Episode 3: The ROI of People Analytics: Driving Tangible Business Outcomes

Summary

Welcome to the inaugural episode of The Peoplebox Show, where we uncover the secrets behind the OKR success of top businesses around the world. In this episode, we have Austin Strong, the Director of Corporate Strategy at Weave, an all-in-one communication platform.

Weave made history by being the first Utah-based company to go through the prestigious startup funding boot camp, Y Combinator, in 2014. Since then, Weave has continued to make remarkable strides in the tech industry. It became a Unicorn in 2019 and went public in 2021, demonstrating its impressive growth and stability.

With a background ranging from managing Deloitte Consulting’s Strategy & Operations practice to leading product compliance testing and lean manufacturing operations, Austin is a seasoned leader and brings a wealth of knowledge and expertise to the table.

Tune in to this insight-packed session to hear Austin talk about:

• The strategies for crafting and accomplishing moonshot goals during uncertain times
• How to achieve optimal alignment within teams and between different departments in large organizations
• Strong’s personal experience with OKRs and key takeaways for others to learn from
• Proven tips, tricks, and best practices to set and effectively manage OKRs

Below are some excerpts from the session hosted by our CEO & Co-Founder, Abhinav Chugh. You can also watch the full podcast below.

Complete Transcript

Abhinav Chugh: Despite the current economic downturn, most leaders still want to achieve moonshot goals— but now have fewer resources. Does it mean companies need to calibrate their strategy and tone down their goals?

Austin Strong: I don’t believe in reducing moonshot goals during economic turbulence. The challenge is not in the goal itself but in the approach or path to reaching it. For example, consider a company with a target of 30% yearly revenue growth. Rather than relying on conventional sales and marketing methods, the company could launch a product-led growth strategy with self-sign-up and self-onboarding that is more cost-effective and requires lesser resources. This shift in approach doesn’t alter the ultimate goal but rather the path toward it.

However, in some cases, revising the annual operating plan and financial model may be necessary, resulting in the tapering down of goals. But you should only do this after exploring all other viable alternatives.

Abhinav Chugh: Your organization went public last year. Has that changed your approach to strategy execution in any way?

Austin Strong: Definitely! Going public brings a higher level of scrutiny to a company’s operations. Instead of being accountable to just the board, and employees, the company now faces examination from analysts, investors, and public shareholders who want to know how resources and time are being managed.

So really, what it did for our strategy was that it forced us to sharpen our pencils. And it has necessitated a more mature and sophisticated approach to managing our OKRs, long-term plans, and metrics, as we are now accountable for these during every quarter’s investor call and the board calls leading up to it.

Abhinav Chugh: As a large organization, how do you foster alignment among teams and individuals to enable quick execution akin to that of a small startup?

Austin Strong: When it comes to effective strategy execution in a large company, there are a few key things to keep in mind. First and foremost, having a dedicated team or person in charge of the strategy and metrics is essential. You need to designate a single person to be in charge of the strategy and the metrics associated with it to avoid ending up in a situation where no one owns it.

Next, it’s crucial to have a written plan that everyone at the company can refer to. This allows everyone to be on the same page and understand the company’s goals and objectives. Additionally, having a written plan encourages debate and discussion, which can lead to a more robust and well-thought-out strategy. It helps in democratizing strategy, implementation, and execution. You’ll be amazed at how many companies don’t actually have a strategy plan written down.

If you think of a company like a military unit, the CEO is like the general who has a big plan for the company. Still, the success of that plan depends on each department (like the regiments or divisions in the military) understanding and working towards the overall strategy. This way, each department can act independently, making decisions and coming up with creative solutions that align with the company’s goals. This leads to faster execution, improved decision-making, and increased creativity while ensuring no department goes off track and does something that goes against the company’s strategy.

Another important aspect of strategy execution is having a centralized tool to track all the information. With a strategy plan involving multiple initiatives, metrics, and KPIs, it’s easy to get overwhelmed. Having a tool like Peoplebox helps to manage this information in a digestible way.

Finally, constant communication is crucial for effective strategy execution. This means having regular meetings, newsletter updates, and other forms of communication to ensure that everyone is up-to-date and aligned on the strategy.

Abhinav Chugh: That brings us to the main question— When did you start implementing OKRs, and how has your journey been?

Austin Strong: We started using OKRs last August while developing our plan for 2023. It’s important to understand that setting up the right OKRs takes a lot of thought and effort, and it’s not something you can just throw up on the wall.

I’ve been with the company for 18 months, and at that time, the OKRs were in a primitive form. But now that we’re public, we need to clearly define what a successful 2023 looks like for us. So we went through a thorough process of defining our objectives, the key performance indicators that support those goals, and the key initiatives that will help us achieve them.

One of the challenges with OKRs is that you need someone to own them; in our case, that responsibility was given to my team and me. Another common mistake is starting too big. People often don’t want to make trade-offs and want to do everything at once, but that’s just not possible.

If you don’t prioritize your OKRs and say no to some ideas or objectives, your OKRs will become too sprawling and complex, leading to exhaustion and abandonment. However, if you focus on just two or three key objectives, you’ll have a better chance of success. 

Refusing to make trade-offs and having too many OKRs can quickly lead to burnout and the feeling that OKRs don’t work when it’s just a failure to embrace the reality of trade-offs.

Abhinav Chugh: How closely do you associate company strategy with individual performance and engagement, and what is your opinion on how it should be linked?

Austin Strong: Yeah, this is something that we’re trying to get better here at Weave— having a solid written strategy plan is key. When everyone agrees on the plan, and it’s clear how your team fits into one of the focus areas, each department and team within that department can create a plan for how they’ll contribute to the company’s goals. You can even break this down to the individual level. If you do it right, it should be a significant factor in their year-end performance review. We’ll be looking at what they did to contribute to the company, which will play a big role in determining things like promotions, coaching, and more.

Abhinav Chugh: How do you strive to build cross-functional visibility and transparency throughout the process? Is access to the company’s OKRs open to all employees, including interns and early hires, or is it only available to specific teams?

Austin Strong: At Weave, we have a strategy plan that includes sensitive information we don’t want to be publicized. We don’t want our competitors to know our exact goals or how we plan to achieve them. That’s why we’ve created a balance between having a detailed strategy plan that only a few key people can access and a simplified version called “strategy on a page” that we give to every employee. 

The simplified version still points everyone in the right direction but reveals less sensitive information. This way, everyone understands what we’re trying to do, but the leadership team has the full details. That’s how we balance keeping sensitive information secure and ensuring everyone is on the same page.

Abhinav Chugh: In your experience, who is the most suitable person or team to manage OKRs in a medium to a large company?

Austin Strong: That’s a great question you’ve got there. When it comes to OKRs, it really depends on the size of the company. For smaller companies, the CEO and the chief of staff often take charge. But as the company grows, they’ll need to delegate the responsibility to others.

In a medium to a large company, I’d recommend that the chief operating officer and their business ops or corporate strategy team own the OKRs. The COO and their team are broad across the entire organization and have a holistic view of the company, which makes them the ideal group to manage the OKRs. However, the ultimate accountability for the objectives and key results must fall on the CEO or the COO. When you delegate the responsibility to other functions, it tends to become too departmentalized.

Abhinav Chugh: How do you increase team productivity when resources are limited, like having less budget and fewer people available? As an experienced consultant and executive, what strategies have been successful for you in this situation?

Austin Strong: I’ve found that two things really work: simplification and communication. First, simplifying goals and focusing on one or two things at a time can help with execution. Secondly, communication is critical, especially in larger organizations. We have regular vital signs meetings to discuss strategy and progress toward our objectives. This helps keep everyone on the same page and ensures that strategy is not just a yearly or quarterly conversation but a weekly and monthly one as well.

Abhinav Chugh: How do you make sure that you stay focused on OKRs? Are OKRs talked about in every meeting or just some of them?

Austin Strong: The OKRs are the main focus of these meetings. For example, the Vital Signs meeting is all about tracking the key metrics, the project meetings are about the progress on the initiatives tied to the OKRs, and the monthly progress meeting is specifically to check in on how the OKRs are coming along.

And during our Quarterly Business Reviews, it’s like a check-in on our OKRs’ progress for the quarter. So the OKRs are not an afterthought or something pushed to the side, they are the center of attention in these meetings. And any other discussions or updates are taken care of outside of the meeting.

Abhinav Chugh: Have you also experienced a shift in accountability as companies grow, where people become more responsible for initiatives and tasks rather than metrics and outcomes?

Austin Strong: In small companies, it’s easy to hold everyone accountable to a single number, but as a company grows, it becomes more challenging.

At our company, with about 800 employees, we’ve found that it’s not always possible to hold everyone accountable to a single number. Instead, we focus on measuring the overall output of a team and then trust the team leaders to determine how to measure the accountability of their individual team members. It’s like empowering the division leaders to rate the soldiers instead of the general. If the departments know what they need to achieve, they can manage at a tactical level and measure the performance of their individual components.

Abhinav Chugh: What is your number one piece of advice that you want to give to other strategy leaders to ensure a very tight strategy execution in 2023?

Austin Strong: So, my advice for ensuring the successful implementation of your strategy is what I call “pre-wiring.” Essentially, it’s about ensuring that all the key players in your company are on board before you present your plan to the CEO. If you just go to the CEO with a plan and haven’t gotten buy-in from everyone else, it won’t be executed, even if it’s a great plan.

So, take the time to meet with all the stakeholders, get their input, and make them feel like they’re a part of the process. This way, when you present the plan to the CEO, you’ve got everyone else in the room nodding their heads because they helped create it. And they’re ready to defend it and make it a reality. It may take longer, but it’s 100% worth it. So instead of trying to dictate from the top down, build up momentum from the bottom up.

That’s a wrap for today!

Summary

In this episode of Peoplebox Analytics Talk, Abhinav interviews Krishna Raghavan, former Chief People Officer of Flipkart, about the intersection of data, technology, and people in HR. Krishna shares his unconventional journey from software engineer to HR leader and discusses the importance of data-driven decision-making in HR. He highlights the evolution of people analytics and the role it plays in solving business problems. Krishna provides real examples of how people analytics can be used to predict attrition, improve candidate experience, and drive employee engagement. He also addresses common myths about people analytics and offers advice for HR leaders looking to build a data-driven culture.

Key Takeaways

• Data-driven decision-making is crucial in HR and can lead to better business outcomes.
• People analytics should be a strategic consulting arm of the company, not just an HR function.
• Investing in data education and producing noticeable wins can help gain credibility for people analytics.
• Data democratization is important, allowing everyone in the company to access and use people analytics.
• Data privacy and access control are essential to ensure employee data is not used for surveillance.
• Starting small and focusing on concrete use cases can help build a business case for investing in people analytics.
• People analytics can enhance fairness, transparency, and accountability in the company.
• Overcoming data fragmentation and ensuring data comprehensiveness and completeness are ongoing challenges in people analytics.
• Resistance to data-driven HR can be overcome by demonstrating the efficacy and value of data in decision-making.
• Data analytics can make HR leaders more effective and help them drive better people outcomes.

Full Transcript

Abhinav (00:00)
When was the last time you met with a Chief People Officer of a $40 billion company only to find out that before taking on this coveted role, he has never spent a single day in HR. When was the last time you witnessed the ascent of a senior VP of engineering to the position of chief people officer

at the largest e -commerce company in Asia. Sounds intriguing? Buckle up. Hi, everyone. I’m Abhinav, co -founder of Peoplebox, and I’m super excited to kickstart the first episode of Peoplebox Analytics Talk, where we invite trailblazing leaders to delve into the fascinating intersection of data, technology, and people. And to make it even more special, I’m delighted to welcome our first guest, Krishna Raghavan. Krishna, in his last role, donned the hat of

Chief People Officer of Walmart owned Flipkart, And unlike most HR heads, his journey has been nothing but a barrier breaking one. Welcome to the show, Krishna.

Krishna Raghavan (00:54)
Thank you so much. It’s a pleasure to be here.

Abhinav (00:56)
Krishna, you started your career as a software engineer, worked in tech giants like Yahoo, Oracle, became CTO of ClearTrip, then joined Flipkart as the SVP engineering, and then became Chief People Officer, a path barely taken. One question that probably would be top of everyone’s mind in our audience is that when you were young, did you ever imagine that one day you would be at Peoplebox Analytics Talk talking to us?

Krishna Raghavan (01:21)
Definitely not Abhinav.

Abhinav (01:24)
but jokes apart, it’s truly our honor, Krishna, to have you here. So let’s dive right into this. Talk to our audience about how did you end up snagging the top HR spot at Asia’s largest startup after a super impressive engineering career.

Krishna Raghavan (01:38)
Yeah, the story is an interesting long one, but I’ll try to keep it short so that I don’t bore our listeners. But I think the journey started way back before I can even realize that it was happening to me, which is I always, I think gravitated towards problems that involved people, culture, teams and building them to scale. Right.

And even my prowess as an engineering leader was always within that space. I didn’t realize that as much until probably much later in my career. And that’s when I went through a sort of a life transformation where I did a program. You can call it midlife crisis or whatever, but I did a program and I sort of discovered where my superpowers lie and where I should spend most of my energies in the coming days, weeks and months. Right. And the answer was.

extremely evident to me in front of me which was go and try to take on a role where I could do talent, culture, building at scale not just within my function within engineering. So that’s when you know lot of things came in place and the opportunity opened up at Flipkart applied for it and little did I know in about a month’s time I got approved for the post and I got there and

and I couldn’t have imagined in my wildest of dreams that the COVID pandemic would follow in a couple of months, but rest is history. But that’s really been my journey to get on to this particular role.

Abhinav (03:09)
That is amazing.

getting into the position of Chief People Officer of the largest startup and then COVID hit, which probably nobody prepared anyone for. And so before we go to the COVID one, I’m really interested to know what was everyone’s reaction? You know, your peers, the HR team, you know, getting somebody as heading the people function who has never got a single paycheck.

which writes title HR.

Krishna Raghavan (03:38)
there are lots of people that tell you, are you freaking crazy? That’s the question I got many a times. Some of my peers in engineering in particular, they said that you’re actually taking a disastrous move and you shouldn’t be doing this. You should be staying in technology. That’s where I think most of your promise lies. And,

Some of my well -wishers were obviously backing me, but I think there were lots of naysayers along the way. And I would say that even within the HR team, there was a lot of, I would say, suspicion of what to expect an engineering guy coming into the post of heading HR.

Abhinav (04:16)
let’s talk about engineering, you know, Krishna, you are an engineer at heart. I started my career as a software engineer. there’s one thing that I’m very sure about that. They love data.

And our audience must be very curious to know How did you leverage the engineering mindset and that data -driven culture in making the right people decisions?

Krishna Raghavan (04:35)
No, it’s a very important question Abhinav. I think one of the first things that I sort of brought into the role and many times I’ve been asked this question. What are the things that you actually brought into the role and what are the things that you actually jettisoned by coming into the role because these are completely different roles. But one of the things that I brought in was this data orientation or the mindset of looking at data for everything, right? And I’d like to start with basics. I think as I entered the function,

The most important thing became the question became what to even measure Abhinav because you know, often times there’s a lot of data out there and companies pride themselves on putting together 40 metrics on a spreadsheet and everybody’s pouring over those metrics. But actually do you need to look at 40 metrics to make decisions? That’s the first question to ask. So it became my, my sort of initial focus became.

You know what, what are we here to do from a people function perspective? How is that in alignment with our overall business strategy? And then go to define the metrics and the metrics, which were important. We had to actually come up with them. In some cases, the instrumentation was also not even there for the metric. And you had to put the instrumentation together. In some cases, the metric was already there. And in the other cases, wherever there is noise, where there were extraneous metrics, we actually just kind of, you know,

remove them from all the dashboards. So that became my like initial focus as I came in.

Abhinav (06:06)
But defining the most important metrics or OKR is one thing, but then getting the data, especially for such a large company, how hard was that?

Krishna Raghavan (06:14)
Very hard. Like some of the metrics were obviously there, instrumented like I said earlier Abhinav. But in some cases, there was a fair bit of data fragmentation and I’m sure we’ll speak to it at some point later in our conversation. But you know, disparate systems, systems used for different use cases. But when you actually look at a metric, the metric is actually a blended metric. It’s an output metric of many input metrics. But these input metrics are actually present in different systems.

So how do you actually get them together? In some cases, the systems themselves were not even talking to one another. So it became plumbing, like what I call data plumbing, which is like, okay, you know what? I want to instrument this metric, but the heck I can’t even figure out how to do it. Let me actually now put my head around this problem itself. Let’s instrument first. Let’s build a data pipeline. That became the first order problem. And I started to solve some of the instrumentation problems.

Abhinav (07:08)
I love the phrase data plumbing. I’m probably going to use it in one of the pitches we use. But coming back to the whole uses of data or say the whole function of people analytics, you know, for most of the companies that I speak with, it starts with hiring a reporting or a people analyst, you know, who would help creating reports primarily for the leadership or the HRBP. Is people analytics more than just creating reports? And if it is, what all does it entail?

Krishna Raghavan (07:36)
Yeah, there’s actually a pretty good paper on this Abhinav and I would urge our listeners to look it up. There’s actually a Deloitte study on a people analytics maturity model. And, you know, there are stages of evolution of how they look at people analytics. Obviously, many of these consulting firms do this, you know, as their primary gig, right? But I think just to summarize, initially, when you build a people analytics function, it tends to be

a data provider function like you aptly described it, which is, you know what data is not my problem as an HR functionary. It’s the people analytics is problem. Whenever I want some data, I send a request. I get data back. Neither does people analytics as a function know why I requested to this data in the first place, but they become more data service providers. Right? That is like, I would say.

you know, point zero on the scale of zero to 10 in terms of people analytics maturity. And I won’t elaborate on the entire evolution path evolutionary path Abhinav, but at stage 10, it’s almost like people analytics is like a strategic consulting arm of the company, not the HR function. You know, like the CEO says, you know what? I need to figure out.

Where do I need to invest in terms of my best talent in the company and what are the skill sets I need to build in though in that talent. Now that’s a very fuzzy question when you ask that at a scale of an organization that could be Flipkart size. It’s almost like people analytics has to anticipate that problem and like a strategic consultant going in tell the CEO this is what I think this is the decision or set of decisions or recommendations I can actually give you.

And in some cases, even short circuit and say out of these set of recommendations, by the way, this is the one I would pick. And the CEO has, you know, the ultimate veto choice to make that decision. But it’s almost like moving from data provider to decisioning for the company, not just the HR function. That’s what I see the entire evolutionary curve to be for people analytics.

Abhinav (09:48)
That’s super interesting. And you mentioned about the report by Deloitte. I actually absolutely love that report and I highly recommend everybody to, you know, all HR leaders to go through that report. Actually it’s authored by a very good friend who is the partner at Deloitte named Nitin Razdan So it’s a fascinating report. Coming back to the usage of people analytics, you know, I think what was every report and talk about that, how useful it is, but

Krishna can you give some real examples of how you use people analytics to achieve some real business objectives?

Krishna Raghavan (10:23)
Yeah, absolutely. I think there’s the holy grail that most companies want to get to, which is the churn prediction model that we called it in Flipkart or the attrition prediction model. We built a model with all the data that we had. This was after I think at least year three of the people analytics journey. You know, we are, we’ve kind of moved along in terms of the maturity curve and we have the data instrumentation in place and all of that.

Abhinav (10:31)
Yes.

Krishna Raghavan (10:51)
It actually turned out to be from a precision perspective. It actually was pretty accurate. Okay. In, in terms of percentages, I think we were able to get to 80 -85 % precision. we were able to employ this in particular teams in the company. Right. and we were able to give this data not just to HRBPs, but actually the line managers, and empower them to actually have these.

conversations with some of their employees that could be on the high risk prediction. So that’s one very, very strong use case. Second Abhinav is I think a lot of companies out there pride themselves on being a top destination for talent. But do you measure candidate experience through the funnel of hiring? And in Flipkart, one of the things that we realized when I was working with the team is that

You know, candidates that got accepted their candidate NPS scores was very good, but the candidates who got dropped at some point in the process because there was probably not a fitment their NPS scores was very less. Now you could argue and say, you know what? I don’t care about the candidates that got dropped, but that does not define a great company because end of the day, your promoters are the ones who also interviewed with you and won’t say, you know what?

I didn’t get through, but I had a great experience through the process. Now the people analytics team was able to give me this data that led to say, if I look at rejected versus accepted, my NPS obviously differs. And these, this led to a series of interventions to improve the experience for rejected candidates as well. Right. That’s my second use case. And the third is we moved away from this annual survey business, which is

once a year, I’ll check employee voice and I will take a set of actions. We moved away and we said we are going to do continuous listening and we are going to actually have a mood score and we’re going to ask you how you feel at a particular day at multiple points in time through a particular week through a particular month. As this data matured, what we found out, this is probably probably common -sensical is that there is a very strong data correlation between mood score.

as the leading factor for attrition. So how do you take action early on from an employee life cycle perspective? Because often companies talk about employee retention. I actually kind of hate that phrase. It’s almost very negative. It’s like you want to go, but I’m somehow trying to hold you back. But is there a reason to stay in the first place? And can I actually engage with you when you are starting to disengage and you’re showing signals of disengagement?

So flip the problem on its head and I think this was one of the biggest shifts that people analytics actually helped us make in the company. So these are two, three use cases that I wanted to talk about.

Abhinav (13:44)
Krishna, the way I look at Flipkart and pardon me if I use the wrong phrase, but.

I look at Flipkart as like the Amitabh Bachchan of business world, you know, the pioneer in setting innovation that everybody look upto And the reason I use the word business and not startup is because even the larger publicly listed companies want to learn from Flipkart. And I believe that the use of data and people analytics must not be an exception here. So give our audience some important learnings from Flipkart, people analytics culture that they can today go and leverage in their business.

Krishna Raghavan (13:52)
Ha!

Yeah, I think it’s a very important question and I wouldn’t say, you know, it’s just people analytics. I think data as a common theme across the company, the data oriented mindset is extremely deep across Flipkart. I think that is something which goes across not just HR, but every function out there. I would probably put across certain big learnings that we’ve had through the, through the journey, right? And in particular, people analytics.

The moment people analytics stays within the domain of HR, you’ve lost the plot. It is not just a HR function or a department that you need to set up in HR. The way you need to think about it is in the business realm, you often have an analytics organization, right? And this analytic organization typically is a horizontal that goes across the company. Initially,

is a data provider then becomes insight provider then starts to actually even recommend and make decisions on behalf of the top management in the company for everything that’s people anybody in the company should be able to access people analytics. So if it remains within the domain of HR, you have lost more than 80 % of the vision of where people analytics can get to. That’s probably the biggest takeaway.

The second I would say is invest a lot of time in data education and I cannot overemphasize this enough even within HR at least I find that the level of data proficiency is probably not where it should be because in today’s day and age there is an explosion of data.

and even within the people realm there will be an explosion of data. The skill actually lies in asking the right questions, connecting the business problem to what we are solving for and asking those pertinent relevant questions and then using the power of data to reveal answers out to you. So if you can’t ask the right questions, you will be barking down the wrong tree many a times. So

Data education is my second biggest takeaway and this has to happen across the board including within HR. Right? And third is produce some very noticeable wins in the company to gain credibility of the function. It should not be something like it’s a pipe dream in year two, year three, by the way, this is the roadmap we have on people analytics and this is what we’re going to deliver. It can’t be that.

Most companies in today’s day and age need answers yesterday, not today. And how do you actually have that business acumen, that urgency and agility in your operation to be able to land some very strong, successful outcomes early on will really establish the credibility of what people analytics can deliver for the company. These are three big takeaways for me.

Abhinav (17:22)
Krishna, I’m so happy that you spoke about the first one, which is data democratization. Because whenever I speak with HR One of the top wishlist for them is the, the ability to provide everybody in the company, even the employees, you know, the power of data. However, the major roadblock

is fragmented employees data in different tools and sheets. And like you also mentioned, right? Some data is in ATS and others on HRIS. ESOP data is sitting on another tool, performance and engagement somewhere else. And there is then tons of data on spreadsheets. How did you overcome that for a company of the size of Flipkart, which has tens of thousands of employees and I’m sure no dearth of tools and sheets.

Krishna Raghavan (18:03)
Yeah, I think if you ask anybody out there on a joking note, Everybody says that this software is the best for this use case and nobody obviously wants to adopt one common platform for all use cases. So what you land up doing is obviously buying lots of different products and services leading to fragmentation and everybody proposes promises the moon when you buy them. But, after that, you realize the actual truth, right? The harshness of.

data fragmentation. So I would say that this was a journey for us, Abhinav, and it’s actually still ongoing. I would say to that extent until very recently, you know. So the way we actually did it is, like I said earlier, we spent a lot of time analyzing what data we want, where does this data reside, and spent time in actually putting together the entire instrumentation pipeline for it.

So we had to build data pipelines across all our systems and all of them flowing into one common data warehouse. Then once we build, you know, the people domain model, the people domain model as in how should we represent an employee, right? The entire data model for us, if you think about it, there are multiple relationships between an employee, a manager, an employee and a skip manager, right?

How do you define the persona of a director and so forth? You define the entire people domain model and the instrumentation pipeline for it. And then what you do is you actually land up building adequate visualization for it because the power of data cannot be revealed, so to speak, or cannot be shown in its all glory unless you have great visualization. So in our case, we also had to pick a platform or a product to visualize.

And then the education thereafter followed. So that’s the journey we’ve kind of been on Abhinav to make sure it’s not been easy at all for sure.

Abhinav (20:01)
I can absolutely imagine it’s not easy. It must not be easy because one thing that we didn’t talk about, is the data sanity. a lot of time data is not in a consumable format. I was talking to somebody and say, you might wonder that it’s so easy to find out the last CTC of an employee, like from a previous company.

And you’d be surprised it’s not because it’s sitting, it’s sitting in notes. Uh, and those are like tens of hundreds of notes. So was that a problem that you also encountered about, you know, cleaning up the data and make it in a consumable or probably a quantifiable way. And then of course, you know, put it into your data pipeline.

Krishna Raghavan (20:41)
Absolutely Abhinav, I would say the two big things that you always think about when you deal with data is you think about data comprehensiveness. Do you have all the data in the first place? Then the degree of data completeness. They are very different by the way, like comprehensive means do you have all the data? Completeness is more the aspect of accuracy. So it’s not enough by the way that even if you…

Take your example, even if you got the CTC of the last employment into a system where you can consume this data, unless you actually refresh this data for future joiners that come in, the data becomes incomplete. So you also need to ensure completeness, not just comprehensiveness, right? So it’s a big problem. And in some cases, frankly, the data is so offline.

it takes a lot of effort to just bring it online. Many of the teams don’t even record this data.

Abhinav (21:38)
Now, Krishna, a bit of a controversial question. You are this round peg in a square hole, bringing this engineering and data driven mindset. Did you get any resistance? Generally, not a lot of companies rely on data when it comes to making people or HR initiatives. How was that going through the journey and was there any resistance?

Krishna Raghavan (22:04)
No, no, I think I would be lying if I said there’s no resistance. There was definitely resistance. Different forms of resistance, right? Sometimes you face resistance when you make a large change, passively or actively, correct? So the active pieces in places like learning and development where you need to define efficacy of your interventions. Sometimes learning and development will say, you know what, we have great participation rates, we have good satisfaction scores. Isn’t that enough?

Why do we need to measure quantifiable business outcomes of our learning intervention? That’s more like active resistance because the question is why? Why do we even need to do that? The passive resistance comes into places where people are not data aware enough and they believe in somebody else’s job to collect the data and take those decisions or they say these decisions are very intuitive. We actually take them based on intuitive thinking. Right? Why do we need to actually bring data into the equation for everything?

So there you face some degree of passive resistance as well. And what you need to keep constantly doing Abhinav is obviously one as a leader, you role model to you actually make sure that you keep communicating the efficacy of data and how it could lead to better decisioning across the HR function and the company itself. So that’s why I said earlier, producing some wins early is going to be important because talking in theory,

is one thing but actually in practice producing some wins and real examples is much more powerful.

Abhinav (23:36)
I love what you said that you need to be the role model to use anything, or I think to drive any change. But Krishna, tell me honestly, you’ve been an engineer, you’ve been an HR head, and obviously in both of the roles you’ve used data extensively. Do you genuinely believe that the usage of data makes someone a better HR leader or a better HR business partner?

Krishna Raghavan (23:59)
I mean, there’s, you know, it’s an emphatic. Yes, I’ve enough. It definitely makes you there’s no doubt in my mind about this. I mean, everybody would obviously say the answer. Yes to this one. But the degree to which it really helps you become a better leader. I think across the board and actually why only focus the point on HR, HR obviously yes. But the way you lead teams in companies today.

Gone are the days where you can just be very intuitive only as a leader and say, you know what, I think this person’s good. This person’s probably not scaling up enough. You have to now move to an era where you can use data to actually really power your decisions. And let me actually talk a little bit about just one small example. I think what we saw as the biggest transformational change in Flipkart.

is when we started to actually bring the power of data to business leaders around people, we defined what we call is a people dashboard. And we said to a business leader, you know what, you look at business, you look at top line, you look at bottom line, you look at all of this. What if I gave you a people dashboard in conjunction with your business dashboard?

And you look at it also to make decisions for your function. How would that look to you? And tomorrow, you know what? Both the CEO and me are going to hold you accountable. Like we hold you accountable for business outcomes, we are going to hold you accountable to those people outcomes as well. So if your attrition spikes, if your diversity doesn’t get to the target that we want you to get to, we are going to hold you accountable. It changed the game It helped leaders become.

better people leaders. At the same time, it drove the people agenda is not just an HR agenda, but it now becomes a company enterprise agenda. And that’s a very, very powerful shift. And this could not have happened without people analytics.

Abhinav (26:07)
Coming to the more challenges, Krishna, and I know you must, of course, be much more aware about them than I do, is the sad reality of HR world that they don’t get high budgets like a tech or sales or marketing would get. So how would you suggest to HR leaders building a business case for their CEOs or business leaders to invest more in people data or people analytics?

Krishna Raghavan (26:33)
Yeah, I think it’s a very, very, very important question Abhinav. I think that’s where probably most of the companies do not have adequate resources to invest in this particular area. I would actually start with focusing on two or three very concrete use cases for the business. Like it’s almost like when we think about building products for a set of consumers Abhinav.

We always think about product market fit, right? I would actually kind of think about it internally as a CHRO or an HR functionary in similar vein, which is we know it’s important, but how do we actually bring these stakeholders onto the table? The CEO to sponsor the investment in this area. Let’s take a big hairy problem that’s facing the company right now. And actually evidence.

how it can actually be solved elegantly through people analytics leading to direct better business outcomes. It could be around staffing. It could be around talent development. It could be around attrition, right? And you could actually quantify a before and after as well saying that this is what I implemented in a particular function. And so therefore you could have a control group established as well. So I would take this approach.

established two, three concrete use cases. And that I think will be a lot more powerful to sell to your most important stakeholders within the company.

Abhinav (28:04)
Do you see HR and people function in general

is now becoming more and more data driven and did the whole pandemic and remote and gig worker had anything to do with that? Was there a trigger? Was that a driver or is it still the same how it was 10 years ago?

Krishna Raghavan (28:20)
No, certainly not the same. Abhinav I think it is definitely improving as we speak. I think people have gotten a lot more data aware HR functions across the board. I think you have to realize that earlier data was more seen as, you know what I need to get data for a particular use case more as a service provider as I described it earlier. now looking at

How can data power decisioning is something which is dawning upon, I think, HR functions across the board. But they are grappling with data fragmentation as a real problem. So the awareness, the intent is there But now the problem is when rubber hits the road, how do I actually get there?

Abhinav (29:07)
Yeah, absolutely. So I want to now move to our last sort of a section or a round, which is about breaking the myths and as you speak to a lot of, you know, business people, HR people, HRBPs about the people analytics I see a lot of myths and I want to make quick answers from you to our audience about, you know, how do you, how do you react to these myths? So one of the first one that we hear very often is about you need to be

have deep data analytics expertise to use people analytics in the company.

Krishna Raghavan (29:40)
The answer is absolutely not. It all depends on the power of the tooling and the product that you actually employ to solve this. And products have evolved to such an extent where it’s about just a set of clicks. And like I said earlier in my conversation, it’s about asking those right questions. You can get the data that you actually want on your fingertips. So you don’t need to be a data scientist, a data analyst to actually use data.

Abhinav (30:06)
And the second one we hear is that People analytics reduces people just to their data and take the human element out of it.

Krishna Raghavan (30:14)
Actually, it’s more the opposite, right? Which is, I think of it as sometimes, particularly in the people realm, we use our own hidden unconscious biases to drive people decisions. And actually data bust those biases, right? Like, you know, one of the most common things is,

people who are not seen in the pandemic, they are probably not doing as much work and they don’t deserve to get promoted. You know, this could be a huge bias that could actually play out both consciously and unconsciously. But if data was there to our rescue, actually it would even make the company a fairer place to work in where these biases actually do not rule. So if you think about it, data can actually be your

I would say your biggest lieutenant, so to speak, as a leader, your biggest supporter to ensure fairness and transparency in a company.

Abhinav (31:18)
Very well said, Krishna. Another one. People analytics facilitate employee surveillance.

Krishna Raghavan (31:23)
Absolutely not. I think as long as you have standard, very, very strong data privacy rules around employee data, and you have very strong access control, determining who actually can view pieces of data, I think you’re in very, very safe hands. It’s not a surveillance tool at all.

Abhinav (31:44)
I couldn’t agree more. And the last one is to start people analytics, data must be perfect.

Krishna Raghavan (31:49)
Not at all. I think it’s a journey. The data completeness, comprehensiveness journey that I talked about is a journey. You don’t need to be perfect on day one. Start somewhere, start small, establish those wins and continue on your journey. Because what will propel you on your journey is progress, not stagnation.

Abhinav (32:08)
This is really, really helpful, Krishna. Krishna, you have moved on from Flipkart now. 31st December was your last date. And our audience will be very curious to know that after such a path -breaking career, what’s next for Krishna?

Krishna Raghavan (32:23)
Yeah, I’ve been still, you know, sort of thinking through, you know, and scouting for opportunities that I think will really interest me. My heart has always been in the realm of startups and I want to see how I can contribute to one or many. And that’s sort of where I’m sort of focusing my energies on in the coming days, weeks and months. I’ll definitely keep everyone posted, you know, what I’m up to next.

Abhinav (32:52)
I can’t thank you enough, Krishna, for this super insightful session. I enjoyed every bit of it, learned a lot, and I’m sure our audience will have so many insights and so many learnings to take from this talk. Just thank you so much and wish you all the very, very best for the next step in your career.

Krishna Raghavan (33:10)
Thank you so much Abhinav. It was a pleasure talking to you. Very, very insightful questions and thank you for a wonderful conversation.

https://podcasters.spotify.com/pod/show/peoplebox/episodes/Episode-1–The-Impact-of-AI-in-Talent-Acquisition-and-Management-e2n9lg8/a-abgqak1

Summary

Recruitment today is broken. Businesses are grappling with challenges like misalignment between hiring and business goals, overburdened recruiters, and poor candidate communication.

These inefficiencies are costly. According to HBR, the wrong hire can cost a company 5-7 times the employee’s annual salary when considering hiring, training, and lost productivity. Additionally, a study by Glassdoor found that a single job opening costs companies $4,129 on average, with costs increasing the longer a position remains unfilled.

In the first episode of The Peoplebox AI Talk, Abhinav Chugh, CEO and Co-founder at Peoplebox, sits down with Suzanne Salzberg, a veteran talent leader with over three decades of experience at leading tech companies like Highspot and TextNow.

Suzanne discusses how AI can streamline processes, from creating unbiased job descriptions to improving candidate communication and making data-driven hiring decisions.

Full Transcript

Abhinav (00:00)
If there’s one thing everyone is speaking about today, it’s AI. It’s going to disrupt every aspect of our professional lives. And some may say it has already started. So what will be its impact on the future of work, especially how we hire, develop, and retain our most valuable asset, People

and which part of the employee life cycle from hiring to retirement is going to see a biggest change What does it mean for the companies and HR? Hi everyone. I’m Abhinav, and welcome to the new season of our podcast, Peoplebox AI Talk, where we invite incredible leaders to go deep into the fascinating intersection of talent and AI. Today, I’m delighted to have Suzanne Salzberg on the show

Suzanne comes with over three decades of experience in the talent space. She has been the head of talent for big tech companies like Highspot and TextNow, and she’s now also a visiting lecturer at the University of

Welcome to the show, Suzanne.

Suzanne Salzberg (01:00)
Thank you, I’m happy to be here.

Abhinav (01:02)
Suzanne, you have been a talent leader for some of the most high tech companies. Let’s start with speaking about the disruption of AI in the talent space. Where do you see its biggest impact in the employee life cycle?

Suzanne Salzberg (01:16)
Well, I can, I can focus on the candidate’s life cycle in the talent acquisition you know context. I really think the, the biggest impact is going to become, honestly from the minute they read the job description because I think I see AI really helping to create complete, unbiased, easy to understand job descriptions. and it’ll also help hiring managers because one of the biggest pain points for talent teams is waiting for that job description.

The next big impact would be how fast a candidate hears back in the process, whether it means they applied and they get a response or if they’re in first, second, third, fourth interviews getting a response because I talk to a lot of candidates every day. And one of the biggest grievances from candidates is that they’re being ghosted.

And then I think the other major impact will be is how recruiting softwares are actually using AI on their backend and taking some of that load off of talent teams.

Abhinav (02:20)
That’s actually so well said. You’ve been in this industry for over three decades. Where do you believe that recruitment is most broken today and craving for AI to fix?

Suzanne Salzberg (02:24)
Right. Well, I feel like it’s changed, but I feel like right now where it is most broken is because recruiting teams have become so much leaner. The hiring is slowed. So full teams got laid off or there’s one recruiter left. And so those small teams have so, you know time management is a huge problem because now they’re doing four jobs. They’re the recruiting coordinator. They’re the travel coordinator. They’re looking at resumes. They’re doing the interviews. And so it’s almost impossible for them to go through a thousand resumes that they’re getting in a day because they’re also doing, I was talking to a previous recruiter that I worked with and she said that she was, you know, a CMO was coming in, took her eight hours to schedule their onsite, you know, with everybody’s schedules and I’m , and so that’s eight hours that she can’t be recruiting.

And so because AI can help with a lot of those administrative tasks, it’s great at doing travel planning, scheduling, you know, screening those initial resumes, like we said before it can really help with JDs. And so I think that that is a huge place where AI can help. I think the other place that it’s broken, is when you’re hiring for technical roles and engineers and they have to take a coding test, right? And that coding test is only as good as the person writing it. And so many times from a talent perspectiveyou know, someone will get a question wrong and it’s not even something that they’re measuring for that this person is going to do. And so I think if AI can write some amazing coding tests or tests that engineers or QA employees can do, like that will be a huge piece that is a big pain point for recruiting.

Abhinav (04:19)
Do you think that recruiters will be replaced because of AI?

Suzanne Salzberg (04:22)
Well, I think if you talk to anybody in talent acquisition, they’ll laugh thinking like, good luck. There’s no, because I mean, honestly, so many times recruitment and talent acquisition is siloed over here. And we just live in our own world and nobody really knows what we do, right? And so like they don’t, and it’s probably bad on us for saying all the work that we do and what we do, but I just don’t ever see, and anybody that does it, knows that they should not be replaced, right? But there is a fear because the people that don’t know really what we do say, oh we can replace the talent team with AI, right? So, you know, it’s that.

Abhinav (05:03)
So you really have had thousands of resumes in a day?

Suzanne Salzberg (05:07)
Oh, 100%. If you just go on LinkedIn and look at some jobs, it literally will say, a thousand people have applied to this job, for one job, right? So if you think that every job pretty much gets 500 to a thousand resumes, so it’s, and if you know, you have 20 recs open, 20 job openings, yeah, you’re getting a thousand a day for sure.

Abhinav (05:16)
Oh my God, and how do, how do you manage that? I don’t think it’s humanly possible to go through them one by one. And you know, it’s destined to have a

Suzanne Salzberg (05:38)
What I do is I help people navigate the job market and the hiring system. And, and so, and when the first thing I help them with is their resume. And so I teach them that a recruiter probably is going to spend… 10 to 20 seconds on your resume. And that is no lie. So that first half of your resume better be good. because we, there’s you know, fortunately The applicant tracking systems have this thing, it’s called quick review. And you can go into quick review and you’re literally just tapping. And, and that’s why people you know are frustrated that the recruiters aren’t getting back to them. But I said, if you knew what a recruiter was doing right now, all of these things, like that’s, like don’t blame the recruiter. Like it’s the situation that’s happening.

Suzanne Salzberg (06:26)
That’s, it’s real. It is a real thing.

Abhinav (06:29)
I had a very interesting talk, I was at, I was at a conference where there are a lot of job boards and they’re talking about what are the things that they are doing to attract candidates. And they say, we are giving them the ability to not only create their you know resume based on AI, we are giving them the capability to actually apply for say 35 different jobs with 35 different resume, all altered on the basis of the job description.

And we were just laughing that it’s not that AI is going to fight humans or, you know candidates are going to fight AI. It’s AI fighting AI. You know AI from the recruiter side is actually fighting AI on the candidate. And good luck to the Boolean searches and the keyword searches because now everyone’s resume will be built based on the job description. How do you see that world? What are the skills that both the recruiters as well as the candidate will now need to do a better matching.

Suzanne Salzberg (07:34)
I always teach candidates that the job description is the final before the exam.

So I will look at their resume and then I’ll say, look at this job description. And when a applicant tracking systems or LinkedIn or any of those companies are matching you to the Boolean searches, right? they’re literally taking keywords from that job description that recruiter put in. And if that’s not on your resume, guess what? You’re not gonna be one of the top 30 people that show. And so

You can do it without AI. I mean, it’s harder, but like I encourage people to have a couple of different resumes. I mean, sure, if you want to make them, you can tell an AI resume. It’s still at the point where it’s a little bit of a turnoff because like, is it real? Have you searched like what this company is looking for? So there’s a trust factor. There’s a trust factor because AI can make this beautiful resume that literally matches the job description. Then you have to question, do I trust this person? Are these, is this data real?

We’re at the point now where we can tell. We can tell if it’s an AI resume. And I’ve been to, when I’ve been to conferences and you have people that, you know AI still misspells things. You can always tell there’s certain little things. It’s like when you get like a scam email, there’s certain little things you can tell that make it a scam. And so I would encourage people still to do it the old fashioned way, but use that job description as your final, like, and you know, focus on what you’ve done to match the job description.

Abhinav (09:07)
And, you know, when I’m speaking with a lot of, you know, talent heads or, you know, experts in the talent acquisition space, one of the big fear that I hear is about the AI bias.

What do you think both the companies as well as the you know AI tools can do to mitigate this bias in the AI -driven, you know world.

Suzanne Salzberg (09:28)
Yeah, I totally agree that bias can cause AI to make decisions that are, you know, systematically unfair to particular groups of people. it can discriminate based on race, biological sex, national, you know everything.

And so because humans are choosing the data that the algorithms use, and even if like these humans are making a conscious effort to eliminate bias, it can still be baked into the data that they select, right? And so you can do extensive testing and diverse teams can act as effective, you know, safeguards. But even with these measures in place, I mean, you’ve heard the old saying garbage in, garbage out, right?

Suzanne Salzberg (10:11)
And so bias can still enter that machine learning process and AI systems can then automate and perpetuate bias models going forward and then you’re in big trouble, right? And so one of the ways that you can help to mitigate the problem, and I think this is where companies really need to focus on, is that businesses should look to engage their data science teams, all the other functions, like very early on in their organization as early as possible. And so then they can assure that the models accurately reflecting the decision -making process and that you know, the data is just weighed accurately. But engaging those people after the fact is not a smart decision. So just getting everybody in early and preventing that bias early is what’s gonna help because if we don’t then it’s just gonna keep perpetuating that bad bias going forward

Abhinav (11:04)
Yeah, and just about those checks, you know, there are a lot of laws coming up about using AI in recruitment. There are recent law in Europe. New York recently passed a law to build more checks when it comes to recruitment. What are your thoughts? Do you think these laws and compliances, will they help the technology or will they become a blocker in the technological advancement?

Suzanne Salzberg (11:28)
It’s probably a mix of both. I mean, it’s going to be a learning process. think it’s good that there, there are laws. I think there definitely needs to be regulation in AI. And it’s, it’s going to be a lot of trial and error, honestly. I mean, things are going to happen. Things are going to break. And I hope they just fail fast and fix them fast. But I definitely think there should be some regulations. just like when GDPR became a huge thing.

Other countries besides ours were like, you know you have to delete our data within three months and you have to put on there, like, do you want us to delete your data? So yes, it was a blocker, but it was also a good thing, right? And so, anytime something new is, that’s what happens.

Abhinav (12:11)
Absolutely. And I want to talk also about the data. You know data has been one of the most important things when it comes to you know making recruitment more effective, making it better. You know the more the enrichment you can do of the candidate profile, the more you know diverse data you can do that. You know in this world of AI, How, What role do you play that data will take in making better talent decisions? And how can AI further put a fuel to that.

Suzanne Salzberg (12:39)
Data plays a huge role in making, you know, better talent decisions. I mean, I talked to you about, you know, when CEOs want data, right? So where does this data come from? It used to be like, just my information, right? And so data can play a huge role in making better hiring decisions. We talked about the analytics in the process, huge, right? So just to give you an example, if you build a great dashboard, you can tell in every part of the process how long it’s taking how many diverse underrepresented groups are in the pipeline.

It’s a lot of pipeline analytics that are really important because if you want to do great diversity hiring, then it has to be intentional. And it starts at the beginning of the pipeline. It doesn’t just happen at the end, right? And so let’s say you have a hiring manager that you notice underrepresented groups are just never getting through.

Like, you know, then that’s when you talk with HR and you say, hey, this hiring manager’s never letting through a person from an underrepresented group, right? And so those, it helps you to make better decisions. It also really helps with, one of the biggest mistakes I think people are making is they’re thinking, oh, we need 30 QA engineers.

But AI can help you build these models based on your revenue goals and all these things. And then you can reverse engineer based on these models, how many people should we actually be hiring? And, and what skills should they actually be? Here’s our problem of company X. What skills should the people we hire actually possess.

Suzanne Salzberg (14:22)
The other piece that’s huge is keeping up with compensation. So companies like Radford, I don’t know if you’re familiar with Radford, but companies like Radford that most people use to like look at what we should be paying people, you know, they have 35 ,000 companies that feed input into their, their software.

They do it once every six months, right? And so like say in 2022, what was happening was because salaries were going so high, so fast in the US companies started going outside of the US to hire, right? Canada, South America, that was happening a lot. And the salaries were half, which isn’t great, but, but companies were like, let’s go to Canada cuz you know, but so what was happening, the salaries in Canada and other countries were going up so fast

Because every company got this great idea. And I would come back and say, hey, the senior engineer is now making this much. And they’re like, what? Like there’s no way, right? There’s people just didn’t believe you. And so if AI could, could, you know, take that data and analyze it and quickly and give me like actual data that I can say, no, look here, that would be amazing. And then because you wouldn’t lose candidates, you know, based on the fan companies were just offering 50 grand more per person just to get them, you know? So the smaller companies were like, what do we do? like you know, so, and then the employees that had brought on, before that from those countries were now like making way less. It was, it was crazy. I’ve never seen anything like it in all my years. So, so I think the data on compensation can really help create better models.

Abhinav (16:06)
I think it’s so interesting, they said that how AI or data can actually help align companies business objectives to their hiring goals. And this is one of the big problem that we see that when majority of the companies, this whole talent acquisition and talent managements are staying silos and completely disconnected. You know, I would love to hear your thoughts on how can companies better align both the talent acquisition and talent management to just build a more cohesive ecosystem.

Suzanne Salzberg (16:35)
This is a big problem that’s happening right now in the talent ecosystem, and it’s looming very large right now. And because of the crazy market that’s been, right now employees and candidates are feeling that, you know, going back three to five years, that was, we’re people first. We care about people. We’re people before profit

Like you heard all these things of like people are a number one goal and which is great but now it’s like it’s a bottom line and all we care about and which is you know as a CEO it’s important right? It’s important that you consider the bottom line, but now so what’s happening is Employees are not feeling like companies are loyal to them so guess what we’re not gonna be loyal to you so when Suzanne reaches out as a recruiter I’m getting people way easier to jump ship from a company because they think well we could have a layoff next month because we’ve already had three so I’m just gonna leave right.

The other thing that’s happening is candidates are seeing this or they’re just been burned because they’ve been laid off three times in the last two years so they’re interviewing, they’re getting job offers and then they’re continuing to interview.

And so I’ve had in the past probably six months where candidates literally called the day before and said, oh, I decided to take another job because they gave me 20 ,000 more dollars. so nobody’s loyal. So everybody’s pretty much just like, sorry, not sorry. You know like you haven’t been, you know, helping me at all.

Companies need to be, especially HR, needs to do a better job in hiring better leaders that are understanding hiring the right people and budget forecasting. So we’re trying to mitigate all of this and we just really need to have better succession planning and it’s just instead of everything always being on fire and oh the first, the first solution is to do a layoff, right? And so companies have to really get back to valuing the people, not just staying there, but actually valuing the people. I feel like that’s, that’s really bad right now.

Abhinav (18:43)
We have spoken about hiring Suzzane, but what about post -hiring? You know do you think that AI will make a difference in providing overall employee experience as well, especially say from the day one right from their onboarding

Suzanne Salzberg (18:58)
Yeah, yes, for sure. think it will. I also think that it really helps people. I mean I talk to people that companies every day that’s like, Oh I had to make this PowerPoint presentation and AI helped me make these beautiful slides like that used to take someone a long time to do. Right. So it helps again now that companies are leaner. It’s helping take a lot of those administrative tasks, even off of onboarding and all of those things.

As in recruiting, I don’t think AI should replace onboarding. I think you should have real people doing people’s onboarding because again, it’s the first impression from these companies. I think, this is a really interesting aspect that I think can help a ton, is

If you ask any employee at a tech company, especially right now, is they’re always having to fill out these engagement surveys? How are we doing? What can we do better? Like on and people are very honest because a lot of times they’re anonymous, right? So what happens is the company fills it out, HR is saying, oh make sure you get your engagement survey filled out. And then they have an all hands meeting and they report the findings and people can do you know anonymous questions.

And the biggest pet peeve of employees is they take the time fill these out, they give you the feedback, and then they never hear anything about what’s going to be done to fix what we just told you was wrong. And so I feel like, because it takes HR teams a long time to compile this data, look at where the problems are, and I think AI can do that really fast, and then also maybe recommend some solutions to helping, right? And so then HR is going to be the one that executes them, but they can get that stuff done faster because HR so many things on their plate, again, it just takes that administrative stuff off. So I think it helps everybody, everybody’s employee experience.

Abhinav (20:53)
Absolutely, being an employee engagement platform. I can 100 % you know relate to what you’re saying. It’s not about the effort to take a survey. It is not even about an effort to go and collect the feedback. It’s about what to do with that. And like they say that you know a feedback taken and not done anything is actually worse than no feedback taken, 100%

Because HR has so many different things to do and so many fire to douse, if you just tell them that this is what your number one thing to increase your ENPS or your retention, they would absolutely grab that. And I think that’s a great opportunity for AI. And my last question, you know, we’re speaking about it today, but I want, you know, I would love to hear your thoughts on that one thing, that biggest impact that you believe that AI will make in the talent life cycle in the next 10 years from now

Suzanne Salzberg (21:27)
For sure. You’re gonna see a lot of trial and error which I said well I think you’re gonna see a lot of trial and error which works, but I think the analytics and seeing where the process is working or not working. I think it’s really gonna help make data driven decisions as well as taking the time off the recruiting team on parsing those resumes that we get so many resumes I would say a lot of it depends on the industry for an example

I have a friend who’s a radiologist, and he literally said to me, there will not be radiologists. AI is going to completely take over, just the radiologists that are reading the scans, not the ones that are doing you know surgery. He said they can do it faster, they don’t make mistakes, and, and people can get that information way faster, right? So like you’re sitting there, you just got a scan, and you have to wait for the results. Painful, right? You can literally get it in 10 seconds.

Suzanne Salzberg (22:44)
And so he said, he literally told me, goes, my job is not going to exist. So a lot of it depends on the industry, right? So that is one job that yes, AI can replace that job.

Abhinav (22:44)
Wow.

Suzanne Salzberg (22:56)
Right? And so, yeah. And I think that what’s happening right now is we’re between generations. Right? And so even Gen Z, like they’ve had human interaction and AI right now, but like say a Gen A, like after Gen Z, they’re fine with no human interaction. Right? They’re like, I don’t want to talk to people because they can’t have a conversation. Right? I don’t want to talk to people. I’m fine if an avatar does my interview. So I’m not saying 10 years from now what’s gonna happen

Abhinav (22:56)
my god.

Suzanne Salzberg (23:27)
But I think right now there’s too many people that were in this flux between the generations where that’s why it’s not gonna happen but hey, I am not naive enough to say that like if you have future generations that have no problem never talking to a human it might not, you know replace it. That’s my biggest fear I would be very sad if that happened, but you know, some of it’s probably inevitable

Abhinav (23:49)
I want to say one thing to your radiologist friend that I am talking to, everybody. Like, I’m hearing this from a programmer. I’m hearing from an SDR. I’m hearing from a copywriter, from a marketer that our job will be replaced by AI. And to be honest with you, I honestly, I don’t think so. I think we humans have an incredible capability to adapt and learn and build new skills that an AI can’t do. So I’m

I’m sure there’s both of us to see that what the next 10 years we do, but I’m very optimistic that it’s going to be something good and not very negative. Well Suzanne, thank you so much for taking the time to speak with me. I just loved our conversation and this industry is moving with such a high speed. And I’m sure when we speak again, we’ll have a different you know AI landscape and new challenges.

Suzanne Salzberg (24:24)
Exactly.

Abhinav (24:42)
I’m sure we both and you know all our audience will be fully prepared for that. Have a wonderful day and thank you so much for your time.

Suzanne Salzberg (24:49)
Thank you. I enjoyed it. Have a great day. Bye.

Abhinav (24:52)
Thank you.