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#135 - Shlomit Gruman | When AI Redefines Work
8 Plays1 hour ago
Shlomit is a people and transformation leader with over 25 years of experience across tech, SaaS, consumer platforms, and banking. She has served as Chief People Officer in complex environments such as hypergrowth, large-scale transformations, and post-acquisition integration. Today, she works with CEOs and leadership teams on operating model redesign and AI-enabled transformation. Her approach combines pragmatic execution, data-driven thinking, and a strong focus on people while leading change.
Shownotes
00:00 - Intro & Context
02:10 - Why AI is not just a tool but a shift in how work itself is designed
14:35 - How organizations can redistribute tasks between humans and AI agents
28:50 - The key success factor: iteration, experimentation, and the right mindset
Links
Guest Linkedin: https://www.linkedin.com/in/shlomit-gruman-navot/
Thomas Linkedin: https://www.linkedin.com/in/thomas-kohler-pplwise/
Thomas e-mail: thomas@pplwise.com
pplwise: https://pplwise.com/
Recommended
Transcript
Introduction to Shlomit and AI's Impact on Organizations
00:00:07
Speaker
Today's guest is Shlomit and I'm really looking forward to this episode already. We met at the Human AI Dinner, which you um mainly hosted, but I could be a co-host as well. um And you're now a transformation and people executive and also four times chief people officer. And I'm really looking forward to this episode when we talk a bit about all the changes regarding AI and what does this mean also for organization and change and
Career Journey and Embracing Change
00:00:32
Speaker
uncertainty. But before we do that, um let's start with a a small introduction about yourself.
00:00:37
Speaker
Yeah, thank you. Thanks, Thomas. And it was a great pleasure to do the dinner together. um i thought it was ah ah an amazing evening. ah So you already introduced ah who I am professionally. um My name is Shlomit. I live ah currently in the Netherlands. We're actually for the past ah almost seven years.
00:00:58
Speaker
um been in organizations for 25 years now, um and also as a chief people officer for 13 years, um worked and lived in five different countries, worked in tech,
00:01:15
Speaker
in ah consumer tech, banking, SaaS, seeing all the different, I would say, use cases, hyper growth, transformation, growth, investments, divestments, acquisitions.
00:01:32
Speaker
ah And at the same time, you know, my motto in life is that I don't know, you know, or more, I don't know than know. So of course, you know, all these experiences and navigating a lot of uncertainty and ambiguity, it builds a lot of muscles, ah ah for sure. And I have a lot of bruises and a lot of learnings at the same time. And I know we're going to talk more about
Integrating Human and AI Worlds
00:01:55
Speaker
it today. um Especially in the times that we are living now, we actually mostly don't know then know.
00:02:02
Speaker
And I'm really happy to be here today. and yeah Likewise. And what so what way made you um organize a dinner or an event um around human XAI?
00:02:19
Speaker
I think was the the like ah the the overall um tagline. um Why did you come up organizing this? I often often see this from providers and that are doing a lot of dinners.
00:02:29
Speaker
But for somebody it's like you who is now in, let's say, um executive also organizing it. I think that's very proactive and great, but what's what's maybe behind that?
00:02:41
Speaker
Oh, okay. Now that's going to be a story. So um first, I'm extremely passionate about bringing these two worlds together, ah the human and AI.
00:02:55
Speaker
I myself started the journey yeah into AI or deeply into AI around eight months ago. um So it happened as part of um experimenting um in an advisory capacity, which is currently what I'm doing.
00:03:13
Speaker
So I decided very intentionally, as mostly I don't know, ah to educate myself and immerse myself in AI. Today I only work with agents,
Transitioning to AI Leadership
00:03:24
Speaker
so I used to lead teams, now I lead agents. i always prefer ah people, I have to say, you know, ah but I fully understand and get how blended the world is becoming and more and more we will see leaders managing both humans and agents. So I got very curious about a topic. I started learning it, going very deep into the different use cases, obviously the different tools that are out there, solving different problems, ah being part of a community also that is all dedicated for that.
00:04:02
Speaker
And then I felt, okay, you know, I have all these ah experience muscles, bruises from leading change, transformation, obviously a lot of judgment that came from this world, um and now also understanding of AI. So I got really, really fascinated with how I'm bringing these two things together. Mm-hmm. So managing change in the AI era with the learnings of transformation, scaling changes, et cetera, and with what I know now about AI. So that's kind of a a big passion of mine. um You can call it leading ai revolution, leading AI transformations, helping CEOs and companies to navigate
Connecting Leaders through AI Dinners
00:04:48
Speaker
things.
00:04:48
Speaker
all these changes and make sense of this moment. And then also to bring leaders together to really reflect on this topic of what actually that means ah when you need to lead both and redesign work in companies.
00:05:04
Speaker
um As I truly believe that many companies don't get that this is not a tool adoption, but work redesign. So that's been a long story yeah into the point of the dinners. The dinners is just an opportunity to connect with other leaders on this topic. And I'm very happy that I was able to find great sponsors that are ready ah to bring people together. ah to to such an event.
00:05:30
Speaker
We had two of them so far. So we had one in Amsterdam, second in Berlin. Both of them also with a company called Juno Journey and obviously yeah different um with yourself in Berlin and different partner in Amsterdam.
00:05:45
Speaker
And it's just been a great
AI in Process Efficiency
00:05:47
Speaker
success. ah We learned from dinner to dinner um in terms of what people want to hear, the conversations, the panel, But it's just like, you know, it's people want to connect and that's a great opportunity to do so.
00:06:01
Speaker
Nice. Now you made me curious on and examples. you You manage agent. What agents and for what workflows? What but what specifically do you do with it? Yeah, so i I have different use cases, starting from um um managing all my expenses.
00:06:20
Speaker
So that's a clear workflow that I had to build. As you know, there are things that I'm very passionate about and i intuitively I enjoy doing and there are things that I less enjoy doing. as So I build a an agent that deals with all my expenses and things that I just need to keep track on on a regular basis. um Obviously, I'm doing a lot of work with AI when I need to create documents.
00:06:48
Speaker
Of course, I apply my judgment, but AI helps me a lot to structure, to get the correct research that sometimes is needed inside context into it.
00:07:00
Speaker
And then we do some iterations around that. I obviously work with the note takers for all my meetings. I analyze them. i also have um a special project in Claude of analyzing all the note takers and all the learnings um out of them. I've been doing some things on Lavable.
00:07:21
Speaker
So it depends. So I... yeah Notebook LM, one of my favorite tools. I've been building infographs there and presentations. So it's either agents um that I have either on ChatChapiti or Cloud or some workflows that I created, um or I'm just using the tools again. And I'm mostly on the pro versions of the tool, so I'm using the more enhanced versions of them, solving for different problems that I need to solve.
00:07:55
Speaker
and Okay. um um I also think that it's really great. um We just got ISO 27001 certified. It's like an information security um norm internationally.
00:08:09
Speaker
And it's really, i would say, intense to get that also for an organization already with um a lot of employees. And um I think we used a consultant plus...
00:08:21
Speaker
um their internal documentation as recommendations and then also build our custom agents just for this ISO certification um with the PeopleWise context, plus also the context from the consultant, plus also with, I would say,
00:08:39
Speaker
um prompting in order to um use also relevant context for relevant ah for different situations um that we could build also a lot of customized documentation for workflows that were maybe already there or we already used and then also um adapted then in the documentation but also um created internal documentation that is then, let's say, the employee-facing documentation out of the official ISO certification documentation.
00:09:08
Speaker
And um I think we were one, so the consultant told us, and also the TÜVSUD, that we were one of the fastest certifications. um Oh, wow. Because we we did already everything AI-native, and we also we really did not have any major issues, right? It was just really...
00:09:25
Speaker
um agentic and AI generative um from day one um in setting this up from scratch. And this was really fast and accurate. And I was really surprised um that we saved a lot of time and a lot of annoying work. um A second example, but I also use it for is... um financial planning. Often I can just, also in the car, I have my, let's say, um voice conversation with JGBT or somebody. i go to a a different, I would say, project type where they have um certain instructions on how to behave.
00:10:00
Speaker
um And then I use a lot of conversations like we hired now these new people. um What's the impact on, for instance, um our um financials? and What do we need to take care of in in terms of infrastructure? Because it's like pre-set what factors we have in terms of cost versus
Redesigning Workflows with AI
00:10:19
Speaker
revenue. And then I always know when going into the car,
00:10:22
Speaker
where we're standing um financially. It's, of course, a draft and so on, and it's also just rough estimations. and But that's often enough until the real financial executive summary is coming from the internal controlling, which is often a bit delayed because it's detailed and it's 100% correct, right? So there are so many great use cases. Did you also see um in organizations that you maybe consult um how AI is used in a good way, but also in a bad way, maybe? um Oh, yeah. I mean, so first, just reflecting on what you said, I think, and that's what I saw about myself, it helps you to become so much more efficient in things. So like the speed of it,
00:11:07
Speaker
Uh, like to your point, right? Leo, I give this data and then I get this output immediately. Like in few minutes. Uh, if I'm thinking like I'm, I'm now like when I need to do some work on excels, I'm, I'm just going into Gemini.
00:11:21
Speaker
I give it some, you know, data or context and then I get an Excel. you know, built for me. This is just incredible. So I'm just thinking like the ah the time that it saves us to do the thinking work and the value-add work, this is just incredible. so ah and and and And another point of reflection from what you said is that the point, and that links me also to organizations, you said something extremely important. We can just build it from scratch, right? Like we build it, it's there, it exists, we go.
00:11:55
Speaker
For many organizations, the problem is that they need to break their workflows. They need to break their work um in order for something new to emerge.
00:12:06
Speaker
And maybe that their workflow was set up in a non-AI native way and then you have a problem. Yeah, but it was... most likely set up in a non-AI native way because when you think about change and transformations you are changing workflows that were built some years ago or processes that were built some years ago and with AI it doesn't make sense anymore.
Transforming Recruitment with AI
00:12:30
Speaker
Or the process can completely look different because you are now bringing agents and humans, or you bring a Gentic AI, which is about agents working with agents, which is a different story.
00:12:43
Speaker
ah So you really have to redesign work And what I've seen, what I've been observing is that, um of course, you know, in organization, mostly people know how to use ChatGPT or some sort of chat, you know, either Copilot or Gemini.
00:13:02
Speaker
ah But the problem is that it's not actually changing the work itself, or rather the companies are not capturing the value of that usage. It's what I call shadow AI. ah There is shadow AI happening, but it's not necessarily structured.
00:13:18
Speaker
um So one mistake that I'm seeing companies are doing, they're treating it as a tool adoption and not as a work redesign, meaning they're not really sitting and looking at every task and getting very, very clear about what has to stay with humans, what has to be augmented with humans, and what needs to go entirely to AI.
00:13:40
Speaker
And AI will do a better job or can already solve for it at the fullest. So, of course, you still want a lot of judgment in the process, but not everything requires humans in the loop.
00:13:53
Speaker
Do you have an example process where this could be um already done, what you described it? Yeah. I mean, let's take even recruitment, right? I mean, I think it's ah it's an easy example in our context. um AI with the right context, if you give it the right context, it can certainly um source for candidates. It can certainly point to the more suitable...
00:14:18
Speaker
ah candidates, resumes. Again, context is extremely important because you want to make sure that AI doesn't have bias in the process. So the humans have to to tell the agent, whatever you're using, is ah what to pay attention to.
00:14:36
Speaker
Absolutely. But then ai can help you to do a lot of the work that before you needed sourcers for it. i mean like I remember that like the previous model of recruitment. You have a recruiters and you have researchers, you have sourcers, you have somebody that schedules interviews.
00:14:54
Speaker
A lot of it today can be done easily by yeah by AI. But... What I see, and I can also mirror that, but what I see in recruitment at the moment, I think um for the sourcing piece, I did not see a product that is really working well. But if you use it well for market mapping, which would be, let's say, the pre-step of sourcing, it works very, very well.
00:15:19
Speaker
um Then also for um building funnel reports, that's not what all the companies do, but I think should do, is to really analyze existing data or past data. And then also just um build a forecast when we see, let's say, an interview, let's say an account executive in Munich for selling to enterprises. um You use this data and you want to show your hiring managers or your company how long it takes for somebody to get into the company and then get productive because to sell an enterprise deal is usually nine to 18 months, right?
00:15:54
Speaker
So every enterprise seller that is in the first year maybe did not even do a sale. Yeah. um So when when this is the context that you can take also in terms of hiring and setting expectations, AI is really great for that because you can showcase diagrams, what you said, um showcase funnel metrics um based on benchmark data data or internal data and also showcase this to the hiring manager to the business to say, hey, in order um to to solve your problem, we need we have this market um we can hire from, then we have a number of available talent or not available talent, just a number of talent. And then the question is how how many of them are available to us. Then when you get our data in terms of recruiting, you can ask for the salary expectations, their target attainment and build a market map to say, okay, in these companies, these people who achieve 100% earn that amount of money. and these are roughly the activities what they in order but what they do in sales cycles in order to achieve the target.
00:16:53
Speaker
If we cannot provide a better job offering than that, we most probably want to hire from there but cannot hire from there because why should they leave? We don't have a better offer.
00:17:06
Speaker
right And if somebody then would change, maybe they did not perform well there, will they perform here? right So I think this is something where you can then really have a lot of honest conversations, but it's just, with again, important that you can do that only if you have the data foundation, the data model.
Data's Role in AI Deployment
00:17:22
Speaker
And a lot of companies, they don't gather this data. yeah It's just somewhere and it's not consolidated, right? Yeah, so so exactly. so So I think there are a couple of things here, Professor, from what you described.
00:17:33
Speaker
One, data is key because, you know, if you're connecting your tools, like it was always the case, right, even before AI, but it's especially important with AI ah because the wrong data, wrong output. Right.
00:17:49
Speaker
yeah that's like ah Or if you are not very clear about what data you are connecting the system to, you're not going to get the result that you're expecting. So data, internal data, external data, extremely important. And apply judgment also on this data, on this raw data that you're connecting it to.
00:18:07
Speaker
And I think the second thing, it's like, let's take recruitment and use this iceberg model, like above the line, at the line, below the line. Everything that has to do with interviewing people and making decisions, this is humans, right? You still want to have recruiters, getting the process, hiring managers, and so on. Then you have the augmentation. You know, you're using AI to help you do the market research. Yes, or the scheduling, as you said. That's also really good for that. that Sorry? there The scheduling. The scheduling. I think actually scheduling is even below the line. Scheduling can be like, just give it to AI. You know, AI will install the process ah for you.
00:18:48
Speaker
But then there are things you want to augment that humans are still in the loop ah in like understanding the data, applying judgment to the data. Interview, the interview itself, it's also another good example where you can use a very sophisticated note taker that will even give you notes based on your values and mindsets that are important to you in the company.
00:19:12
Speaker
And still, you're assuming you're in the driving seat to make the final decision. So I think that's a very good example where where you take a process, And you redesign it with AI, thinking very intentionally data, which systems are connected, and then being very, very intentional in designing what stays with humans, what is being augmented, and what, just give it to AI, you know, and free up all this time for more meaningful work.
00:19:42
Speaker
And this is just one example, right? I mean, I can talk about, I i built an onboarding plan using an AI. And that was just an incredible. Like in half an hour, I got like top-notch onboarding plan.
00:19:57
Speaker
um Like when you need to do some massive hirings and you want to build a plan on what's going to happen in week one and week two and week three, including like a bootcamp.
00:20:08
Speaker
Just by giving some context about the company, I got like in like an incredible onboarding plan, which of course, you know, needed some iterations. That's that's normal.
00:20:20
Speaker
But again, the speed of of which, you know, I got it, something that maybe two years ago would have taken several days to build.
00:20:30
Speaker
Yeah, and also several stakeholders from several perspectives, right, that all need to sign off something and do something and now you can just propose a proposal that is already maybe considering their perspective when you prompt it in the right way and we have and when you have access to the right and information. um And then...
00:20:49
Speaker
they can also um review stuff faster or accept stuff faster. And then, um yeah, you're just faster and more accurate. I also um used it for um internal onboarding purposes that we have a very specific plan and so on. And then I think it's really structured, but there are still the, let's say, human challenges to find the right time for that and right and then making sure that people are really doing it. So that's maybe the human part. Yeah, there is the still still the human part, precisely. and And I just like on something that you said, I just wanted to add, prompting is extremely important, no doubt about it. And there is also the concept of superprompter.
00:21:28
Speaker
I would say that context is like the key. Context is the king, is the queen, whatever. So it's, ah yeah, prompting is good. But what is really important is that whatever you are using, and that's why data is important and and connecting it to everything that is happening in the company,
00:21:48
Speaker
AI needs to know your context. And and the more it knows its kind your context, whatever it is, it can offer you better solutions. Whether it's like getting to know you as a person or the company or what you're trying to achieve.
Balancing Data Privacy and Regulations
00:22:02
Speaker
So content context management is becoming key in really getting the most out of AI. Yeah.
00:22:10
Speaker
And I think therefore you just need to find a fine line, right? What is... um what is the right data you can also feed into. um Yeah. And also what you maybe want to augment or synthesize as data um when things getting maybe too personal or too sensitive. And I know the US s and the EU have different perspectives and opinions on it. yeah I think maybe the USA way is a bit extreme on unregulation and maybe the EU is a bit extreme on regulation, right? So maybe something in the middle is also fine to to start with.
00:22:48
Speaker
Yeah, no, I think safety and security and governance around it is very important. Otherwise, people are not going to trust it. Yeah. Yeah.
00:22:59
Speaker
So what what I also um think is necessary that um Testing there is ah is really important, but from testing to getting it into production, that's a different piece. And I think this is where the human aspect is so important because ultimately you need to change um habits and behavior um on different workflows. it's It's actually nothing than basic change management actually. um How do you do that properly to adapt let's say, ai ah AI testing process or test process in a testing environment into a company-wide um
Iterative Learning and AI Adoption
00:23:38
Speaker
process. we do We did not and do that really properly and roll it out. We we are somewhere in the middle with some processes, um but still definitely not there that I could say, oh, this is rolled out.
00:23:50
Speaker
It's working. The company is using it. It's part of the DNA and core process. um So that would be interesting for me also on how you do that. Yeah, I love this question because, you know, for me, this is the product mindset that is just now being amplified. And what I mean by the product mindset ah is you always need to approach everything with a huge degree of iteration and it's a mindset.
00:24:19
Speaker
and The mindset of ah launch and learn, launch and learn, or test, fail, learn, you know, whatever you want to call it, ah is that, of course, you know, you have context, you have data, which is a big part of the discovery, the discovery process in everything that you do.
00:24:38
Speaker
And then you have to define what is the smallest move that I can next take, like experiment with. Then you go out, you get feedback, and you build on that feedback.
00:24:50
Speaker
And I think that's extremely important in every aspect of the company to adapt this mindset. With AI, it's becoming more critical because it's a lot of, ah ah you can experiment endlessly.
00:25:02
Speaker
today. you're going just You have an idea, you go and build something, you try it out, you get feedback, great, you amplify. No, you kill it, you move on to to the next ah to the next thing.
00:25:14
Speaker
So it's sometimes also hypothesis that you need to to test and and get input on. So I think it's really mindset. It's the mindset of constant iteration. It's the mindset of curiosity, of constantly learning,
00:25:31
Speaker
of It's okay to fail, you know, as long as you are not destroying the company because of course certain things you have to be very careful about. Like you have to create the guard wells um and create boundaries around it.
00:25:45
Speaker
But let's say most of the things we can we can test and we can learn. And sometimes fail and that's okay and we're also going to learn from it. So it's just this mindset of constant iteration and treating everything that you do not as a process, but as a product.
00:26:04
Speaker
Now, I'm not against the word process. I think it's a good word. And eventually you need a workflow. Needs to be clear, like step one, two, three, et cetera. But the mindset should always be like, you know, we don't know. Things are changing so fast that we constantly need to iterate.
00:26:24
Speaker
And most of the things that we are doing are up for iteration, whether it's with our customers, that their needs are changing constantly and there is feedback that is coming from them. Uh, or something that like you need to build
Fostering Safe Experimentation Environments
00:26:38
Speaker
internally. Also the needs of your employees are are changing different stages in the company and you need to iterate.
00:26:45
Speaker
So from my perspective, it's a lot about the mindset, then creating the environment. that encourages that. I mean, if it's I can talk a lot about psychological safety and that it's okay to test and it's okay to learn, that it's okay to talk about your learnings.
00:27:05
Speaker
This is where leadership comes into play. Because at the end of the day, if people don't feel safe, they're not going to experiment. They're not going to They're going to play it safe.
00:27:16
Speaker
And then that's going to kill your ah like constant iteration mindset. And the incentivization is also important, right? Because if you, let's say, and create the right workflow that is then eliminating your job from a narrative, that's also maybe not what you what you then naturally will do.
00:27:33
Speaker
Yeah. No, no. I mean, and I can understand it's very human, right? It's very normal that people will be fearful of their jobs and about their future.
00:27:45
Speaker
And I think this is also where leadership comes. So beside, you know, the mindset and creating the right environment is to allow people to see the opportunities in all of it. Because I can say, you know, and I i can talk openly about the fact that, and I have it from my own experience, careers are changing.
00:28:04
Speaker
in front of us, right? So it's, if people are looking for stability, it's very difficult to find these days. It's very difficult to guarantee that, oh, things are just going to be stable the way it is for the next foreseeable future, because we really don't know. And the experience that we have for the past, um especially five years or so, five, six years, is showing us that we can't predict anything.
00:28:33
Speaker
You know, it's like, it's so many unknowns. And the only thing that you can absolutely control is yourself.
Conclusion: AI Fluency and Leadership
00:28:41
Speaker
You can absolutely control your mindset. You can absolutely control your skills, your knowledge, your learnings.
00:28:50
Speaker
ah And that's something that leaders, I think, have a huge responsibility to show people the path to it. You know, like, hope is willpower and waypower. You need to have both to have hope.
00:29:05
Speaker
And that's the job of the leaders. You know, you are showing them the way. Of course, it comes from your own will, but you can show the way and really encourage people, even with this AI, like,
00:29:16
Speaker
to explain to them what is a AI fluency, help them to develop ai fluency, encourage them, encourage them to experiment and learn and constantly be in this iteration, their personal even iterations process. So yeah, I mean, I think it's ah it's a lot about ah just looking at yourself and what you can do.
00:29:42
Speaker
huh That's a great final word. We're already at time. Shomit, thank you so much for the episode. It was really great having you and hope to see you soon. Yeah, thank you. Thanks for the conversation.