Beyond the Resume: Rethinking How Leaders Assess Talent

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Mo: [00:00:00] Welcome to today's episode of People and Strategy. I'm your host, Mo Fathelbab, President of International Facilitators Organization. People and Strategy is a podcast from the SHRM Executive Network, the premier network of executives in the field of human resources. Each week, we bring you in-depth conversations with the country's top HR executives and thought leaders.

For today's conversation, I'm excited to be joined by the co-founders of Teslify, Namrata Kamdar, COO, and Abhishek Shah, CEO. Teslify offers AI-powered pre-hire assessments to measure the skills of candidates in an effort to improve the hiring process. Welcome, Namrata and Abhishek. 

Namrata Kamdar,: Hi, Mo. So happy to be here.

Mo: Great to have you both. Uh, Abhishek, I wanna start with you. Tell us about your career journey and why did you [00:01:00] create Teslify?  

Abhishek Shah: So hi, Mo. It's so nice to be here. So I came to this problem as a founder of Achinac Tech, who was trying to build teams very quickly, and we would, we would meet people who looked perfect on paper, interviewed extremely well, but actual fit sometimes became clear only after they joined.

At the same time, we were almost certainly overlooking capable people because their resumes did not tell the right story. That never sat well with me. Hiring can change person's life and shape company's future, yet we were making these decisions largely through resumes, credentials, and instinct. So Teslify begin with a simple question: What if we gave people the opportunity to show what they could do before we judge them, [00:02:00] where they studied, where they worked, or how confidently they interviewed?

That idea, proof over pedigree, is still at the heart of what we are building. What drew me to the mission was the human side of it. A better hiring process should not only help the employer make a stronger decision, it should also leave candidate feeling that they were seen fairly. And you see, uh, the quote behind my desk?

Uh, it's, it's like always do the right things, right things and be fair. So that's what we stand by.  

Mo: Thank you, Abhishek. And, and Abhishek, when leaders come to Teslify looking for a solution, what's the moment that makes an organization realize its current screening process is failing? 

Abhishek Shah: So I think it is rarely one dramatic moment.

It usually appears as a [00:03:00] pattern. So recruiters are overwhelmed, hiring managers keep asking for more interviews, strong candidates disappear during the process, and people still leave after joining And the clearest warning sign is when the team cannot explain why one person is more suitable than another without saying they felt very strong about this candidate in the interview.

So another sign is when every hiring problem produces another round of an interview. That usually means organization does not trust the information it is getting early in the process. So when screening works, the discussion becomes much cleaner, much clearer, and instead of debating personalities or polished resumes, the team can talk about job-relevant evidence and watch what each candidate can [00:04:00] demonstrate. 

Mo: And, and what are the specific costs and time, quality, and organizational risk of recruiting the wrong hire, Abhishek? 

Abhishek Shah: So I think, uh, salary is the most visible cost, but it is often not the largest one. There is manager's time or extra t-extra work that is absorbed by the whole team, delayed projects, customers impact, and then the cost of starting the search again.

And more than anything, time lost, which is there is a strong candidate you may have rejected while making that decision and that also you are losing. So I, I think there is monetary cost, but there is also human cost to it. A capable person may have been placed in a role that did not suit them, and that experience can damage their confidence as much, as much it affects [00:05:00] the company.

So that is why I try not to call someone a bad hire. More often, it was a poor match or a prediction failure in the process. So no system can improve or remove every, uh, like completely remove every hiring risk. But a thoughtful process can prevent many avoidable mismatches.  

Mo: Okay. Well, thank you. So now let's talk about some use cases of AI-powered skills assessments in practice.

Uh, one example comes, uh, with your work with inDrive, a delivery and rideshare company. Uh, inDrive was manually evaluating every candidate, with assessments consuming eighty-five percent of the hiring team's total effort, uh, and time, uh, of three weeks. Um, so what was the first thing that had to change to reduce time to hire?

Uh, Namrata, can you, uh, can you take that one for us? 

Namrata Kamdar,: Yeah, definitely. [00:06:00] So inDrive is a global mobility company. So when we were talking to them, the-- based on what they described, it was not just simply like, "Hey, let's add assessment." We tried understanding what is team looking for. Genuinely, what do they need to know before hiring manager spends time either, uh, going through resumes or interviewing s-somebody. 

So once we understood the process, we started working backward from that role. What does the success on this role look like? What capabilities candidate needs to have, and how can we measure that early and consistently throughout all the candidates? So once those questions were answered, we understood that the skill assessment needs to fit in at the beginning of the process because when recruiters are evaluating all the candidates manually, they could spend hours and hours of time.

So there's only certain time, like maybe ten hours a week, that recruiter can spend to interview the people. We wanted to make sure [00:07:00] that time is utilized for them to interview the right candidate, the right fit, people who have already demonstrated the skill that they can do the job. Now the interviewer is just trying to understand that how would they fit in the position.

Like they have already demonstrated that skill. So that was the main process that we tried to demonstrate using skill assessment. So the... Another thing that happened was the quality of the hire. So now, because they already were sitting down with the right candidate, the quality of people coming in were much better because they already knew what the job would look like before they even started their first day Um, I think that was the biggest lesson out of, uh, all this assessment, uh, sequences that were put for them, that the early signal in the process and everything else became faster and more focused with them.

Mo: A-and Namrata, another outcome from transitioning to the AI-powered skills assessment was an eighty-two percent [00:08:00] increase in learning and development participation for inDrive. Uh, what led to this increase?  

Namrata Kamdar,: That was very interesting outcome, um, because assessment was not just ending at the hiring process.

It was used to understand the skill gap even after people joined. So the generic training that candidates were given was not one size fits all. When the assessment results came out, we were able to identify what are the candidate strengths and where do they need development, and the whole conversation changed after that.

Manager were not like, "Hey, how well do you already know, like, this part of the job?" Like, "You already got this, but there's this area that you need improvement on." And now the assess-- like, training gap felt more personal. It was not one size fit all, and people were willing to participate when they were told that, "Hey, this is the one area you are lacking, and let us help you become better at it."

So it was assessment b- didn't just [00:09:00] become a hiring, um, ga-gate, like, hey, stopping people, but it became b- basically like a development, uh, map for the candidate out of basically employees after that.  

Mo: Mm-hmm. And Abhishek, what leadership lessons did you take away from working with inDrive? 

Abhishek Shah: So, uh, Mo, I think, uh, the biggest, uh, lesson was that you should not automate the process before deciding what good looks like.

So it's tempting to begin with the technology, but the important conversation comes first, and that is, like, what does success in this role mean? Which skills matter now? Which skills can be developed later? And what evidences could give team the real confidence to fill that position? So the other lesson was to start with a focused problem.

You do not need to redesign the whole hiring across the entire company from day one. [00:10:00] Choose one role that has the most pain or pain is quite visible, involve the recruiters and hiring managers, measure the outcome, and learn from it. So like people should support the change when they understand the reasoning, and, uh, they would support the change and can see the results. 

Leadership is as much about building that trust as it is about selecting the technology. So it's not just about technology. It's about how you implement this technology and whether you ask the right questions or not. 

Mo: Building the trust is always gonna be important, right? 

Abhishek Shah: Yeah. 

Mo: Absolutely.  

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Mo: So next, let's transition to how video screening can be incorporated in the hiring process. Uh, so Namrata, you worked with Unity Communications, who was dealing with two compounding problems: candidates dropping out mid-process and new hires leaving too soon. Uh, how did you diagnose where things were breaking down? 

Namrata Kamdar,: So when we looked at Unity Communications processes, so we were looking at like, "Hey, how did candidate's journey was from beginning to end, and why were they dropping off in the middle of the process, and what was early iteration?" It was not two separate problems. We wanted to look at that as a one uniform thing.

Why were the candidates leaving? How long did they have to wait between the stages? Were they asked same questions at different stages of their [00:12:00] interview? And then why did, uh, after they joined the company, why did they leave early? Was the work they were given different than what they were expecting based on the job description or what they understood during the interview process?

So the early process that they had relied totally on resume and conventional interviews. It did not give employer great insight about their communication or real working behavior. And for candidate also, it was basically they didn't have clear idea other than like, "Hey, what am I actually going to do on the job?"

Like, "Hey, I'm supposed to know the Excel, but am I going to do, uh, basic accounting on Excel or am I supposed to do VLOOKUP and macros on the Excel?" So the real job sense was not given to them. So inter-- basically giving short video interview became more realistic process in the earlier interaction. So company got to learn more about the candidate, candidate learn more about the job that they are going to be actually involved in. 

Great thing about that, it reduced [00:13:00] surprise on both the sides. Like n-no party was as surprised like, "Hey, what am I getting into?" They knew what was this relationship going to look like. 

Mo: So Namrata, the video response screening feature was also a meaningful part of what changed the evaluation dynamic at Unity Communications.

Um, what are some of the benefits of video screening into the hiring process? 

Namrata Kamdar,: Um, if used thoughtfully, a video interview can show you a lot of things that resumes cannot. Let's take a customer-facing role as an example. Instead of, uh, asking candidate, "Explain me the time when you handle difficult customer," let them demonstrate a real-life example handling difficult customer with using video interview. 

It goes from show to-- telling to showing, basically. It also removes the friction for scheduling. Candidate can respond at their own suitable time. Every person receives the same relevant prompts, and the [00:14:00] recruiter basically review the response against a consistent rubric. Video interview is not-- does not basically become, uh, or enable to skip the human judgment.

The question must be relevant for work, scoring must be consistent, and accommodation must be available. Candidate should understand how their responses would be even used. If done well, video interview gives people another way to demonstrate their capability. If done carelessly, it can introduce new bias, so the design really matters.

Mo: And Abhishek, uh, candidate drop-off fell by seventy-five percent and the recruitment cycle shortened by forty percent. From your perspective, what does a shorter, more structured process actually signal to candidates about a company, and why does that matter for the quality of who applies? 

Abhishek Shah: That's a really good question, Mo. 

So, uh, I think it tells candidates that company respects their time, knows what it is looking for, and can make the [00:15:00] decisions. The strongest candidates usually have options, not one, but multiple options. And if they face long silences, repeated questions, or interviews that seem disconnected from the role, they start to wonder whether the organization operates that way internally too.

And I think structure does not have to mean cold or impersonal. In fact, clarity can feel very human. Tell people what to expect. Ask only what is relevant. Communicate promptly and close the loop. When candidate drop off by seventy-five percent and recruitment cycle becomes like forty percent shorter, the operational gains are important.

But the deeper result is trust. A respectful process keeps the strong people engaged long enough for both the sides [00:16:00] to make the clearest decision. 

Mo: Thank you. Thank you. Uh, and you mentioned that quality of hire matters. Let's talk more about how standardized role-specific assessments and practice can help. 

Uh, Namrata, you worked with the US District Court for the Southern District of New York to standardize the recruiting process for fairness. How do you approach designing an assessment program with fairness in mind? 

Namrata Kamdar,: Fairness begins well before assessment is built. You first need to understand the role, identify the skill, identify, like, who genuinely needs, uh, those skills to necessarily perform.

Like, what are the genuine skills required? If it's not something relevant for work, it should not influence the hiring decision. T-the first struggle TA is having basically to identify the difference between keywords and resumes and, like, taking those keywords from resumes and matching that with the real capabilities of the candidate.

So stop watching or reading the story people write [00:17:00] about their themselves. The next thing to create the consistency and fairness is all the candidates receiving comparable questions, same conditions, and similar, uh, scoring criteria. To provide reasonable accommodation, keep the human oversight and regularly examine the process to make sure there is no unintended differences between the groups. 

When we started working with US, uh, District Court for Southern District, the fairness came in with basically all these assessments they were creating was conducted in person in a supervised setting. All the candidates got the same questions, and they were evaluated un-into the same criteria. So fairness is not just a label that vendor can just stick it on the algorithm.

It's a process organization should be able to explain, examine, and improve. So the idea, like as-- Let's take an example of somebody working in IT, like having a degree in IT or somebody who's passionate about IT. They both have the same [00:18:00] shot at the job Without the bias of resume. So they both having the same opportunity to demonstrate their skills, whether you have degree or you're passionate about something because you have the skill set.

That's what the fairness is all about. 

Mo: Uh, and Abhishek, there were thirty-two specific role assessments. Uh, that's a significant investment in customization. Uh, what's the argument for this level of precision?  

Abhishek Shah: So, uh, Mo, we invested in about thirty-two role-specific assessments because I think job title is not a job description.

Two roles inside the same organization may require very different judgment, very different communication style or technical knowledge or attention to detail. A generic assessment is easier to deploy, but convenience is not same as accuracy. If we ask candidates to invest their time, [00:19:00] we should measure something that is genuinely connected to the work they may be doing.

And thirty-two assessments were not customization for its own sake. They reflected the fact that every different role requires different evidence, and that specificity also improves the candidate experience. Even when someone is not selected, the process feels more legitimate when questions clearly relate to the opportunity that they applied for.

Mo: Uh, thank you. And, and Anuradha, how did introducing these role-specific assessments improve the quality of hire in the long run?  

Namrata Kamdar,: Um, what it did is it created more consistent and job-relevant foundation for the decision. So hiring managers could compare different candidates based on evidence connected to each role rather than relying on just different interviewers' impression.

So what we should be careful with the phrase [00:20:00] quality of hire is it's not something you can honestly declare on the day a person joins. You need to follow the whole process on what happens afterwards. How quickly did the person become productive? How did they perform? How well they are doing against the expectation of what they were expected to do six months, 12 months down the line?

Whether did they stay or did they grow with the company? The real long-term value of a structured assessment program, it only gives organization repeatable starting point and the data it can learn from. So you connect what you measured during hiring with what actually happened on the job. Did they... Did it improve the process overall?

Retention is another good one, like retention of a good hire, not overall retention of all the employees. Like of a good hire, you connect what they demonstrated during the hiring stage with the performance and what they learned throughout the process. So if they did very well upfront, are they still perform top perform of the year later? 

How well [00:21:00] was their scoring when during the assessment phase? So it's no longer about the feeling, it's the measurement, uh, that you do with the... That's what the quality of the hire comes from. 

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Mo: Uh, and Namrata, for an HR professional listening right now who's thinking about introducing AI assessments but doesn't know where to start, what's the one piece of advice you'd give them before they do anything else?

Namrata Kamdar,: Start with one role, preferably a role that has a high hiring volume or repeated hiring difficulty or early attrit-attrition. So before you choose a, uh, tool, sit down with the hiring manager. Define what a successful person in this role would have to actually do. Separate the skills that are essential on the day one from the ones that can be learned, uh, during the first three months suppose of the job.

Then build a short, relevant assessment around the essentials and test alongside the existing process. Look what happens after people join in. Does stronger assessment result connect with faster ramp-up or better performance or improved [00:23:00] retention? Let the evidence guide the rollout. Put the skill result before resume.

Don't try to decorate after you have made the decision to hire somebody. Like, put it before, uh, you are even offering them.  

Mo: Thank you. And Abhishek, where do you see the intersection of AI and skills assessments heading in the next three to five years? 

Abhishek Shah: So Mo, I think assessments will become less like a static test, and it's going to be more like a realistic work experiences.

So candidates may solve a problem, handle a customer conversation, analyze information, do live programming, or complete a simulation that reflects the actual role. So that becomes especially important because AI can now help almost anyone create a polished resume or a job application. And the job application and resume may look perfect on paper, but employers [00:24:00] still need a reliable way to understand person's own actual capability.

AI can help personalize assessments, surface patterns, make the evaluation faster, but the decision must remain explainable, and humans must remain responsible for making those decisions. Resume is not going to disappear, but it will lose its monopoly. The best companies will be the ones that can recognize genuine capability, actual skills in people than the traditional process that would have missed them. 

Mo: Last question for both of you. I'll start with Namrata. What is one piece of advice that has shaped your work or personal life? 

Namrata Kamdar,: Uh, one piece of advice that I would give is stay curious 

Mo: Brilliant. Thank you. And Abhishek? 

Abhishek Shah: So for me, it is do not [00:25:00] confuse confidence with capability. Give people a genuine chance to show what they can do. 

That belief shape, shaped Testlify, and, uh, it has shaped how I try to work with people as well. And Mo, before we close, Namrata and I are excite to, excited to share that we are working on an upcoming book, The Easiest Yes: Why the Best Hire is Rarely the Easiest One, which brings these ideas to life through a practical story about making better hiring decisions with evidence and not instinct.

Mo: And that's where we'll end it for this episode of People and Strategy. A huge thanks to Namrata and Abhishek for your valuable insights. 

Abhishek Shah: Thank you so much, Mo. 

Namrata Kamdar,: Thank you.  

Mo: Thank you both. 

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Outro: SHRM