How AI is changing the recruiting process

Mihir: Hello, and welcome to the first of a three-part webinar series focused on innovating the candidate experience. My name is Mihir Gandhi, and I am the Head of Marketplace Operations at Eightfold.ai. As a hiring manager for nearly two decades, souring, hiring and retaining talent have been essential themes in my career managing at a hyper-growth company like Lyft where I was the first general manager for their Flagship region in northern California. I’ve acutely felt the pain of hiring rapidly and hiring right. I’m thrilled at how Eightfold’s addressing these challenges and so much more. Specifically today, we are diving deep into strategies for recruiting and talent acquisition and how AI is changing the recruiting process. We are joined by our esteemed guest, Ashish Mediratta from Tata Communications and has a robust content to cover with regards to how Ashish and Tata Communications approach AI and talent acquisition.

First, a little bit about Eightfold. At Eightfold, we have created a talent intelligence platform for enterprises that leverages artificial intelligence to hire, engage and nurture talent. Talent-centric applications built on this constantly-learning platform enables enterprises to manage their entire lifecycle from prospect to candidate to alumni. With over 100 customers including Tata Communications, AdRoll, Hulu, Grand Rounds, Nutanix and more, Eightfold has helped companies vastly improve their talent acquisition, talent diversity, and talent management capabilities.

Historical Legacy products like ATSs were developed to replace tracking paper resumes and as such provide very similar workflows. The now ubiquitous, onerous online application processes that companies require from applicants is unduly hard on both applicants and companies replacing paper problems with digital ones. Eightfold was born in the AI era specifically to address and solve challenges with employment in today’s society.

More information than ever is being communicated about jobs, companies, and candidates. These reside on job boards, career pages, social profiles, professional profiles like GitHub and Dribble and many, many more. And companies have more information than ever in their systems. They have ATSs, HRISs, CRMs, et cetera. Of course, hiring managers have specific visions as to the skills and experiences that they are building on their team. More data isn’t necessarily better. It just means there are more places to find different information and try to cobble together a more holistic view of a candidate.

Is it humanly possible to take in all of these data and identify if candidates fit or their potential to excel in a role or their career trajectory let alone doing this for thousands of candidates across hundreds of jobs? This is where Eightfold’s talent intelligence platform comes in. Eightfold was designed to improve the lives of candidates, of recruiting, HR, hiring managers, employees and alumni. It both aggregates and digests these data marrying internal data like your ATS with a plethora of sourcing and recruiting tools you could be using with externally available information to create an enriched talent repository. Eightfold’s tip platform uses these data to help surface what candidates are good at today and what they’ll be ready for in their next steps in their careers. This drives better talent strategy and execution.

Once the Eightfold platform has ingested robust data from Legacy and public profiles for each person and created a rich profile of each candidate, Eightfold calibrates each open role according to the specific needs of the organization, drives improvements for recruiters and hiring managers’ intake experience, provides an instant pipeline of qualified candidates, improves the overall candidate experience, improves your employee referral experience and, of course, drives retention to improve internal mobility. After our discussion and before our Q&A, Jason Gray, Eightfold’s Director of Sales Engineering, will give us a brief demo to bring these words to light.

A quick example of one of our customers, Hulu was using a host of tools to assist recruiting such as Jobvite, LinkedIn Recruiter, agencies, job boards and sourcing tools. They were also receiving more applications than their team could keep up with. Given their hot growth, the volume of applicants and the number of tools they were using, the Hulu team found that highly qualified candidates were slipping through the cracks. Hulu then implemented Eightfold which aggregated data across all these tools and sources. Hulu was then able to have a single view on their entire talent network. On average, recruiters saved four hours per day and quickly stopped using in-mail because their talent pipelines were full of highly qualified candidates.

The next example actually comes from Tata Communications. We’re going to go ahead and skip it since we have the source of truth from Tata Communications with us here today. As I mentioned earlier, Jason will be giving us a demo of the Eightfold platform before the Q&A. For now, let’s get into our conversation. I’m very excited to welcome our featured presenter, Ashish Mediratta. Ashish joins us from Tata Communications where he is Head of Global Talent Acquisitions and the Associate Vice President of Human Resources. With nearly two decades of experience in the talent space, Ashish has held leadership roles in esteemed companies such as Wipro, DennPack, Nokia, and Huway. In today’s conversation, we’re looking forward to hearing how Tata Communications is innovating the candidate experience and how Ashish is using AI to turbo-charge internal mobility in recruiting capabilities. Ashish, welcome to the webinar.

Ashish: Thank you so much, Mihir. I’m truly excited to be here.

Mihir: Great. I understand you had some slides prepared, and you’d like to walk us through.

Ashish: Yes, for sure. Before I start off, a quick one minute about myself and my organization. I’ve been well over at this organization for almost two years now. Wow, what a phenomenal journey it has been in terms of AI. Tata Communications is a $3 billion organization. It’s part of the Tata group which is having more than 100 organizations. TCS, that means one of the better-known brands of the Tata group.

When I look at the page that’s in front of us now, what have we really thought of? What have we realized from the current acquisition function? We gave ourselves this acronym for ROADS which is standing for real-time, on-demand, quickly online, do-it-yourself and social media-enabled. This has been our real sense from the thought behind our entire talent acquisition strategy. We have been using a design-thinking approach where we have retaught our processes current perspectives of the hiring managers as well as the candidates.

The last in our part has been the recruiter. What we look at is can we have an optional everything in our process that makes the role of the recruiter king in the future to a large extent? I would realize our recruiters as becoming talent acquisition business partners from the guys who understand the business so well. They are actually marketing the organization and these areas. With that perspective – can you hear me?

Mihir: Yes, we can hear you.

Ashish: Okay, great. One of the things that – the first point that struck people when we talk about AI in the recruitment world, what will the recruiters do? In fact, recruiters in my own team asked me this question, point-blank, when they saw the presentation which is going to follow in the next few seconds. My only answer to them was, “Folks, what we are going to create now is probably going to be one of the earliest things done in the industry. If you are the ones who are the early adopters of this technology, rest assured, your CVs are going to be so powerful. You’re going to be hot properties in the market.” Trust me, two years down the line, what have I to show? Maybe one or two attritions in a team of 30. Again, nothing much to regret on that in terms of the top talent being lost. No, none of that sort. I’m pretty happy with what we have as of now. I think let’s move to the next one.

This page, as you see here, I think this is the heart of the engine that we are driving. On the left-hand side, you see it says new job acquisitions list. How can we imagine an auto-sourcing engine? Then, we have a profile pool just having internal jobs, market applications. It’s got our own database and employee referral. All candidates that we are getting to job boards, agencies and to social media, how do we enable an engine that is smart enough to reach out to prospective candidates? In the coming slides, we will show you a few samples of the areas where we’ve actually grabbed the coat on this one.

In fact, that makes me truly, truly excited that, yes, we have been able to make a paradigm shift in some of these areas. The next slide being auto-cleaning up profiles, again, thinking about how do we get AI to look at the social fitment and the cultural fitment of a candidate to the organization? We’ll talk about that as well.

The next piece being auto-assessment. Can we have an engine which is smart enough to read the job description and then decide what kind of assessment from the pool of assessments that we have in our organization that it can administer those assessments automatically to candidates? With that perspective, we are talking to our own internal IT team to get that platform where all these assessments can be picked up by AI and administered to candidates.

The next one that you see here is a pre-screening video interview. There are many and more solutions out there which are offering pre-screening videos, simply and straight-forward. All along that we would noticed we have mentioned notifications to candidates. We want the system to keep engaging the candidates all throughout. Imagine if you have a database having 100,000 resumes which are possibly even six months, one year or two years old can we have a smart database with a notification to candidates that, “Ma’am or sir, it’s been almost a year since you interacted and shows you’re- learning or some new certifications, et cetera. We would be happy to have you upload or share with us your latest profile.” Imagine the impact that it causes and the amount of peace it provides to candidates and to us.

The next piece that we see here is an auto-scheduler. Again, that’s to be much- these days. I think we can move to the next slide, please. I mentioned to you a little while back about auto-sourcing. The first, the bar you see over here, the first two IJP which is internal job posting, as we call it, for our internal market. Up here, I think this is where we have done the most significant change for our organization. Actually, I can say we have turned the paradigm upside-down.

Up to now, what happens in most organizations that at least I’ve come across in my career is that employees have visibility to jobs in the organization. However we ask the employees that a job came by my friend, and he did not apply for it. Though you were entrusted in this, what happens? They’re going to say to you, “I was on vacation,” or, “I was not well for a few days. I just didn’t get a chance at all. I was so busy. I had no chance to look at the internal job market. I happen to lose on so many job roles.” It’s always the focus has been on employees to go on a hunt on their own. That’s where I think using this AI from Eightfold, I think we have made a complete paradigm shift.

The algorithm, now what it is doing is it is taking a look at the profile of all employees. The moment a new job acquisition is raised, the algorithm matches our employee database. Those are for them who are available to apply. We have known around having been in a role for 18 months, et cetera, et cetera from very basic availability criteria, the algorithm does the match, the criteria. Basically, those employees who are the best fit will get notified by the algorithm.

Of course, the employees need to log in for the first time and set the other logins. Having done that, they can be rest assured that the algorithm is going to be hunting roles for them in the organization. It can always be argued that is this not something like internal fortune. But that’s the way we have positioned it in our organization that we are trying to replicate the external job market situation. There are agencies outside who would not take our permission to reach out to our people and offer them options. That’s where we want to turn the paradigm that hiring managers now know that, their people, if they’re not happy in doing what they are doing, then the organization is having a platform to allow people to navigate internally within the organization. The same benefits applies to them also. If they are getting tangled in their role or it’s been too long and they’re getting frustrated, the same benefits and teachers of this tool can apply to them. With this approach, I think we have just changed the paradigm completely.

Take the next example. It’s called a TC. I’m sorry for the language here. It’s called Talent Cloud. That’s our internal language. I’ll probably change this one before sending it back over. Employee referral, my experience, again in organizations, has been that employees reach out to HR folks. They reach out to hiring managers requesting them that, “Please see if you can do something for my friend. He’s a great person and fantastic experience,” and all the good stuff. However what happens in real life is, with that at least, that the algorithm is now suggesting the best jobs to employees from the CVs of their friends and relatives. They no longer need to chase after anybody in HR or with the hiring managers requesting around, no need. You just upload the resume of your friend, and that’s it. The algorithm does the rest, to you and to your friend, what should be done.

Coming to the next slide that’s on this tray now, it talks about auto-screening and social profiling. Here, as you again look to the right, we’re talking about a masking feature. Now, as an organization, we, as Tata Communications, pride ourselves on diversity. The challenge for us or the target for us this year is to hire 35 percent diversity globally.

Over here, how do you ensure and this is a perception that’s carried by our CEO, how do you ensure that there is no bias at the very initial stage of the screening process? Then, the recruiter has submitted resumes to hiring managers. How do you ensure that they’re not getting biased? With this feature that we have from Eightfold, the algorithm is actually masking the profile, the CV really and the photograph, mail ID, LinkedIn ID. Everything gets hidden, even the pronouns are hidden. The hiring managers are now being asked to do their shortlisting based on the merits of the profile that they see, skills and experiences. Part of them looking at the name or the photograph and then trying to figure out what they ought to do next. I think, again, a huge thanks to the Eightfold team for doing what they have done.

Here’s a sample of a normal profile that a recruiter would see. Go to the next page, please. This is the masked profile. There’s the photograph. All the good stuff is hidden. Now, the hiring manager needs to make a decision. I think this is being appreciated by our top management as being a real, I would say, radical change from the past. The next one, please.

This stage, I think, is the one where we talk about the benefits to our employees and to the managers regarding the usage of the new internal job market platform. The benefit to managers is that they don’t have to do much. The algorithm is matching the best of internal employees. Those are the digital notifying them. These profiles are now also visible to hiring managers. Then, these employees have applied for the job. They get to see and also match ratings as to what the algorithm’s getting. To the right side of this page, as you’ll see, the benefits that we have accrued so far in terms of diversity, we have seen a big jump by five percent already. We are going for more. When you see our offer, instantly, that’s how many offers are being made in the stipulated number of days.

Our target was 80 percent. We went on to 84, 85 and now 87 percent. This was probably around the highest points that we have reached. I think in terms of the advantages that we are seeing for niched skills especially on high-technology areas, I think that’s where we have seemed to grab the coat. The kind of calibration that we have achieved with the algorithm with the profiles that we see that is a huge, huge benefit to our recruiter. Mihir, over to you.

Mihir: Ashish, thank you so much. I specifically love the way that you framed the internal mobility of your own employees as Tata, as a whole, having an internal marketplace. I think that resonates especially in the HR and recruiting front where talent is so difficult to come by. We have such a deep focus on recruiting. The retention is really as, if not more, important to ensuring that you have the right talent in place. Thank you so much for walking us through that.

What I’d love to do over the next few minutes is maybe just a few questions for you, Ashish, to help us understand a little bit more about how your operations work at Tata. You guys have a massive organization, obviously a great brand name and incredible product. Why were you motivated to improve talent acquisition and talent management at this time?

Ashish: We look at talent acquisition and talent management as two of the most people-intensive areas. In our quest to improve the stakeholder experience while dealing with HR processes in the organization, we feel that the greatest impact we can give to our employees is through technology interventions.

We live in an organized world. I can’t, frankly, recall the last time when I tried to hail a cab on the roadside. The cabs and the cabbies, they end up locating you and very, very quickly. We are literally taking our HR services to employees rather than them having to go looking. I think that’s been the essence of what we’ve been trying to do.

Mihir: As you do that, how do you think about refining your current processes versus moving to an AI-powered solution? Help us understand the decision to move to AI versus maybe existing or Legacy solutions.

Ashish: Our Legacy systems, as most organizations would have in the form of ATSs, we already have success factors that we use here. Other organizations have Taleo and a host of other products. What we believe or hear is that our technology is not replacing humans. It’s merely improving the quality of work that’s been put in by recruiters and HR personnel. We are basically automating the time-intensive or redundant tasks. We are taking this ideology further.

AI is not only automating our processes in our Legacy solutions. It’s also going to impact the decision-making of our stakeholders. I can give you data and facts. About a year and a half back, we, as an organization, were recording one offer on a base of 12 resumes which is now down to one of six. That’s significant in terms of the time and the effort that people are saving. It’s for recruiters, most importantly the hiring managers. They’re able to get people faster, better and that’s what really counts.

Mihir: It’s great to hear you talk about AI as an assist for human capital and human power. With the number of AI solutions out on the market today, why did Tata ultimately choose Eightfold?

Ashish: That’s an easy one for me to answer. You have a great product that is loaded with functionalities. On top of it, you have a strong leadership at the top level that is driven towards innovation. I can vouch from personal experience how these customers can pick people. They are very, I think, able in delivering a product team who are very understanding and responsive to customer requirement. I really hope that other customers that are working with you, they have a similar experience. There will always be challenges. They will be largely to do with making customizations on our existing Legacy system. I’m sure that’s one apprehension everybody would have. That’s where I think the Eightfold team has swung the needle.

We have another organization with which we have tried the AI-based solutions. Honestly, I do not like to name over here on the platform here. We found that the integrations with success factors as a big thing. I was not working at all. This is the first thing that we had a chat with the top leadership of Eightfold from around March 2017. Within a month, we are actually very, very surprised to note that they had integrations done and complete with the successes while we were testing and calibrating on the product entirely. These things gave us a lot of confidence that we are dealing with the right people who are very like-minded in terms of creating something new for the future and extremely responsive. I think those have been the clinkers for us.

Mihir: Great. Thanks so much, Ashish. I’d love to jump into another question. Before I do that, I’d love to also ask the audience. Some of you have already typed in some questions from the audience. We’ll be doing Q&A a little bit later in the webinar so just a reminder to type in your questions, and we’ll get to them as we can with Ashish at the end of the presentation. I want to move and touch on something you talked about a little bit earlier, Ashish. You mentioned that HR team feels that AI is a solution and an ally. I think we’ve also heard from others that, perhaps, this can be a threat and potentially eradicate some of the roles within the HR sourcing, recruiting functions. Can you talk a little bit about how your HR team feels about AI as a solution, as an ally versus a threat?

Ashish: Sure. I think that’s an easy one, again. For us, there has been unanimous acceptance of the AI-driven solution in both the HR team as well as the talent acquisition team in particular. What we have seen is that the quality of work, the quality of outcomes and stakeholder experiences are getting better with these solutions. I suspect that in maybe five years’ time, literally, every organization will have solutions of this nature. I think that’s what I am also emphasizing in our team here that being ahead of the curve from the industry while creating solutions and services of the world is just going to enhance your own credentials. You’ll be the early adopters, have a strong resume. Maybe you’re the ones that are going to help other organizations evolve. That’s been an easy one for us to motivate our people.

Mihir: Thanks, Ashish. I love the way that you frame that. I want to go back to one of the things that you touched on earlier in your presentation where you talked about internal mobility and viewing Tata’s human capital and talent capital really as an internal marketplace. I also love how you framed that outside recruiters won’t hesitate to contact your own employees, so why shouldn’t your own team be able to do the same thing? Can you give us a little bit of a sense of what the Tata employees say about the internal mobility capabilities and opportunities and the impact of internal referrals on the organization in the past year?

Ashish: Before I go there, I’ll give you some data points. Almost 28 percent of our entire hiring happens through internal mobility. This is one data point that gets reported to the board on a quarterly basis. That’s the, I’d say, important and seriousness given to internal mobility and internal careers for our employees. Given that as a background, it’s been very easy for us to implement the mobility solution. It’s easier now for employees than ever before to view the relevant opportunities for themselves. It’s easy for them to keep track of the status of their applications. This was a huge pain point earlier. There are so many employees taking part in the internal mobility process. How do you just keep them informed of what’s happening? At what stage are they in the hiring process? That’s getting done by the system now.

They get to know more about the opportunities and the people they would get to work with along with the job description. They get to see that and I think most important to be proactively informed about any new developments regarding upcoming opportunities that feed their aspirations. With this, I think AI is working with employees enabling their career growth. We are getting some very good feedback from employees for this specific solution and likewise for referrals. We want to really follow up with anyone to seek the board for CVs of your friends and relatives. You can be assured that the relevance – they will search for relevant profiles and match them with the jobs that are being created and keep you and your referral candidates notified. I think that’s the beauty of the solution.

Mihir: Ashish, thanks so much for this. I feel like we could go on for a long time. I know we’re just scratching the surface. There’s actually a lot of blood, sweat, and tears that go into not just implementing the solution but the process and operational changes that you and your team have put in place to enable this kind of a transformation. I’m sure that we will have many more opportunities on future webinars and panels and certainly during our Q&A later during this webinar.

Right now, I’d love to turn it over to Jason Gray, the Director of Sales Engineering at Eightfold, to give us a demo of the Eightfold platform, a bit more tangible examples of how Tata and others have been using the Eightfold platform. Again, I’d love to remind the audience to continue to pipe in some questions. We’ve got some great ones loaded up for Ashish right after the demo. I’m looking forward to jumping back into that dialog, Ashish. In just one sec, we’ll jump back there. First, Jason.

Jason: Thank you so much. Thank you for having me and thank you, Ashish. I’m going to jump in right into our screen here. You’ll see my browser where I’m going to upload my resume. Three things I want you to take away from this is one, how this deep matching and natural language understanding improves the candidate experience, the recruiter’s experience as well as the internal employees and referrals and then, finally, how this with deep matching and so forth drives these efficiencies and what not. As a user, as a candidate, I can come onto a website. Rather than searching across thousands of different opening positions, I can simply upload my resume.

In this case, I’m going to upload a software engineer resume. Automatically, the deep matching looks at my resume and the job descriptions and the calibrations from Eightfold and predicts what’s best for me. These predictions are really key through our semantic understanding. Now I can see software engineer, that’s what I do. I love that. Solutions engineer, that’s more forward deployed. Because I’ve been customer-facing in the past, it probably will recognize that. Senior data engineer, actually I’ve got a little bit of data science in my past. That’s a priority hire.

Finally, product manager. I did do a little product management and liaison stuff in the past. These predictions are really interesting for me. As a user, I already know this. I just know that these are great matches. I’m going to apply to these two positions. Automatically, I’m going to get a thank you. I’m going to get also other data like what great blog posts relate to my position, what team members I might know at the company. It really streamlines the experience for the candidate. The recruiter, now on the flipside as we go to the recruiting view, I can see all of the positions that I am owning as well as a nice feed of updates around those positions that I’m actually working such as software engineer. I can see that I’ve got recent applicants, leads. I’ve saved a pipeline, one that shows AI as the top 30 percent senior software. We’ll talk a little bit about that in a moment.

Then, two, likely to respond and 26 new leads. Because there are certain individuals who have been in a position a real long time or maybe they’ve moved fairly quickly in terms of their trajectory, Eightfold exposes that so that I know those would be good people to go after. Then, I drill into this now. I’m actually going to see all one view. I no longer have to go and spend hours upon hours on job boards, you know the names and try to find applicants until my eyes bleed. I can see them right here in a five-star matching mechanism through Eightfold that shows me their overall relevance, their skills, their title, ideal candidates that we’ve targeted or calibrated this role that may be an employee or maybe someone outside the company that we’d love to have.

Let’s find similar people, school, work, experience, all very important. As I look through this too, I might also have a scenario where now I’ve got a very skewed team to men. I’d like to really target women, so I can filter on that and look at the diversity there. There’s also unconscious bias smoothing. What I mean by that is perhaps I’m a recruiter that is always going after engineers. I always go after ones that, perhaps, graduate from Stanford, Berkeley or MIT. Now, I may not notice that top Canadian school like Miguel in Montreal or top agent school in China or India. Those are now identified as very similar and ones I should go after. Taking off this diversity element, I can now also drill into these individuals like Ankur and see what is it that makes him such a great fit? He’s got eight years of relevant experience. That’s not just total experience but relevant experience. He’s also top 30 percent in senior software engineers. Well, what does that mean? Well, this candidate took almost four and a half years to reach this level compared to the peers that took six. He’s an overachiever. That’s great. Sometimes, these resumes are padded.

Let’s go out to the public data like GitHub and Stack Overflow and see are they contributing to these projects, open-source, et cetera? What kind of answers and things are they doing on Stack Overflow? Wow. There’s a lot of great elements that they’re truly active participants. This drives a tremendous amount of engagement for me to go after this candidate. I can see all the recruiting activity, personal information if we want to show it. We start to see all the matching mechanisms in these blue boxes where Eightfold semantically pulled out the right matches from this person’s resume against the job calibration. Also, too, I may be interested in actually sending this to the hiring manager or perhaps even a recruiter should have a masked profile. I don’t see their actual name and ethnicity. I don’t see their personal info. I don’t see the schools they went to. It’s strictly based on merit, and that’s really key. This is a quick demo, so we’re going to keep moving on here.

We also look at campaigns and nurturing people. How can we do deep matching and leverage all those existing applicants we have in our applicant tracking system, 140,000? Now, I can go and actually grab a blog post from our website or somewhere else and say, “I want to target these applicants that are passive.” Matter of fact that reduces 140,000 to 10,000. I may also say this is a diversity element back to the fact that we’ve got a skewed male team. Let’s go after female. That’s 1,800 applicants. I can continue to add additional elements to refine the audience including ATS information from that ATS integration to any ATS that you may or may not have.

Ultimately, what this means is that we’re targeting people with this great matching of parsing the blog to who they are. We get these wonderful open rates, 50 to 70 percent. I was in market automation many years past, three to five percent open rates were amazing. Well, 50 percent open rates for a recruiter is astounding. Now, we see that engagement by the cities, by locations. We can see the recipients and how many opens they have. I can go ahead and checkbox and say, “You know what? I want to go ahead and share these people with my hiring manager right away and get that started.”

Finally, to close out, when we look at internal mobility, I can come in and see what jobs are right for me automatically matching me to those specific elements. In this case, if I am under HR and I have admin privileges, I also can see who is a high attrition risk because we want to go after those people and make sure that they find the right career path. They can go to the career planner, find projects and/or openings and/or mentors that best represent what the matching sees in it for them, a whole variety of matching elements. So to remember here is driving a great candidate experience, a great recruiting experience and allowing for those internal mobility and a self-service model that makes it disarming for anyone to get involved with. With that, I’ll close out. Hopefully, this has piqued enough interest. Reach out through the website to the sales department. We’d love to do a deeper dive demo with anybody. I’ll kick it back over to Mihir to finish this off.

Mihir: Thanks so much, Jason. I really appreciate that overview. Just as a quick plug on the website, it is Eightfold.ai. Please, feel free to go there to learn a little bit more information about the Eightfold product. Ashish, we’d love to jump back into a couple of questions that the audience has asked of us. Do you have a few minutes left before you have to head back to your duties?

Ashish: Yes. Please, go ahead.

Mihir: Fantastic. Where we dropped off just a second ago before the demo was around the operational challenges that your team has faced. Can you describe how you actually address those operational challenges and how you prepare your team for changes like implementing new tools to drive adoption to actually move the organization forward?

Ashish: Great. Like I mentioned earlier, challenge number one for us was the integration with the existing ATS that we were using. The next thing for us was to do customizations. There is an existing tool, and we want to bring in new changes and functionalities. In some instances, what we are doing is, for example, for the masking tool, we have practically migrated our hiring managers onto the Eightfold platform to basically look at the profiles, give their feedback and then do the process practically on Eightfold.

For other parts, for example, the internal mobility piece, we are using the auto-match functionality very well and the triggers and the tracking. We have literally done a huge amount of customization. I think that’s been the clinker for us with Eightfold, the flexibility that’s shown by the product team and delivering team to make changes and do them very, very fast. In terms of our recruiters, the experience they are able to provide, I think we have also tried to drive their behavior with the help of incentive.

Almost 15 to 20 percent of their benefits and compensation is in the form of variables. We have made adoption of technology and deliverables. For example, I mentioned to you about conversion ratio. That jumps from zero to 12 then an offer from 12 resumes down to one offer from six resumes has been a big thing even for our recruiters. We give them benefits for their performance toward diversity. We run programs like the star of the month and the champion for the quarter for diversity. We are basically trying to model their behavior and make superstars out of those who are doing the best of things in the interest of the hiring managers, of the employees and of the company. You are rewarding the right behavior, and we are seeing good results.

Mihir: Ashish, I think it’s a novel approach. It dovetails very nicely with the way that you talked about the future vision that seems to be being realized as we’re talking of the recruiters and HRBPs as true business partners that are helping move the organization forward and really be ambassadors for the organization both internally and externally. Part of the process of doing that is building massive trust with the hiring managers. Can you talk a little bit about how AI tools have helped your – and some of the processes and operations and innovations you’ve talked about have helped drive some of that recognition and trust from the hiring managers with the recruiting team?

Ashish: Very early in our hiring process, we hold that this company calls between the hiring managers and the recruiters. What we did was almost a year ago, we started having our recruiters to make a demo of the Eightfold product to the hiring managers. What happened actually on the ground was they realized that their job descriptions was usually gibberish. Then, they started playing with the job descriptions on the Eightfold platform. Then, they realized the power of every single word that they write.

Usually, in almost a two-page job description, maybe three lines are absolutely critical to the hiring manager. That’s where in that experience of discovery call, they realized that those three lines were produced so beautifully and clearly crafted by them. We could show them the change in the profile, before and after. I think that’s where the conviction spread in the organization that we’ve got the right product. The quality of CVs that they were receiving, the calibrations on these profiles, those were the big difference in driving the behavior of hiring managers, two words, Eightfold.

Mihir: Ashish, I love how you called job descriptions largely gibberish. When I suffer from bouts of insomnia, I generally open any careers page and start reading through job descriptions. I find that seems to solve my insomnia much faster than any other type of natural or herbal remedy. I think we share a similar version for that. Ashish, I think with that we are running just short on time.

I want to thank you so much for your time and your insights, your valuable guidance on how to really change the process and operations within the organization and use tools such as Eightfold and others that you’re employing to drive that forward. I personally found this really enlightening. I learned a lot from you. I’m looking forward to not only working with you as a partner but also learning from you and helping drive Eightfold products sweep forward to help more customers with their recruiting and retention capabilities.

Ashish: Great. It’s been a pleasure and honor for me to be on this platform. Thank you so much for this opportunity. If there is any way I can be of help in the future to any of our colleagues who are listening in, you can reach out to me over LinkedIn and I will be happy to help and learn from you. I look forward. Thanks so much.

Mihir: Thanks so much, everyone. This concludes this webinar. Please, tune in tomorrow morning at 10:00 AM Pacific for the second of the three installments on this webinar series. Thanks so much.