Ep 815: Do You Know How Work Gets Done?

0:00Many organisations are investing heavily in AI, with some already cutting jobs on the strength of what it could do within their company. Yet very few can actually accurately describe how the work actually happens inside their own businesses. So what are they missing? And what is it costing them? Keep listening to find out. Support for this podcast comes from Cyber, the professional network for cyber security. If you're hiring for cybersecurity roles, you'll know that they are more complicated and time-consuming than more generic requisitions. The briefs are full of acronyms. Filtering through candidate pools is incredibly complex, and outbound response rates are a fraction of what you see elsewhere.

0:47Specialist agencies will take away the burden, but at a high cost. And every week a role stays open, your organisation is at heightened risk. Cyber Recruiter is built to close that gap, saving you time, effort and money in the process. Every Cyber Member profile carries a trust score. Certifications are validated through Credly and identity is verified at sign up. Through their custom search agent, CIRA, your team brief in a role in plain language and get a qualified shortlist within minutes. You can find out more by going to cyber.ai slash hire. And cyber is spelt C-Y-B-E-R-R. That's cyber.ai slash hire.

1:35There's been more of scientific discovery, more of technical advancement and material progress in your lifetime and mine, than in all the ages of history.

1:50Hi there, welcome to episode 814 of Recruiting Future with me, Matt Alder. AI adoption is accelerating, but for most organisations it's still on a very ad hoc basis. And few leaders can say with confidence what's being used, what's working, or what ROI they're getting. Some companies are already going further, cutting roles on the assumption that machines can simply take over tasks from people. What rarely gets examined is what those decisions do to the people who remain, the culture they work in, and the customers they serve. So what do organisations need to understand about how work happens before they let AI reshape it?

2:34My guest this week is Sam Nafisi, Chairman and CEO of Protoscore, a data analytics company that studies employee engagement, productivity, and collaboration in large enterprises. In our conversation, Sam shares what his data reveals about AI use and productivity, the cultural consequences of automating roles, and why visibility into work has to come before automation. Hi, Sam, and welcome to the podcast. Good morning, Matt. Happy to be here. Thanks for having me. A pleasure to have you on the show. Please, could you introduce yourself and tell everyone what you do? Great. My name is Sam Nafisi.

3:13I'm the CEO of a company called Protoscore. Fantastic. And tell us a little bit about Protoscore. At a high level, Matt, it's a data analytics company that looks at employee engagement, retention, collaboration, and kind of productivity as a whole, using AI and algorithms to kind of give assessments and outcomes for large enterprise organizations. Fantastic. And you've got a huge amount of really interesting data that we're going to dive into. But to start with, just a bit of a general question. In terms of AI, from what you're seeing, how are organizations actually adopting it? Because there's a huge amount of hype out there.

3:53What are you seeing as the reality? I think the reality, we're probably using a baseball analogy in the second or third inning of AI adoption, migration. And we believe, with a lot of data that I'll share momentarily, it's been more ad hoc. It's been more by the seat of our pants, reactionary, and let's adopt AI, let's get all the tools that we're fully familiar with, ChatGPT and Claude and Gemini and all the tools without really looking at the impact on the organization, on outcomes, and on customer relationships. So we think that that's a big area of focus as we move up from the third inning of the baseball analogy to the sixth and seventh and more maturation of this kind of movement.

4:40Absolutely. And there's obviously a lot of money being spent on these tools. And I was talking to a CFO the other week, and they said the biggest challenge they have around AI is almost software creep in terms of buying more and more sort of point solutions and AI-enabled tools that might sort of overlap with each other. So a lot of money being spent. What kind of visibilities do companies actually have on what's being used and whether it's working? Great question. One of the many components of our software in Protoscore is giving that visibility. And I'll give you a similar example to what you gave with the CFO conversation you had.

5:15I was speaking to a CEO of about a 4 ,500 employee organization literally last week, and they had adopted licenses for Claude and spent hundreds of thousands of dollars, I don't remember the number, but a significant amount of money on those licenses. And realized quickly using our tool, Protoscore, was that so many of the employees were using non-Claude AI tools, primarily ChatGPT. And the assessment was, well, why am I paying for all the Claude licenses if the rank and file are using ChatGPT, let's look at this. Is it an adoption issue? Is it a training issue? Are they more comfortable with ChatGPT because it was more of a legacy product?

6:00So that kind of nuance and visibility is what we're trying to provide. There's a lot of money being spent around AI tools, like you mentioned. What are we getting out of it? What's the return on that investment? And what else are you sort of seeing from the AI use that you're tracking? Okay. There's no doubt we see productivity increase. We have data that we released a couple of weeks ago that kind of medium users, moderate users of AI see a productivity jump of 15, 20%, but heavy users are in the mid 30 % range of increase in productivity versus their peers. So what we do, Matt, is we look at and assess employees within the same peer group.

6:44So basically identical roles and looking at their flow of activity during the day. And we see, as I said, for heavy users of AI tools, productivity increase in the mid 30 % range. Interesting. Interesting. And do you know what it is that makes people heavy users? What's going on with the adoption there? Why are some people adopting it? Other people aren't. I think it's inherent to new tools, right? As human beings, you know, I'm probably the least user of AI within our organization, for example. I'm also the oldest person here. I'm the guy with no hair and white on the sides. So I think it's part of that.

7:23Part of it is training. Part of us is reluctance. Part of us getting stuck in our old ways. And I'm giving myself as an example, as I've picked up adoption, as my team has kind of got me on board of Claude and using it and improving, it's fascinating of how it's made me productive. And it's kind of organized my life and my different areas of my day, looking at my workflows. One of the things we do, Matt, is we look at workflows and optimize the workflow during the average day of an average employee. Interesting. I suppose on that point, what impact is AI having there? But particularly on things like the culture and how employees communicate with each other when the work is starting to become automated.

8:09That to me has become my big, big mantra in the last two, three months. And the impact of culture was not foreseen by any of the organizations we talked to. And you hear it out there in Meta's press release and Salesforce and Microsoft. So here's the analogy. We adopt AI and because of it, we could potentially use a tool like ProtoScore to look at workflow automation, workflow optimization. and we get rid of X number of employees because of chatbots and agents are able to resolve those issues. What happens to the culture for the remaining employees in the organization when that RIF reduction in staff, reduction in force happens?

8:54It was a completely unforeseen outcome and byproduct of the AI migration. So we've been the big advocates of let's assess that before you pull the trigger. Because it leads to an incredible erosion of culture, disappointment, and what's the outcome look like for the end user clients? They never looked at that issue. Let's get rid of X number of employees. AI can do the work, resolve the tickets, customer service, whatever the areas were that AI quickly became adopted. Unforeseen was the circumstances of the outcomes of what the customer experience looked like. What do the remaining employees in the organization that communicated with their colleagues that are no longer there?

9:39I think that's really interesting because there are some kind of fundamental mistakes being made at the moment by almost assuming that humans and machines are the same. That actually, you know, you can break a workflow down into tasks and a machine can do those tasks. Therefore, a machine should replace the human. But as you say, there's so much more to it, isn't there, from the human aspect of things? Completely. Here, I give this example often, but imagine a sales department and a customer service department that historically were very enmeshed. There was the pre-revenue, the revenue, and then the customer success would take it from cradle to grave in the history of that relationship with that client.

10:20They communicated all the time. So imagine for an organization that may have reduced its customer success team because of the repetitive functions there are now using AI for onboarding, training, QBRs, all the stuff that customer success does. Now, historically, the sales team would be communicating with their colleagues on the CS team. What does the sales team do now when the colleagues are no longer there on CS, but it's a chat bot? Yeah. Do I write an email to the chat? Do I slack it? What does he respond to me? Do I go have coffee with it? It's a whole nuance that it's unknown. And that's where I touch about the culture issue because you have the remaining people in the sales organization, in my analogy, still with the company, while customer success may have been reduced to a fraction of its former employee count.

11:15Support for this podcast comes from Pebble. I know that many of you listening are hiring internationally. And you'll be all too familiar with the wall of paperwork, rules and approvals. And you know just how much this red tape can slow things down. That's what Pebble is designed to fix. Pebble makes global hiring simple through embedded compliance and AI-driven workflows. The Pebble platform takes the delays and guesswork out of going global. So you can move fast without adding risk. The bottom line is that anywhere is possible with Pebble. With global hiring simplified, founders and HR leaders can spend their time on focusing on what and where comes next for their business.

11:59Pebble has a special offer for recruiting future listeners. Pebble is normally$399 a month per employee, although a no-brainer for what you get. But right now, there is a limited time offer on their site that makes it even easier to get started. Go to HiPebble.ai before it's gone. That's H-I-P-E-B-L.ai. Terms and conditions apply. And has your sort of data told you anything else about how work's changing, how people communicate? Were there some sort of surprising findings in there? Yeah, look, we're all about outcomes and both outcomes internally for the organization and then ultimately externally in the customer relationship of that company with its clients.

12:49And you've seen this public information, you know, people have let go of thousands of employees only quickly to rehire back some of them, right? And our thesis has been before you have that reduction of force and migration to AI, let's stress test it. Let's look at the data around it and ensure that your employees are kind of sound and healthy that are remaining. And most importantly, the customer outcomes and the customer success remains as it was, or if not improved, because then it's going to be really, really disadvantageous to the company to migrate to AI when you have potential churn or attrition of your customers.

13:32I think there's an interesting point here as well, because there is a great deal of fear in the workforce around things that could be seen as surveillance. We've seen a very well-publicized case of meta using people to train AI to do their jobs and then laying them off. With software like yours, when you're collecting that detailed data on how people work, where's the line between visibility and overstepping into surveillance for you? Great question. And that's been the number one question asked of us in the five years of our history. A lot less now, but certainly in the beginning, the pronounced question was surveillance, big brother, you're micromanaging people.

14:19We don't do any of that, Matt. And the distinguishing difference between us and some of the tools that do that is we don't look at your mouse clicks and URLs and keystrokes at all. But more importantly is the employees themselves have their own dashboard within Protoscore. So nothing is hidden from them. So when I come in as an employee, if I'm a Protoscore customer, I come in, I have my dashboard. I see what Sam is doing and I see what my peers in the identical role are doing. So we give full visibility to the employee, to middle management and to the C-suite. I think that's interesting. That must be interesting from an onboarding perspective as well.

15:01When people join the organizations, they can actually see the shape in which people work and how they use the software. Yeah, completely. Here's a perfect example that we had a company whose onboarding would take up to 90 days. They deployed ProtoScore. They looked at what a good onboarding and a good employee onboarding outcome looked like. Post-deploying ProtoScore, they reduced their onboarding time to 45 days. So we showed what a good employee was and how to replicate it. and any employee that's coming in and it's good and wants to thrive and succeed loves a tool like ours. By the way, I give this analogy a lot.

15:4220 years ago, salespeople detested Salesforce. Like, oh my God, you're looking at my pipeline. You're looking at my call volume. Leave me alone. I don't want to get involved in Salesforce. Obviously now it's ubiquitous and salespeople can't live without it. So we feel a tool like ours, if it's properly deployed and adopted, it allows the flexibility for the employee to potentially be remote, be a hybrid employee of three, two, four, one days in the office versus remote, and give the employer the engagement visibility they want to provide the flexibility to the employee. So it could be a win-win if used properly.

16:24As a final question, if we sort of look out into the future, where is this going? What does the future of work look like, particularly as AI gets even more embedded into organizations? That's a great question. I think of that 24-7, Matt. And we believe, partially selfishly, is that for any of this AI strategy to work, you need activity-level visibility. For AI to work properly, you need granular activity level work and workflows to be able to optimize those. And tools like ours provide that visibility to management. Exactly what does Sam do in his eight to five, eight to seven, whatever my duration of my workday, what do I do exactly during the sequence of my day?

17:14And by seeing the activity level at that granular level, you're able to show it, replicate it for other employees within that group and cohort. And then if you want to have AI come in, that's the intelligent way to deploy AI when we fully assess the workflow, and then we optimize it, right? The issue and hiccups have been people have tried to optimize and jump to AI without having visibility to workflows. And the last thing I'll say is this, I think this idea, this morbid world that we're all going to be replaced with AI is BS. I truly don't believe that extreme is going to happen. I think we're going to push back and there'll be a modicum of that middle of the road.

17:57We're going to need people. People want to talk to people, engage with humans at some level. I'm not saying AI doesn't have a big part in the future of work. It absolutely does. We're seeing it. It's done great for different roles in different organizations. But this idea of humans being obsolete and the obsolescence of human beings is way overly exaggerated in our belief. Sam, thank you very much for talking to me. Absolutely, Matt. My pleasure.

18:47for listening. I'll be back next time, and I hope you'll join me.

19:17Thank you.