In this special edition of the Futurum Tech Podcast, Daniel Newman takes a look at some of the trends and challenges in the future of work with interviewees Edward Bagden, Associated Director of Flight Operations and Safety at LIFT Academy, and Greg Bowen, CTO and SVP of Digital Acceleration at Dell Digital, the IT organization that supports Dell. Thanks to Dell Technologies for sponsoring this important episode.
In the next decade, there are many exciting ways that AI and other emerging technologies will change the future of work. One of the ways AI will—and actually already is—changing the workplace is as it relates to inclusive talent. Removing bias from the talent recruitment processes and creating a culture of diversity and inclusivity are areas where AI-powered technology can help companies make significant inroads. On that topic, on the podcast today my guests and I took a dive into LIFT Academy, a company that is on the cutting edge of using AI for recruiting and retaining talent for the commercial piloting industry.
LIFT’s Edward Bagden shared that the company’s goal is to remove the socio-economic bias historically associated with gaining piloting skills. Rather than limiting the company’s talent pool to those who can afford to fly, they focus first on finding people who would be good, solid team players for their company, and then provide the training they need to be successful. Pretty cool, isn’t it? Finding people who have a passion for a job, then training them to do the job makes a lot of sense. Even better? LIFT guarantees results. Based on an algorithm the company has developed to determine what will make the most successful piloting candidate, the company guarantees a piloting job to every candidate it accepts to its training program.
Another way AI is changing the future of work relates to the ability of an organization to empower workers. Dell Digital’s Greg Bowen discussed the concept of supporting distributed and remote workers by creating platforms that enable real-time collaboration. The future of work is both employee and values-driven, he says, and AI is part of that shift.
In terms of challenges AI faces, there are many things that make tech adoption and rollout a challenge, the most important of which is culture. Some of the key things we discussed as it relates to tackling the challenge of culture include asking these questions:
- Are all leaders in the company on board with the tech changes rolling out?
- Do all of the employees know and understand how AI will actually help them do their jobs better, rolling value up the job chain, rather than eliminating their jobs completely?
- In terms of automation, do they know that RPA is here to do things like scheduling, minimizing errors, data entry—things that take a lot of time but don’t produce a lot of value?
These are just some starter questions that we covered during the course of our conversation, but in order for companies to be companies of tomorrow, they must face these cultural challenges today.
My guests today acknowledged there are ethical dilemmas surrounding AI, including bias in algorithms and the fact that we don’t always understand why AI makes the decisions it does. In the future, Bowen says, there will be a movement toward incorporating public audits into algorithmic models to see where biases lie and reverse engineer them when possible.
Not sure how to get started on your digital transformation journey? We recommend starting by developing a strategy and outlining the outcomes you want from AI and taking small, agile steps to make them a reality. For example, Dell recently took one problem: The challenge of finding better ways to incorporate people on the autism spectrum into their hiring process. The company held a hack-a-thon to find new technologies that could help solve that particular problem. It was a low-investment tactic on the part of the company, but paid huge practical dividends. These are things that any company can do to start making value-driven steps forward.
Want to know more? Check out Dell’s recent study Realizing 2030, which forecasts how emerging technologies like AI and the Internet of Things will shape how we live and work by the year 2030. The study, created in collaboration with Institute for the Future (IFTF), includes useful data and information that will help businesses navigate the coming decade.
Daniel Newman: Welcome to the Futurum Podcast Interview Series with Daniel Newman of Futurum Research, and in this special future of work edition, I am happy to be hosting two thought leaders talking about the future of work.
But before I do that, I want to go ahead and let everybody know that this episode is sponsored in part by Dell Technologies, and we will be having actually one of Dell’s Senior VPs and CTOs on the show. But let me quickly introduce our guests today. We have Edward Bagden. He’s the Associate Director of Flight Operations and Safety at LIFT Academy as part of Republic Airways. And we have Greg Bowen, Senior Vice President, Digital Acceleration and CTO at Dell’s Digital Office of the CIO. Those are a mouthful, but they sound like really, really important responsibilities, and I’m really excited to have both of you here for this future of work episode.
Now as a little bit of background before I give you guys the mic and the chance to introduce yourself, this show is not only a future of work special recording, but it’s also based upon a study that Dell did called Realizing 2030, which looks into the future of work. And in the show notes, I will be sure to leave a link there because if you’re interested, this study that Dell did had some fantastic data and some really exciting things. And at a high level today, we’re going to talk about some of these trends, some of the findings. But without further ado, I want to give a chance for my esteemed guests to go ahead and introduce themselves, tell a little bit more beyond your job title, what you guys do, and what interests you. So Edward, I’m going to go ahead and start with you. Edward, welcome to the show.
Edward Bagden: Hey. Thanks so much, Daniel. Great to meet you and great to be on the show. By trade, I was an airline pilot originally. Several years ago, I made a shift towards aviation management and joined the management team at Republic Airways, which is a regional airline based in Indianapolis, Indiana but flies all over the United States, Canada, Central America, Mexico, the Caribbean. Do about a 900 flights a day for American, Delta, and United Airlines. Started my management job at the airline and then had the pleasure of joining the team at a newer company called LIFT Academy about a year and a half ago. And we were started by the airline as an ab initio training program to create a pipeline of the best trained airline pilots and guaranteed career path for them to join us at Republic.
Daniel Newman: And I have to imagine that your work is just full of technology and the implications of technology as we’re training future pilots and those that have such an important responsibility. As a regular flyer myself, I certainly have the utmost respect for those of you who do get behind the controls and appreciate that. So Greg, welcome to the show as well.
Greg Bowen: Well, thanks for having me. As you said, I’m responsible for Digital Acceleration at Dell Technologies and Dell Digital. We felt this was so important that we put an organization around it. It’s transforming not only the technology that we use, trying to implement more and more of Dell Technologies’ own capabilities internally but also the way we work and how we organize our people. And so we call this the Dell Digital Way. It’s a people, process, and technology transformation, and my group leads that. We also put together an artificial intelligence, an AI CoE, which attempts to standardize the job family for data scientists around the company as well as develop a platform for being able to accelerate and share the production of AI models around the organization. Prior to coming to Dell Technologies four years ago, I spent 16 years at Amazon.com leading an AI team as well as various functions around customer support, operations, and supply chain.
Daniel Newman: Wow, what an interesting role. So many of us have a little Amazon AI wandering around our house. So I think my son, who’s three years old, absolutely loves asking Alexa to play Baby Shark. And so occasionally, I have to unplug it, and then he goes, “What happened to Alexa?” So it’s really kind of neat to see this next generation that’s growing up from day one.
But myself as an author, I actually just published a book called Human Machine. It’s my seventh book, and I’ve been looking very closely at the future of work and our partnership with machines. And it sounds like I’ve been looking at it and studying, and you guys have been doing it in many ways, now at Dell, Greg. And of course so much of what you guys do in airlines, Edward, comes down to simulation, leveraging new technologies that can get people more experiences without necessarily having to always put them behind the wheel, you could say, or in the cockpit. I’m not sure the nomenclature. I’m not a pilot, just a regular flyer, but there’s so much going on.
But today, I wanted to dig into that experience 2030, and the outline, I want to kind of cover three areas. So I’ll give you guys a little preface, and you guys have seen this. But just to make sure everybody out there also knows, there were a number of trends first of all that were discussed, and I want to focus in on three of them. And then I want to talk a little bit about some advice that you guys could potentially give about how companies can overcome some of the challenges and concurrently really help their employees progress in this age and then finally maybe some pragmatic, practical go-to market activities that business leaders that are listening to this podcast can potentially act upon.
But let’s dig into the trends first. The research really focused in on three things. It was inclusive talent, empowered workers, and AI fluency. So let’s first off start talking about human machine partnerships. And by the way, I love that choice of words because it kind of same as the book title. But Edward, let’s start with you. How do you see these human machine partnerships making it possible to find and match people’s unique talents and capabilities inside their organizations they’re working for.
Edward Bagden: So inside of the organization, when I think about talent selection and using machines to help do that, a unique approach we’ve taken at LIFT, which hasn’t really been done by any other flight training organization in the United States, is to use a computer based aptitude test to actually identify the best candidates for the program before they enter into it. So every student that comes to our flight training organization actually is guaranteed a job as an airline pilot at the other end, so it’s critical that we find the right people on the front end before they begin training.
So they take kind of a multi-panel aptitude test that uses a variety of different input methods, and we use Dell hardware to provide that experience. And we use the results out of that. We feed that in actually on the back end once the students’ progress through training and are actually hired at the airline. And we actually have used some machine learning to compare the data that we acquired during their training and during their career at Republic to help us make better decisions on the front end when finding the right people for that opportunity.
Daniel Newman: You said something there that I want to reiterate because I don’t think everybody maybe grasped it, but you said everybody that gets brought into the program is guaranteed a job, right? Is that correct, or you said-
Edward Bagden: That is correct. And I should qualify it by saying after successfully completing the training program, but we have a very high retention rate for our students that come in.
Daniel Newman: I wasn’t so worried about the… I figured there were probably a few caveats, but in today’s day and age, coming into a training program, going to university, hasn’t tended to guarantee very much. How do you think that’s impacted the business, the culture, the success? Do you guys find that that sort of up-front commitment has really helped raise the bar of the people coming in?
Edward Bagden: Absolutely. I mean, traditionally when the cost of entry to aviation is money, that doesn’t necessarily yield the best results on the back end. When we were actually at Dell Technologies World, I was on a panel with someone from McLaren, and we were talking about, are the best Formula 1 drivers in the world, are they all the best drivers, or were they the people that could afford to invest a million or so dollars that’s required in carting and leading series to get to Formula 1? If you went out and actually ran an aptitude program on the people that had the competencies that led to successful outcomes in race car driving, you may see a different population. And we want to take that same approach to aviation.
So instead of letting someone who can afford aviation training, which is very expensive, we’ve brought the cost down and subsidized it and ensure that we actually find the right people on the front end. And for them to invest their time and effort and money into that and for us to do the same, we want to make sure that we have the right people flowing in. And yes, I think it’s absolutely driven quality on both people entering and people exiting our program.
Daniel Newman: So what’s faster, the F1 on the ground or the airplane?
Edward Bagden: Zero to 60, the F1 car’s always going to win, but after about 190 miles an hour, we got them.
Daniel Newman: You got them toasted?
Edward Bagden: Yeah.
Daniel Newman: I still love Talladega Nights, and I just want to go fast, so I love it. But yeah, your example really resonated with me because like I said, I have teenage children. I have a little guy, and I have a 17 year old. And one of my biggest concerns is always, what is this college and this investment really guarantee in a day and age where what they’re learning in their first few years of school is barely even relevant by the third and fourth year? It’s very interesting to hear companies making such a big commitment to training in an agile way with their people. We’re going to train you and develop you and get you ready, and when you come out the other end, if you tick the boxes, you’re going to have an opportunity to be part of our organization, which certainly has to make people really be excited. And it must be very competitive for people to get into the program.
Edward Bagden: It is competitive. And something that I want to emphasize and kind of go off what you’re saying there is that there’s a lot more to airline style flying than what’s required by the Federal Aviation Administration for a private instrument or commercial pilot’s license. It’s a lot of competencies. A lot of it has to do with systems management, automation management, decision making. We actually teach all of that from day one. So we’re taking the same skills that we typically teach to a pilot when they’re hired at Republic as an airline pilot, we’re bringing that all the way down to the first level. And yeah, we’re very excited about the promise that we can give to all of our applicants. Looking at our industry, 637,000 pilots required worldwide in the next 10 years, so there’s a definite shortage we’ve identified, and we’re trying to make sure that we’re ready to move forward into the future.
Daniel Newman: Noted. Very, very interesting. Hey, Greg, the second thing in the trends that really captured my attention was empowered workers and the empowered workforce. So it’s something that leadership talks a lot about, “Oh, we got to empower workers.” And this isn’t new per se to the technology era. It’s always been something in change management. Employees are always like, “Hey, we need our workers to be able to do more, make bigger decisions, help keep the company moving. It also creates more excitement.” But there is a big technological impact for empowering workers in the workforce today. Talk a little bit about the technologies that workers need to become fluent in to succeed in this human machine era. And concurrently, how does that help drive the kind of culture that companies would need to be able to digitally transform and compete in this age?
Greg Bowen: Well, if you think about what’s going on today, work is becoming more distributed, more remote. Dell Technologies, for instance, has a program that really allows employees to work from anywhere in the world and contribute directly to the value of the business is creating. So when you think about what you need to have when you’ve got a remote distributed workforce, you need platforms that enable real time collaboration. Things like Slack are out there that really drive that feeling of being face-to-face, being able to collaborate in real time, and have projects that are being shared in real time. Things like GitLab and GitHub were primarily a technical code sharing application but are becoming more and more a way just to share projects, and so people can work on them collaboratively in real time. So that notion of being able to work distributively is really empowering to a workforce and not being forced to being chained to the desk anymore.
And then in the study that we published, there’s a company in London called ETCH, and they’re enabling a platform that allows people to be paid based on when they want to be paid, not on the pay cycles of the company. So imagine that ultimate empowering is determining your own pay schedule and allowing that to happen in real time. And that’s the way that work is going to be done in the future, much more employee driven and delivering value across the organization.
Daniel Newman: And of course, we’re going to get paid with cryptocurrency, right? It’s going to be part of the trusted blockchain. I’m just going to throw six or seven more buzz words out, and then we’ll proceed to the next question.
But no, you know what? You’re totally spot on. And what I loved about it was really what you were talking about is the human aspect. I think so many people think of it as digital transformation, for instance, as tech first, and I always have said it’s kind of people first. It’s culture first. It’s tech enabled. And so, you’re enabling people through these tools. You mentioned Slack or collaboration tools. You mentioned giving people more flexibility and freedom and that applications can do that for them, giving them more choices of the tools they use and how to secure them.
And this kind of leads me to third trend. I’d like to get a little bit of feedback from both of you on this one but AI power. So Edward, I’m guessing specifically in your business, I’m sure there’s many applications for AI. One of the more prominent AI applications a lot of people talk about is the machines themselves, the planes, right? And for a long time, the planes have obviously had a lot of capabilities to leverage technology to do a lot of the point A to point B. And again, this is where I, as the reader or consumer of content versus you having been behind the controls know what the delta is, but a lot of people say things like, “Oh, in the future, planes will just fly themselves,” and things like that. So that’s the negative impact that people are all talking about. But AI can be an enabler too, right? Do you see AI as something that can really help pilots be better at their job and help airlines be better at delivering customer experiences?
Edward Bagden: Absolutely. With regard to AI flying airplanes, I think we’re still a ways out from that. The computers that the airplanes have installed in them are pretty rudimentary still and running on older, extremely robust hardware, the kind of things that you want to run mission critical applications on, things that are not running on multiple layers or the most cutting edge technology. So as far as the airplanes, flight control systems, fly-by-wire, all of that are pretty rudimentary.
But AI can be incredibly valuable when it comes to looking at, let’s say, a detailed radar map or looking at meteorological data. Just like AI has the ability to look at an MRI or medical imaging and make observations that a doctor may not be able to, we believe that artificial intelligence could probably do the same thing by looking at a radar map. It may be able to draw conclusions or see things that the trained human observer may not immediately realize. So in the cockpit, those kinds of technologies are huge.
As far as the rest of our operation, artificial intelligence, machine learning, can be incredibly valuable when it comes to scheduling the resources that we schedule. So when you talk about having 100 aircraft and 600 students and 150 flight instructors and classrooms and simulators and curriculum, to be able to optimize and maximize utilization resources using human capital, not very efficient and very difficult to do. So we’ve been working with some of our vendors to use things like machine learning to make sure we’re optimizing our schedule on a day-to-day basis.
Daniel Newman: Yeah, I think some of those are great examples. And Greg, I’m sure you’re seeing it through the customers that you serve, the enterprises you partner with, and probably within your own enterprise, but where do you see AI playing its part in upskilling, for instance, in driving the workforce forward as opposed to some of the negative connotations that have come with AI?
Greg Bowen: I think at its heart, AI and automation that’s driven by AI has the power to reduce and sometimes actually eliminate the tedious, non-value added work that an employee has to do. So back to your point, this is really about the human experience, about the employee experience. And imagine how mind numbing it is to copy data from one system to another, one application to another eight hours a day, how error prone that can be as well. So we’ve created systems that automate some of that movement of data around.
In almost every case we’ve applied automation to a problem like this, it hasn’t been about reducing the workforce. The labor that was applied to that tedious, non-value added work is actually moved up the value chain. And sometimes, we actually have AI assisted programs that allow them to do more creative work than just copying, pasting data.
Another example is in the software development process. So there’s just a lot of things about setting up an environment or getting a dev machine going or finding a method that you might’ve used in the past. We’ve actually created chatbots that sit side-by-side with the software developer that they can ask questions in real time and actually get something provisioned for them versus having to submit a ticket for it. So it allows that interaction to be very seamless, very fluid, very flowing, and it takes the tedium and those tedious tasks out of the work experience. So I think it’s really going to become more and more of a partnership and an ability for people to put their creativity at work versus having to do those rote, repetitive tasks.
Daniel Newman: I think you hit few things on the head. And I probably asked you the question in such a way that you sort of propelled me forward because the next thing I wanted to get onto was talking about businesses overcoming some of the negative connotation. And some of what you just mentioned, effectively making people’s work more interesting, helping assist them with routine and mundane tasks, and that’s something we certainly spend a lot of time on when we were writing the book, looking at there is this upside. There is the bringing everyone’s level forward.
And of course, it’s important to mention, and everyone may not love the way it sounds, but you have to be willing. The employees have to be willing, the team has to be willing, the ownership has to be willing to participate in doing things differently. And that’s why culture’s so important, and that’s why transparency’s so important. So you sort of covered what I wanted to talk about in terms of companies embracing the technologies to fill gaps. You talked about RPA. You talked about some simple automation and AI.
But ethics and transparency are a whole other thing. Edward, as your organization is starting to leverage the technologies, what is the kind of idea around being willing to talk about what’s really behind AI, what’s really behind automation? Have you guys thought about that much yet? Have you started to figure out how do you communicate to your workforce, your approach, what you plan to automate, how it’s going to work? Is it something that people need to know, or is it something that less is more, and we’ll deal with the issues as they come up?
Edward Bagden: Well, when we talk about what I was talking about most recently with regard to resource scheduling, I think that a lot of our leaders in the scheduling operation at LIFT have been deeply involved in the conversations with our vendors as we develop these technologies, so their buy-in is pretty high. And I mean, ultimately, these are not the kind of jobs, as Greg was saying, that people are necessarily hyped to do when it comes to allocating resources and scheduling them on a time grid every day. I think everybody in these departments realizes that their time could be better put forward to solving some of the issues that requires an actual human to do, so whether it’s more customer facing work or things like that. But yeah, I think it would be important to explain the rationale and how things work so that the end user can use it more effectively and be more comfortable with the actual software driving it.
Daniel Newman: And you kind of have to think about the employees too. So you talked about the scheduling leaders, and to them, they might be… You see them kind of wiping their hands of it and being like, “Great, I can focus on spending time with the pilots and spending time training.” But sometimes, those who are impacted by a schedule, for instance, and they said, “Hey, AI set this schedule or an automation tool set this schedule up, and I feel like someone else got a better… They got the better flights. They got the better routes. They got the better training times. How did this all work?”
So sometimes, like I said, the upskilling at the management level can be great. But then do you share with the students, with the other employees, with the flight instructors who are impacted by not any longer necessarily being able to go to their boss and say, “Hey, can you do this for me,” because it’s like, well, they’re not really doing it anymore? Does the transparency help that forward, or are people just kind of left to cope with whatever the outcome ends up being?
Edward Bagden: Well, some of these technologies are still forthcoming and under development. But I mean, the rules that are in place today for how we assign schedules, aviation is traditionally a seniority based industry, so you receive a number when you’re hired. You move up one when someone above you leaves, and then people fill in below you. So we have pretty rigorous algorithmic rules for how schedules are assigned. But regardless, whether AI is creating the schedule or not, there still needs to be an opportunity for the employees to speak with a human being, someone in the scheduling department, someone in leadership, to voice their concerns and ensure that things are being treated fairly and that every employee’s been treated with respect and dignity.
Daniel Newman: So as Greg mentioned, the chatbots that are set up to be side-by-side, they don’t answer everything. They just maybe help with some of the day-to-day basics and making people more efficient in their work. But that chatbot eventually has to be like, “Human, I’d like to talk a human.” And I think that is a challenge. I mean, this isn’t a new challenge either. This goes back to when credit card companies started putting you through the automated telephone prompters, and it was all about efficiency and all about being able to be 24 hours. But it probably frustrated just as many people as it helped. And this is the same with technology. Our obligation isn’t only to deploy the new technology but to make it helpful and to make it something that people really like.
And Greg, that kind of leads me back to you. You talked about some of these technologies as enablers, but working at Dell, building a center of excellence, what are your thoughts around ethics? What are your thoughts around transparency knowing that so many people… Driverless vehicles for instance, I’ve heard endless ethical dilemmas on that one, right? What happens with the dog, the baby, the storefront, and the other car? What does the car do? Well, that’s an extreme example, but day in and day out, AI is only as good as how we train it in such. So kind of what’s been your take on this whole dilemma in building your center of excellence?
Greg Bowen: This is coming up more and more. And if you think about AI and ML algorithms, look, they’re created by humans. Humans are inherently biased. Our brains are actually wired to make decisions to keep ourselves alive. So there’s bias in the way we actually operate as human beings, and a lot of that bias creeps into technology in unintentional and in many times, unavoidable ways. Look, our culture, our class, gender, the way the brain works, they shape decisions that we make when programming. And our models are designed to learn and change, and they often become black boxes, so we don’t even sometimes understand why a model makes a decision that it does. And it’s really hard to go reverse engineer that decision. So you couple the inherent bias in the way we program, and then there’s bias in our training data, right? The data itself is biased because it’s oftentimes a reflection of the society that we live in. And so it’s really hard to engineer that out.
And so what you mentioned, that transparency, that is becoming more and more valuable and important in this world that we’re headed to, understanding what are the sensitive attributes in a model and knowing what data impacts those sensitivities so that we can understand what the model’s designed to do. Interestingly, more and more, there’s a call to do public audits of models so put together a synthesized dataset that will exercise the model and understand where the biases are. So how different is the outcome of the synthesized data versus the natural data that’s being driven through the model? All those things are ways that we can start to identify bias and more importantly, try to engineer it out of the models that we’re creating.
Daniel Newman: I think that’s super interesting. And we’ve all seen when models have been built and AI has been left for humans to interact and create freely without limitations, the inherent and intentional bias that are laden upon them. So it really is up to us as humans to be super engaged. So that’s where these human machine partnerships become so critical. We can’t just leave it to the machine, and we also need to increase and enhance the communications so people understand this is a work in progress, kind of like compute. I mean, we’ve become so accustomed to what compute can be done in our device that sits in our pocket. But that was a evolution that took many, many years to get to where we are today, vehicles changing, airplanes changing, computers changing. Well, AI has come on the scene pretty fast. Not to say… I’ve met people who studied it 20 and 30 years ago, but the way it’s suddenly become used day in and day out in our business, I think people mistake it for being perfect when in fact, it really is a collaboration.
So speaking of collaboration, I want to sort of come to a wrap here because both of you bring really unique experiences. And thank you guys again so much for your contributions to our podcast so far, but you bring a lot of experiences. So for the listeners of the Futurum Tech Podcast, what I’d really love is to have you guys share some of the practical wisdom from your own experiences of the ways that companies can drive greater collaboration, build culture, embrace human machine partnerships in the workplace, and help the customers, help your own organizations realize the potential, the future, and of course, realize 2030.
Greg Bowen: So from my perspective, one of the most important and most practical steps to, whether it’s digital transformation or bringing AI in to empower the workforce, is to just get started. Knowing that in many companies, that’s the first and that’s the hardest step because there’s this really strong desire, especially at enterprises, to create a complete project plan where every box is ticked and everything is mapped out over a three year to five year period of transformation and knowing that that’s just not going to work because of the pace of change that’s happening right now. So my suggestion is to take a very agile approach, work with your business partners and the technology organization to determine a set of outcomes that you want to achieve through AI.
I’m going to give you an example of something that we recently did at Dell Technologies. We partnered with SAP, and we did a two day hackathon to develop technology that’ll help people on the autism spectrum find, acquire, and keep jobs. What we find is that 85% of people on the spectrum are unemployed, but we believe that 60% of them have skills that would be very valuable to an organization. So we think there’s huge opportunity here, not only for the business, but for potential employees. So we created a two-day hackathon. We had 30 cross functional teams from around Dell Technologies. We worked with people on the spectrum. We interviewed them, and we started developing applications.
One of the best ones that I saw from that was an application that used natural language processing to review job descriptions, simplify them down to key technology skills that a person on the spectrum can understand because often, a job description is full of buzzwords and noise that’s just too much for them to process. They can then apply for that job. And the last piece of the technology routed the application straight to a hiring manager versus through an HR department because one, direct connections with hiring managers prove to be more successful in the process with someone on the spectrum, and two, shortening the hiring cycle is another benefit for people on the spectrum.
So that was a two-day exercise, very low investment, that had the opportunity to present big results for a company. So just take a practical step and get started with low investment and an agile approach.
Daniel Newman: I do love that. I always hear the metaphor, how do you eat an elephant? One bite at a time. And I think so much with digital transformation comes down to that, analytics, big data, AI, all the technologies, is just taking steps, doing something, picking one particular challenge your organization has and looking at how the technology can help you to solve that challenge as opposed to trying to think about how it might fix or be applied across an entire organization. So I think that’s really great advice. Edward, what say you?
Edward Bagden: I mean, I’m excited about all of the technology that we use every day in the workplace, the collaborative tools we use. We use virtual reality and take it around to STEM fairs and high schools and different places to give people a real in-cockpit experience of what it’s like to fly. But what I’m most excited about is diversity that we’re promoting in the industry as well by using technology to help our selection process, to reduce barriers to entry. With all the data we’re collecting throughout the training process, and then using things like machine learning to draw observations off that at the end, I think ultimately, we’re going to get a much better vision of what the right candidate for an airline pilot looks like. And that inherently will drive diversity because it’s, again, it’s not based on socioeconomic status anymore. It’s based on ability.
What is really important about that, and it kind of hearkens back to one of the things Greg was talking about earlier. We’ve got our digital competency evaluation that we run on each applicant, but at the end of the day, it is a human decision that determines if it’s the right person. These are just inputs, and we must always be cognizant of the inherent bias in some of the selection processes we’ve created. So I think it’s important to use technology to help find the best people to create valuable pathways to drive diversity in our industry. And at the end of the day, make sure that we’re aware of the potential biases in the technology that we’re using.
Daniel Newman: Both great answers. Obviously, we have so much opportunity to participate in that process, to learn from the bias, to improve algorithms, to improve our datasets and train the machines to do better, and that’s upon us. And I believe as a society and as an industry, tech companies are doing this every day, and I think it’s going to take time, and people need to have some level of patience with it. They need to have some level of confidence in it, but at the same time, they need to hold the organizations, the enterprises, and the companies that are utilizing it to a high standard to make sure that this technology does in fact make society better.
So Edward, Greg, I want to thank both of you, tremendous show. Love talking about human machines. As I mentioned, this podcast was sponsored by Dell Technologies, and we appreciate Dell Technologies being a part of it.
Their study Realizing 2030 was really outstanding. If you’re interested in the future, I really recommend that you take the time to click on it, read it, download it, process it, and think about it because the stuff we talked about today, if it interests you, this was all covered in the study, and there is so much more to it.
For Futurum Tech Podcast, the Interview Series, I want to thank everybody for tuning in and listening. Click that subscribe button. Join us again in the future as we bring more interesting, exciting, and thoughtful leaders onto the show. For this episode, we’re out of here.
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