Episode 130

Ani Misra on Objective Hiring is Embracing AI and Cultural Diversity in Recruitment

TIM: We are live on the Objective Hiring Show. Today we're joined by Ani. Ani, thank you so much for joining us.

ANI: Thank you so much for having me here, Tim. It's a pleasure to be here to discuss the very topical discussion and very controversial one probably as well. Could become controversial as we deep dive into it. Yeah. Yeah. So good evening, and thank you so much for taking the time as well. Yeah.

TIM: It's absolutely our pleasure. Yeah. And whereabouts in the world are you joining us from today?

ANI: I'm from Amsterdam. It's a cloudy Amsterdam today, unfortunately, but definitely I hope that we will get some more sun. This weekend and this week as well. So yeah, can't complain. Can't complain. Yes.

TIM: And I'd love for our guests just to hear a little bit more about yourself, just so they can start to understand who they're listening to today.

ANI: Absolutely. My name is Adindita Mishra, and I'm originally from India, but I've been living and working outside of India for over a decade now, and I've been in data, AI, and analytics for over two decades. Primarily focusing on how do we really solve business problems with analytics, be it AI applied or generative AI, as we hear these days quite a lot. But also, how do we really create insights to basically make data part of the decision-making? So that's being my focus. Currently I'm working at Decathlon as the director for AI innovation and trust. Also very topical and interesting topics. Definitely let's deep dive into how you really make that objective hiring really objective.

TIM: Yes, exactly. Let's do that. And it's such a difficult topic. And as you say, almost a topic that's become politicized in the last six months that I probably wouldn't have guessed or predicted a few years ago, I have to say. But maybe we can start in a slightly less controversial area to begin with. And thinking now about candidates when they're going for roles, when they're interviewing, there's a tendency, maybe almost like a cliche. overly technical candidates, maybe not necessarily presenting themselves best in their interview. Maybe sometimes there's almost a negative correlation between how technically skilled someone could be and their soft skill presentation skills. And so I feel like that's maybe sometimes a challenge for hiring managers to still evaluate them fairly. I'd love to get your thoughts on this.

ANI: That's a great question. And this is indeed a big one, especially in the engineering AI and data science world. And, as you correctly pointed out, not everyone is a natural communicator, right? And that's okay because we can't hold that against the candidates, right? So the worst way to assess, in my opinion, is that especially for the technical talent, it is just by asking them to explain things verbally. So some of the best engineers I have worked with. They aren't the most polished speakers, but they are absolutely geniuses in their field. So what I do normally, and I always love to also give, is not the talk about the problem but also the solution, which is that instead of focusing too much on the verbal skill, what I like to do is let them demonstrate their problem-solving by giving them a real-world task like a case study. Sometimes I pay, if possible of course, then I pair them with a future teammate and see how they collaborate. And also what I've realized is that not everyone can answer complex questions on the spot, and that shouldn't disqualify them, right? So I give them time to prepare as well. So great talent doesn't always come in a polished package. But I think that as hiring managers, especially currently in the current situation, it's our job to see the past—basically, past their communication—but really look into their potential.

TIM: Yeah, this is really interesting for me to hear because I imagine you must have gotten some pushback at some point if you're trying to promote hiring candidates this way, because this is going against the way that most companies do it, where they really overindex on interview performance. I feel like there's a cultural fit interview. There's a technical interview. It's often, I'd say almost fully interview-based as an evaluation tool, whereas you're saying that's going to miss a certain segment of candidates. Have you received pushback on this?

ANI: I did. I did. What I try to do normally, that's why, is that I do fit in and fit them into the usual interview process but provide a case study up front so that they can prepare. And depending on the company and the pushbacks, the type of pushback that I get, so I tried to fit. Adapt to the existing process, but try to make sure that, okay, I could also do that to fit my requirement and my ways of assessing in the way that it should happen. So definitely it's always a mix. It's always a balance between the current process without jeopardizing the existing company's culture of recruiting but also bringing in the new culture. So eventually that could change them. Existing process as well. I believe that people do see the benefits of it. The moment you start hiring good candidates for the roles with the current evaluation process I've also seen that people start to think, Okay, maybe we should change the process as well. So that's a culture change to change management that I think every company has to go through these days. I think hiring managers should just rely on their instinct on how they should select and find the right candidates. Final scene with the message on screen: “Hard work always pays off.” Ahmed smiled with satisfaction.

TIM: Yes. Yes. I find it quite amazing how many hiring processes are set up to really not at all select the candidates that you really want to need. And that even sometimes small things could be filtering out some of the best candidates. And I can think of just straight away a few anecdotes off the top of my head. Someone I worked with at Aluba when he joined a business a few years ago. He was heading up the software engineering team, and he was planning on doing a lot of hiring. I think he's going to hire a hundred engineers in a year. And so the HR team said, Okay, we'll help you. We'll do the screening for you. We'll do the resume screen and the first phone screen because it's a big volume. It's okay, sure. Yeah. But what are you going to look for? Like, how are you going to evaluate them? Because. Someone in HR skills is a long way away from having software engineer skills. There's not a lot of crossover, and they kept pushing back, and eventually they just said we're going to filter out the weirdos so you don't have to speak to them. Okay. And so that was their criteria: anyone who wasn't really extroverted and smooth and savvy or what have you. Was going to be cut down. And he was like, Hold on, every engineer I've ever worked with was the opposite of that. The ones I've, one of the ones that may be a little bit stranger. Okay. Because that often correlates with them being really great engineers. And so that was a scenario where it was like their hiring process was negatively correlated with who they're looking for. And it's really dangerous. I think if companies don't think about this,.

ANI: Exactly. And that's a very interesting point that you pointed out. It's also about diversity, right? Sometimes we often forget that the people who match the current team are not the best choice, probably for the team as well, because then you are losing the concept of diversity, because yes, it's, uncomfortable probably, but that's the whole point of bringing and mixing, balancing the right skill in the right place to solve a business, solve a problem, right? So that's an interesting point that you mentioned as well.

TIM: And as much as companies can improve their selection process, at the end of the day, a lot of candidates who are technical are still going to have to go through interviews, more traditional interviews. Any general tips you can think of for technical candidates to do better in those interviews, especially if, yeah, maybe communication skills aren't their number one skill?

ANI: Yeah. I do think the most important thing is problem solving. Because that's exactly what people or hiring managers are looking for in a candidate. Because at the end of it, with data or insights or AI, we're actually solving a business problem and helping people to make a decision, not just based on their intuition, which is also grounded in truth through data, right? So that's essentially the point. And I do also believe in the human-centric approach. So I do of course also emphasize that the emotional quotient or emotional intelligence should also be there, that, okay, we are just not building a technical solution for people. They can use it, but it has to be desirable as well. So I do believe that's the balance of that. Intelligently emotional intelligence with the intelligence to solve the problem that really can create a holistic way of looking at a problem and eventually solve it. So that's—I think that's definitely—I always emphasize, and what I always see also from that is that people become more innovative, right? People start to think. Out of the box when you also challenged it by thinking from different aspects of it. And that should come also from the human aspect that we bring in. So I think that both the right brain and the left brain should be really equally used and leveraged. When we see something that is challenging?

TIM: And is it about then having quite a wide balance of different types of candidates or different types of people on your team? And then as soon as I start thinking about that, I think about a lot of companies hiring for cultural fit, which to me always feels like it could be at odds with a diversity of mindset because it almost sounds like you're trying to get people to fit into a mold, but we want people to think differently. Is that a conflict, or am I missing something about cultural fit? Do you think

ANI: It's a great question because culture is important, right? So cultural fit is definitely one of the important things that should be considered as well, but I do also feel that people having different working styles should not be a showstopper because every team needs a mix, right? If everyone on the team is structured, you might miss innovation. And if everyone is a big thinker, nothing is executed, right? So I always ask myself, what does this team need right now? If we are scaling fast, we probably need executors more. If we are in a new space where we really need innovation, then we need problems, and then we need problem solvers who can think beyond the box, right? But here's the trick. No matter the mix, we need to set clear expectations, right? So that we know, okay, this is our shared goal. This is what we need to achieve. And that understanding the common understanding has to be there. And that's—I think it's a leader's role to also set that boundary and the clear way of driving, which direction we should take, which destination we should be going to, and. The path can be very different depending on the problem that we are in. And that's why I think that while cultural fit is important, having different mindsets, having different people, and having different ways of thinking are also very much appreciated as well. If someone thrives in ambiguity, for example, make sure they know how to work with structured teammates. If someone needs clarity, we need to make sure that they are in an environment where that's provided, right? It's not about hiring one type of person, but it's about balancing the team to reach the same goals.

TIM: One other area of hiring I've often thought sometimes descends into a very subjective type of evaluation is soft skills, and I'm using air quotes—let's say human skills"—and just as a simple example. So communication. I reckon if you asked 100 people to describe to me what good or bad communication skills are, you'd probably get a hundred different answers. And so I find that often with the soft skills, they're just inherently subjective. Another one is even just thinking across cultures. I'm not an expert in Dutch culture, but it's always struck me that the average Dutch person is pretty direct in their communication. The average Australian is pretty indirect. So I would say that if a Dutch person came to Australia for an interview,. I could easily see how they might be perceived as slightly rude or abrasive. And an Australian went to Holland for an interview that might be seen as very—just tell me what you think. Like you're too, you're waffling on; just get to the point. And so I'm not sure one communication is better or worse than the other. It's just different. So do we also have that issue of just, yeah, being too fixated sometimes on soft skills that are just harder to measure as well? Is that where we get into trouble?

ANI: That's a, that's actually a very interesting question for me because I have worked in so many cultures, including in Australia as well. I actually feel that Australians are way more direct than, for example, me as an Indian; we are definitely extremely high context, and Dutch is indeed low context, and indeed there is. You need to be very much aware of when we communicate with a person who is not exposed to this cultural aspect or acknowledging that the other side, the interviewer, might be coming from a different culture and they might have a different way of communicating than the interviewer should be aware of. That's the cultural difference, right? When we communicate how we operate and how we think, it's probably not them. Not the solution or not a formula, but a book that really helped me a lot to understand it at the very beginning is the culture map, right? By Erin Meyer. And that's it; it tries its best to really. Map people on different skills and different aspects from different dimensions, including communication and leadership style. So that definitely I always use and keep in mind by understanding that, okay. It's. If I'm interviewing a person who is from a high-context region, then I would definitely expect a little more explanation of a problem rather than just a very straight, direct answer. So I might have to direct. My questions and my way of getting the answer in a way that I get it right. So it's indeed a very, very good question. I think that we often get, yeah, we often get that. Yeah. Get that problem in the interviews because we just don't understand why the other person is reacting or giving a super like I am doing right now, a super high context response to your question. But it's also sometimes helped with the storytelling, for example, right? So there's always an advantage. So when I select, for example, an inside storyteller in my team. And not just an engineer who needs to build code. But I would probably go with somebody who can tell a very elaborate story and explain the business in very simple ways. What is that they need to do based on what the data is telling us? There's always an advantage to mixing the right culture and acknowledging and understanding what that is and the benefit of that culture as well.

TIM: Let's see, a few people now have mentioned that book on this podcast. I really have to grab myself a copy because it must be very informative.

ANI: It is. It is. Yeah. I highly recommend it.

TIM: And I suddenly thought maybe I should do that; that'd be really interesting. An analysis of all our podcast guests by how long their answers are, grouped by where they come from, to see if there's any kind of correlation between their culture; that would be really interesting. So, in low-context culture, they just are very direct and straight to the point. They will be challenging podcast guests, I think, because I'd be doing all the talking.

ANI: Exactly.

TIM: One topic we have to talk about, of course. Those two little words, or two little letters, I should say, are AI, which is changing the world in so many different ways. And of course, hiring is no different, starting to just creep into hiring, maybe more used on the candidate side than the company side from what I can glean so far. But I'd love to hear your thoughts on it. How companies should be thinking about using AI and hiring. Should they just be automating the whole thing? Should it still be like an AI human process? What are your thoughts on how companies should be integrating this?

ANI: Yes. I think it should be a mix. I always emphasize humans in the loop, always, right? And that's definitely not because that AI cannot do it, but AI cannot do everything yet. Maybe in the future we will see a different AI. I don't know. But for example, at Decathlon, we don't use AI for hiring yet, right? But I do see the potential, especially in making the process more efficient and helping with balancing the biases. If we can use it right now, for example, in our recruitment process, we are manually screening resumes and taking notes and interviews. And eventually we are processing it collectively and relying on it. It's like a collective gut instinct to reduce bias, but I foresee that if we apply generative AI or even applied AI, for example, we can summarize a candidate's answers from an interview more thoroughly, right? We, but it doesn't replace real judgment, right? So that's what I really want to highlight in that part. So here's the thing: AI should be an assistant. Like a co-pilot, but not the final decision maker because it cannot measure things like curiosity, adaptability, emotional intelligence, as I mentioned before, or how well someone works with a team. Again, they don't know what holds in the future. So definitely It can help taking notes. It can never be a selector. It can definitely support humans to be more efficient, but the final decision must always be made by humans with the focus on potential, rather than just a very theoretical, qualitative, or quantitative way of measuring it.

TIM: I'm wondering if there's an interesting analogy to sports recruitment because I think 25 years ago or so, baseball started to use analytics and recruitment that movie Moneyball with Brad Pitt brought to popularity, and then football started in the last eight or nine years. And I heard an interview just today with Sir Jim Ratcliffe, the recent new owner of Manchester United. And he mentioned the fact that recruitment has been. Dreadful for 10 years, they spent more than a billion pounds and as a gaping hole that he says they have no data analytics in their recruitment at all. And it's one of the leading reasons why they've tanked so badly. Meanwhile, these smaller clubs like Brighton have gone and bought all these amazing players for a very small amount of money that has just been worth so much. And so it's been this really interesting breakthrough in football where they're using data to replace some of the more legacy ways of choosing whom to. By being more based on the gut feeling intuition. And so I feel like we're just waiting for that to happen in regular jobs in regular business. Do you think that will happen? Or is there some reason why that can't translate from, like, sports recruitment into recruitment of other people?

ANI: I think it will happen, but there are many risks, right? So the AI that we are using has to be trustworthy. So it shouldn't have bias, you I'm sure you are aware of that. The what we have seen in Amazon in the past, right? So there's always a risk that if AI is not trained on diverse hiring data, it can dream and reinforce those biases, right? So Amazon's AI hiring tool was automatically filtering out female candidates just because past hires were mostly male. While AI can definitely help scale the hiring processes, I think we definitely need to make sure that the AI system that we're using in the process must be fair, must be transparent, and must be accountable as well. So that's, I think, crucial. To make sure that we embed that the potential of it is huge potential. I absolutely believe in data analytics and could definitely help us make better decisions as again, an assistant, as a co-pilot to give us indications of all right, so signals that, okay, all right. This could be a better candidate because. X, Y, and Z reason. So the why behind something becomes more explicit, but I don't think that should be the only thing; that shouldn't become the decision maker.

TIM: Yeah, my feeling on this is yeah, I also agree that we should apply those standards to AI. But then I also think we'll. We don't apply those standards to human recruitment, and human recruitment is already so broken. There are already all these studies around; just as an example, in Australia, one from a few years ago, to cut a long story short, they found that on average, if you apply to a job in Australia with a Chinese name, you have only one-third the chance of a callback or else equal to applying with an Anglo-Saxon name. So there's just a lot of discrimination already in the human process, and nobody really measures that or monitors that. I see how AI has some limitations, but is it a case of so do humans? So could AI not already do it better than humans, even if it's imperfect? Maybe.

ANI: I I actually tend to agree on that. And that's why I mentioned balancing the biases, because we all have unconscious biases, right? So let's be honest about that. So I. I think I'm not sure I used to, and I probably still tend to hire people who click with me, like liquid, right? And what does the click mean? It's basically a bias, right? So I've made this mistake myself. I remember interviewing someone I had a great connection with: same interests, same ways of thinking, and similar semantics, and I was ready to hire them. But. When I really stepped back and compared the actual performance in the interview process and looked at a holistic view of the others I spoke with, I realized that someone else was probably technically even stronger. So that was a real learning moment for me: diversity doesn't always feel comfortable. But that's exactly why it makes sense. the team stronger, right? So research shows, and I think I'm sure you know about it, that diverse teams can make decisions 70 percent of the time better or most of the time. So because they challenge each other. So I think it is very important to really understand that recognition. That we have biases and sometimes it's hard for us to probably really rectify those biases in the moment, but that's why AI could definitely help us if we can make it right, and that's why that could definitely become a very handy tool. For us, for sure,

TIM: Yeah. You mentioned before the kind of AI interview assistant. I think that's an absolute no-brainer. At least already at the moment with current technology, you could do a great transcription and great summarization of any meeting. And then. You could easily have the AI interviewer also score the candidate on the scorecard that the interviewer had, and it would be very interesting to compare human scorecard evaluation versus AI scorecard evaluation. Imagine if there was a big discrepancy at some level, and then the AI explains their decision, and the hiring manager or human interviewer explains theirs; it could help to uncover some of those biases that we might not even be aware of as well. Also. I feel like there's a lot of upside.

ANI: Exactly. And I think there are right now many measurement frameworks that are out there to measure eyes, which are out there, just so you don't even have to train them in front. You definitely have to eventually, but they did. They're doing a pretty good job, and you can definitely select based on which model out there is performing the best in terms of summarizing content, which is more trustworthy, which is more reliable, and which is really getting the inner meaning of the whole conversation and summarizing it in the right way. So I think if we select the right AI assistant or the right model to be our assistant in terms of AI, I believe that this definitely could help us a lot, but we need to be aware that it also comes with a lot of data literacy, right? So if he starts thinking about, okay, this is AI; it must be right. And that mindset has to be changed too. So that's, I think, I believe that it's not about just usage, but also having the higher literacy of understanding what is right and what is wrong from the human aspect that makes the right partnership more efficient.

TIM: Yes. And what I hope doesn't happen is that companies then train their own, I don't know, CV screening tool, their interview tool to say, Hey, here's all the decisions I've made previously. Make more of those decisions. Because all that's going to do is scale the current bias. as opposed to having something that's unbiased.

ANI: Exactly. So it has to be a very conscious decision on training the model in very diverse data. If we want to build a diverse, ideal workplace, .

TIM: I'm sure you've seen this trend. I've certainly heard a lot about it, which is that the candidates themselves are using Chachapiti or other large language models to write their CVs, write their resumes, and sometimes even apply automatically to different roles, like by filling in the web form automatically. And so what I keep hearing is that, Oh, we're getting all these applications. A lot of them look amazing on paper. They seem to match the job almost too well. And so this seems to have been causing a screening issue. Do you have any tips for how to differentiate between this AI-generated BS fluff and the real deal?

ANI: I love the question, and I'm really going through this hell as well. You can delete the word, absolutely. We are in a world where anyone can create a perfectly worded resume in 30 seconds with the AI, right? But does that mean they can do the job? Not necessarily. So I believe that the hiring managers need to look beyond the resume, right? So if you are just scanning a list of skills and experiences, you are not actually hiring for ability; you are hiring for who knows how to write a good resume. So instead, and probably going back to my first response, what I said was that I like to really understand and reveal the real experience and expertise. So I ask questions like, If that's a question, yeah, it's an interview. Then I ask questions like, Oh, tell me about a time when something went wrong in a project. What did you do? And how did you really overcome this? And, or I could also ask questions like, can you explain the concept that you just mentioned to me? Like I'm a five-year-old kid because I don't want to be anyone's grandmother. So I believe that AI can generate passwords. And polished responses, but it cannot fake the deep, the depth of expertise. So the best way to separate fluff from reality is just getting candidates to show and just not tell. So that's totally—I think that that should work.

TIM: And that's once you've gotten them into the interview stage. I guess the challenge a lot of companies will be having though is, who are we choosing to bring to interview? Because the problem is the resume looks so good. There are a thousand of them. Maybe, I don't know, 500 of them might look good. Yeah, are you more practically trying to validate stuff ahead of time, even pre-screening candidates for skills or anything like that?

ANI: Yeah. I think in the beginning, I think it was a challenge, but now I realize the moment I see an overly fitted resume. and reusing the words that are exactly there in my job description and some jargon, which normally we do not use in conversation or even resume writing. That's an amber flag for me immediately. I also do balancing because sometimes people are using AI. Intelligently as well to enhance their current resume. So if I see there is a human touch or a bit more spelling mistakes, sometimes that also gives away, okay, people actually probably at least wrote one sentence on their own and made a mistake; that definitely shows probably that they have written it. But sometimes also I see that they put some effort in, a little bit. Customize the AI-generated content in a more human-centric way. So that definitely gives away a little bit of the idea that, okay, somebody used AI, but definitely to enhance rather than just blindly. Yeah. Generating their CV to fit the profile. But it's—I must say this is definitely more difficult than. Earlier, selecting from 500 to five probable potential candidates.

TIM: Yeah, it's getting harder and harder. One thing I heard someone mention was, oh, a quick comparison to the person's LinkedIn profile. And so they reported finding what looked like, oh, the perfect candidate based on their resume. But it almost felt too good to be true. They went to their LinkedIn link, and the profile is completely different because it's because you're not going to—you can only have one LinkedIn profile, whereas you can have as many CVS as you want when you're applying for jobs. So that was a little interesting hack, but it's tedious to do and pretty manual. I'm not

ANI: Yeah, no, it's it's, but also let's think about it. The trend of using somebody else to write your CV for a particular job is also not unknown. So today people are using AI, but. In earlier days, people used to use somebody else to write their ATS fit or their CV for certain roles. So it's just replaced by AI, which I understand. So I believe that's also a matter of how do we really select and understand the potential of the potential candidates beyond this fluff. Password jargon words. Yeah. Resume. I think we have to deal with it for the time being until we get to a point that, yeah. Gives us a better idea on how we really select them.

TIM: Yes. And. I feel like some candidates listening to this might say, Yeah, my resume is full of AI-generated fluff, but so is your job description because a lot of the job ads—I'm sure that's one—must be one of the leading uses of Chachapiti on the company side is to flesh out a job ad because it's, so much easier just to do it. Ask Chachapiti to write it, then write it yourself, but do they run the risk of then falling into the same issue if they just sound a bit too generic and end up being this kind of long laundry list of requirements? And do we then risk not having a really well-targeted ad to find exactly the candidates that we want?

ANI: I, I do that. I do that because I try to simplify job descriptions. I try to always focus on three to five core must-haves instead of hundreds of nice-to-haves or should-haves. Kind of skills and that definitely helps me clearly to separate the potential candidates that I would like to at least screen, for my role, right? So I think one of the reasons I do that as well. I started to do that when I was made aware that only six, I think, men tend to apply for the job even if they meet 60 percent of the qualifications right as in the job that's mentioned in the job description, but do you want your men and women to only apply if they have qualified for all 100 of the qualifications of the skills that are needed for the job right? So I don't want to really front screen anybody who is knowing that this is something that has come out in the many research over the time. So I definitely try to be very objective, and that probably goes with your objective hiring. And also making sure that I give the right idea and set the right expectations. Also, what is the need for the role? Because that's also something very important. It's not me looking for somebody; it's also somebody else looking for a match for the role. So if we think from the mutual understanding and, yeah, requirement perspective, I believe that it definitely works quite a lot.

TIM: I've often thought that hiring would be a lot more efficient if both parties were just as brutally honest with each other as soon as possible and if the job had included like a day in the life of the role and who else is in your team and, of course, the salary, the remuneration, this and that, and the candidate gave you the resume with everything you needed on it so that we could just get down to it a little bit quicker. Would you be up for that kind of process, or do you think there needs to be like a little bit of a dance where you're holding back information through the process, and eventually you find out everything once you start the job?

ANI: I don't appreciate it myself. So I normally am brutally honest. If I'm hiring somebody, that's okay; this is the state of the company probably, but this is the problem you want to solve. And I see that. Let's see. You think this is a challenging role for you. This is a challenge you want to accept or not because, at the end, people do find out, right? Hiding the facts of the challenge doesn't help anybody because then I have seen so many cases from my perspective as well. I joined a company, and it was completely a different point. The situation was painted very differently. Whereas when I joined, it's like completely different in terms of the challenge being more aggressive. Of course it's nice, but at the same time, it's always nice. To know that a friend, because then you either come with super high energy if you are a problem solver and if you like that, or it will be like, Oh my God, this is really worse than I thought it was. So I need to really take 10 steps back to do my job. So then it basically slows your progress back, right? No matter where you want to go. So it's so good to have the right setting expectations and the right goal as well. Yeah. Yeah. Right level.

TIM: What do they say? Most of life's disappointments come from a mismatch between expectations and reality. And I can tell you the first two people I ever hired, which was 10 years ago now, into a role. I was working in a company with a kind of Excel hell environment where we didn't have a data warehouse every month. There was this ridiculous process to combine eight different Excel spreadsheets into one report for a CEO that was, oh my God, such a disaster. A lot of VBA code and a lot of copying and pasting basically. And so I wanted to hire an analyst to palm off some of the crappy bits of my job basically so I could do something slightly more interesting. And we hired someone, and I overpitched the role. I said, Oh, lots of interesting data, da dah. Lots of opportunities, which there were, but I should have said, you know what, the first four months are going to be pretty tedious because we have to automate some basic reporting, and then we can start to do some analysis. But I didn't really pitch it that way. The first candidate joined, quit on day five, went back to the market, did the entire thing again, and hired someone else. Quit on day two. All right, so that's like a ridiculous waste of time for them more than anything. And then for me, as the hiring manager, completely demoralized, having wasted all that time and money. So of course you want to avoid that. So it's better to be honest as soon as possible. I think that was my takeaway anyway.

ANI: Yeah, I think that might be it. We always learn from making mistakes in the hiring process, right? So thank you for sharing that. Yeah, that's—I think that's indeed—I believe that's a mutual understanding of the problem, and the state is super important to really be part of the same team because, at the end, that's the whole goal, right?

TIM: Yes. And this goes at any level as well. Like I remember in 2022, there was a company here in Sydney, a tech company that had raised a bunch of money. On the day they went bust and they shut down, they had 40 roles on LinkedIn open, and my friend had joined the week before. And so it turned out that the company had made, I don't know, only like a hundred thousand dollars in revenue, and they'd spent like 10 million. Okay. So it's just complete bullshit that The founder was running. Of course that's, that's an extreme example, but. It always pays to be as honest as you can in hiring. think

ANI: I think it's tricky.

TIM: side as well,

ANI: Yeah, I think it's tricky for the startups. I've heard so many nightmarish stories about startups that aimed to be a scaler but didn't know how to do that. And then a great concept, but doesn't know how to really get to the point where actually things can scale that. Just pure what I was talking about as well. That's why you need the balance in the teams, executors, and innovative thinkers. They could both, together, make sure that, okay, you have a great thought, but then make sure that it's really usable and desirable so that we can scale. So yeah, this is me. Yeah, that's really sad.

TIM: One thing we've done for years, because we're a startup, is we just show people our bank balance when they join or when they're about to join. We tell them exactly how much money we have, what our runway is, and what our revenue is per month. So they have all the facts. If it's too risky for them, okay, no worries. Like it's, as long as we've given them the information, if they join willingly, then all is good. We're in it together.

ANI: Yeah, I think that transparency is the way to go to get the trust, right? So you need to trust each other. Yeah. That's great. Yeah.

TIM: One thing we haven't touched on yet is working styles. We've touched on it a little bit, but. So we spoke before about the kind of high-context and low-context communication styles across different cultures. But then also some people who need clearer direction, others prefer the more open-ended challenge. Have you seen this map to cultural background? Is it more typical than someone from certain cultures would expect? One or the other. What's your experience here?

ANI: I think that mindset is very individual. However, there are certain aspects of how people approach a problem and a challenge that could come from a cultural background. For example, I think that there are certain cultures that see the glass half full, and the other culture could always see the challenge as a glass half empty. And there are some cultures that may just think, okay, there is a glass and there is some water, and that is 50 percent full. Empty doesn't matter. There is some water in it. So I think it's individual. However, somehow, sometimes I've seen there is a cultural. Aspect in it, but I haven't seen it in any study or anywhere so far, so I can't really confirm that from the study perspective or research perspective. But I think that's just the way of looking at a problem, and how can we really either be super enthusiastic to solve the problem without just looking at everything that could go wrong? And some cultures or some individuals, also maybe from the culture or not, cannot guarantee that they could really think of a problem by thinking of one. All the risks, or not even risks, all the problems that could occur even before starting to think about the solution. It's—it could be individual, but there could be a link with the culture as well. But I believe that's just the way you start thinking of a problem.

TIM: Yeah, I haven't seen data one way or another by culture, but I've seen a meme, the meme, which has it's like a normal distribution and it's Americans view of everything. Everything is great, good, or pretty good. That doesn't get any worse than pretty good. And then someone from Eastern Europe is dreadful crap. Very bad. It is as good as it ever gets.

ANI: Yes. And that's what I didn't want to say, but I have seen that indeed in the Eastern European mindset, there is really a lot of prethought even before they even start thinking about it. And there, I know the historical background as well, which is probably coming from it with the struggle during the Soviet era. Maybe we're going to get political, but that's something that we... Did have an offshore delivery, near a shore delivery center in Romania. And I think we had a lot of challenges getting anything done because there had to be so many confirmations; they needed reassurance they needed to just start solving a problem or getting. starting a project, essentially. So every risk had to be addressed, which is very good practice, but sometimes it's just not possible. Whereas sometimes Indians Way do way better than they're like, Okay, let there be a problem. Sometimes we say too much. Yes. Yes. Even if you don't know, we just say yes. But there are some mindsets there that also say, okay, let's take the challenge; let's try how far we can go, and maybe we will be stuck, and we will get to know a lot of risk as long as you raise them. I believe that's how we can just get things done.

TIM: Yeah, it's probably good to have a bit of a mix. You want this sort of gung-ho, underthinking, overconfident, let's just do it. And then someone says, Hey, you know what? Hang on, let's at least think of these three problems. Because if one of these comes up, we're screwed. So maybe a nice

ANI: Agree. Agree. I definitely appreciate the view of also being overly apprehensive about everything because then I can select 10 from your apprehensions, and the rest we can park. Let's start.

TIM: There's a scene in one of my favorite TV shows with a politician, and she's trying to get one of her policies up, and she pitches it to a team, and she says at the end of the meeting, Look, I can see you hate it, so you can find all the holes with it. Okay, let's play that devil's advocate kind of role. That's

ANI: Exactly. Absolutely. Absolutely. Oh, that's a good tip. Which one is it?

TIM: That's the thick of it, which is actually about politics in England The Americans remade that as Veep. If you've seen Veep, one of my favorite ones as well.

ANI: Yeah.

TIM: And if you could ask our next guest any question about hiring, what would you ask them?

ANI: I think that's a great question because I was just thinking about it. And I think that I would definitely ask, What's a hiring mistake that changed the way you hire today? So I think everybody makes them have made some kind of mistake during the hiring process and eventually learns how to tackle the problem in the next run, and I would definitely love to hear that from the next guest.

TIM: Yeah, and we're lucky they might share more than one mistake. They've been hiring for long enough. If they've only made one, they're doing pretty well. I have to say, I'm sure I've made way more than one.

ANI: too.

TIM: Yeah. Yeah. Okay. Excellent. I'll level that up for our guests sometime next week and. I'm interested to hear what they say. And it's been a really interesting conversation today. We've covered off a lot of different ground. We've managed to keep it not too controversial in the end, I think. So that's comforting; we're unlikely to be canceled anytime soon. So that's good. Thank you so much for joining us and sharing all your insights with our audience.

ANI: Thank you very much. It was a pleasure. Have a nice evening.