Episode Transcript
[00:00:03] Speaker A: Welcome to INA Insights, where prominent leaders and influencers shaping the industrial and industrial technology sector discuss topics that are critical for executives, boards and investors. InA Insights is brought to you by Ina AI, a firm focused on working with industrial companies to make them unrivaled. Segment of one leaders to learn more about Ina AI, please visit our website at www. Dot ina dot AI.
[00:00:40] Speaker B: Good morning. Welcome to another episode of our AI startup podcast series hosted by Ina AI. Our guest today is Mister Ashutosh Garg. Ashutosh is the CEO of Eightfold, which uses AI to provide companies with a talent intelligence platform, helping them manage their current talent and attract new talent more effectively.
Eightfold has raised over $400 million so far and currently employs over 700 people.
Prior to Eightfold, Ashutosh co founded Bloomreach. Bloomreach is a multi channel marketing optimization company that helps its customers meet its customers where they are. Specifically, it personalizes the e commerce experience by unifying real time customer and product data so businesses understand what their customers really want.
Ashutosh, welcome to our podcast. We're super excited to have you and I'm looking forward to talking about your journey to being a two time founder in the AI space.
Just to kick off, for those not familiar, could you explain what Eightfold does?
[00:01:47] Speaker C: Absolutely. First of all, thank you for hosting me. Super excited to be on this show.
Eightfold, as you mentioned, is in the talent intelligence space.
What we realized is employment is the most fundamental thing in our society.
If we can help people with a better career, better job, better employment world will be a much better place.
And through AI, can we learn from different experiences people have had globally, what education they have had, how they have progressed in their career to better understand what skills they have, and more importantly, what they can learn, what is their learnability?
With that, we have built what we call a talent intelligence platform. It's a new category that we define.
Today, Eightfold is used by hundreds of companies across the globe, in hundred plus countries, across 20 plus verticals, and 25 plus languages.
Where these companies are adopting AI Eightfold to better understand what is the talent they have?
What is because that talent has, what are the skill gaps? How this skill gaps are going to impact their business over time? How should the organization look like in future?
How can they grow their current employees so that these people can deliver and help the needs of the organization over time? And wherever there are skill gaps, help them attract and hire the right talent quickly and efficiently.
[00:03:18] Speaker B: These are all pressing problems for every company. Ashutosh would love to speak more specifically about industrial manufacturing companies on the manufacturing line. These companies see a lot of turnover. How does your company specifically help solve those questions?
[00:03:36] Speaker C: This is a fantastic question, right? If you look at these industrial companies, manufacturing companies, I mean, they are stagnants in these companies, especially with the field worker, where the turnover could be 50%, could be hundred percent, and there's a huge cost bringing startup, scaling them around the needs of your organization. And sometimes six months, a year, two years later, they start teams. Today we work with mining companies as well. We work with manufacturing companies and we work with companies like Starbucks of the world, right.
Which struggle with the similar issues.
And very first thing we are able to do, or two things that we are able to do is one, is help these companies build a network of talent. Their own talent pool is what we call.
So for example, if you have a manufacturing plant in the middle of, let's say, Illinois in us, right, or somewhere else in India, there's only so much talent in that vicinity.
And many of the talent that comes and joins these companies is a local talent that lives in that vicinity.
Today, what enterprises are not doing is building a relationship with this talent. So even if this talent has joined them or has left them, right. They are constantly engaged with organization.
See this? Someone who's getting paid $20 an hour, right. Or $15 an hour. They may have a medical issue and they may leave. They may have some family thing and they may leave. Right.
Today, companies don't have an ongoing relationship with these people. So first and foremost, what we are able to do is build a network, a community of this talent for these enterprises.
And what it does is that it enables these companies to attract the talent and hire the talent. Very, very.
The second challenge these companies run into is very high attrition over there. What we are able to do is provide the right kind of upskilling, career mobility opportunities as these people are coming in. Sometimes you may get tired in working in one plant and you may want to move to another plant, right. You are working in one line of work over there and you may want to another one, right. Sometimes these are physically challenging ones.
By enabling the right career, pathing up, skilling, internal probability, we are able to reduce saturation dramatically on one side.
And by building this network and quickly identifying who may have the skills, and even if they don't have the skills, who can learn these specifically, we're able to dramatically reduce the time to hire and cost of hiring.
[00:06:22] Speaker B: Got it. That's very helpful. I think the upskilling part you mentioned was critical, right. I think a lot of people really, once they're in the workforce, find it very hard to leave the workforce and upskill and move on to their next job. I think giving them the platform to do so is critical. When I look through your website, I noticed a lot of different case studies. Is there one that really stands out to you most or that you felt was really transformational to a client and you bring up all the time?
[00:06:56] Speaker C: I would say all these are very transformational.
So for an organization, if you're able to reduce their time to her by 40%, that's very, very meaningful.
In many organizations where we have been able to improve diversity by 30%, that's very meaningful. But the one, I would say one example use case where we are getting used a lot right now as well, which truly stands out for me, is working with public sector, with the state governments.
So state governments, one of the things they work on is reducing unemployment in the state.
As people are getting unemployed, they file for unemployment benefits. They go to the state and say that, okay, I don't have a job. Help me out here without Eightfold. The only thing these state governments can do is just doll out the money to these people.
There's a saying, right?
You give a man a fish, you feed them for a day, you teach them how to fish, right? You feed them forever, right?
What these people really need is the right job.
But quite a few times the reason why these people unemployment is a plant got shut down somewhere, some company got shut down.
So what we are able to do with Eightfold is build a network of companies looking for people, people looking for jobs as they are getting unemployed, but also a network of career supporters who can help upskill these people so that they can get another job.
And by building this three way marketplace, we are able to make a huge trend in the unemployment space.
And it is something which is very close to my heart.
[00:08:39] Speaker B: That makes sense. I'd love to hear a little bit more about how your company has also developed. You started this company in 2016, analytics. AI was in a completely different place there. The core problems you're looking to solve have been around forever in the last eight years. How has the industry changed? How has your offering changed?
Where do you see it going? What's, what's next on your roadmap?
[00:09:06] Speaker C: I think that the way, I mean, great question one is as a startup, right?
You're going through a journey, and the day you are starting the company, the way the market is, the need of the markets, they may change over time. What is available in the market will change over time.
And the good thing is, in our case, we realize that what is more fundamental problem and what are transitory problems?
See, unemployment rate may go up and down.
There was a time over the last four years, five, six years, where we had the highest unemployment rate ever and with the lowest unemployment rate ever.
We've gone seen both of those cycles over the last five years. So those market dynamics will keep on changing.
But what has not changed is people need jobs, they need employment, they need career to grow and succeed. And companies need people to grow and succeed. That fundamental need has changed.
The focus did change for companies. There was a period when companies were very focused on just hiring talent from outside.
Then there was a period when companies are focused on retaining their talent.
Now the focus is lot more on how do I develop my talent.
But the focus on talent and the need of talent hasn't changed. And because you focus on this fundamental problem, that need for talent, and we solve for that. And hiring, retaining, upskilling are just means to an end, has enabled eight foot to keep growing, no matter how the market has unfolded. Right.
The second thing is, from day one we said it is a data problem.
Can we use data to better understand what people have done, and then by virtue of that, understand what they can do next and connect them to the right opportunities?
And wherever there is a gap, can we help them understand how can they fill that gap?
So I think that data problem has stayed the same. Now the AI has advanced a lot. A lot more algorithms are available, a lot more infrastructure is available. So that the level of automation and intelligence we can provide to these enterprises is unprecedented.
So we have adopted and just written and constantly incorporating as the new technologies AI is coming in and improving, while staying focused on that fundamental problem of talent and deleting to large enterprises.
[00:11:46] Speaker B: Got it. I think what you've said is it's really important to really be thinking about the customer and all of the different problems they'll have and really come up with a full suite of solutions that no matter how the market moves, you have them covered. It's about being customer centric and truly understanding their problems to some extent.
[00:12:08] Speaker C: Exactly. And see the market will change. Even within an organization, you may have part of the company that is thriving at the part of the business that is not.
So once you understand that dynamics, that it is going to be there.
Second is one very interesting thing about eight port is we don't focus on one specific knowledge worker segment.
So it's not that we just focus on engineers or doctors or salespeople or product people or data analysts or frontline workers, right?
We focus holistically on the talent relevant to that organization and that has also made us sticky and valuable to all these companies because their need for different kind of talent will keep on changing over time.
And all these are part of the need.
So it's more like that in a human body, right? You all these parts, right? But any point in time, the part that is hurting you and put all your energy over there, right? And that can keep on changing. So thinking about the problem holistically is also very important.
[00:13:22] Speaker B: Makes complete sense. Ashutosh, would love to switch gears for a second and focus on your journey till today. Tell us more about your background and your professional life before. Eightfold.
[00:13:34] Speaker C: Absolutely. Grew up in India and maybe that is especially growing up in a small town. Later seats for thinking about career and employment in me.
Got my undergrad from it, Delhi and for better words, fortunately, but exposed to machine learning even back in those days.
So my undergraduate thesis was in computer vision and machine learning in IIT. After that came to us with my masters and PhD from Illinois Urbana Champaign, again getting a PhD in machine learning, then joined, I mean after short standard IBM joined Google research where I led all their personalization efforts.
And what was interesting about that is I was building models over there to better understand what someone is going to search for next on Google. Building models to understand what someone is going to read on Google News, that's what they will be excited about.
And while it seems like yesterday personalization was such a new concept back in those days, then I left Google to start another venture below reach in the e commerce space where again we were building models to understand what people are going to shop for next.
And now at eight would be a fundamentally again, building the modest to understand what someone can do next and learn next.
So last 25 years of my career, professional life have been all around understanding personalization recommendation and then applying it to real world problems.
[00:15:12] Speaker B: Makes sense. I was going to ask around, hey, you've done a lot of marketing work. You're now in the talent management space. These are somewhat different fields. Did you, was there a specific challenge at Bloomreach or was there a specific insight that then inspired you to start Eightfold?
[00:15:30] Speaker C: I would say two things. One, what is my fundamental strength? Is it marketing or is it data science?
And I would say it is data science is not marketing. And those are all different verticals where I can apply that skillset, right?
But more importantly, right, why I started Eightfold and why in HR space, right?
And maybe the answer was little bit surprise you and not the standard answer.
What I find is strange is that even when people are successful, they're always about, how can I chase more money? How can I start another company in the space that I know so that I can build a bigger business?
But there are many other people who can do that.
But if you're fortunate enough to be in the right place at the right time and successful enough, right? Can we try to beg on some really big hairy problems that will have a big fundamental impact on our society?
It wasn't that. Okay? HR is a big market. I didn't even know what HR is. I started 8th, I didn't even know how big the market is. I never did the market analysis.
The only thing I knew is employment is go to people. Let's just focus on that and make it better. Life will be better.
And even if I fail, it's okay, because I have already one successful thing going on for me.
So it was more driven by that desire to do more basic fundamental work for society than anything else.
[00:17:06] Speaker B: Yeah, it's truly a core problem that every company is dealing with. It's one of those really structural issues that needs to be solved. And question is, will it ever be solved? Because once we solve one part another, not an issue, but the goalposts will always change.
[00:17:26] Speaker C: Actually true, I would think of it the opposite.
The goal post for me is not those enterprises.
The goal post for me is that individual, see, and that's the beauty over here. The problems are intermingled. If you make it better for individuals, companies automatically benefit.
So really, truly on a daily basis, I think more from the individual's perspective, not from the enterprise perspective. Now, why that is very interesting is humans have this innate capability of keep getting better and better. They can do so much more.
Even when we think we are done, there's nowhere more to go.
We keep surprising others and we are able to do more.
And our thing with Eightfold is that can we keep on maximizing people's potential? It's not about where they are, but more about what else they can do. How can they go even further?
And I think that is never going to change. People will always have a desire to do more and they have ability to do more, and can we help them with that?
[00:18:39] Speaker B: That's a great segue into my next question. Actually, talking about upskilling, what do you think you did differently as a second time founder after your first venture?
[00:18:52] Speaker C: The very big difference was thinking about the market and the problem differently.
It wasn't about, let me worry about solving today's problem in today's market.
But let me think about what is more fundamental problem and that is more widely applicable. Widely applicable.
So it wasn't that let me post all the talent problem for ETL sector.
And the challenge with that is had we started the company like that in pre COVID time, you would have died.
Let's say we started even the problem. To say that, let me solve the problem for software engineers. The Jenny, I would be like right now, very, very scared. What will happen to us, right? So I think the biggest thing I would say the second time founder was think about the market, the impact, something that is going to be relevant for times to come.
[00:19:48] Speaker B: That's a good heuristic for sure. Really solve a core problem rather than just getting too specific. You never know where the market's going to move. Let's talk about more generally, artificial intelligence, almost its emergence in the last couple of years. In terms of the applications specifically, let's talk about the commercialization of this technology.
When you speak to all of the different companies that you work with, what are the main drivers and barriers for large scale enterprises adopting this technology, and how do you typically tackle these challenges at Eightfold?
[00:20:22] Speaker C: So a few things. One, I would say over the last two years, things have changed.
Two years back, sometimes the conversation was, why AI?
Do I really need AI?
Every once in a while a customer will, a prospect will come back and say that you guys have great AI, but I don't know whether I really need AI right now or not.
I think with the popularity and hype around AI, people are no longer asking that.
One of the barriers that does still exist is people don't understand the technology yet. They don't understand the application.
There is some fear around regulations, there's some fear around how it will impact different functions in my company.
Really. On one hand, I think AI and tailwinds of that, all that, that is goodness.
But that is also moment in time. Right now, AI is hot today. It may not be hot in a year from now, and it doesn't matter. What matters is are you able to drive real outcomes, outcomes and pivoting your customers, your prospects, to think about what are the outcomes they can try through AI and really focus on outcomes versus the technology behind it.
And once you do that, some of the fear, everything goes away.
Second thing is, as you're doing these things, it's very important you do that this in a transparent fashion so that people understand how this technology is working, behaving in what is the likely output even before they see the output.
And if you do it in transplant fashion, you again further switch their fears around unknown.
So focus on outcomes and do it in a transparent fashion. And if you do these two things, I think you can get a very good adoption.
[00:22:13] Speaker B: That makes sense. I think. Yeah, with any new technology, it's, there's always a fear of if it goes well, goes well, but what can go wrong as well, right? And so being transparent is, is key. I think more specifically, there's been a question around AI, safety regulation, things like that, specifically in the HR and recruitment world. What are the key ethical considerations that people are talking about, and how are you tackling these? How do you think about these problems?
[00:22:49] Speaker C: I think the biggest fear people have in the recruitment space is how society has had bias for the long, longest time.
And if any AI is learning from the data, and if that is the case, is it's going to perpetuate those biases or not.
That is the biggest fear or consideration or everything. And every regulation is around that.
So as any organization building a solution in this space, what you have to do is embrace these regulations. These are going to be there.
In fact, not just embrace, welcome them and say that, yes, we need these regulations, right. Because some of those fears are real.
And even if we are doing the right thing, others may not be doing the right thing. Right.
So those regulations are going to be there. And as part of the thing, very first thing we did well before these regulations even surfaced up, is created an AI ethics council in the company, chaired by external folks who have been extragulators to review, evaluate everything that we are doing on a regular basis.
And that has been very, very effective for us.
The second thing is, this is where it's not about just using some technology, but understanding the technology.
Like, I've been doing this now for 25 years, so I have some experience in this space.
So building these models with equal employment opportunity in mind, and what it does is that seeing that our models need to be fair, irrespective of who this individual is, they will be focused on their skills for sales, their race, gender, ethnicity, age. And so those factors, right, building rich analytics so that we can further validate and make sure our algorithms are doing the right thing, organizations are doing the right thing, and doing all of this in a transparent fashion so that everyone understands what's going on.
And with all those things, now, we have seen great success.
[00:24:58] Speaker B: That's good to hear. It's good to hear of people thinking about these problems proactively. I think this has become a large question that you see all over the news. As someone who's not in the space, it's reassuring to hear of practitioners who are proactively thinking about it and embracing the regulation rather than in new industries, really sometimes pushing corners of how far they can go. So great to hear that as one of the closing thoughts in this conversation. Generative AI is having a pretty big moment right now, but as someone in the space, some of these advancements might not be completely new to you.
Are there any applications use cases, whether they be more consumer facing or enterprise facing, that you're most excited by and look forward to in the future?
[00:25:47] Speaker C: Personally, the one that I'm most excited about, which is again, relevant to what we are doing, is upscale, making education skilling available to muscles at scale, making sure that each one of us is able to develop and succeed on a daily basis.
And instead of sharing what AI can do, can we instead use AI to do better?
So putting generative AI for good, right? I mean as a for a good use cases, right? Saying that what do I need to know today to be most successful in my job?
What do I need to learn today so that I will be successful tomorrow as well?
And if I am able to use genitive AI to help people on a daily basis, right?
Think about providing each and every person an AI coach so that they are able to do better at what they are doing.
And if you do that, I think every other problem will get solved, right? Humans have innate potential to do wonders, but even helping the doctor to do better tomorrow, even helping that pharmacy person to do better tomorrow, like every person, how can we enable them to do better tomorrow?
And that's what we are focused on at eight four.
[00:27:12] Speaker B: That's amazing to hear. Ashlosh, thank you for joining us today. I think everyone has a few things to think about and take away from this conversation, especially a lot of our listeners in the industrial space. Hopefully we're in a position in this industry to adopt these technologies in the near future.
Thank you.
[00:27:33] Speaker C: Thank you.
[00:27:39] Speaker A: Thanks for listening to INa Insights. Please visit Ina AI for more podcasts, publications and events on developments shaping the industrial and industrial technology sector.
