Founder's Story - The Hiring Disaster: Why 50% of Your Employees Will Quit (And How to Fix It) | Ep. 298 with Manouj Gupta Founder of ACHNET

Episode Date: January 13, 2026

In this episode of Founder’s Story, Daniel Robbins sits down with Manoj Gupta to unpack why modern hiring fails so often and how AI agents are reshaping how companies evaluate talent. Manoj explains how ACHNET’s AI agent, iJupiter, unifies resumes, interviews, and assessments into a single system that helps leaders make clearer, faster, and less biased hiring decisions. Key Discussion Points Manoj breaks down the real hiring disaster most companies ignore: nearly half of employees leave within one to two years because they were never the right fit to begin with. He explains how fragmented systems, gut instinct, and rushed decisions force leaders to stitch together incomplete signals under pressure, creating costly mis-hires. ACHNET was built to solve this by designing hiring around clarity first, not speed or volume. The conversation dives into how AI agents conduct structured interviews, evaluate candidates consistently, and rank talent objectively while keeping humans in control of the final decision. Manoj argues that AI doesn’t remove the human element but removes inconsistency, fatigue, and bias from early-stage evaluation. The result is faster hiring without sacrificing quality, and a level playing field for candidates who would otherwise be filtered out. Takeaways Manoj reframes the future of hiring as a mindset shift rather than a technology shift, where clarity replaces time as the marker of quality. He explains why speed and quality are no longer trade-offs when evaluation is designed correctly from the start. For candidates, honesty and evidence of real outcomes matter more than resume fluff in an AI-evaluated world. The episode makes a compelling case that AI agents will not replace humans in hiring but will fundamentally change how humans make decisions. Closing Thoughts This episode offers a rare inside look at how AI agents are already transforming enterprise hiring from the ground up. Manoj’s perspective challenges long-held assumptions about interviews, resumes, and decision-making, pointing toward a future where people are placed where they actually belong. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

So, Manoj, AI and hiring. This is so newsworthy right now. You have a lot of different opinions and thoughts. And I'm glad to have you on because I want to talk to somebody who's building the software, making the platforms, working with AI agents, which we know are the future. And I know that's what you're doing with ActNet. So Manoj, I'm very curious around why you even started this company. What was the spark? What happened, whether it's in your business or what you saw with other businesses, that made you say, this is something I need to start and I need to start it now?

Yeah. Okay. Great question, Dan. And thank you for asking me and I appreciate you speaking to me today. So, Dan, here is the thing. For years, I've seen the companies invest heavily in sourcing, branding, and pipelining. but when it comes to actual evaluating people, the process had not evolved. Decisions were pretty much made, driven on resumes, fragmented interviews, gut instinct, and so forth.

What stood out to me was this contradiction. Businesses were becoming more data-driven everywhere, except at the moment when it comes to hiring people or selecting the right people. Resumage said one story and interviews said another story and assessments were done from different systems and altogether, the leadership was expected to stitch all these things together under a time pressure. So I thought that how about we try to create something which is one unified platform which can do all these things together and give the executives a clear

view into the person whom they are trying to hire. The gap which we were looking wasn't about hiring faster or slower. It was about the absence of a unified, objective way to understand a candidate end to end. Once we saw that clearly, building ACNET became less about creating another tool and more about rethinking how the talent should be evaluated and what we can do so that the hiring managers can take a decision with clarity.

That's what made me think about creating this platform. So in my experience, I found a lot of managers aren't really taught, like, how to effectively hire. And what I've seen for many years was that people are placed in the incorrect positions, like you mentioned. People put someone in a position that is not really to their strength. And then what happens in the end, they have to be fired. When it, in my opinion, it wasn't always their fault. I'm very interested, though, in how to do. how you're leveraging AI agents to help in this process. And when did you realize, though, that, like what I'm saying,

that humans sometimes are the bottleneck and it's not the technology. So what are you specifically doing with AI agents to solve this and how do these AI agents work? Okay. So we have got, we have created an AI agent underlying AI agent called I Jupiter, which is our main engine, software engine, which is AI agent, if you may. And the way it looks as we ask the hiring manager to define what role they are hiring for. That clarity has to come from the manager. Now, once that role has been defined, after that AI agent state that

definition of that role, goes through all the resumes, and look from the context perspective, what the manager is looking for, hiring manager is looking for. Searches through the resumes, finds out, ranks them what is the best resumes they have. It finds out based on the role.

Then it sends out, it takes the interview or conducts the interviews on on behalf of the manager, hiring manager, like me and you are talking, the agent also come online in terms of avatar and it interviews the candidate. And then it assesses the person

whether that person is fitting into the role. So this is all AI doing the assessment of the candidate. And then if for the required, if the company also wants them to give some technical assessments like engineering drawings, to do some engineering drawings or write some code, then the agent gives those questions to the candidates as well, and then puts all these things together. Your resume, what has come out of the resume, how you have done the interview and how you

have done the technical assessments and then ranks those candidates. So, and it gives the hiring manager a good report which tells them what are the, what are the positives of the candidates and what are the, if there are any negatives, then it mentions a few things there. And so that manager can now, can take the decisions clearly on the candidate. So that's how it works. I needed this when I used to hire in corporate. I would do so many interviews that I'm not even kidding.

I've fallen asleep in interviews before and I felt like I couldn't always be consistent because of what was happening in the day, how it was feeling. There's a lot of factors that obviously as humans that we have that AI does not. So I feel like I didn't always give a consistent interview with every single person. This to me is huge. Now, some people, if I mention this, they free. freak out when you talk about AI and hiring because I think they just don't understand it, right? Or they think, oh, it removes a human touch.

What do you think that they get wrong about thinking this way when it comes to AI in the hiring process and what you're doing? Again, one important thing which we have to understand is AI is not or the engine which we have got is, We have made sure that it is not biased. So everybody who comes onto the platform is being put on the same level no matter what. So that is the first thing. Second thing is once the assessment happens, suppose once the assessment happens,

then final decision is to be taken by the hiring managers themselves. It is not that AI has taken a decision and given it to hire hiring manager or has been taking the decision and companies are going to hire. No, we want hiring managers still to look at the decisions coming out from the AI agent and so that it is more of a tool for them to select the candidates rather than, you know, blindly accepting whatever AI is saying. So we expect that hiring manager to go through the reports, all the reports which have been given, and then take the decision.

So human is not out of the picture. Final interview we still expect. Just let's say I get 500 resumes for a job or 500 candidates apply. Now, how much time it will take for us to evaluate 500 people at one go? it's not possible. With an AI agent, we can evaluate and take the interviews, whatever the process we want to fit and whether we want to go through the resumes that can be done within hours. As a founder, I'm thinking a lot about my goals for 2026.

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Taking the interviews can be done within the single day. obviously if scheduling can be done with a single day, and interviews can be taken in the single day as well. Then the technical assessment can also be done the single day. So 500 candidates can all be evaluated probably in a day or two, and the reports can be generated, and then the ranking can come out, and then the hiring managers can look at those rankings

and call those people instead of five, 500 people now, they can call three or four people or 10 people for the job interview, for their final interview and take the decision. So that's the help hiring manager is getting here. And what I used to happen before is if I get 500 resumes, then the recruiters get involved. Recruiters will, nobody goes through 500 resumes. Who has got time to go through 500 resumes? You will take maybe 10 or 20 or 30 and give it.

it to the hiring manager to take it forward. What about those 450 people who were in the line they are not looked at? Now it's a level playing field and everybody can be evaluated properly. So it's a good thing for everybody. If there's a people who are the right fit, they will get the job. If the people are not the right fit, then they might be used into some other places where they can be fit. Man, I love that. That's a great way to look at. at it. And I mean, yeah, you normally, that's why it's so hard to get many jobs, I think, because you saw, you know, some roles, there could be thousands of people that are applying. I remember applying to roles so many years ago and just seeing, it would show you like, you know,

3,000 people applied to this position. I'm thinking, like, there's no way that I will ever get this job, right? It's so hard to get into the system. So the fact that you're basically like cloning yourself and you can have AI go through all of those 3,000 in a day or two days. And then you can really evaluate. I think this is great for companies because I think they can find more talent. You can find more talent, talent that you may not have found before. When you're, the fact that you, you know, you're working with, you know, technology that for a lot of corporations and enterprise, I'm sure they've never used.

And this is something so new. And any time, you know, you're selling a service to, I think, a corporation or enterprise that is new, there's always challenges with that. So what for you has been the hardest part convincing these companies that, you know, using ACNET, using these AI agents and how much this can benefit their organization? Yeah, I think convincing once they have, once they understand. the vision and once they have seen taking the demo, I think convincing has not been very difficult for us. They can clearly see the value.

And once they started seeing the reports which have come out and what we have done is we have run, we have taken about 150,000 interviews. Before we have, launched the product, right? So our AI is very well trained in how to evaluate people within the different industries. So when we go to the companies and when we show them the report and we give them open, open field, you come in, you tell us what you're looking for, they just have to say, like,

we want to hire for, let's say, aviation engineer. let's say. You just say that. The AI will draw the whole job description for you. Then you can look at the job description and tweak it the way you want to tweak it. You can add to it and put your context what actually you are looking for in the candidate. And after that, the AI will do all the job for you. So once we and when the report comes out, they themselves say that, or the questions when the I asked in the interview for the candidates, they themselves are saying,

the customer themselves say that some of the times we don't even think about the questions, or we miss out on the questions which are very relevant because we are against time or we are, you know, so these are some of the things which AI is able to help them with. And the clarity which they get it about the candidate is something which, which they have liked it.

So, you know, as we are rolling out, we are seeing more and more customers are opening to it because they can see the value. And because it's not biased, they are happy that they can get some candidates which they might have lost otherwise. So now that you've done so many, do you have any data that you can publicly share?

around, you know, maybe impacts on this or maybe even, you know, the interviews that it's done, any data that would be interesting around either the person, the interviewee or on the corporate side, like is, what's the outcomes from this? Yeah, I mean, we can, we have got the data to share. I mean, every customer which comes in, they want to see the data and we are giving them the data to show, or we are showing them the data. data, which we have trained our AI agent on. And that's how they are able to take the decision on us. So there was, there was always a phrase that people used to tell me when I had to do a lot

of hiring. It was, you know, hire fast. And I think the reason is because we, you know, the hiring process takes a while. You're always so busy as, as a business leader. And you need to get people in because if they're not in, you can't really, your company doesn't function. So at the same time, when you hire fast, as a human, you can unfortunately compromise the quality, which I think is the amazing part of leveraging AI. So how do you see now in 2026 these people from human resources, C-suite to business leaders, how can they leverage this now currently without the compromise? Yeah.

So then the biggest shift leaders need to make is mental, not technology. Okay? That is important. For decades, we have equated thoroughness with time. More interviews meant better decisions. More rounds meant lower risk. That assumption no longer holds. In 2026, quality will come from class.

not duration. CHRO needs to move away from evaluation by accumulation. Collecting more resumes, more interviews, more opinions, and towards evaluation, and more towards evaluation, they have to go more towards evaluation by design. That means defining what good looks like up front. That is important.

and letting systems evaluate consistently against that definition. So if the clarity is there, what exactly they are looking for, and you just feed that in there, and I'll let the system do the job, or AI agents do the job from there. Another critical shift is ownership. Hiring decisions can no longer sit across five stakeholders with no single source of truth. I mean, everybody is taking their own decisions,

everybody is coming with their own inputs and so forth. Leaders need a unified view that allows them to decide. So if the system is there, right, I mean, it can give you one clear indication, this is what this talent is about, and then take that base and take the decision. All five stakeholders can then take that base and take the right decision about that candidate. So that's something which is going to take. And finally, speed and quality should no longer be treated as tradeoffs, right? With AI-led structured evaluation, organization can move faster because they are evaluating better.

That's what I feel. I love that you can get more resumes, the more the better, and you can have the AI do more, where it's kind of the opposite as humans, right? Like, you didn't want to have 300 people apply to a job because you could never get through that. You want like 20 people so you know you can get through 20 people. I'm very fascinated with AI agents just in general. I mean, the things I hear about AI agents,

like I have a marketing agent, now I have a hiring agent. I have, you know, this agent, that has sales agent. And there's so many great things from a business perspective. I think this technology is going to completely transform everything that we do. How do you see? Like what's the picture? If you could paint me a picture like five years from now, three years from now, maybe five is too long, right?

Because things change so fast. Three years from now, what do you think in this process of business is going to be automated? Like, what does the world look like then? So I think few things. One is right now the process is the evaluation process is all broken. I feel auditor is not there. It is there, but it's looked very differently. For example, you send a resume.

Their recruiters will take the resume and then they will read the resume and then somebody else will go for the interview. Maybe they will take the interview. somebody else goes for the interview, then there is a system which is going to take the various evaluation. I think all this will go away. I feel what will happen

if you look at maybe in a couple of years or four years or five years, whatever we may say, that hiring managers will be self-served. What you need, you just input that data, and after that, everything will be all, you know, touch-free or touchless.

The AI agents will work behind the scene. They will get you the candidates, the best candidates available, based on what is the clarity you have provided to them. And it will sort out from so many candidates and give you few people which you can look at them and take a decision. So your decision will be faster, quicker. At the same time, it will be more clearer that the person you have hired is the right person for your job. That's how I think.

So it will become more touchless if you may. Right now, we have got so many people involved even for one hire, right? So that will shrink or that will cut down, I feel. Well, for a hiring perspective, I hope that what you said is true around less people and being involved. It's like less minutia. And I think more people will be hired better for their job. And it just reminds me of like strength finders, the book. Like I am so big on wanting to put people in the place that really fits to their strength.

instead of hiring people for the wrong job because I personally have been fired more than one time and I always felt it was because I was not really hired for the right role. And, you know, which I think this can eliminate a lot of that. Final question here, because I think this is going to be really big right now. People want to understand if I am filling out a resume, they're filling out a resume almost for AI. I hear this in the news all the time. People are like, I fill out my resume for AI, knowing AI is kind of the first step.

Does it really matter? Or is there like one or two things that people should be doing when they're filling out a resume or filling out for a job? Yeah. I think, Dan, great question. You know, let's look at why people get into the wrong buckets within the company. Let's first talk about that. People want to get the job because they want to live their life, they want to earn money, and so forth, right? Now, why they're not happy is because what they're doing, they got the job, but what they're doing is not the right fit for them.

That's the reality. And what happens is once they get the job, the managers find out, they also find out that they are not, they leave themselves. and the manager later on finds out that, you know, they are not the right fit. What is more important is rather than AI writing a resume for them, AI can write the resume. I mean, you can get the grammatical mistakes out of it. But I think what is more important is one should be honest what they are good at. And they should try to sell themselves for what they can do better.

And that's what is more important for, that will come very helpful in the evaluation when they are going through the evaluation. What people don't do is that they write the resume, but they put a lot of fluff in there. What is more important is that put the responsibilities and what are the success stories which they have, which are behind them. put some data around the success stories which they have and let then the AI do its own job. And I'm sure if they are good at what they do and their data is there. I don't see any reason AI not selecting them or putting them in front of the hiring manager. I think that is very important. No, thank you for sharing that. I've been thinking about this. I keep reading about it. So I was excited to get someone like yourself on to really dive into these things. I love all things AI. Good, bad, amazing, ugly. I mean, there's so many incredible things. We can solve so many problems. And I hope we try and use AI for good more than we use AI not for good in the future. That's my hope. And obviously, people

yourself building these type of companies. So if people want to get in touch with you, they're a corporation, they need to use the service, how can they do so? So they can go to www.acknet.com and they can fill in the request that they want to have their demo of the platform. That is the right way to get in touch with us as of now.

Well, my nose is Gupta. I mean, you've got to come back. when you've, when you've hit like 10 million or 50 million, you know, data points, come back. I would love to hear more and share what are you seeing. I think the interesting part too will be that. So, you know, how many people will get hired and then how long do those people stay versus when just a human does it and AI wasn't involved? I think there's going to be so many interesting points in 2026.

Love to have you back on to talk through these things. I think people would just understand how better they can use the service more and more. And thank you so much for joining us today in Founder's Story. Thank you. Thank you, Dan. Thank you. I appreciate for having me here.