Alice Sesay Pope (00:00)
we are in a trust recession and AI is here to stay. So we all have to embrace it. But as we embrace generative AI, let’s ensure that we have the right strategy that our customers can trust

Shelley E. Kohan (00:38)
Hi everybody and thanks for joining our weekly podcast. I’m Shelly Kohan and I’m very excited to welcome Alice Pope. Welcome Alice to our show.

Alice Sesay Pope (00:47)
Hi Shelley, it’s a pleasure to be here with you today.

Shelley E. Kohan (00:51)
It’s great. So you are a Fortune 100 executive and you’re author of The Trust Algorithm, How Leaders Build Trust with Generative AI, which is going to be our topic today. It’s quite the hot topic these days. And what I find super interesting about you, Alice, is that you have literally managed all the devices in my house right now. And what I mean by that is you were

Alice Sesay Pope (01:13)
Ha ha ha ha.

Shelley E. Kohan (01:15)
previously the global vice president at Amazon.

Where you ran a 10,000-person organization across Alexa, Prime Video. You were responsible for over 2 million subscribers, 30 plus global sites, on and on and on. So I believe you are one of the few operators that have actually really worked in a major way with generative AI. So would love to get your perspective on.

What’s headed for us in retail? So I’m anxious to jump right in.

Alice Sesay Pope (01:49)
Yeah. Well, I believe there’ll be more personalization. There’ll be more personalization, faster way to analyze the data, understand customer preferences. That is definitely what what what I will see happening. I’ll also see new services being added as roles evolved, as

Perhaps some transactions that required a person being handled by automate by agentic AI or generative AI that will give room for for for people to add even valued other valued services. I was reading something recently about IKEA. I don’t know if you saw that, but

As they automated with Genitive AI, their associates became more interior decorators. So I believe the customer experience is going to be enhanced because people will have bandwidth to connect with customers in ways they perhaps could not do so and be still cost-effective.

Shelley E. Kohan (03:00)
Definitely have heard about that and we’ve we’ve been talking a lot about that. And I think a big key to that success is the trust as the algorithm for AI access. And I know your book was written about that, but I also feel in retail, especially with a lot of brands, that the leaders right now are feeling very a lot of pressure to kind of keep up with AI.

And it comes from the board of directors, it comes from competitors, it comes from the market, comes from consumers. So let’s look at what does this pressure to keep up look like in real life.

Alice Sesay Pope (03:35)
Yeah. The pressure is real. The pressure is real. Everyone wants to work extremely fast to demonstrate they’re more efficient, they’re more product, they’re more productive, their cost to serve has decreased. And generative AI has a lot of advantages to lower costs to serve. But we also have to be cautious that in that process, that we have a clear framework.

That customers are comfortable, that they can trust the decisions that brands are making. But it even goes further than that: that the associates working in those retail organizations can trust that the corporations are making the decisions that are in their best interest.

So it’s it’s it’s an interesting time. In the book, I start with saying that we’re in a trust recession. We have more data than we’ve ever had before. Yet people are skeptical. And sometimes, even the way how in a retail organization you send an email to a customer to engage them, there’s the pause because customers don’t know: is this real or is this a scam?

Shelley E. Kohan (04:45)
That’s so true, Alice. So e every time I’m on the phone or I make a phone call with a company, I’m always asking myself, is this a real person or AI? So I think you’re right. I think there’s

Alice Sesay Pope (04:56)
Yeah.

Shelley E. Kohan (04:56)
a lot of kind of hesitation out there right now. And I do think retailers are really in a tough spot. So you you don’t want to fall behind, because then your competitors will get ahead of you, but you don’t wanna speed so fast ahead that you’re making the wrong decisions, right?

Alice Sesay Pope (05:11)
Mm-hmm. They are. The best places when I think of retail is figure out within your brand where is their customer friction and eliminate that friction.

And as you’re deploying these different use cases for generative AI, ensure that the experience you’re delivering is actually at the same standard that you were delivering before or better. Because if it deteriorates the customer experience, you might lose some customer loyalty, and customers have lots of choices these days.

Shelley E. Kohan (05:46)
Definitely. I like your idea. Like, you know, first start small with the friction points for whatever the retailer or brand is seeing with their existing target markets. can you think of or your rem share a story about when a company went so fast they actually had to stop and pull back an AI rollout?

Alice Sesay Pope (06:09)
Wow. I think in 2022, 2023, and even maybe even up to 2025 when companies were rushing so fast to launch generative AI solutions, everyone in the organization was launching solutions, and in some cases these features were similar.

And they weren’t tested properly. And when they were launched, there was hallucination. And when there was hallucination, there were customer complaints that escalated. And teams realized that they had to pull back. That they had to have some guardrails and a governance framework on how they launched these solutions. So we want to move fast, but ensure that as you’re launching these different features, that you have a plan.

That you have a plan, and I would I would suggest I would rather launch three things and launch those use cases extremely well than launch 15 to 20 use cases, and all of them are subpar or below customer expectations because customers are smart and they have very high expectations. So I would say

When solutions were launched in an uncoordinated manner and there was pressure to launch quickly, and the testing wasn’t as robust as needed. So, in in some cases, you can test

A generative AI feature in pilot face, and it’s great. And you’re all sitting at a table and you’re like, Wow, this is great! Customers are gonna love it, but you haven’t thought of all the customer since scenarios, and you launch it, and it’s a big customer detractor, and customers hate it, and they will let you know.

Shelley E. Kohan (08:01)
For sure. I want to go back to something you mentioned a minute ago about this ownership issue. Cause I think that you’re right. A few years ago, I mean, you have a lot of functions in retail. You have IT operations, marketing, you have people in the stores, online, e-commerce, on and on. So who actually is the owner of AI inside an organization that can coordinate these efforts?

Alice Sesay Pope (08:25)
And that is the million-dollar question. Great question. In many organizations, it’s not clear. And those gray lines has been problematic and what has caused a lot of chaos. For every use case, there needs to be an owner.

Because when something goes wrong, and it will, someone needs to be accountable to turn it off and know how to turn off that feature. someone needs to be accountable of how that feature is performing on a regular basis and when to and how it’s monitored and governored, governed. And I believe in the past.

As operators went to the board, it was tell us what use cases you have and how great they’re performing, and what the roadmap is, how fast are we going to launch all of those? But now organizations are stepping back, and the questions that boards are saying is: how are we governing these use cases? What is our mitigation plan? And if something goes wrong, who is accountable?

How would how do we detect if something goes wrong? And those are some questions that perhaps weren’t discussed and need to be a major discussion. And I would say even in terms of governance, it used to be the function that legal and compliance would think of the how governance should look like.

And now it needs to be something talked about in the very front design phase when the IT marketing and operators as well as legal and compliance are coming together.

Shelley E. Kohan (10:05)
Yeah, it is I think it’s really challenging to figure out that sweet spot because as much as you know, IT and legal are great at what they do, I’m not sure from an AI perspective that the AI strategy should own by those functions. There’s a lot of use cases where

marketing or stores or other functions in the business have the intellect and business knowledge to really put forth an AI strategy that resonates with that target market as opposed to just looking at the guardwell guardrails aspect of it. So it is it is tricky to figure that out. Do you think we’re gonna start seeing chief AI officer? I know we’ve already seen that a little bit, but is that going to become a predominant role in the C-suite?

Alice Sesay Pope (10:49)
believe it will. In some cases, it will be in the C suite. I’ve seen, you know, recently I’ve seen different flavors of it. I saw one organization that had a report to their P chief people officer that I thought this is kind of odd.

Shelley E. Kohan (11:03)
Interesting.

Alice Sesay Pope (11:04)
I’ve I’ve seen a report to the CIO. I’ve seen in other cases where it’s within the COO. It will, we’re in a learning phase. And

We’ll we’ll see how it ev it evolves. It probably also has a lot to do with the company culture on where on on where that function lands. And there may be some organizations that it becomes a C suite role that reports directly to the CEO. But I’m even noticing their chief AI officer cer certifications and programs now that some universities are having. So it it definitely is a it’s a new thing and it’s it’s kind of hot right now.

Shelley E. Kohan (11:46)
That’s really interesting. So I wrote an article, it must be six years ago, maybe more, about what I called the

Alice Sesay Pope (11:51)
Ha ha ha

Shelley E. Kohan (11:53)
knowledge deficiency gap. And that was the sales associates having less knowledge than the customers coming into the store. So fast forward six

Alice Sesay Pope (12:01)
Mm-hmm.

Shelley E. Kohan (12:01)
years now, and consumers then became ahead of the retailers in terms of knowledge and technologies and using.

different types of technologies. And if you look at the past year, I almost feel like the customer and consumers are actually ahead from an AI perspective. And now the company spent the last year trying to catch up. And now I think the companies are ahead of the customer. So do you have any insights about who’s who’s actually ahead of who? Is it the customer? Is it the companies? I’m sure it varies greatly, but generally, who’s being left behind right now?

Alice Sesay Pope (12:37)
It depends. It does vary. there are still some customers that are very savvy, and in the era of generative AI, they can teach themselves stuff with the generative AI tool. They can say, give me every information I need to know about this product. What are the different price points? Who are the part competitors? Who are so

Consumers can go to and and then they can compare. I if if they’re anyone like me, when Claude gives me an answer, then I go to Copilot, then I go to Gemini and say, How can I compare contrast what what model is giving me the best information?

I think the challenge is going to be how training changes. The training within organizations, how do we train sales professionals? How do we train customer support professionals in this era where information changes so quickly and is readily available?

Shelley E. Kohan (13:39)
Yeah, I think keeping up has always been a big challenge. I think actually retailers and brands are a lot more agile today than they’ve ever been and they’re very quick to kind of reset. but the other question I have is so how how how can companies measure, I would call it the company, the customer readiness to adopt AI in their shopping journeys. How can you measure it and how can companies

Just stay in that sweet spot of maybe being a little bit ahead but not too, you know, so ahead that it it the customers gives pause to, you know, what the company’s doing. How do you hit that sweet spot?

Alice Sesay Pope (14:19)
It’s understanding your customer friction points again. Do you do you understand the customer and know the things that have been painful for them to interact with your brand or the things that have required customer effort?

So I started measuring customer effort. How that question is, how easy is it for you to interact with this brand? Or sometimes that question is, how how much effort does it take for you to get your problem resolved? And if we can reduce the effort and give customer the confidence that we have increased their ability to understand the value that they have with the product, or we can provide them.

information before they even know that they have a product a problem those are gonna be the winners so for instance within a a device business if you’re able to send the customer a proactive message to say your network in your home is not working properly and therefore your Kindle won’t work and perhaps you are you have

You have permission up front to send them a text message. But once again, they have to trust that it’s not a scam. It’s not some. So it’s so it’s it’s it’s we’re in a very interesting time right now. They’re deep fakes. There, there’s even fakes in terms of people’s voices. I heard something recently that someone was able to mimic the voice of a leader and

within a banking environment and had a customer support professional transfer thousands of dollars and it was a deep fake that had mimicked that voice exactly.

Shelley E. Kohan (16:10)
That is that’s just so scary to think about that. I wanna go back to this customer readiness conversation real quick, because I I wanna give you an illustrative example. so we know, so two years ago, we knew the the example out there was, yeah, you’ll get a light bulb on your doorstep from Amazon, and then you open it up and you say, Wow, why do I need a light bulb? And then two hours later your light goes out.

Now that back then was

Alice Sesay Pope (16:36)
Mm-hmm.

Shelley E. Kohan (16:36)
kind of freaky, right? Consumers weren’t yet ready

Alice Sesay Pope (16:38)
Mm-hmm.

Shelley E. Kohan (16:39)
for that. today, you know, I have Oral B sends me a text message saying, your replacement brush needs to be replaced, your brush needs to be replaced, right? A replacement message.

Alice Sesay Pope (16:48)
Yeah, yeah.

Shelley E. Kohan (16:50)
I’m okay with that. But where are consumers on this, you know, predictive side of AI where they are being told up front that this product’s going to expire or

You know, your light bulb’s gonna go out. Have consumers moved past that or are they still a little bit sheepish sheepish on that?

Alice Sesay Pope (17:09)
I think we have to give customers the choice so that customers still believe that they have some control of their personal information and their preferences.

Give them the and they should be able to opt in or opt out. Because in some instances where we be may believe that we’re helping the customers, there are other customers that say this is really creepy and I don’t want that. So give the customer the option. Let them have some control.

Shelley E. Kohan (17:39)
And how do we then also, so you have the customers, and I do think you kind of have to somehow figure out which customers are ready and which ones aren’t. I know that’s easier said than done, but also now how do

Alice Sesay Pope (17:49)
Mm-hmm.

Shelley E. Kohan (17:50)
we then prepare the employees for this shift with AI and how do we build their trust because we know the employees are the front lines to the customer.

Alice Sesay Pope (17:59)
We’ve lost a lot of trust, I believe, within the past two years with the employer and employee relationship. and it’s because of a a a variety of reasons. Some of the forced return to office implications. There was some trust there where that existed. there was also instances where employees weren’t sure that they should

Give their opinion and their insights about customer preferences because they thought if they did, could it be automated and they would lose their job? but I believe we have to step back, work together within our organizations to have a conversation. What does the future of work look like? What does the future of retail look like?

And how do the employees as well as technology fit in that space? And be and have those hard, honest conversations. What roles will be eliminated? Because some will. According to the World Economic Forum, there’s 170 million jobs that will be created by 2030.

But there will be 92 million eliminated. So that’s the net game. If organizations can begin to seriously, and I use the word seriously because I believe we focus more on cost reduction, but if we can focus on the future of work,

At the same time thinking about cost reduction, we can get to that balance where we’re improving the overall customer experience at a better cost, then we’ll be able to provide better value, which will in turn lead to increased revenue and better overall customer loyalty.

And we need to spend the time to develop the strategy. So as we’re thinking about moving fast, and I talk about that in the book, don’t move so fast that you don’t have a clear roadmap and that you don’t have a strategy that you can communicate clearly to your employees that they can feel excited about, inspired, and that they can trust.

Shelley E. Kohan (20:19)
I love that. And I believe that is so true. And that trust factor is just so important, both with employees and with the customers as well. So the other thing that you talk about, if we could just take a moment to talk about AI as advisor and that really shifting to AI as autonomous reactor, so how does that raise the stakes on trust, governance, and accountability?

Alice Sesay Pope (20:43)
Wow, the stakes are really high. Because AI as an advisor, it depends on what kind of advice you’re giving, right? If you’re giving advice maybe about a fashion trend, not so bad. Give me a wrong advice. I can say, okay. If you’re giving an advice about my health, don’t make it wrong. You know, I may or may not trust that.

And in that that’s the same case if you’re giving advice about someone’s financial situation or their insurance claim.

Those are high-stake instances where the advice has to be or or the recommendation has to be right. And I would say the same thing when it’s an agentic feature that is taking action on behalf of the customer, we have to make sure that if the action is wrong, it can be reversed. And then we have to think about the impact that it has on a person.

and their livelihood. so so we we we need to really make sure that we spend the time up front thinking about all the use cases, thinking about the risks to the customer. What are those things that if we get it wrong erodes trust and if we lose that trust we can’t get it back.

Shelley E. Kohan (22:08)
Thank you, Alice, so much. Really enjoyed our conversation today. Do you have any closing thoughts that you’d like to add before we end our podcast?

Alice Sesay Pope (22:17)
Yeah,

we are in a trust recession and AI is here to stay. So we all have to embrace it. But as we embrace generative AI, let’s ensure that we have the right strategy that our customers can trust

and our associates that we work with can also trust. Thank you for having me here today, Shelley.

Shelley E. Kohan (22:40)
Well thanks, Alice.