Behind the Numbers: Using AI at Work: Part 2—How companies are using AI and tips for employees on how best to use it

Summary

Behind the Numbers: Using AI at Work - Part 2

Podcast Information:

• Title: Behind the Numbers: an EMARKETER Podcast
• Episode: Using AI at Work: Part 2—How companies are using AI and tips for employees on how best to use it
• Release Date: April 4, 2025
• Host: Marcus
• Guests: Arsene Alice (AI and Technology Analyst), Gajo Sevilla, Henry Powdery (SVP of Media Content and Strategy)
• Description: This episode delves into the practical applications of AI in the workplace, exploring how companies are leveraging AI technologies and providing actionable tips for employees to effectively integrate AI into their daily tasks.

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1. Introduction and Icebreaker

The episode kicks off with Marcus welcoming listeners and introducing the returning guests, Arsene Alice and Henry Powdery. To set a light-hearted tone, the hosts engage in an amusing discussion about extreme running feats:

• Henry Powdery shares, “It took him 20 months. That's a long time, Kevin. The greatest.” (01:11)
• The conversation humorously touches upon human endurance limits, emphasizing the challenges of long-term commitments, paralleling it subtly to the complexities of AI adoption in businesses.

2. The AI Backlash: Understanding Employee Resistance

The core discussion begins with Marcus referencing an insightful article by Grace Harmon, highlighting significant resistance among employees towards Generative AI (Gen AI) adoption.

• Key Statistics:
  • 41% of C Suite executives believe Gen AI is causing internal strife.
  • 31% of employees admit to sabotaging their company's AI strategies.
  • Higher sabotage rates are noted among Gen Z and millennials, with 1 in 10 workers tampering with performance metrics to undermine AI's effectiveness. (03:12)

Reasons for Resistance:

• Diminished Value and Creativity: 33%
• Fear of Job Replacement: 28%
• Increased Workload: 24% (03:12)

Gajo Sevilla probes, “Are we heading towards an employee AI backlash?” leading to a nuanced discussion:

• Gajo: “I mean, I wouldn’t say it’s wide scale, but possibly in certain situations, certain industries... if employees are left to their own devices.” (04:03)
• Henry Powdery: Emphasizes the leadership’s role in mitigating backlash by clearly communicating AI’s role as an augmentative tool rather than a replacement. “If companies could tell that story more clearly... empowering them with resources...” (07:04)

3. The Perception Gap: Executives vs. Employees

A significant chasm exists between how executives and employees perceive AI adoption within their companies.

• Survey Insights:
  • 70% of executives believe their AI strategies are strategic and successful.
  • Only 40% of employees share this sentiment, a 30 percentage point gap. (05:03)

Henry Powdery attributes this disconnect to inadequate communication and resource allocation: “...leader ship not giving any resources out to their teams to figure this stuff out.” (07:04)

4. Identifying Business Value: Overcoming the Productivity J Curve

Discussing the challenge of translating AI technology into tangible business value, the hosts reference Professor Eric Brynjolfsson’s concept of the "productivity J curve" where initial productivity may dip before rising.

• Gajo Sevilla: Advocates for aligning AI solutions with specific business outcomes such as cost reduction through automation or revenue growth via AI-driven personalization. “You’re trying to solve problems... AI is just a convenient and measurable tool...” (10:45)
• Henry Powdery: Suggests evaluating AI’s impact from a financial perspective, focusing on both revenue acceleration and cost reduction. “What can generative AI help you accelerate... reduce expenses...” (12:03)

5. AI Accuracy and Reliability Concerns

A critical examination of AI’s reliability, particularly in information accuracy, is presented through a study by Columbia Journalism Review and the Tao Center for Digital Journalism.

• Study Findings:
  • AI models incorrectly cited sources in over 60% of queries when tested with real news excerpts. (14:19)

Henry Powdery: Acknowledges the complexity of the news ecosystem and the inherent challenges for AI in accurately attributing sources. “The language models already have a challenge... the language model doesn't say I don't know...” (15:34)

Gajo Sevilla: Highlights the programming limitations of AI models, noting their tendency to generate responses even when uncertain. “They’re just programmed to have answers and solutions.” (15:38)

6. Practical Tips for Using AI at Work

The episode concludes with actionable advice for employees on effectively utilizing AI tools in their professional lives.

From Alex Fitzpatrick of Axio:

1. Be Specific: The more precise your requests to AI, the better the outcomes.
2. Follow Up: If the initial AI response is unsatisfactory, refine your request with clear instructions for improvement. (17:39)

From Henry Powdery:

1. Use Audio Interfaces: Recording ideas verbally to overcome writer’s block and streamline the proposal-writing process. “...record myself... transcribe the whole thing and just talk for 30 minutes...” (17:39)
2. Leverage AI Training Features: Utilizing tools like Claude Styles to train AI on personal writing styles, ensuring outputs align with individual voices. “By training it on some of the pieces that I'd written... it does a lot better job now.” (18:55)

From Gajo Sevilla:

1. Start Small with AI Pilots: Implement AI on a single team or department to measure effectiveness before wider adoption. “Pilot projects give us good feedback and most of the kinks are worked out...” (19:56)
2. Measure Success Effectively: Establish clear metrics such as time saved or error reduction to evaluate AI’s impact. “You could use time saved... error reduction...” (20:10)

Additional Tips from Marcus:

1. Check AI’s Work: Always fact-check AI-generated content to ensure accuracy, especially given the high incidence of AI hallucinations.
2. Be Polite: Incorporating courteous language can enhance AI chatbot interactions. “Researchers have found that using words like please and thank you improves AI chatbot performance.” (21:10)

7. Conclusion

Marcus wraps up the episode by thanking the guests and the production team, emphasizing the importance of ongoing dialogue about AI in the workplace. The hosts encourage listeners to apply the discussed strategies to navigate the evolving AI landscape effectively.

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Notable Quotes with Timestamps:

• Marcus: "If someone close to me said, 'Marcus, I'm thinking about running for three days straight,' I'd be like, don't." (02:15)
• Henry Powdery: "A lot of this is driven by a need to get more efficient, more nimble, more creative." (07:04)
• Gajo Sevilla: "The narrative should be that it's a tool that can help augment but not replace your employees." (07:33)
• Henry Powdery: "The bottom line is the bottom line where we're using these tools in order to run more efficient and more profitable businesses." (12:03)
• Marcus: "Check its work. Make sure you fact check all that stuff." (21:10)
• Henry Powdery: "Using audio as the interface... just let you kind of go." (17:39)

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Key Takeaways:

1. Employee Resistance to AI: Miscommunication and lack of resources contribute to employee sabotage and resistance towards AI adoption.
2. Leadership’s Role: Clear communication and positioning AI as an augmentative tool are crucial in minimizing backlash.
3. Bridging the Perception Gap: Aligning executive optimism with employee experiences through strategic implementation and support.
4. Ensuring AI Accuracy: Vigilance in fact-checking AI outputs is essential, especially in information-sensitive areas like news.
5. Practical AI Usage Tips: Specificity, follow-ups, audio interfaces, pilot projects, and personalized training can enhance AI’s effectiveness in the workplace.

By addressing these areas, companies can better integrate AI into their operations, fostering a collaborative environment where technology empowers rather than replaces employees.

Transcript

[Marcus]

And rapidly changing market Speed to insight is everything. AI search is the newest feature on your marketer Pro plus. It helps you streamline research, delivering context driven answers in seconds. No more endless searching, just relevant insights to power your strategy. Stay ahead with AI search. Why wouldn't you? Exclusively on Pro plus. Learn more on our website eMarketer.com hey gang. It's Friday, April 4th. Henry Garjo and listeners, welcome. Welcome to behind the Numbers and E Marketer video podcast. I'm Marcus and today we are discussing more using AI at work. This is part two of our two part episode. Part one was on Monday, if you want to go check that one out. For today's conversation, I'm joined again by the same folks we have with us, Arsene Alice, covering AI and all things technology. He lives in New York. It's Gajo Sevilla.

[Gajo Sevilla]

Hey, happy to be back.

[Marcus]

Hey, fella. We also have our SVP of media content and strategy. He lives up in Maine. It's Henry Powdery.

[Henry Powdery]

Hey, Marcus.

[Marcus]

Hello, sir. Today's fact, we start there. How long would it take you to run around the world? Any guesses?

[Henry Powdery]

Sounds like a trick question.

[Marcus]

I know it should be, but someone actually did this. So Englishman Kevin Carr did it. Just amazing. It took him 20 months. That's a long time, Kevin. The greatest. And that's a decent pace as well. It take most humans most of their life basically to run around the world. What a trip, though. The greatest distance run in 24 hours, close to 200 miles, which is what, nine marathons by Lithuanian Alexander Sorokin in August of 2021. Uh, he did the run in Poland. Um, Dean Karnaz holds the unofficial record for the longest run without sleep, 350 miles, which he ran over three and a half days back in 2005.

[Gajo Sevilla]

Without sleep.

[Marcus]

Can't be good for you.

[Gajo Sevilla]

No, no, no, Dean, who.

[Marcus]

If someone close to me said, marcus, I'm thinking about running for three days straight, I'd be like, don't, okay? I don't care about the records. Stop it. Oh my goodness. Remark.

[Gajo Sevilla]

Three days.

[Marcus]

Wow. Yeah, I'm exhausted already. It's barely midday. Three days. Trying to stay awake is tough. You kept running. Well played, Dean. Anyway, today's real topic, using AI at work, part two, how companies are using it, and some tips on how best to use it in the workplace. All right, gents, for this one, let's start here. So Stu runs a team sent me this article and it was from Grace Harmon, who writes for. She's part of our tech team basically.

[Gajo Sevilla]

And yeah, she's an analyst for the AI Tech Briefing. And that was a great piece that she put together.

[Marcus]

Fantastic piece, right? 41% of C Suite executives said adopting Gen AI is tearing their company apart and creating power struggles. So this was according to. This is what she wrote. This is according to Reuters 2025 survey. It found 31% of employees admit to sabotaging their company's Gen AI strategy, with even higher shares of younger Gen Z and millennial folks doing so. One in 10 workers are tampering with performance metrics to make it seem like AI is underperforming. Why, you may be asking. Grace explains that the main reasons for obstructing Gen AI strategies included AI's risk of diminishing their value and creativity, 33%, fears about taking over their job, 28% and a bigger workload, 24%. Gaja, I'll start with you. Are we heading towards an employee AI backlash?

[Gajo Sevilla]

I mean, I wouldn't say it's. I wouldn't say it's a wide scale, but, you know, possibly in certain situations, certain industries, I could see that happening. Especially if, like we said in the previous episode, if, you know, AI implementation is done a bit carelessly, saying, oh, you know, we need to adopt, we need to innovate, but at the same time, if, if employees are left to their, you know, own devices. Pretty much like this story that Grace put up. It says 49% of executives said employees are left on their own to figure out Gen AI. Yeah, I can see that being a point of friction because all of a sudden, above, over and above, what they're doing now, figuring out AI and how it makes the company better becomes something that they need to be taking care of as well, right?

[Marcus]

Yeah, Yeah. I mean, Henry, we talked about this bit in the last episode about how it seems like a lot of tech executives, the people building the AI, aren't trying to sugarcoat this. Matteo Wong of the Atlantic, writing as early as 2016, OpenAI CEO co founder Sam Altman said as technology continues to eliminate traditional jobs, new economic models might be necessary, such as Universal Basic Income. Ubi. He warned repeatedly since then, the AI will disrupt the Labor Market, telling Mr. Wong's colleague Ross Anderson of the Atlantic 2003 Jobs are definitely going to go away, full stop. AI employee backlash. How likely?

[Henry Powdery]

I mean, I think it's likely and clearly happening based on this survey. And I think it's the responsibility of leadership in this example to change the perception. Because if the C suite executives in this survey feel like it's tearing their company apart. They're obviously making it a priority while at the same time not giving any resources out to their teams to figure this stuff out. So that's going to leave people feeling, you know, a, that they're just trying to be forced out or eventually their role will be replaced by an AI. And that's, you know, that could be the case in some positions, but in many that is not the case. I think a lot of this is driven by a need to get more efficient, more nimble, more creative. I think over the past few years, companies have been asked to do a lot more with less. And one of the ways I look at Gen AI is perhaps giving companies a chance to reclaim some of that, you know, mental load backed for the thinking, for the strategizing, for the developing. And I think that if they could tell that story more clearly to their employees, there'd be a lot less backlash, while at the same time empowering them with resources to be able to learn how to use all of these things.

[Gajo Sevilla]

Yeah, I agree with that. I mean, I think the narrative should be that it's a tool that can help augment but not replace, you know, your, your employees and, you know, using AI for things like, you know, support or, or just doing the more menial, you know, time sucking tasks. That could make a big difference in a eight, you know, in an eight hour workday. Right?

[Marcus]

Yeah.

[Gajo Sevilla]

If applied properly again.

[Marcus]

Right, yeah. Saying we want to take this off your plate and then saying, so we can have you do this other stu as opposed to we want to take this off your plate. And people are looking around thinking, okay, well then what am I supposed to do? There does seem like there's to be a chasm. There does seem like there is a chasm between how employees view AI adoption, how they think it's going at their company versus how the C suite executives see things. There was a writer survey. We were citing them in the first episode on Monday. They found 70% of executives felt their company's approach to AI had been strategic, successful and that the business was AI literate. That number falls from 70 to closer to 40% for how employees view their businesses AI adoption. So a 30 percentage point chasm between how companies think it's going higher level people and then the people on the ground. And so, Henry, it does feel as though the AI use at work, it could end up feeling a bit ad hoc. I was thinking this a few days before seeing these numbers, actually. I was thinking, don't businesses need to outline a clear AI strategy And then two days later found this writer survey. And in it, they had specifically 90% of execs saying their company has an AI strategy. That number falls to 57% when employees were asked. So a big part of this. Part of this has to be just confronting this thing head on. It's kind of like we were talking about on another episode about kids using AI for homework. It's one thing to. You can't ignore it. You have to address it and say, okay, if you're going to use it, this is the right way to use it. This is the wrong way to use it. Not just, let's hope that they don't use it. Let's kind of ignore the elephant in the room.

[Henry Powdery]

Yeah, I love that. And I also think that the other question is, are they communicating what they're going to do as a company when they realize what they're trying to gain by using AI? If it's time savings, what are we going to do with that 30%? I mean, I could think of three positions on my team I would love to hire for in order to grow and do different things. But, you know, we're all working under our own budgets and our own realities. And so I think that, you know, not to be idealistic, because of course, you know, one side of efficiency is unfortunately, you know. Right. Sizing a business. But on the other hand, it can be, you know, investing and building into business. And so I think the communication has to be as clear as possible.

[Marcus]

Yeah, yeah. So let's talk about how to figure out how people can figure out where AI and businesses and employees of those businesses can figure out where best to use AI. Professor and senior fellow at the Stanford Institute for Human Centered AI, Eric Brynjolfsson was saying there's always this difficulty of translating even the most amazing, or maybe especially the most amazing technologies into productivity and business value, calling it the productivity J curve, because it sometimes gets even worse. Worse before it gets better. He was saying, we saw it with electricity, the steam engine, early computers. We're seeing it now. The real challenge, the bottleneck, is figuring out how to identify business value. So, Gaja, I'll start with you. How do you identify business value when it comes to figuring out where to inject AI into the company?

[Gajo Sevilla]

I would try and match AI solutions to certain outcomes. So you're trying to solve problems, whether it's cost reduction. You could do that through automation, maybe cutting down on repetitive workflow, things like invoice processing, data encoding, you know, taking that into consideration, or even, like, revenue growth. You know, through, you know, AI driven personalization, dynamic pricing, chatbots for your, for your sales and marketing. Clearly you're trying to solve for specific problems, right? And sometimes it's a situation where you could have any types of solutions to fit that, but then AI is just a convenient and measurable and available tool that can quickly show you that it's working. Right?

[Marcus]

Yeah. Yeah. Henry, how about for you? Where do folks start when they're looking at this? Do they just write down on the whiteboard a list of all the problems that they're having and then figure out, okay, let's prioritize them, let's rank them, start with number one and then go from there. How do we find the tool to fix the problem?

[Henry Powdery]

I mean, I think that's one way, but when you're talking about business value, you're talking about money, right? And so I think that's what you need to look like. You look at it from two sides. What can you do that's making you money? That gen AI is going to help you accelerate and what is costing you money, that generative AI can help you reduce that kind of expense. And with the product side, I think it's, you know what, what more can you make is by personalizing all of your messaging, do you get an X amount, lift in conversion rate and what does that translate into, you know, the cost per acquisition? I think there's, there's a lot of those equations that need to be worked out, but I think that's where you start because the bottom line is the bottom line where we're using these tools in order to run more efficient and more profitable businesses.

[Marcus]

One thing that is, it's kind of a bit of a paradox because on the one hand, people are being told, maybe people having a bit of cognitive dissonance or what, you know, people are being told, use AI, it's making things faster, more efficient, better, improving the quality, etc. But then they're being told, slow down, these tools aren't perfectly accurate and you can't trust everything that you're, you're, you're getting from the answers from these things from, because they're hallucinating, which is when they make up answers, when they don't have the, when they can't find the actual one. There was a new study from Columbia Journalism Reviews, Tao center for Digital Journalism, and it found serious accuracy issues with gen AI models used for new searches. This is an article from Benj Edwards of Ars Technica. He was explaining that the researchers tested eight AI driven search tools by providing direct AI excerpts from real news articles and asking the models to identify each article's original headline, publisher, publication date and URL. And they discovered that the AI models incorrectly cited sources in over 60% of these queries, raising significant concerns about the reliability incorrectly attributing news content. Henry, I mean, how, what do you make of this new study about AI model inaccuracies and how do people get around this kind of industry wide issue?

[Henry Powdery]

Yeah, I mean I'm not surprised that this is what they saw when looking at news specifically because when you're talking about news online, it is a much completely different ecosystem than informational queries like things that Wikipedia or content marketing sites are going to show. The news landscape is full of small players, scrapers. I mean, I think even the study cited how often that Yahoo News was the source, which was just aggregating the original source of news. So I think the language models already have a challenge when it comes to disseminating the most authoritative news sources for these queries. And at the same time, a lot of the top publishers that perhaps have the most trust and authority are blocking these crawlers in their robots TXT protocol. Even though the study did note that they did find some instances where they were going around it. I just think it's a really, a really complex environment and I'm not surprised that the language models are struggling with it. And it's more problematic because the language model doesn't say I don't know when it's confused, it makes up an answer. I think that was one of the problems that they noted in the study as well.

[Marcus]

Why is that? Why can't these models just say I don't know.

[Gajo Sevilla]

I think they're just programmed to have answers and solutions. Right?

[Marcus]

Yeah.

[Gajo Sevilla]

And rather than saying no, I, I don't know that or I can't act that they'll give you something that's less than useful.

[Marcus]

Yeah, I mean that's people though, isn't it? I mean, I can't remember. It's hard. I feel like conversations with people, it's very rare that when you ask someone something they say I don't know, they will confidently just kind of say an answer and then you kind of run with it or they'll kind of talk around an answer and try to figure out in real time. So. And these things are designed by people, so maybe it's just a reflection of society. Is that people don't ever say to you I don't know.

[Gajo Sevilla]

Yeah. And you know what? Even voice assistants hit that Wall. If you talk to, you know, to Google or see if they don't know, they'll just say, I don't know. But I found this on the web.

[Marcus]

Yeah.

[Gajo Sevilla]

In other words, figure it out, because this is all I have. Right? Yeah, but, but I guess the language models don't have that built into them, so they need to reason an answer. As with anything, the data readiness is the huge factor there. Are they using clean structured data or just basically rehashed aggregated news, which in itself is problematic.

[Marcus]

Right, yeah. Let's end the conversation with some tips for using AI at work. I'll start with two from Alex Fitzpatrick of Axio recently outlined five in an article. I'll give you two of them. Be specific. The more precise you can be with your request, the better the outcome. And then number two, follow up. He says, if your AI's first output is off the mark, try to follow up. Try a follow up request with instructions for improvement. And again, be specific. Two from him. Henry, I'll go to first. What two would you offer? What two tips would you offer on how best to use AI at work?

[Henry Powdery]

One of the things I've been really experimenting with and it's been helping me a lot is using audio as the interface. And that means recording myself. So somebody wants me to write them a proposal rather than just opening a blank page and starting to kind of type away. What I'm starting to do now is just hit record on an Apple note and transcribe the whole thing and just talk for 30 minutes, talk out all of my ideas and then give that transcript to something like Claude and Query and use that to try to come up with an ultimate proposal. I've been using that for longer form things. I've used it for writing a newsletter. I find that it is a huge time saver and it really just kind of takes that blank page syndrome out of the equation and just lets you kind of go. So that's tip number one. I think that's a really interesting one.

[Marcus]

And that's something that people say you should do when you're speaking to a person as well. They're like, get your ideas out there, talk them out, talk them through. So that seems.

[Henry Powdery]

And not everybody can do that by typing. I mean, there's a bit more. It's more imposing to be staring at that blank page. But I found that just recording yourself is great. I work from home, so it makes it a little bit easier for me to do that than if I were sitting in an office surrounded by a lot of people.

[Marcus]

Got you don't even think about it.

[Henry Powdery]

And then my second tip is I've been using Claude Styles a lot more. So if you use Claude, you can train it to write or respond in a way. You can give it past examples of your writing or past examples of reports or any kind of example that you want to emulate. And it does a really good job of helping you come up with a style standard so that once you train it, it's much easier to get work that comes out of it that really feels like you like the example I said with the audio recording the first time I started feeding it into the AI and asked it for, gave me a memo based on my transcript. It sounded very robotic and very not me, but by training it on some of the pieces that I'd written over time and really getting it to hone in on my voice, it does a lot better job now. So Claude Styles have been a real helpful tip.

[Marcus]

Very good. Gaja, what do you have for us?

[Gajo Sevilla]

Yeah, so mine are more general, but I think can be applied to a lot of situations. So I think when piloting AI tools, you should start small. So just use it on, on one team, one department. You know, that way you can, you can, it's. It's easier to measure whether what works, what doesn't, and then that can be replicated. And I know we've done this at eMarketer as well. We have pilot projects which, you know, give us good feedback and most of the kinks are worked out when it's rolled out into, into larger groups also, I mean, find ways to measure success. I mean, AI can be so nebulous, but you really want to know how it's helping, what are the benefits? So, I mean, you could use time saved, perhaps for certain tasks. A big one would be error reduction. If you can manage to tailor AI so that it could help in those areas, then that's something you can bring back to your, you know, your manager, your board, and say, look, this is working, let's do more of it. Right?

[Marcus]

Yeah, so those are mine. Very nice. The two I'll note quickly again from Mr. Fitzpatrick's article, but I think are really, really good. One of them is check its work. You know, there is a disclaimer at the bottom of the lot. These models, especially the free ones, saying, it's AI, it's a work in progress, but still they make stuff up. So just, so spot check, make sure you fact check all that, all that stuff. And then secondly, I thought this was interesting. He says, be polite and he was like, no, really? Researchers have found that using words like please and thank you improves AI chatbots performance. So yeah, another. Another good tip there. That's what we have time for for today's episode. Thank you so, so much to my two guests for hanging out with me at the start of the week, the end of the week. Thank you to Gajay.

[Gajo Sevilla]

Thanks again.

[Marcus]

Yes, sir. Thank you to Henry.

[Henry Powdery]

Thank you.

[Marcus]

Absolutely. Thanks to the whole editing crew, Victoria, John and Danny. Not Lance because I asked him to help me with my new camera and he ghosted me for a week. Unbelievable. Thanks to Stuart though. He runs the team and Sophie who does our social media. Thanks to us at Truestory. Thanks to everyone for listening in to the behind the Numbers show. That's not true. An email is a podcast. New marketer video podcast at that. We'll see you again on Monday, hopefully. Happiest of weekends.