(00:00:01) – Hey, everybody, it’s Jenni from GrowthMode Marketing. You’re listening to Demand Gen Fix, the podcast where our team of GrowthModers and our guests discuss the ins and outs of demand generation, and why we believe it’s the key to long-term sustainable growth, especially in the HR tech industry.
(00:00:20) – Hi everyone. Welcome back to another episode of the The Demand Gen Fix podcast. This is Deanna and today we’re going to be talking about selling AI in the tech market. It’s a new frontier. Of course, AI has been around for years, but it has really taken off in the last year or two within the HR tech market, and it seems nearly every solution includes some level of AI baked into its capabilities. And as marketers, many of us are leading with this technology in our message positioning as everyone tries to ride this wave in the quest to win more market share in the very crowded HR tech landscape, but how are HR tech buyers responding to this positioning, and what makes this sale different from the past HR tech solutions? Here to talk about this with me is my friend Bennett Sung.
(00:01:10) – Bennett is an HR tech marketing leader who has been around the block, and he currently serves as fractional CMO at MeBeBot, which I love saying that name, Bennett.
(00:01:20) – I love it, love it.
(00:01:23) – It’s an AI technology tool designed to improve employee productivity. So Bennett, welcome to the show.
(00:01:30) – Thank you Deanna. Appreciate it. I’m very excited to talk about this topic. As for the past almost two years, we’ve been living and breathing this particular challenge of seeing a pivot in the sale buying experience of HR tech solutions that have embraced this AI capability. And it definitely takes me back to my product marketing times in 2006, when I was introducing the applicant tracking system in the cloud. It has a very similar feel in terms of the buyer; the real emphasis on technical;
ITs contribution to the buying process. And so, I look forward to talking about all my different experiences of it, talking about do we include AI in our messaging, or do we stay away from AI? What is the buyers appetite? Are they fearful of the word AI? All sorts of things for us to think about, which I get really excited because my background really stems in consumer behavior.
(00:02:29) – That was when I graduated college. Back in the day, I was very obsessed with why do people buy? How do people learn all the interesting aspects that seem to now be surfacing itself again and playing a really important role of understanding the buyer persona?
(00:02:44) – Yeah, I think the really interesting thing about AI in the tech space is it’s not new to the market. AI has been around for years and certain technologies in the space have had them, but the way that companies are marketing them is much different because it used to be like it was in the background doing the work. Now I feel like you walk the trade show floor at any of the industry events, and every booth almost is leading with the AI.
(00:03:14) – It’s for sure. I remember when I was at Virtual Edge, we had a solution built into the applicant tracking system that was built on the foundational aspects of what we know in modern AI is machine learning and natural language processing. It was focused on matching job descriptions with resumes and creating a score, and then it learning more and more over time how to perfect that message, how to perfect that matching experience.
(00:03:41) – And so, it wasn’t called AI back then. They were people… we were just addressing a pain point. People wanted to figure out how to scale this matching process because recruiters were receiving hundreds and hundreds of applications, and they had to scour through each one of them and look at every resume, look at every little detail, and then determine which ones am I going to shortlist, which ones I’m not. And the technology at the time that was introduced was really an early stage version of AI. Still, it was on premise-based. It was built into the servers. There was like cloud-based AI tools and such, but it gave a good foundation of what we can expect AI to really flourish into. And then certainly as I progressed into other organizations, I started, my bulk of my recent experience has been in conversational AI. Everybody knows that as a chatbot. Chatbot being really at the early stage, interesting enough, it was interesting. Oh my goodness. I can ask questions, I can get answers. I can do it at two in the morning.
(00:04:39) – If I was a frontline worker at a restaurant, I can apply for my next frontline worker position because it was available for me to apply for. So, the evolution of AI and in today’s modern market, in today’s HR tech space certainly has evolved considerably. And I think the next big, the biggest milestone that hit that really hit us was the whole open AI’s ChatGPT. And really, the dawn of the scalable chatbot is what I would consider it. Now everybody will give it a different name, but it was the first time that the chatbot like conversational AI technology was getting a very positive review. Oh my goodness, it could help me write my email. Oh my goodness, it could help me, it could help me summarize my meeting that I just had. There were so many things people were getting, individually, were getting really excited about, including myself. As a marketer, AI tech stack for marketing is I use a variety of different tools. I’m definitely concerned about enterprise security and making sure I know where that data is going.
(00:05:35) – So, and what kind of data I could put in and not be concerned that it’s going to be outputted with somebody else’s query and prompt. So, it’s a fun time to be in the world of AI. And I think they’re interesting aspects from what I’ve observed back in, I think, March of when we saw really the onslaught of AI features being rolled out and announced to the point where George Larocque had a whole conversational session about, hey, here’s what all these new AI solutions can do, right? It’s like, oh my goodness. So, it was an exciting part, but it certainly got people… At least it opened the eyes of a lot of folks, but I think it created more noise and confusion, and we’re still at that trough of the hype cycle. And hopefully over the course of this year, we’ll start to get to a plateau where we’re much more in this practical era of AI in terms of it being more adopted and more understood by everybody in the organization. And there’s so many things that companies are navigating right now to, which is frustrating for companies that are selling AI solutions, where we’re starting to significant delays in sales.
(00:06:41) – Not that sales wasn’t fast in the first place in the enterprise world, right, but it certainly was. It certainly has slowed things down because they’re taking a step back and taking a deep breath and realizing that, oh, we have to create a policy. Oh, we have to… we should be concerned about responsible AI. What is responsible? I mean, we have to create a governance team. All of these things, these are all aspects that organizations are putting in the forefront before even starting to think about what solutions to vet for different use cases. And that’s the other complexity is what use cases do we want to apply to for our organization? It runs the gamut.
(00:07:19) – Yeah, it does for sure. So, when you think about the state of the market in AI and HR, here’s what our friends at Gartner are saying. They’re saying AI is posed to change the future of HR forever, disrupting established views of work, talent, roles and skills. So, many HR leaders feel a sense of urgency about moving forward with AI in the HR function, and 38% of HR leaders have explored or implemented AI solutions to improve process efficiency within their organization.
(00:07:51) – They’re also saying 76% of HR leaders believe that if their organization does not adopt and implement AI solutions, such as generative AI in the next 12 to 24 months, they will be lagging in organizational success compared to those that do. But yet there is a reluctance from prospects to enter into unknown territory. And it makes me think of an article that I read, gosh, this was probably months ago, and don’t ask me who wrote it. It was one of the HR publications that’s pretty well known. I do remember that much, but what they said was what an interesting time in the market it was, because HR leaders were embracing a technology they don’t trust.
(00:08:37) – Let’s talk about that, because that just came up from a commentary. So, my colleague Lydia Wu, who’s up in Toronto at a people analytics conference, just made reference to an interesting observation. She said HR professionals are perfectionists. AI is far from being perfect. It’s a bit of a conflicting characteristic. If they are perfectionists, how long is it going to take for HR to really trust AI to do the work as designed? And I think that is really one of the major characteristic differences in the buying process, is that back in the day, when you just bought an applicant tracking system, all you were doing is vetting for it.
(00:09:17) – Did it create an application in my ATS? Can I move it from one stage to the next stage? Can I create an offer from it? It was just basic foundational work. It’s like, did it do the work with the AI you need to get because it’s making recommendations. This is the thing that the EEOC, who I’ve had a conversation with, is really stressing that AI that makes recommendations, those are the tools that are going to get the most scrutiny, because that is where bias gets introduced most easily. So, in that situation, you have to learn how to connect the dots. You have to understand how the AI works by design. Not so much, does it work? Yeah, you can say it works. It did this, but did it work as it was designed? And that is the intricacy that folks have to now put towards, and the rigor that people need to put towards their vetting process of solutions.
(00:10:14) – So, one of the things like when, Bennett, you and I were brainstorming on what we would talk about today that you brought up that I think is really important for us as marketers to know is the key here is HR leaders have to take into consideration there’s more now that AI is being introduced. And as you pointed out in that conversation, there’s more people getting pulled into that evaluation and decision process.
(00:10:41) – Yeah, especially on the enterprise buying experience. We already know it’s a major team sport. It is not one person sole decision making capability. That is that is maybe a characteristic of a SMB type of organization. But it’s far from being the case in the world of enterprise organizations, where it is one little solution added on to their existing tech stack can have a ripple effect, positive and negative. So,m the reality is, in that situation, the enterprise buying experience has still primarily been owned by HR leadership, or TA leadership, or LND leadership, whatever that niche functionality that the HR organization or people ops organization is trying to support. In the world of AI. It is now…The buying experience is co-led. So, co-led by representation from HR and representation from the CIO.
(00:11:45) – Or most recently the CISO is now becoming another major player. All of them concerned about the security risks that AI introduces into an organization’s kind of infrastructure. It’s no longer HR being able to take full control and run with the process. It’s now requiring them to partner and build that one team experience with their peers in the IT organization. That is a new balancing act that solution providers are now having to adjust their buying process with (inaudible word). If you just focus on HR, IT’s going to come. We’ve had it here, we’ve had (inaudible words). HR is leading it, and then all of a sudden we learn, oh, IT already started that project. But HR was not ever brought into the fold, so HR wasn’t even familiar with it until we had asked them, have we talked to IT about their initiatives and vice versa? IT will be doing something and not even realize that HR, so there is this new muddy relationship right now between HR and the CIO, CISO which will gain more clarity as both of them march down the same pathway. In this experience of trying to figure out how to integrate AI into their organization, because it really is an integration experience.
(00:13:10) – Yeah. Well, and I imagine legal and compliance are getting more involved as well, because there’s a lot of conversation in the market about the risk and the ethics behind using the AI, and everyone’s trying to figure that out. They want to streamline and become more efficient and use less people to get the same type of work done. To hire faster, to you name it in the HR world, right? There’s an AI solution, or 50 that can now solve that.
(00:13:38) – Right.
(00:13:39) – But the last thing that these organizations want is for this AI to get in place, and it creates this big ethical problem and these risks, or it doesn’t work for the organization. And there’s not enough, I think, case studies, or proof out in the market for these buyers to feel confident at times that they’re not introducing some level of risk, or that their workforce is ready for this type of AI.
(00:14:06) – It’s interesting because from the surveys I’ve read, a lot of the employees are very excited about the future of AI in their individual roles. Most of them have already tested out a lot of AI tools on their own, whether or not in accordance to their policy or not. The reality, I think, it’s human nature to be curious about, oh, what kind of; how can this help me with my job, which is a positive thing to have. We want employees to get excited about AI because it is going to become an integral part of their job, and because it also starts to minimize or reduces the friction of fear. A lot of AI is not a replacement, it’s an augmentation. I hate to use a marketing jargon word of augmentation, which it’s become, but it’s an ally. It’s like the bank. It’s at your digital ally, it’s a digital assistant, however, you want to call it, but it’s helping you in some way get your tasks done faster. And so it is the ethical side again, comes down to a lot of different aspects. First and foremost, it’s really about making sure you have a resource, or resources to turn to, to simply keep up.
(00:15:10) – Literally keep up with the daily changes, or daily introductions of new laws. When you look at AI; I look towards (inaudible words) and their progressiveness, or their conservativeness around AI. I mean these countries; (inaudible words) is always many steps ahead of the US when it comes to rigor around data privacy and all those things that require organizations to have these ISO certifications or Soc2 certifications and such that are no longer oh, we’ll just do it for the goodness of business. No, you’re going to do it for your existence because these are certifications that help create that confidence with the buyer that you do prioritize and take concern about the issues and how your AI actually works. And I think, again, as a marketer, one of the big things I’m finding is the amount of, again, reminds me of back when the cloud was introduced to the applicant tracking world, I had to write a ton of technical documentation; technical marketing pieces to communicate what the cloud was about disaster recovery policies. The same thing is happening in AI.
(00:16:20) – We’re now having to document, like, intricately market texture, a visual diagram of here’s how AI works and where it makes decisions. Where are the checkpoints? Where do I introduce feedback? All of these little things have to now be documented, because a lot of the regulations that are evolving, especially in workplaces like New York and California, you actually have to show evidence, evidence of how did the AI make a decision on candidate A or candidate B? And if you can’t provide that evidence, you’re going to be in jeopardy. And that we’re starting to hear from a lot of other solutions that are out there in the marketplace. I think the interesting story around AI is who’s accountable for these decisions. It is the accountability of the AI tool of making decisions has been put to the test. If we look outside, slightly outside of the HR space, we just saw a whole case up in Canada with Air Canada and the Canadian Supreme Court, where Air Canada has a chatbot on their consumer page. They had a passenger inquire about bereavement travel.
(00:17:31) – They gave him an answer. They took advantage of that answer and then tried to recoup outcomes or recoup the dollars that they were expected to get back because of what they were told the bereavement travel policy was. And of course, Air Canada testified; their defense was that’s not part of our organization. That’s a third-party technology. Well, the Supreme Court came back from Canada and says, no, that chatbot is an employee of your organization. It is a representation. It is representing you as a brand. It sits inside on your website where consumers go. Everything about this situation, we gave the plaintiff, the passenger, the retributions and just gave Air Canada a little bit of a slap on the hat to say, by the way, your chatbot is an employee, so you are going to be held accountable. The same conversations are happening in the whole workday conversation, lawsuit happening as we speak. It’s being challenged about who’s responsible for the actual decision making. And that’s always been a big question mark around technology and responsibility of the decision making as more technologies get sophisticated and are becoming decision support tools.
(00:18:41) – So it’s going to be interesting to follow that particular case and see if; how does a court decide in terms of is the employer responsible for the actions of the technology, or is the solution provider the one responsible for it? It’s going to be a very interesting; it’s going to be an interesting time as we continue to see out those types of cases, which again, I hope it’s not going to delay or create delays in the buyer’s mindset in terms of their experiences about their willingness to move forward and get started, because we know HR profession leans on the side of being perfectionist. The reality is AI doesn’t work that way. AI is actually very much like marketing. It’s a big experiment. We’re constantly experimenting with it, right? It’s like there are different; because that’s the only way we know what to do, or know how to gain confidence is to really get firsthand experience, which is, again, one of the things when I look at some of the content pieces that marketing is having to put out there, one of the things we wanted to help organizations understand at MeBeBot was, you know what? We actually offer you a pilot program.
(00:19:52) – This is your chance. This is going to be a customer led; it’s going to be a MeBeBot led pilot program to help you get fully set up and help you get the solution out as it’s designed. We’re going to teach you how the AI works, and then we’re going to enable you to continue to test it out. Again, it’s a risk-free on both sides. At the end of the day, there’s some time that the employer will need to put in for the pilot, but the reality is they’re going to get a really quick learning experience about how AI works. And that situation is priceless, that education. All you do is read what AI does on a piece of paper and that does you no good. Get your hands dirty. That is, get your hands dirty. Get started. If we’re going to wait for decisions to be made in the courts about certain things, or we’re going to wait for all the things to line up, put together; there are some AI solutions that you can get started with now and not to hesitate.
(00:20:44) – And that’s what we want to really encourage, is it still gives you the essence of AI without the big risks. Let’s just say that as I listen to a lot of really ultra-conservative industries like financial services, I hear their CIOs, their heads of; their chief AI officers, the new title flying around again. Interesting enough, financial services, they’re extremely forward thinking on AI, but the way they; how they have approached the AI, instead of going to their customer first, where a lot of times companies put all the investments, oh, we’re going to make all the investments we can in revenue facing products. They said, we need to still learn about what AI is all about. What is this conversation? How does AI create better job descriptions? How does AI do this? Well, their immediate approach, work with our internal customers, our employees. That’s how IT is going to learn, and we’re all going to learn about how this AI really works, and how it’s potentially going to impact, when we do release it to customers, how is it going to take shape and form.
(00:21:51) – (inaudible word) used to redefine the market. While the integration of AI promises to streamline operations, it introduces complexities that demand careful navigation. For companies aiming to excel in the competitive HR tech sphere, success hinges on addressing the specific challenges of potential buyers, distilling AI’s benefits into a compelling value proposition and emphasizing their standout features. We are out of time, but not out of thoughts on this topic, so this episode marks part one of a two-part conversation. In our next episode, I will continue to explore this topic with Bennett Sung from MeBeBot, so be sure to tune back in next week to hear the conclusion of this discussion in episode 60 of the The Demand Gen Fix podcast.
(00:22:41) – Thanks for joining us on The Demand Gen Fix, a podcast for HR tech marketers brought to you by GrowthMode Marketing. I sure hope you enjoyed it. Don’t forget to subscribe for more perspectives on Demand Generation and B2B marketing strategies. Plus, give us a like, tell your friends. We’ll see you next time.
