybe bring it into a lived experience?

Nyeleti: When did governance actually fail you?

Nyeleti: Not in theory, but right in the room.

Alice: Yeah, wow.

Alice: So there was a situation where a customer called in for a problem they had.

Alice: And while they called in, in addition to the natural language processing within the IVR, the phone voice response was responding.

Alice: But the Genetive AI solution with the voice assistant, they had a voice assistant in their home.

Alice: Both of those systems were working in parallel and giving the customer conflicting information.

Alice: It was a very embarrassing situation.

Alice: And the customer was frantic, wrote to us immediately, ended the transaction, knew something was going wrong, and wrote a letter to our CEO.

Alice: And go ahead.

Nyeleti: I’m just, I’m, yeah.

Nyeleti: I don’t even know where to begin, you know?

Nyeleti: I’m just trying to think of the customer and that situation just been completely paralysis mode there.

Nyeleti: What do I listen to, you know?

Nyeleti: And how do I trust this thing?

Nyeleti: And how do I get myself out of that?

Nyeleti: So yes, go ahead.

Nyeleti: Like when it just goes top up there, like how do you manage such a crisis?

Alice: Well, first of all, what was really hard about that situation is that when we were listening to the call, we weren’t able to detent.

Alice: Was that the IDR responding or was that the voice assistant responding?

Alice: So it took hours of us trying to look at the code, trying to understand the interaction to figure out where the breakdown was.

Nyeleti: And when you look at the actual breakdown, right, because we understand that agentic AI solutions is a series of data that we feed into it, right?

Nyeleti: So it’s working on a dataset that it already has.

Nyeleti: So whether it’s one solution or the other, where is the conflict and why would they be different outputs if it’s feeding off from the same database and if I understand that theory correctly?

Alice: Yeah, well, what’s unique about generative AI is it can pull information.

Alice: It can pull information from the internet, it can pull information from different sources, it can pull information from data that has been now, a customer has put into the database.

Alice: So it pulls information from various places.

Alice: I don’t know if you heard.

Nyeleti: So then in the sense it recommends, right?

Nyeleti: But then also what you’re saying that it also refinance on recommendations because the loops of information that I go into it are constantly expanding.

Alice: Exactly.

Alice: Because it’s constantly expanding, it can pull information that’s incorrect.

Alice: And that’s why we have to also have an audit process.

Alice: You know, generative AI has given the world great advantages, but it’s not 100% accurate.

Alice: So we need to ensure that we’re able to audit the outputs and give it some guardrails.

Nyeleti: It makes me think about, you know, a lot of the systems that many of us are using and, you know, that audit part of it and how it becomes so much an integral part of your work life, of your personal life.

Nyeleti: Now you’re talking to your language model system about anything and everything and often kind of forget that this isn’t necessarily audited.

Nyeleti: This is picking up information from all sorts of stuff and not all of it is, you know, it’s not the holy grail.

Nyeleti: And do you think that constant usage of it makes us lose sight of that reality?

Alice: Yeah, I think so.

Alice: Constant use of it.

Alice: Sometimes the great surprises we get when it gives us an output so quickly that we agree with.

Alice: But then for some of us that use it on a regular basis, we’ll notice that sometimes we make a request and 75 or 80% of the output is accurate, but there’ll be something in the script and we’ll say, oh my gosh, that’s wrong.

Alice: And we change it.

Paula: We make those modifications.

Nyeleti: And especially when we’re applying it into the work context.

Nyeleti: And I’m just wondering how often is a due diligence applied?

Nyeleti: And you spoke about the governance model and different operating systems to ensure that what we get out of it isn’t taken as fact, but it’s routinized.

Nyeleti: How much you suggest organizations ensure that?

Alice: I’m not sure that all organizations are able to do it yet.

Alice: And sometimes the larger the organizations, the harder it is to really do an effective audit.

Alice: But interesting enough, I was talking to an executive leader that was at a much smaller company.

Alice: And she was explaining to me that at her company, people use different models.

Alice: And I was like, wow.

Alice: You know, I thought, because in large companies, it’s controlled in what large learning models you could use.

Alice: And she said, some people use Geminis, some people use co-pilots, some people use chat TBT.

Alice: And I’m wondering, wow, are you putting company information into all these things?

Alice: How do you preserve confidentiality, privacy, or even the IP of the organization?

Alice: So it’s something that leaders need to step back and think about.

Nyeleti: Yeah.

Alice: And ensure that as we’re making these decisions, that we’re enhancing trust and not eroding trust, because if we erode trust, we lose loyalty.

Alice: And it’s easier to build trust, but when you lose it, it’s much harder to regate it.

Alice: And sometimes you can lose customers in that process.

Nyeleti: And that is the core of your thesis, right?

Nyeleti: That trust is quite easy to build, but when you lose it, you know, regaining it is not as easy, and you often don’t always get the opportunity to regain it.

Nyeleti: Back to that example that you shared, you know, why are you able to regain trust in that situation?

Nyeleti: And when do you just consider it a lesson learned and that’s done and we just move on?

Alice: You know, it’s really interesting.

Alice: There were times when we launched solutions and a customer might have been in beta, and they said, you know what, I’m not going to be your guinea pig.

Alice: Take me back to an old version.

Alice: I don’t want this.

Alice: But then there were other customers, and I respect that, that we let the customers know that we were trying different models.

Alice: And they said, I don’t want this.

Nyeleti: Yes.

Alice: And then there were other customers that say, OK, I’ll experiment with you, but guess what?

Alice: What you say is not going to be gospel for me.

Alice: And guess what?

Alice: I don’t want you taking an action on behalf of me.

Alice: So we have to meet customers at the trust level that they’re at.

Alice: We can’t force them into it.

Alice: And as we become better, I believe trust will increase.

Alice: Yeah.

Alice: As the models become more sophisticated as well.

Nyeleti: But I like that idea of giving the power back to the customer, so they get to decide how far they go with an experiment.

Nyeleti: Which takes me just generally into the field of experimentation.

Nyeleti: So many organizations want to not only stay on the curve, but be ahead and try new things.

Nyeleti: Because if you’re not innovating, your competitor is.

Nyeleti: And so in a lot of organization, there’s a lot of different experiments.

Nyeleti: And we spoke earlier about the human factor.

Nyeleti: How do we ensure that there is room for experimentation within an organization, across different teams, but at the same time, we are learning from each other and it’s collaborative?

Alice: Yeah.

Alice: This is a leadership moment.

Alice: It really is a leadership moment.

Alice: Leaders must lead with integrity.

Alice: They must be transparent.

Alice: And at the same time, inspiring.

Alice: Right?

Alice: And people need to believe that they’re in a safe environment where they can experiment.

Alice: What I’ve seen, the bad side of some of the generative AI implementations is that teams are no longer willing to collaborate the way they did pre-generative AI days because everyone wants to come out as a hero.

Alice: So because they want to come out as a hero, they don’t want to share information.

Alice: When you don’t share information and best practices, what happens is sometimes you have duplicative solutions within a company.

Alice: And that’s terrible for a company because you don’t get the return on investment.

Alice: You overuse your tokens, which is costly.

Alice: But if leaders start out communicating a compelling vision, creating a safe place where there’s psychological safety, give associates a clear understanding of how their roles will evolve, and demonstrate that we want an environment where there’s true collaboration, we will have more success.

Nyeleti: I appreciate you saying that this is a leadership moment, and it’s really much, it’s a very defining moment.

Nyeleti: And in that example, when instead of sharing best practices, you’re holding back, and often because everybody wants to be a hero, that’s what you’ve just said.

Nyeleti: I mean, that’s a behavior based on an underlying fact, and I would assume it’s fear, because there’s so many unknowns.

Nyeleti: I mean, we look at the news, and so many big organizations are letting people go because of AI, because of, you know, I’ve heard headlines saying that, you know, certain populations of the company could not be re-skilled for AI.

Nyeleti: So it does drive a lot of fear, which would explain some of that behavior.

Nyeleti: What are your thoughts on that?

Alice: Yeah, the World Economic Forum says that by 2030, there are going to be 170 million new roles, right?

Alice: But there will be 92 million roles eliminated.

Alice: So that means there’s going to be a net gain of jobs.

Alice: If we have 170 million new roles and 92 eliminated.

Nyeleti: Yes, that’s a very inspiring and happy story.

Nyeleti: Why is it that I don’t see that in the news headlines?

Nyeleti: And how can leaders inspire the organization with that knowledge?

Nyeleti: And because that helps me, it’s, I just need to re-skill.

Nyeleti: I need to know how my contributions can be valid for the future workplace and align myself to that goal, as opposed to say there’s only going to be five people who make it out of the 20, and I need to just be one of the five.

Alice: Yeah.

Alice: Great point.

Alice: Re-skilling, and I talk about this in the book, re-skilling needs to be a priority.

Alice: And initially, as people were launching these generative AI solutions, re-skilling wasn’t thought about.

Alice: People were just thinking about, we’re going to reduce cost, and we’re going to reduce cost by eliminating roles.

Alice: But the NVIDIA CO said something about three weeks ago that I thought was very profound.

Alice: He said that companies that are talking about their productivity improvements by reducing jobs, he said that’s a cop-out.

Alice: He said if you’re using generative AI effectively to get the true returns on investment, your associates will be more productive, you’ll be able to do more, and because you do more, your revenue will increase, and you’ll need those people.

Alice: So as organizations are thinking about their overall transformation strategy, and I hope more organizations are thinking about an end-to-end enterprise AI strategy, not just a feature that we’re going to launch this feature, and this feature is going to eliminate these jobs.

Alice: But if we think about generative AI from a holistic transformative way, we will also consider how will jobs evolve?

Alice: What training will we provide?

Alice: So our associates are prepared for that evolution.

Alice: And if we do that, associates will trust it, customers will trust it, and I believe we continue to enhance the loyalty of some of these brands.

Nyeleti: You mentioned one of my favorite words, which is evolve, you know?

Nyeleti: How do you continue to evolve in this era?

Nyeleti: You know, how?

Nyeleti: I’ll ask you that question.

Nyeleti: How can one evolve in the A&I age?

Alice: Yeah, we need to learn together.

Alice: And so that means in organizations, we need to listen to the inputs of people at various levels, people at the entry levels, people at the mid-level management, people at the executive levels.

Alice: It can no longer be a situation where an executive sits in their office, develops a strategy and that strategy is it.

Alice: This is a moment where we’re all learning.

Alice: Because week over week, almost daily actually, the generative AI tools have new capabilities that are super advantageous, but we all need to learn together.

Nyeleti: Yes.

Nyeleti: And in the workplace, does that look like more hackathons, opportunities for having labs?

Nyeleti: Is it new information sharing forums?

Nyeleti: What does it look like to bring senior leaders and young associates together to discuss these matters?

Alice: There needs to be numerous forums.

Alice: And you name some of them.

Alice: Hackathons, show and tell meetings.

Alice: It needs to be leaders that are willing to travel.

Alice: Like for me, I had teams around the globe going to some of the different sites.

Alice: And when I went to Japan or in Tokyo, and then when I was in Cape Town, South Africa or Costa Rica, hearing first hand from associates, their ideas and their suggestions, and each of those suggestions were always extremely valuable.

Alice: It’s IT professionals talking to the people that are interacting with customers on a regular basis.

Nyeleti: Yes.

Nyeleti: And one thing to pick up from that, you know, having time, face time with the people that are actually in front of the customers, but also people who are in manufacturing, and how can AI support some of those processes, is that you have hard evidence on the actual problems that people are dealing with on a day-to-day basis, as opposed to, you know, give me the 10 most used use cases in scenario A.

Alice: Absolutely.

Alice: Absolutely.

Alice: In every industry, I believe there’s a generative AI application.

Alice: Yeah.

Alice: And we should look at the very tough manual task.

Alice: How do we simplify that?

Alice: How do we automate that?

Alice: How do we reduce the workload of those repetitive tasks for our associates?

Alice: But also, how do we continue to think of ways to build trusts that lead to increase customer loyalty?

Nyeleti: And this, Alice, I love what you’re saying.

Nyeleti: How do we continue to build trust?

Nyeleti: It brings me straight to your book, The Trust Algorithm, How Leaders Build Trust with Generative AI.

Nyeleti: We’re going to make sure that we link the book so that our listeners can get an opportunity to read it.

Nyeleti: But if you had to summarize, what are the three top key takeaways that leaders should live with after reading this book?

Alice: Make sure that you know why you are launching a generative AI solution.

Alice: Have a clear problem statement.

Alice: Two, ensure that you have a playbook to implement it.

Alice: And three, although I say three, it’s the most important to me.

Alice: Include your people.

Alice: Think about your people.

Alice: How do you lead them with integrity?

Alice: How do you lead them in a way that they believe in this transformation and they embrace it?

Nyeleti: Excellent.

Nyeleti: So what I took away from that was, first of all, the why establish the reason behind the solution, how the playbooks, the frameworks, but also the who.

Nyeleti: Who do we need to bring along and how do we bring them along?

Nyeleti: So I think that’s well put.

Nyeleti: And lastly, because Let’s Talk Loyalty and Loyalty TV is often listened primarily, by Loyalty Marketing Professionals and use it in a place where you’re looking at enterprise-wide transformation.

Nyeleti: But if we go back to a manager that’s handling a Loyalty Program, they can have what we’ve discussed today about AI and the innovation around it and how we need to elevate the customer experience.

Nyeleti: What’s the takeaway that Loyalty Marketing Professionals should take?

Alice: Speak up and do not be intimidated by the technology or your tech teams.

Alice: You all have data, you all have insights about how to increase loyalty, how to improve the brand perception.

Alice: Speak up and don’t ever be intimidated that the tech people have all the answers because they don’t.

Alice: And they’re learning just as much as you are during this generative AI era, and your voice matters.

Alice: And your voice matters because you give insights and you’re thinking about customer loyalty all the time.

Nyeleti: I love that because you’re so right, we have the data.

Nyeleti: We know who our most valuable customers are.

Nyeleti: We know what they’re vying.

Nyeleti: We know what they’re searching for.

Nyeleti: We know the questions that they have.

Nyeleti: And we play such a crucial role in making sure that that data is fed to the rest of the organization, all the way into product development and different parts of how we organize ourselves.

Alice: Yeah.

Alice: Yeah.

Alice: Your voice matters.

Alice: Your voice matters.

Alice: Speak up.

Nyeleti: You know, with that, Alice, I mean, I see why you have just received the honor of being one of the 50 women to look out for for boards because you clearly do speak up when you go, and your opinions are rich and well thought of.

Nyeleti: So thank you so much for all that you’ve shared today.

Alice: Thank you.

Alice: It was such a pleasure to be with you today.

Nyeleti: And I will leave it at that.

Nyeleti: And I’m just encourage everybody to pick up the book and make sure that not only are you building innovative solutions, but it’s really about the underlying trust and making sure that it elevates the customer experience.

Nyeleti: Thank you for watching.

Nyeleti: Thank you.

Paula: This show is sponsored by Wise Marketeer Group, operating The Wise Marketeer and Loyalty Academy.

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