Paula:
Welcome to The AI Factor, the podcast that dives into how AI is transforming business, communications and the world at large. In each episode, we bring you powerful conversations with innovators and industry leaders who are making AI work in the real world. Today’s episode is part of our AI Unleashed series where we talk with customers, developers, and operators on the front lines of AI transformation. Our guest is Danielle Anderson, Vice President of AI Customer Implementation here at IntelePeer. Danielle has helped countless organizations move from AI ambition to AI action, and she’s here to help you do the same. From setting your AI strategy to managing the cultural shifts that come with agentic AI, we’re unlocking the blueprint for long-term success. Let’s get into it. Danielle, welcome.

Danielle Anderson:
Thank you, Paula, how are you?

Paula:
Very well, thank you. I’m doing good on this hot summer day. Apple Intelligence came out about a year ago, and it’s really played a pivotal part of Apple’s recent developments as it comes to artificial intelligence. One of the things I like about Apple Intelligence is its branding. Apple is so spot on and great with branding, and they talk about Apple Intelligence as a personal intelligence system designed to enhance user workflows. So Danielle, you are someone who you help companies day in and day out integrate AI into their business. How much does naming a narrative matter when it comes to getting user and employee buy-in for AI tools?

Danielle Anderson:
It matters more than most people think. I think it’s important to, from a contextual standpoint, give a name to the system that really is going to help alleviate some of the noise and frustration for all of your employees and be a support function to them to help reduce some of those administrative tasks, take that away from them. And give them an opportunity to really focus on some of the more complex issues that your customers most definitely need, and really be able to remove some of the problems and the muck from the system and get them to a place where they can give you some really impactful results for not only your customers, but for the business as a whole.

Paula:
Nice. And I know we are going to talk a little bit more about this in the back, kind of getting that employee buy-in into this transformation. So the back half of the call, we’ll definitely dive into that deeper. But before we get there, let’s talk about some best practices. There are numerous essential building blocks for success with any project implementation. We need to talk about what steps every organization should do before launching their AI initiative. Danielle, what are the top three foundational steps every organization should take before rolling out AI?

Danielle Anderson:
The first one is to clarify the business problem that you’re trying to solve. If you really can’t tie AI to an operational pain point or revenue opportunity, it’s not necessarily going to be as beneficial for you and your team won’t understand the importance of it.

Second is to assess your data readiness, and I think this point probably needs to be stressed more, but at the end of the day, if you have garbage in, you’re going to have garbage out. So if you are not prepared to be able to give AI the information that it needs in order to be able to best assist you, you need to do a little groundwork on getting that data ready so that you know exactly what you want from AI, and AI has the tools that it needs in order to be able to help you.

And then third is about cross-functional alignment. There’ve been so many times where I’ve had conversations with executive sponsors, project managers, and individuals that are on the front lines, and there’s not necessarily a complete agreement around how we’re going to use things and how we’re all going to work together as a team to bring a good AI solution to market. And the best way to me to do that is to just remove the silos, make sure that everybody owns the ultimate vision and goal of moving forward with AI, and then you come together and have a plan on how each individual organization is going to help with achieving that goal.

Paula:
Excellent. So quick question. I always talk about, I think almost every episode of AI Factor at some point or another, not directly saying garbage in, garbage out. I love your boldness.

Danielle Anderson:
Sorry.

Paula:
Oh, no, I love it. I love it. What about those companies who just really never had a solid data foundation and they want to start with AI, but they’re looking at pretty much garbage and a mess? Is there anything that they can do specifically that would help them? Maybe it’s coming to a starting point, like you don’t have to have all your data from day one of your business, but data from the past year or something. Are there any tips you could give regarding that?

Danielle Anderson:
If you’re not completely ready to run, I think that it’s best to start with the crawl option. Bring the data that you have to the table that hopefully is fairly clean, and then use that data to come up with the use case that you have for AI to help you reduce some of your pain points and move forward with the right mindset, hopefully, some now new data discipline type of strategies. And then add capacity and various different use cases as you and your company mature along the pipeline of having good data to avoid the garbage in, garbage out, and as you continue to evolve with the AI component that you’ve now added to your organization toolkit.

Paula:
Oh, I love it. And I think that’s super helpful for those folks. I personally believe that a lot of organizations are out there with a lot of data that isn’t “clean” and they are interested in going down the AI path, but they have a big kind of task in front of them, which is getting some degree ready. So I really appreciate those insights. What’s the biggest misconception about being ready for AI?

Danielle Anderson:
I would say the biggest misconception is also waiting for perfection. Most of the new things that kind of come up aren’t necessarily always perfect. The first MP3 player that came out wasn’t perfect, the first search engine that came out wasn’t perfect. But it went to market, it made sure that it had some trials, they continued to learn from it, test it, put it through different use cases until they got to a point where it was going to be sustainable and efficient. And that really is how you need to approach AI. I don’t think it’s any different than any other technology we’ve already dealt with as a human race from the first people that created the wheel to the people who have now bought us authentic AI.

Paula:
I love it, and I appreciate not waiting for perfection because if you are waiting for perfection, you’ll never get anything done. So how would you align AI initiatives with business goals? And how would you advise people not to spend their time chasing those “shiny objects?”

Danielle Anderson:
To me, the best way to avoid chasing shiny objects is to always tie your AI use cases to measurable KPI, key performance indicators, revenue, reducing costs, reducing handling time, the key drivers that we know help to make businesses profitable. And as long as you always begin with that end in mind, you don’t have to worry about whether or not you’re chasing a shiny object. If it just so happens to be shiny, that’s great, but at the end of the day, if it’s actually giving you measurable outcomes, it’s definitely served its purpose.

Paula:
I appreciate that. And it’s funny, on our recent podcast I just did with our partner team, we were giving partners some advice as to what to do, and one of the things was in addition to setting KPIs, it’s sort of setting that baseline, where are you today? And then from there, establish the KPIs so you kind of know what you’re working off of and where the points of improvement are. I think that’s applicable across the board, whether you’re a partner of ours or whether you’re a customer, those KPIs really couldn’t be more important to establish those and make sure you are working toward them. And hopefully a few of them will be shiny. We like shiny things. So even with the best intentions, AI implementations can go sideways. Everything in life goes sideways. Let’s talk about what not to do and how to recover if you hit a wall. Danielle, what’s the most common mistake you see in failed or stalled AI implementations?

Danielle Anderson:
With any AI company that you work with, they are going to be the experts in their AI solution that they’re trying to deploy with you. And what I’ve seen time and time again is that people underestimate the amount of feedback and assistance the company will need in order to complete the implementation. Because while we’re experts at IntelePeer, we need you to help us so that we can become experts on your company so that we can really understand what you do and the issues that you have. So I think the biggest pitfall I see is basically that data gathering that we’re talking about around some of the data discipline, and having proper conversations around what you really need and bringing those individuals the table so that we can have a good discovery session to help us to prepare to create those good outcomes, those successful KPIs that you need so that we can optimize the IntelePeer solution for our clients.

Paula:
I never really thought about it like that. I love it. So how can teams avoid pilot purgatory?

Danielle Anderson:
If you fail to plan, you plan to fail. It’s an oldie, but it really works. I’m a strong champion for always setting a date. Even if it’s penciled in, if it’s a dream, if we’re not even sure we’re going to be able to get there, we need a stop-gate for the pilot. We need a stop-gate really for any milestone that we’re trying to achieve. And I don’t think that those milestones, like I said, they’re penciled in, they’re not permanent, but we have to begin with a plan for every go-live, for the sites that we’re going to add, for any additional use cases that we want to do, and of course it adjusts as we move forward.

So just like using AI, you kind of do that crawl, walk, run strategy. You do the same thing with your project plan because as you go about finishing one pilot, moving forward with the next, and adding more sites to it, you learn more and that schedule that you penciled in now becomes something you feel more comfortable putting in ink. Sometimes I think that people are like, “Oh, we can wait on that one.” And I completely disagree. I think that if you know what your goal is and you set that goal, it makes it a lot easier for the entire project team to rally around achieving it.

Paula:
Wow, I like that. I think I need a Danielle in my life to help me set dates for things, but as you were talking, I was kind of laughing to myself. I’m like, that makes total sense. And pencil it in. It doesn’t have to start out being in pen. What advice do you have for leaders when AI project isn’t delivering as expected? And that could be part of the, you didn’t plan right from the beginning, although I don’t think your customers face that, Danielle. I think you’re a planner, but what advice could you give to those leaders?

Danielle Anderson:
[inaudible 00:10:57] like I’ve been again, every one of these, with them, but the road to success is really just paved with your failures. Do a retrospective. Let’s look at lessons learned. This is just something that we’re learning and we’re all kind of working together to find out what goes best. Let’s just be honest and transparent about what happened so that we can take that information and use it for future use cases. I mean, it’s something that we have to reframe. Maybe we have to relaunch the AI solution. But with the outcomes that we’re able to achieve, it’s really important to understand what went wrong, what went well, and how we can replicate that part, and then how we can mitigate the issues that may have caused us to have so many of those bumps and bruises as we were kind of going through the plan. That’s also to me why from the previous question, why that plan kind of starts in pencil and ends in ink because we learn with each one of the failures ironically enough that we have along the way.

Paula:

Wonderful. And I’m going to quote Nick Cage and I think Nick Cage was quoting Thomas Edison. I don’t know if this was true or fabricated for the movie, but what was it Edison said? “I didn’t fail 1,000 times making a light bulb. I’ve succeeded in making a light bulb.” He kind of just totally bypassed the failure. I don’t think I quoted that right.

Danielle Anderson:
I know what you’re talking about though. It’s the perfect reframe.

Paula:
It’s a great way of looking at it. You just reframe your failures. They weren’t failures. They were like getting me one step closer.

Danielle Anderson:
Exactly.

Paula:
So AI transformation isn’t just technical, it’s emotional, organizational and personal, kind of like what we were talking about in the beginning of the interview. Successful change management can make or break adoption. So Danielle, tell us how change management for AI differs from other digital transformations?

Danielle Anderson:
On top of managing the various different components from a technical aspect that you have to change, you really have to manage the adoption and the emotional impact that it’s going to have on your company and your customer base. That’s really just the human element along with it. The best way to me to manage that is to make sure that you’re incorporating those individuals along the way so if they feel like they’re a part of the process and they get to be a part of the feedback loop, to make it easier for them from an emotional standpoint.

Paula:
Okay, that’s fair. I like that. What strategies have you seen work well in helping employees adopt new AI powered workflows?

Danielle Anderson:
Letting them be a part of the user acceptance testing process is really the kicker. Also, choosing the individuals that are going to be a part of the pilot is essential to success. Bringing your early adopters, the individuals who like new technology want to try out new things, or going to try to break it, for lack of a better word. Because those people will become your social media butterflies that will spread the word about what it can do and what it’s going to bring to the organization before you even have an opportunity to announce it to a group. And that really helps to kind of raise awareness and to make sure that the company is excited about it and employees are ready for it. Because it’s one thing to hear it from the executives and leadership, but it’s another thing to hear it from people like you who are going to benefit from it every day. And that’s extremely important, not only in AI, but to me in any transformation that you actually complete.

Paula:
I like that and I agree 100%. I’m a proponent of not filling the room but getting at least one constructive naysayer as part of the project. And I’m saying that because you don’t want someone who’s just like, “Absolutely not. I’m never going to adopt this.” But you want someone who will have an open mind, even if they’re not keen on it, they have an open mind and I feel like they bring a degree of levity and kind of a real world perspective of how are the other employees, not all employees are early adopters, so how are they to be receptive of what we’re rolling out and what we’re doing? Any thoughts or any kind of stories with a constructive naysayer, shall we call them?

Danielle Anderson:
I like to call those individuals aggravated fans.

Paula:
Aggravated fans?

Danielle Anderson:
Yes. I bring those people in really early. I think it’s good to have a champion and then that aggravated fan that’s going to come in and literally pick it apart as many different ways as they possibly can. That gives you some very different perspectives, both at the various different ends of the spectrum. And it really helps to build trust in the system when you turn that person around and you make them understand the benefit that they’re going to receive. And they go from aggravated fans to just a raving fan. And that to me is … It’s hard, it can be trying, but it’s the best way to increase the adoption of your solution with your direct reports.

Paula:
So Danielle, who should be involved in change management and what roles are critical?

Danielle Anderson:
I think the most critical role is the executive sponsor. That’s the person that’s got the strategic vision, hopefully a very good view of the expected outcomes and the KPIs and the one that’s going to gain us the most buy-in from the employees that report up to them.

Paula:
I like that. And I think if you have a strong executive sponsor, they’re great at helping you break down any walls that may exist with other executives.

Danielle Anderson:
Absolutely. They’ve hopefully thought four or five steps ahead, so they’re already prepared for some of that and they kind of help guide you through that as well.

Paula:
Excellent. So agentic AI isn’t just a tool, it’s a teammate, but that shift requires a new mindset. Let’s explore the cultural change needed to work with AI and not just use it. How do you get teams to see agentic AI as a coworker rather than just another system?

Danielle Anderson:
I think for that, it’s really about showing the impact that new agentic AI will kind of bring to the table, and it’s about clearing some of the noise from their system so that they can focus on their higher impact work, like I said earlier. And so I try to position it as operational leverage. That make sense? We’re removing friction, we’re not necessarily bonding as coworkers, but I’m somebody who is helping to relieve you from a lot of these administrative duties so that you can really focus on the hard and fast things that’ll really help increase our return on investment and bring us to hopefully a better standing at the organization.

Paula:
Yes, I concur with that completely and I myself have been using it as part of my work. And it’s funny, I mean, it’s as you said, it’s not perfect. So sometimes things don’t work out in my favor, but by and large, it’s amazing how much of that administrative ankle biting it handles. So what mindset shifts do leaders need to champion when it comes to working with AI?

Danielle Anderson:
In the future, it’s not going to be about knowing everything. It’s really going to be about who can orchestrate with a various different set of tools to be able to find out all the information and intelligence that they need at that time. And so it’s about trusting your AI agents, trusting your team to evolve and giving them the tools necessary so that they don’t have to become experts or know everything in one field, but they definitely need to know how to use those AI tools in order to be able to find out.

Paula:
Yup. So any advice for balancing excitement and fear in the workplace when it comes to AI? I think some of the fear is driven by media who they’re looking for the clicks, so of course they’re going to talk about the scary elements versus the positives.

Danielle Anderson:
I think you really just have to do it scared. I was at an event last week and they were talking about how afraid people were when the TI calculators came out. You remember those Texas Instruments calculators, and they’ve really helped us out a lot, but people were super afraid of those devices when they came to market. But if you really lean into that fear and kind of do it anyway, I think that there’s a light at the end of that tunnel. We just have to get over that fear, go through that dark tunnel and come out to that light on the end where it’s a little less confusion, a little more realization that, oh my gosh, this is great data, great stuff that we can utilize and just try something before it’s been completely vetted, if that makes sense.

Paula:
It totally makes sense. And as you were talking, it’s funny, I started thinking, I think even just computers in general, I remember when we first started using Word and it wasn’t as, I’m going to say user-friendly or idiot-proof as it is today. You had to consciously go in and save your stuff versus it now just automatically does it. So I think also as the AI matures, there’ll be a degree of user-friendliness added to it that should hopefully break down some of the fear that we might see from our colleagues.

Danielle Anderson:
I completely agree.

Paula:
Well, I’m glad, that makes me happy. So before we wrap up, I like to switch gears. Danielle, we end each episode with a quick round of rapid fire questions. It helps listeners get to know you a little bit more than your title and the wonderful information you’ve just provided us with. I’m going to give you three questions. You can respond with a single answer or if you want to actually expound upon your thoughts, we’re always here to listen. So let’s kick this off. What’s one tech tool or app you can’t live without?

Danielle Anderson:
I don’t know if I could choose just one. I love everything with an AI add-on. So whether it’s ChatGPT or Copilot or an AI tool embedded in my project tools, anything that’s about smart acceleration, I’m trying it. I am practicing what I preach and I’m really strongly focused on the outcomes and the key performance indicators that are important to me, one being my return on investment and the ROI that I’m getting out the solution. And a big strategic focus for me when I’m using any of these tools is how I can find a way to scale my impact, hopefully really large, and scale down the amount of workload I have, if that makes sense.

Paula:
Oh, that makes total sense. And I think everybody should look at it like that, truthfully. This isn’t one of my three rapid fire questions, but you made a comment in passing to me about how you have an AI agent for almost everything.

Danielle Anderson:
I do.

Paula:
Now, is this just like you went into ChatGPT and set up agents?

Danielle Anderson:
ChatGPT, I’m doing it in Claude, Perplexity. I’ve got so many and I pit them against each other, and I’m trying to see who’s going to give me the best answer. Cursor actually allows you to sample them all. And I think that’s probably the worst thing that could have happened to me because I’m like, oh my gosh, I don’t have to change platforms. I can just use one. So yes, I am making them compete. It is the war for information, if that makes sense.

Paula:
So I’m totally going off of these rapid fire questions, but what’s the most favorite agent that you’ve developed thus far?

Danielle Anderson:
Honestly, ChatGPT does give me the ability … It does have a lot of my information because it was the first one that I used. So I use it from a health standpoint with helping me with my diet and exercise routine and kind of my own personal motivation. And I think just because it happens to have the most data, it’s my favorite because it really has become my partner, my personal fitness guru, and it’s really helped me to kind of go through a funk and get out of a funk when it comes down to some of my workouts, if that makes sense.

Paula:
Oh, yes. And now as you’re talking, I’m like, wow, I should do that now. My thing is I need the AI to actually do the exercising for me.

Danielle Anderson:
That’s a good one right there. I need that.

Paula:
All right, back to our questions. What’s your go-to song or if you want performer or a band when you need a boost of energy?

Danielle Anderson:
So ironically enough, I’m not really into musicals, but when I heard Hamilton, I think I learned every song. And My Shot by Lin-Manuel from Hamilton is my absolute favorite. It’s motivational, it really pumps you up. It’s about legacy, it’s about challenge. When I need a good pick me up, I put that song on my phone and let it go through the speakers, and it really does make me feel motivated to do whatever’s in front of me. If fear comes up and I put on My Shot, fear is gone.

Paula:
Excellent. And last question, if you could co-work with an AI version of any fictional character, who would it be and why?

Danielle Anderson:
I’m going to have to say Olivia Pope from Scandal because she’s very strategic, cool and poised under pressure, always five steps ahead. So if I could pair her with my real-time data and instincts, I’d be absolutely unstoppable.

Paula:
Wow. Well, Danielle, you strike me as a person who is cool and poised under pressure yourself.

Danielle Anderson:
Thank you.

Paula:
So I’m going to thank you for joining us today. I really always enjoy speaking with you. It’s so insightful and you have such a positive outlook on things. It’s very refreshing. So thank you, Danielle.

Danielle Anderson:
Well, Paula, I want to thank you for giving me my shot.

Paula:
Always a pleasure. Thanks for joining us on The AI Factor. Huge thank you to Danielle Anderson for sharing real world insights on how to roll out AI the right way. If I have my way, I’ll have her back in October to talk about some AI implementation nightmares. If there’s one takeaway, it’s this. Success with AI isn’t just about the tech. It’s about your people, your culture, and your readiness to evolve. Be sure to subscribe to The AI Factor on your favorite platform. And if you enjoyed today’s episode, leave us a review and share it with your colleagues who are ready to take their AI plans to the next level. Until next time, keep unleashing the power of AI.