WEBVTT - Smart Talks with IBM: Combatting Hiring Bias: Recruiting a Diverse Workforce with Intelligent Automation 0:00:00.040 --> 0:00:05.000 Hey everyone, it's Robert and Joe here. Today we've got 0:00:05.040 --> 0:00:06.960 something a little bit different to share with you. It 0:00:07.080 --> 0:00:10.680 is a new edition of the Smart Talks podcast series, 0:00:10.720 --> 0:00:14.319 which is produced in partnership with IBM. This season of 0:00:14.360 --> 0:00:18.640 Smart Talks with IBM is all about new creators, the developers, 0:00:19.040 --> 0:00:22.600 data scientists, c t o s, and other visionaries creatively 0:00:22.640 --> 0:00:27.120 applying technology and business to drive change. They use their 0:00:27.160 --> 0:00:30.640 knowledge and creativity to develop better ways of working, no 0:00:30.720 --> 0:00:34.840 matter the industry. Join hosts from your favorite Pushkin Industries 0:00:34.920 --> 0:00:38.560 podcast as they use their expertise to deepen these conversations. 0:00:39.040 --> 0:00:41.800 Malcolm Gladwell will guide you through this season as your 0:00:41.840 --> 0:00:45.000 host to provide his thoughts and analysis along the way. 0:00:45.320 --> 0:00:48.600 Look out for new episodes of Smart Talks with IBM 0:00:48.680 --> 0:00:52.040 every month on the I Heart Radio app, Apple Podcasts, 0:00:52.159 --> 0:00:55.480 or wherever you get your podcasts. And learn more at 0:00:55.520 --> 0:01:03.600 IBM dot com slash smart Talks. Hello, Hello, Welcome to 0:01:03.640 --> 0:01:07.400 Smart Talks with IBM, a podcast from Pushkin Industries, I 0:01:07.600 --> 0:01:12.480 Heart Radio and IBM. I'm Malcolm Gladwell. This season we're 0:01:12.480 --> 0:01:17.199 talking to new creators, the developers, data scientists, ct os, 0:01:17.400 --> 0:01:21.720 and other visionaries who are creatively applying technology in business 0:01:21.760 --> 0:01:25.640 to drive change. Channeling their knowledge and expertise, they're developing 0:01:25.680 --> 0:01:30.240 more creative and effective solutions, no matter the industry. Our 0:01:30.280 --> 0:01:34.119 guest today is Angela Hood, founder and CEO of This 0:01:34.160 --> 0:01:38.520 Way Global. Angela's mission is to eliminate discrimination in the 0:01:38.640 --> 0:01:42.440 hiring process. Angela is a serial entrepreneur who saw the 0:01:42.480 --> 0:01:46.640 potential to use automation technology as a way to combat 0:01:46.720 --> 0:01:50.680 the human biases that lead to unfair hiring practices and 0:01:50.720 --> 0:01:55.520 a less diverse, less competitive workforce. On today's show, you'll 0:01:55.520 --> 0:01:58.840 hear how automation makes it easier than ever to connect 0:01:58.960 --> 0:02:02.520 businesses with the right candidates, why automation is such a 0:02:02.600 --> 0:02:07.000 powerful tool to mitigate bias, and how Angela's own experiences 0:02:07.040 --> 0:02:12.080 with discriminatory hiring inspired her to take action. Angela spoke 0:02:12.120 --> 0:02:15.840 with Jacob Goldstein, host of the pushkin podcast What's Your 0:02:15.840 --> 0:02:20.240 Problem and former host of nprs Planet Money. Jacob has 0:02:20.240 --> 0:02:24.160 been a business journalist for over a decade, reporting for NPR, 0:02:24.440 --> 0:02:27.320 The Wall Street Journal, the Miami Herald, and is the 0:02:27.360 --> 0:02:30.640 author of the book Money, The True Story of a 0:02:30.720 --> 0:02:37.519 Made Up Thing. Okay, let's get to the interview. Can 0:02:37.560 --> 0:02:40.160 you tell me just you know we're gonna get into 0:02:40.200 --> 0:02:43.560 it a lot, but very briefly, what is this way global? 0:02:44.480 --> 0:02:49.200 So our technology is built specifically to match all people 0:02:49.320 --> 0:02:53.360 to all jobs without bias. And the last part is 0:02:53.440 --> 0:02:56.600 the hardest part and also the most important. And where 0:02:56.680 --> 0:03:00.160 did the idea for the company come from? So I'm 0:03:00.240 --> 0:03:03.639 a female engineer, and um, you know, I'm going out 0:03:03.639 --> 0:03:07.920 into the workforce after graduating, and I think that just 0:03:07.960 --> 0:03:10.040 like everyone else, I can just put my name up 0:03:10.080 --> 0:03:13.280 at the top of my resume and submit this to 0:03:13.760 --> 0:03:18.280 companies and they'll know entertain me for an interview. And 0:03:18.320 --> 0:03:21.400 what I found was because of the type of engineering 0:03:21.520 --> 0:03:24.680 role I was looking for, which is in the construction industry, 0:03:25.360 --> 0:03:28.160 that was not the case. The recruiters and also the 0:03:28.240 --> 0:03:32.239 hiring managers at these companies would see my name and think, well, 0:03:32.280 --> 0:03:34.160 I don't think that we want a woman, or we 0:03:34.200 --> 0:03:36.600 don't think that she really understands the job because it's 0:03:36.600 --> 0:03:39.400 out in the field. And so a mentor of mine said, 0:03:39.480 --> 0:03:42.720 why don't you use your initials, which are conveniently a L. 0:03:43.640 --> 0:03:47.760 And so I would submit my resume as a L 0:03:47.840 --> 0:03:51.120 hood and people thought I was a man. And so 0:03:51.160 --> 0:03:53.280 then at the same company, for the same job, I 0:03:53.280 --> 0:03:56.400 would get interviews, and that was the first moment where 0:03:56.400 --> 0:03:59.000 I realized there was a lot of bias in the market. 0:03:59.480 --> 0:04:02.480 Turns out that there's a lot more biases, and we 0:04:02.520 --> 0:04:05.320 work to correct for all of them. It's funny that 0:04:05.400 --> 0:04:08.000 kind of story shouldn't be shocking, right, Like I know 0:04:08.080 --> 0:04:10.040 that I shouldn't be shocked by it, and yet I 0:04:10.080 --> 0:04:13.920 still kind of am. So clearly there's a tremendous amount 0:04:13.920 --> 0:04:17.600 of bias in the world, and bias in recruiting. And 0:04:17.680 --> 0:04:20.080 you know, we're familiar with these kind of stories of 0:04:20.160 --> 0:04:25.039 human bias, but but now there's this new problem, right, 0:04:25.080 --> 0:04:29.080 which is algorithmic bias. What is that tell me about 0:04:29.120 --> 0:04:35.479 algorithmic bias? A lot of algorithms are underpinned by machine learning, 0:04:36.080 --> 0:04:39.040 and machine learning very simply can happen kind of two 0:04:39.080 --> 0:04:42.840 different ways. You study what has happened in the past, 0:04:43.240 --> 0:04:47.160 and you try to duplicate that faster and more efficiently, 0:04:48.040 --> 0:04:53.160 and so that in this context would be called supervised learning. 0:04:54.000 --> 0:04:57.880 And that seemed like the logical place for nearly every 0:04:57.920 --> 0:05:02.440 recruiting technology to start the problem with that is that 0:05:02.480 --> 0:05:06.440 there's been so much historical bias that all you would 0:05:06.440 --> 0:05:10.800 really be doing is capturing that company or that hiring 0:05:10.880 --> 0:05:17.159 manager recruiters bias and duplicating it really fast, very efficiently, 0:05:17.400 --> 0:05:21.120 So you would just be expanding bias much much faster 0:05:21.240 --> 0:05:26.080 than a human could. The flip side is something that's 0:05:26.160 --> 0:05:30.480 called unsupervised. So this is where you build a system 0:05:30.680 --> 0:05:34.440 essentially a black box. It's doing all types of calculations 0:05:34.440 --> 0:05:39.159 and decisions internally, and then it's not biased in theory, 0:05:39.640 --> 0:05:42.760 but you have no idea what it's basing its opinions on, 0:05:42.880 --> 0:05:48.320 so it can kind of create bizarre results. Also, it's 0:05:48.360 --> 0:05:51.520 not explainable, and so then you get caught in this 0:05:51.680 --> 0:05:54.840 catch twenty two of I don't want to do bias 0:05:54.839 --> 0:05:58.520 at scale, but I need to be explainable. So what 0:05:58.560 --> 0:06:02.400 do you do? After thirteen failures, we finally figured out 0:06:02.440 --> 0:06:05.599 a way to do this. The methodology that we finally 0:06:05.600 --> 0:06:10.320 found that generated the results that runbiased was not ever 0:06:10.520 --> 0:06:16.160 allowing the math model or the computer to see the 0:06:16.200 --> 0:06:20.640 information that causes bias. So we had to not let 0:06:21.080 --> 0:06:26.400 gender enter into the system, ethnicity couldnot enter into the system, 0:06:26.480 --> 0:06:30.560 things like that, and so then the logical question is, okay, well, 0:06:30.560 --> 0:06:33.400 so if you don't allow those pieces of information to 0:06:33.520 --> 0:06:38.680 come in. How can you then enable qualified people that 0:06:38.720 --> 0:06:43.479 are also diverse to surface without bias? And the answer is, 0:06:43.520 --> 0:06:47.840 when you remove these factors, it happens naturally. And we 0:06:47.920 --> 0:06:51.320 learned this by testing. We've had fifteen and a half 0:06:51.360 --> 0:06:54.720 trillion matching events go through our system and almost it's 0:06:54.760 --> 0:06:58.240 been now almost a decade, and through this we've learned 0:06:58.240 --> 0:07:01.560 a lot people are very diverse. If you will just 0:07:01.680 --> 0:07:05.280 remove your own bias, you'll start seeing them. So it's 0:07:05.320 --> 0:07:08.280 a it's an automated version of what you as an 0:07:08.279 --> 0:07:12.120 individual did, uh before you started the company, when you 0:07:12.720 --> 0:07:15.840 switch from putting your full name on your applications to 0:07:15.920 --> 0:07:20.360 just your initials, effectively hiding your gender. Yeah, it started 0:07:20.400 --> 0:07:23.080 with that. What we also learned though, is even if 0:07:23.120 --> 0:07:27.080 you conceal your name, there are words where maybe someone 0:07:27.240 --> 0:07:30.160 is a waitress in a previous job, and so then 0:07:30.200 --> 0:07:33.920 the persons like, oh, that's a female, right, So then 0:07:34.080 --> 0:07:35.960 we had to go one step further. We had to say, 0:07:36.040 --> 0:07:40.600 now we have to neutralize these gender specific words inside 0:07:40.720 --> 0:07:44.320 resume so that a person cannot look at the document 0:07:44.520 --> 0:07:49.840 and still sus out ethnicity, gender and other biasing attributes. 0:07:50.680 --> 0:07:55.000 It's remarkable that after hiding prejudicial information from the computer, 0:07:55.680 --> 0:07:59.920 like a candidate's gender or ethnicity. A qualified, diverse workforce 0:08:00.200 --> 0:08:04.800 assembled naturally as a result for the overburdened recruiter. That 0:08:04.920 --> 0:08:09.480 means there are huge advantages to using intelligent automation. That's 0:08:09.480 --> 0:08:14.720 a win win. As the conversation continues, Angela explains how 0:08:14.720 --> 0:08:19.960 IBMS technology enabled her to simplify her customers hiring processes, 0:08:20.480 --> 0:08:23.960 and she also shed some light on how far intelligent 0:08:24.040 --> 0:08:28.560 automation has come in the past few years. How does 0:08:28.600 --> 0:08:32.400 intelligent automation look different today than it did, say, five 0:08:32.480 --> 0:08:39.719 years ago, it actually works as the first thing. The 0:08:39.720 --> 0:08:45.359 The level of innovation that has taken place is absolutely incredible. 0:08:46.160 --> 0:08:49.319 And here's the thing about it is people have had 0:08:49.360 --> 0:08:52.760 some negative interactions with things that said that they were automated, 0:08:53.320 --> 0:08:55.439 and they're now they're like, I don't want to use it. 0:08:55.720 --> 0:09:00.680 The level of innovation that has happened is absolutely incredible, 0:09:00.920 --> 0:09:04.000 and for them to not try something because they tried 0:09:04.040 --> 0:09:06.840 something a decade ago and it didn't work, that's just 0:09:06.960 --> 0:09:11.520 completely the wrong approach. We're going to see massive innovation 0:09:11.880 --> 0:09:14.240 over the next five to ten years too, and you 0:09:14.280 --> 0:09:16.160 don't want to miss that. You don't want to say, 0:09:16.200 --> 0:09:18.360 oh I said on the sidelines because I had a 0:09:18.400 --> 0:09:21.160 bad experience a decade ago. So I think, if you know, 0:09:21.200 --> 0:09:25.920 if you're anywhere involved in technology or business growth, you 0:09:26.040 --> 0:09:28.760 need to be part of this. This is your economy 0:09:29.160 --> 0:09:36.320 in play a role. So what is a digital employee? Right? 0:09:37.120 --> 0:09:41.680 So our partnership with IBM, Watson Orchestrate is around the 0:09:41.800 --> 0:09:45.559 dig So D I, G. E. Y is a diggy 0:09:45.679 --> 0:09:49.960 who's a digital employee. And I always think of it honestly, 0:09:50.160 --> 0:09:52.440 is more of a concierge. You can have all of 0:09:52.480 --> 0:09:56.400 your job descriptions living inside a box, for instance, and 0:09:56.440 --> 0:09:58.680 so there's all the job descriptions and you're like, oh, 0:09:58.760 --> 0:10:03.880 I need to find someone for this job. Watson goes 0:10:03.920 --> 0:10:09.800 into box, grabs the job description, and then sends that 0:10:09.960 --> 0:10:14.920 into this way system and this way automatically surfaces up 0:10:14.960 --> 0:10:19.680 to three hundred qualified people from diverse organizations. Right, So 0:10:20.200 --> 0:10:23.320 now the recruiter has not had to figure out where 0:10:23.320 --> 0:10:26.200 are they going to source these people from. They haven't 0:10:26.240 --> 0:10:28.640 had to sort out how they're going to reach out 0:10:28.679 --> 0:10:33.680 to diverse organizations because we have partners and so now 0:10:33.800 --> 0:10:36.640 that part has been taken care of, and then Watson 0:10:36.720 --> 0:10:41.080 Organistrate does the next step, which is sends out communication 0:10:41.240 --> 0:10:46.720 to the candidates that you are interested in automatically, and 0:10:46.760 --> 0:10:49.560 then you get to sit and wait for these people 0:10:49.720 --> 0:10:53.000 to respond back to you of their interest in discussing 0:10:53.040 --> 0:10:56.000 something with you. Now all of this has been automated, 0:10:56.200 --> 0:10:59.640 and essentially what I just described could easily take a 0:10:59.720 --> 0:11:03.040 person in three weeks to go and identify all the talent. 0:11:03.679 --> 0:11:06.319 So you take three weeks and you put this down 0:11:06.360 --> 0:11:10.600 to roughly three or four minutes. Now it's absolutely incredible, 0:11:10.760 --> 0:11:13.720 and I think it gives recruiters the time to do 0:11:13.760 --> 0:11:16.160 what they really want to do, which is talked to people. 0:11:17.600 --> 0:11:21.080 How did you decide that automation was the right tool 0:11:21.240 --> 0:11:26.720 to fight bias? That was a journey, as I think 0:11:26.760 --> 0:11:31.880 a lot of entrepreneurship is an innovation. When we hire technology, 0:11:31.880 --> 0:11:34.800 we're hiring technology to do a job for us. So 0:11:34.960 --> 0:11:37.000 what is the job to be done here? It is 0:11:37.040 --> 0:11:43.920 to identify qualified talent without bias. So when you start 0:11:44.000 --> 0:11:48.440 breaking this down, you realize that if humans could do it, 0:11:48.520 --> 0:11:51.360 we would have already done it. There's been a desire 0:11:51.440 --> 0:11:55.199 to have this happen for many, many years, and we 0:11:55.200 --> 0:11:57.960 were not successful at it. And the reason why is 0:11:58.360 --> 0:12:01.840 Bias is not discrimination. These things get confused all the time. 0:12:02.000 --> 0:12:06.559 Bias is a product of our survival mechanism. We are 0:12:06.600 --> 0:12:09.920 always going to survive as humans, and so we we 0:12:10.000 --> 0:12:13.559 need these survival skills. That's part of bias. So we're 0:12:13.600 --> 0:12:15.400 not going to get rid of it. And it's not 0:12:15.559 --> 0:12:20.320 a character flaw. Bias is just inherently human and we 0:12:20.400 --> 0:12:24.520 are human. And the best purpose that I think technology 0:12:24.559 --> 0:12:26.959 can serve is the fact that it can do some 0:12:27.000 --> 0:12:29.839 things that we can't do. We have to be very 0:12:29.880 --> 0:12:32.880 careful about how we engineer it. Our own technology was 0:12:32.920 --> 0:12:37.680 engineered with removing bias is the priority. But we can 0:12:37.760 --> 0:12:41.080 really have technology make us better humans because it can 0:12:41.120 --> 0:12:44.720 do things we can't do. Despite the potential to vastly 0:12:44.760 --> 0:12:48.800 improve the way we hire, most companies still think automation 0:12:49.080 --> 0:12:53.920 is inaccessible, perhaps a luxury to aspire to in the future, 0:12:54.480 --> 0:12:57.280 but we live in a time when companies are hungrier 0:12:57.280 --> 0:13:01.640 than ever to fill positions quickly. Jacob asked Angela what 0:13:01.840 --> 0:13:06.120 automation can deliver for businesses today and how a company's 0:13:06.160 --> 0:13:11.200 creativity is linked with its diversity. How prevalent is intelligent 0:13:11.240 --> 0:13:16.960 automation in talent acquisition workflows today? So our data says 0:13:17.200 --> 0:13:23.800 that in enterprise that roughly seven percent have adopted some 0:13:23.920 --> 0:13:27.840 level of truly automated technology. But when you look at 0:13:28.040 --> 0:13:30.520 the job market in toll like, you know, if you 0:13:30.520 --> 0:13:33.640 look at the millions of employers we have, it's you know, 0:13:33.760 --> 0:13:39.520 less than three percent have adopted automation. These are companies 0:13:39.559 --> 0:13:43.920 that have a smaller workforce to do a great amount 0:13:43.920 --> 0:13:47.480 of work. They're recovering from a pandemic, they need help, 0:13:47.720 --> 0:13:52.440 and they think that automation is expensive, and it's actually 0:13:52.440 --> 0:13:55.599 the opposite. It's not expensive at all. And so I 0:13:55.800 --> 0:14:00.000 would encourage businesses that are mid market and small businesses 0:14:00.360 --> 0:14:03.760 to embrace technology in a way that they haven't done. So, 0:14:03.920 --> 0:14:06.520 I mean, there's one more piece of sort of what's 0:14:06.559 --> 0:14:09.360 going on now that seems really interesting in the context 0:14:09.400 --> 0:14:12.440 of what you do, and that is the incredible demand 0:14:12.679 --> 0:14:15.160 for workers right now. Right there's I don't know, ten 0:14:15.240 --> 0:14:19.000 million plus job openings, there's the great resignation, and so 0:14:19.080 --> 0:14:25.120 I'm curious how automation is helping both companies and workers 0:14:25.480 --> 0:14:31.280 through this process. Now, there's never been a job market 0:14:31.360 --> 0:14:34.120 like we are living in right now, and so we 0:14:34.320 --> 0:14:36.960 have to think of as employers. We have to think 0:14:36.960 --> 0:14:40.160 of how do I attract this talent. The other thing 0:14:40.200 --> 0:14:43.360 about the volume of jobs that are open is, if 0:14:43.360 --> 0:14:46.360 you just do the simple math, there's two jobs for 0:14:46.400 --> 0:14:49.560 everyone person looking for a job. Okay, so that is 0:14:49.600 --> 0:14:53.760 astounding to begin with. But of the jobs that we 0:14:53.840 --> 0:14:58.000 have available in the market, most people do not have 0:14:58.520 --> 0:15:03.600 the skill set required to fill those jobs. Inside the 0:15:03.640 --> 0:15:07.320 talent pool that is actively looking for a job. So 0:15:07.360 --> 0:15:09.320 now you have to go out and you need to 0:15:09.520 --> 0:15:12.880 be looking for passive talent. You need to be cultivating 0:15:12.920 --> 0:15:15.520 a relationship with the people that do have the skills 0:15:15.520 --> 0:15:18.320 you need. When you go to them, you need to 0:15:18.360 --> 0:15:20.080 be able to say two things. You need to be 0:15:20.120 --> 0:15:24.240 able to say, we use the best technology to identify 0:15:24.440 --> 0:15:27.440 you because you were special, and we really want you 0:15:27.480 --> 0:15:30.440 to come to work for us. That's number one. Two 0:15:30.600 --> 0:15:32.960 you need to say. And when you get here, we're 0:15:32.960 --> 0:15:35.640 going to help you automate those parts of your job 0:15:35.720 --> 0:15:39.040 that you've never really enjoyed before, because we want you 0:15:39.120 --> 0:15:42.640 to be able to dig in in the areas you're 0:15:42.680 --> 0:15:45.600 passionate about, because you're going to be happier and you're 0:15:45.600 --> 0:15:48.320 going to have a better work life balance. That is 0:15:48.320 --> 0:15:50.840 how you win talent in this market. Yeah, what have 0:15:50.920 --> 0:15:54.080 you heard back from recruiters about about this? You know, 0:15:54.600 --> 0:15:58.160 increased integration of technology. So one of the things that 0:15:58.200 --> 0:16:01.400 I think has been maybe the most prizing is that 0:16:01.520 --> 0:16:05.240 it's really opened up the communication between hiring managers and 0:16:05.320 --> 0:16:09.160 recruiters inside the same company. And there has long been 0:16:09.200 --> 0:16:13.720 a silo of hiring managers putting out job descriptions and 0:16:13.800 --> 0:16:16.400 saying recruiters, you know, go find people that make this, 0:16:17.400 --> 0:16:21.240 and then the recruiter needs additional support because they're getting 0:16:21.320 --> 0:16:24.640 questions from the candidates or there's some questions around what 0:16:24.720 --> 0:16:28.880 are the real job specific requirements and they have trouble 0:16:28.880 --> 0:16:33.680 getting those answers from the hiring manager. Hire managers very 0:16:33.720 --> 0:16:36.760 busy and they have their own job to do. So 0:16:36.920 --> 0:16:40.920 by making this more efficient, you start getting much better 0:16:40.960 --> 0:16:45.480 interactions between the entire company. And in this current market, 0:16:46.200 --> 0:16:50.040 companies are truly desperate to find the talent that they need. 0:16:50.480 --> 0:16:53.800 The people want to be found, and now the technology 0:16:53.880 --> 0:16:56.720 is there to help make this seamless. So that's the 0:16:56.840 --> 0:17:00.840 automation piece. Let's talk about the day city piece sort 0:17:00.840 --> 0:17:04.919 of you know, landing here right. So on the diversity side, 0:17:05.200 --> 0:17:09.119 how does how does a diverse workforce help make a 0:17:09.200 --> 0:17:15.160 business more creative. A lot of the big consulting firms 0:17:15.160 --> 0:17:18.000 have dug in for the last decade and said, is 0:17:18.080 --> 0:17:22.879 there really an R o I around diversity, And uniformly 0:17:23.240 --> 0:17:27.840 the answer has been yes. There is increased profits, a 0:17:28.240 --> 0:17:32.200 more consistent workforce, meaning people don't want to leave. There's 0:17:32.240 --> 0:17:34.960 not the same level of attrition when the workforce is 0:17:35.000 --> 0:17:39.400 more diverse, and better recruiting numbers. So all of that 0:17:39.560 --> 0:17:42.480 is like the outcome. But I think the key thing 0:17:42.560 --> 0:17:45.879 to understand is the why behind this. The why is 0:17:46.560 --> 0:17:51.480 that when you're diverse, you come to solutions, and you 0:17:51.560 --> 0:17:55.560 come to questions and challenges from a different perspective. And 0:17:55.640 --> 0:18:00.240 when you have a diverse workforce that is collaborating and 0:18:00.520 --> 0:18:05.080 bringing their creativity to the market and you are using 0:18:05.720 --> 0:18:09.439 their insight to develop better solutions, You're going to create 0:18:09.480 --> 0:18:12.399 better solutions. You're gonna going to get those solutions to 0:18:12.440 --> 0:18:16.760 market faster. You're going to understand positioning of your value 0:18:16.760 --> 0:18:20.119 proposition inside the market. All of these things happened with 0:18:20.200 --> 0:18:24.959 far more clarity when you have a diverse workforce. You 0:18:24.960 --> 0:18:28.359 mentioned earlier that you failed was it thirteen times? And 0:18:28.720 --> 0:18:32.760 I'm curious if sort of getting through those failures and 0:18:32.880 --> 0:18:35.960 working your way to success was a place where you 0:18:36.000 --> 0:18:40.840 did some creative problem solving. I would say that would 0:18:40.880 --> 0:18:46.800 be an understatement at moments. Uh, there are times where 0:18:46.840 --> 0:18:49.119 you know, I just say, like thirteen failures kind of 0:18:49.119 --> 0:18:52.000 in passing. But there were times where I felt like 0:18:52.160 --> 0:18:56.119 I was close to breaking as an innovator. And the 0:18:56.160 --> 0:18:58.639 fact that was like, there's just non solution for this, 0:18:59.680 --> 0:19:04.320 that our team failures is incredibly gut wrening. But I 0:19:04.400 --> 0:19:07.399 was fortunate I had very supportive investors and so we 0:19:07.440 --> 0:19:09.919 got through it. Uh, And I'm very proud of the 0:19:09.960 --> 0:19:13.199 company we are today because of those failures. So just 0:19:13.280 --> 0:19:16.159 to to wrap up, let's let's talk a little bit 0:19:16.160 --> 0:19:19.560 about the future. We've done the past, we've done the present. 0:19:19.680 --> 0:19:22.000 Let's talk a little bit about the future. I mean, 0:19:22.160 --> 0:19:25.879 how do you think the hiring process will look in 0:19:25.920 --> 0:19:29.080 the future, whatever, five years, ten years, And in particular, 0:19:29.119 --> 0:19:34.280 what role will will automation, intelligent automation, augmented intelligence, what 0:19:34.440 --> 0:19:38.840 role will will all that play? Well, if you look 0:19:39.080 --> 0:19:43.000 back in decades ago, there were people that would work 0:19:43.080 --> 0:19:45.639 for the same company for ten twenty years, and that was, 0:19:45.760 --> 0:19:49.639 you know, not that unusual. Now, very uncommon, and in 0:19:49.680 --> 0:19:53.119 the future, I think it will be absolutely rare. I 0:19:53.160 --> 0:19:56.560 think we're looking more likely at people that will work 0:19:56.600 --> 0:19:59.720 for multiple companies. We're seeing that with the rise of 0:19:59.760 --> 0:20:04.600 the economy, we obviously are seeing people love to work remote. 0:20:05.359 --> 0:20:08.320 I know when we have an active job that goes 0:20:08.359 --> 0:20:13.160 out into our marketplace, and if it is remote and 0:20:13.200 --> 0:20:16.760 also prioritize diversity, you will have twenty to thirty times 0:20:16.760 --> 0:20:19.840 more applicants. So I think that we're going to start 0:20:19.880 --> 0:20:25.040 seeing companies really investing in those two attributes, trying to 0:20:25.119 --> 0:20:28.840 keep as many jobs remote as possible, just because it 0:20:28.920 --> 0:20:33.520 attracts talent that companies are really struggling to find right now. 0:20:34.119 --> 0:20:37.120 And I think the level of automation is going to 0:20:37.160 --> 0:20:41.520 continue to increase, that will continue to increase an investment 0:20:41.520 --> 0:20:44.280 over the next five to ten years. In twenty years, 0:20:44.320 --> 0:20:47.000 I think we will all look back and say, why 0:20:47.040 --> 0:20:50.240 did we all do these crazy parts of our job? 0:20:50.760 --> 0:20:53.960 Why didn't we automate those It's because we were waiting 0:20:53.960 --> 0:20:59.520 for technology like Orchestrate provides. Do you have any specific 0:20:59.560 --> 0:21:04.159 advice for businesses that want to incorporate technology and automation 0:21:04.200 --> 0:21:07.720 in their in their business and their work. I would say, 0:21:08.040 --> 0:21:11.439 realize that you use automation every day. You use AI 0:21:11.600 --> 0:21:15.000 every day, So when you're using Google Maps or something 0:21:15.080 --> 0:21:18.679 like that, you're you're using your smartphone, you're accessing this 0:21:18.800 --> 0:21:22.439 kind of technology as a consumer, as an individual, There's 0:21:22.560 --> 0:21:25.400 no reason why you should worry about adopting it as 0:21:25.440 --> 0:21:28.840 a business, and don't feel intimidated by it. You are 0:21:28.960 --> 0:21:32.440 absolutely ready to use it and your business is ready 0:21:32.480 --> 0:21:35.520 to benefit from it. Just don't have that fear. We 0:21:35.680 --> 0:21:38.320 certainly is a company work with companies of all sizes. 0:21:38.440 --> 0:21:42.840 We have companies that have five to tend employees only, 0:21:42.920 --> 0:21:46.439 and we have some that have hundreds of thousands employees. 0:21:47.040 --> 0:21:49.560 That's the great thing about automation is it doesn't care 0:21:49.600 --> 0:21:51.520 the size of your company. It will work for you. 0:21:52.440 --> 0:21:54.600 Angela's fun to talk to you. Thank you for your time, 0:21:55.040 --> 0:21:57.879 congratulations and making it through to thirty And if you 0:21:57.920 --> 0:22:01.080 really think about that, that's a It's a really impressive 0:22:01.160 --> 0:22:04.200 level of persistence. Like I could imagine failing a few times, 0:22:04.200 --> 0:22:07.119 but I would have given up at nine or something. 0:22:08.840 --> 0:22:12.639 It's seven. At seven, I was like, I'm a crazy person. 0:22:16.960 --> 0:22:20.440 It is vitally important to get hiring right. What could 0:22:20.440 --> 0:22:24.800 be more essential to an organization's success than deciding which 0:22:24.840 --> 0:22:28.600 human beings make up that organization. If we let our 0:22:28.680 --> 0:22:34.040 biases go unchecked, we end up excluding qualified candidates, leaving 0:22:34.040 --> 0:22:38.480 our workforce is less diverse and therefore less competitive because 0:22:38.520 --> 0:22:41.960 of it. Angela made an interesting point earlier that I 0:22:42.000 --> 0:22:44.760 want to go back to. She said that bias is 0:22:44.800 --> 0:22:48.960 not a character flaw, it's a survival instinct, and that 0:22:49.040 --> 0:22:51.760 the best purpose technology can serve is to make us 0:22:51.800 --> 0:22:55.639 better humans by doing things for us that we can't. 0:22:56.440 --> 0:22:59.760 Bias is in human nature and we'll never truly get 0:22:59.840 --> 0:23:03.120 rid of it, but the first step to minimizing its 0:23:03.160 --> 0:23:07.080 impact is to acknowledge it's a problem we need help with. 0:23:08.240 --> 0:23:12.639 Intelligent automation can make hiring more efficient. When we allow 0:23:12.680 --> 0:23:17.080 computers to mitigate our biases, better hiring is the result. 0:23:17.840 --> 0:23:20.720 Sometimes to build the best team possible, we have to 0:23:20.760 --> 0:23:23.920 know when to listen to our human instincts and when 0:23:23.960 --> 0:23:28.119 to set them aside. On the next episode of Smart 0:23:28.160 --> 0:23:32.320 Talks with IBM, how to use data creatively in order 0:23:32.359 --> 0:23:36.480 to solve novel problems, we talk with YouTube content creator 0:23:36.720 --> 0:23:41.720 and IBM's senior Data science and AI technical specialist Nicholas 0:23:42.160 --> 0:23:46.280 Renaud smart Talks with IBM is produced by Matt Romano, 0:23:46.760 --> 0:23:51.680 David jaw, Royston Deserve and Edith Russelo with Jacob Goldstein. 0:23:52.240 --> 0:23:56.480 Were edited by Sophie crane Are. Engineers are Jason Gambrel, 0:23:56.960 --> 0:24:02.440 Sarah Brugare and Ben Holliday. Theme song by Granmasco. Special 0:24:02.440 --> 0:24:06.679 thanks to Carli Migliori, Andy Kelly, Kathy Callaghan and the 0:24:06.720 --> 0:24:09.720 Eight Bar and IBM teams, as well as the Pushkin 0:24:09.800 --> 0:24:13.119 marketing team. Smart Talks with IBM is a production of 0:24:13.119 --> 0:24:17.680 Pushkin Industries and i Heeart Media. To find more Pushkin podcasts, 0:24:18.000 --> 0:24:21.439 listen on the I Heart Radio app, Apple Podcasts, or 0:24:21.560 --> 0:24:26.280 wherever you listen to podcasts. I'm Malcolm Gladwell. This is 0:24:26.320 --> 0:24:33.160 a paid advertisement from IBM.