Nav Singh and Kyle Forrest discussed the integration of AI and human work, emphasizing the shift from “humans plus AI” to “humans times machines.” A poll revealed that most organizations are in early stages of AI integration, with 7% investing in people-related topics. Examples included Nobel Prize winner Demis Hassabis’ use of AI for protein folding and Amgen’s drug discovery. Valvoline’s AI-driven hiring process reduced time from 6-8 months to 13 days. Salesforce achieved 50% internal job fill rates through AI-powered reskilling. The discussion highlighted the need for a balanced approach, integrating AI to enhance human capabilities and redesigning work processes for optimal outcomes.

Introduction and Background of Speakers

• Nav Singh introduces himself, mentioning his recent join at Eightfold and his previous experience at Palo Alto Networks.
• Kyle Forrest introduces himself as the future of HR leader at Deloitte, focusing on how AI and automation are impacting the world of work.
• Both speakers express excitement about the AI journey and the potential for humans and technology to amplify each other.

Poll on AI and Work Orchestration

• Nav Singh initiates a poll asking participants to rate their organization’s effectiveness in orchestrating work between people and AI.
• Kyle Forrest notes that the majority of responses are in the early stages of AI integration, with a significant portion still in isolated use cases.
• Nav Singh agrees, stating that many customers are still in the early stages of figuring out business use cases for AI.

Inflection Point in AI and Automation

• Kyle Forrest discusses the inflection point in AI and automation, comparing it to previous technological shifts like cloud and mobile.
• He highlights the rapid pace of AI advancements and the need for organizations to invest in both technology and people.
• Kyle Forrest shares Deloitte’s research showing that 93% of AI investments are in technology, while only 7% are in people-related topics.

Optimizing Human and Machine Collaboration

• Kyle Forrest emphasizes the importance of optimizing human and machine collaboration to drive business value.
• He shares insights from Deloitte’s research on the growth curve of businesses and the need for organizations to invest in both technology and people.
• Nav Singh agrees, sharing examples from customers like Nobel Prize winner Demis Hassabis and Amgen, who have redesigned work using AI.

Examples of AI-Driven Work Redesign

• Nav Singh shares examples of AI-driven work redesign, including the Nobel Prize winner’s use of AI for protein folding and Amgen’s drug discovery.
• He also provides an example of Valvoline’s use of AI agents to streamline the hiring process.
• Kyle Forrest discusses the importance of starting with impactful use cases to gain credibility and time to address other challenges.

Poll on AI Use Cases

• Kyle Forrest launches a poll to understand where participants would like to start with AI, focusing on talent life cycle and transactional inquiries.
• Nav Singh notes that the responses are consistent with what they are seeing in the startup ecosystem.
• Kyle Forrest highlights the importance of addressing tier zero and tier one inquiries to free up capacity for other tasks.

Framework for AI-Assisted, Augmented, and Powered Work

• Kyle Forrest introduces Deloitte’s framework for AI-assisted, augmented, and powered work, explaining the different roles of humans and AI in each.
• He emphasizes the importance of mapping workforce roles against this framework to understand the future of work.
• Nav Singh shares examples of how customers are using this framework to redesign work and improve efficiency.

Customer Examples of AI-Driven Transformation

• Nav Singh shares examples of how customers like Salesforce and Bear are using AI to redesign work and improve talent management.
• He highlights the importance of internal mobility and reskilling workers to adapt to new roles.
• Kyle Forrest discusses the 4R playbook of Salesforce, which includes redesigning work, reskilling people, redeploying them, and rebalancing work between humans and machines.

Challenges and Opportunities in AI Adoption

• Kyle Forrest and Nav Singh discuss the challenges and opportunities in AI adoption, including capacity constraints, data silos, and rigid roles.
• They emphasize the importance of starting with impactful use cases and addressing these challenges to drive business value.
• Kyle Forrest introduces the concept of the human and machine blueprint to help organizations plan their AI journey.

Conclusion and Q&A

• Kyle Forrest and Nav Singh conclude the webcast, summarizing the key points and addressing questions from the audience.
• They emphasize the importance of a human-centric approach to AI adoption, focusing on outcomes and tasks rather than just experimenting.
• The webcast ends with a reminder of the HRCI and HCI recertification credits and an invitation to check out more insights and information on the HCI website.

Nav Singh 0:09

Thank you, HCI, and thank you to everybody for joining. I’m Nav. I joined Eightfold only about five months ago. Before that, I was at Palo Alto Networks, spent about 10 years in cybersecurity, leading the network security and AI practices. Over the course of my career, we’ve talked to many, many customers—thousands of customers—and especially over the last couple of years, have talked to many customers about their AI journey. So really excited to be partnering with Kyle here to bring some of those nuggets for you in terms of what customers… and how customers are redesigning work and how they are becoming successful in this age of AI.

Kyle Forrest 0:49

Hey folks. Kyle Forrest, I am the Future of HR Leader at Deloitte, which means that I spend all my time with CHROs and business leaders understanding how the world of work is evolving in response to many different trends that are out there impacting the world of work, of which AI and automation is certainly one of them that has been getting a lot of attention. And from our perspective at Deloitte—I know shared by Eightfold—this is a moment in time that is not “humans plus AI,” right? Just adding on AI and automation capabilities to the way work gets done today. It’s about “humans times machines,” right? Thinking about how humans and technology can amplify each other to drive, you know, outsized value for organizations in new and different ways than maybe we have been able to do before. So really excited to dig into the conversation with folks today. But before we do that, we’re going to start with a simple poll.

Nav Singh 1:48

The question for us is, please be as honest as possible, no wrong answers here: On a scale of one to five, how effectively does your organization orchestrate work between people and AI today? So choose one if you haven’t started, AI could be minimal. Or two, if there is… you know, AI is used in siloed, isolated use cases. Or three, if it’s AI is assisting workflows. Four, if there is actually humans and AI working together in collaboration. And five, if you are fully agentic today.

Kyle Forrest 2:26

We’ll take a look as some of the answers are coming in from folks. Kind of a quick jump out in the lead on number two. We’ll give it another moment here.

Kyle Forrest 2:48

Number two is staying in the lead and increasing its lead right now. Nav, maybe based on what you’re seeing here, any surprises?

Nav Singh 3:03

No, I think this is very similar to what we are seeing from customers as well. We are very early in the innings of AI, right? We are so early, and customers are looking at AI, you know, first stage: just writing better emails, right? Customers are trying to figure out what are those business use cases where AI can be used, and then looking at how AI can be used in work. And some of those early customers, who may have chosen three and four, are looking at work redesign. And that’s what we are excited to talk about today.

Kyle Forrest 3:37

Yeah. And one thing that I’ll just share, folks, especially as some of you are maybe catching, you know, the seven-ish percent so far that have responded at number four, right? Everyone’s at different places on their journey for a variety of reasons, and that’s okay, right? Sometimes it’s very easy to pay attention to what’s in the news, or on LinkedIn, or other, and think, “I’m behind.” But hopefully moments like these acknowledge that everyone is at different places in their journey, and you need to move at the right pace that works for the humans in your organization to actually put these technologies to use in the right way that drives business value. Which is a little bit of what we’re going to share some more about here today. So let’s go ahead and close the poll so that we can keep moving on.

And why I shared a little bit of that language with respect to the poll, and why it’s okay, is to acknowledge that we are at an inflection point. Right? I think everyone would acknowledge that there’s been so much energy in the last three years from an AI and automation capability perspective. And it is a different moment than some of the recent technological inflection points, like the move to cloud, like the move to mobile devices, and things that certainly changed the way the business world works, but didn’t do it at the same speed at which it appears to be doing now. Number one. And number two, the speed at which new capabilities continue to get released. Right? I think it’s something still like every six to seven months, the large AI firms are like doubling in compute power. So just the capabilities of what we’re able to do on a year-over-year basis continue to change dramatically. And so at Deloitte, you know, we started seeing something in the market where organizations were having some challenges actually beginning to show value with this moment. And we actually took a look over a few-month period of time at what organizations were spending their dollars on in relation to where they were asking for Deloitte to help. And 93% of the spend was being invested in technology-related items, right? “Help me implement this use case,” or “implement this new technology,” or “something related to the data,” versus 7% of dollars being spent on those people-related topics, right? And our kind of perspective is that’s not the right ratio, right? Because in a world where every organization is going to have access to these AI models that are changing at the speed that they are, have access to, you know, maybe equivalently similar technology stacks, it is that spend on your humans that’s going to be your competitive edge, your differentiator, right? It’s that human edge for organizations. And so, you know, we saw that through some of the research that we did in the fall.

And that then led us to really reflecting on what is going on in the world of business right now, right? And if you see here, the gray line is kind of a normal business growth curve, right? Businesses start, they hit maybe an acceleration moment, then they start to plateau, and they make a choice: Do I do a new product launch, merger and acquisition, new market entry, etc.? And we’ve been seeing over the last couple of years, few years, like, there are businesses that are taking off in kind of this green, right, higher growth moments, right? And when we look at kind of the world of work, businesses across industries and sectors, and forecast out what might it look like for a business at this inflection moment in time, we think a lot of this moment is going to come down to what choice is your organization making relative to your investments in technology, humans, or both, right? And so our perspective is that for businesses to really take advantage in this moment in time and maybe catapult them into new phases of growth, new sources of business value, it’s really going to be optimizing for the human and machine together that’s going to be really critically important to kind of jump the curve into next stages of growth, next stages of business value. And I know, Nav, that’s something that you all at Eightfold are seeing in the market, not only with your customers as well, but something that you all believe in terms of how you can help empower your customers. So we’d love… do you want to share some more about kind of what you’re seeing and hearing in the market from an Eightfold perspective?

Nav Singh 8:20

Absolutely, Kyle. And couldn’t agree more, in terms of humans and machines, you know, working together to drive optimal business outcomes. What I’m also seeing is that, just aligned with the theme of this webinar, customers who are at the leading edge, customers who have been able to generate the most output and outcomes from this have really redesigned and rethought work. So let me give you some examples, and I want to share examples with the audience from different kinds of sectors. So let me give you one example from the academic world and then a couple of examples from the corporate world. The academic world example is the Nobel Prize that was awarded in Chemistry. So the 2024 Nobel Prize was awarded to Demis Hassabis, who is the co-founder and CEO of Google DeepMind. He has famously said that he is not a chemist, and yet he was awarded the Nobel Prize in Chemistry. He was awarded the prize because he invented AlphaFold, which was able to predict the folding structure of proteins for 200 million proteins in a matter of months—something that would have taken chemists years, if not centuries, literally centuries, right? And the reason he was able to do that is because he did not treat this as a chemistry problem, but as an information and search problem. So he rethought the process, applied AI in that fundamentally new way. Another example from the corporate world in, again, a very similar field, but in the corporate field: Amgen has been able to triple its protein engineering velocity and halve the time it takes to discover new drugs. And the reason is because, again, they rethought the work in the light of AI. For example, they said that instead of searching the proteins in the finite number of proteins that are available in nature, instead of that, use AI to synthesize proteins, synthetic proteins, and study them. And that’s how they were able to apply AI, study them, and cut their drug discovery times in half, right? So those are a couple of examples. And then let me share another example of one of our customers, which is, you know, Valvoline Instant Oil Change has multiple retailers. One of their leading retailers is Quality Automotive Services. They are one of our customers. They are growing; they opened 30 stores last year. They said, “We are fast growing, can’t hire fast enough.” So they said, “How can we rethink the hiring process instead of people applying, recruiters manually going through resumes—resumes increasingly look very similar—and then, you know, being able to talk to the top few candidates?” Right? There’s a human scale limitation. They worked with us to say, “How can we apply agents and how can we rework the hiring process?” So they said that instead of recruiters going through resumes, “What we are going to do is we’re going to use agents to give every applicant a chance to interview. As soon as somebody applies, they can click a button and say ‘Interview with an AI agent.’ And because that AI agent is built on our 10 years of talent intelligence—we have more than a million skills in our database—we can see what are the adjacent skills. We are able to see the potential of a person, not just what they have done in the past, but what they are capable of doing in the future, and are able to figure out the candidates who are most likely to succeed in that role.” And so they were able to give a chance to everybody, cut their hiring times, and candidates had a great experience. They said that this was actually, you know, they did not see bias, they did not see judgment. They just saw a focus on skills. And the recruiter was not rushed because the AI agent doesn’t have to go to a meeting in 20 minutes, right? So they were able to fundamentally redesign their hiring and reap benefits there. So those are a few examples I wanted to share where it’s not just applying AI to the existing process. Like you said, it is about what we can do in this new world.

Kyle Forrest 12:19

What is actually really important about that, as an example, is when I chat with many organizations, they’re often struggling a little bit with information overload or use-case overload from a “Where do I get started on this journey?” right? And I think there are powerful examples like that that demonstrate an easy way to start making an impact that maybe doesn’t require you to do all sorts of data cleanup or process simplification. It starts making an impact for your workforce, gains their credibility that you are able to drive value, right? And then also gains the time to address some of the things like data or process simplification that then positions you to take the lessons learned from the first deployment, use case, and continue to amplify impact as you continue to move from there. So that’s… I mean, that’s such a tremendous example from what you shared. So we want to ask a second question as well. Hearing that example, knowing that organizations are really still, because of the volume of use cases, figuring out where they are on their journey, we want to hear from you all, like, where do you think are the places that you’d like to start if you haven’t started already, or share, like, where have you started and you think there would be the most benefit from actually moving from “we are constrained just from human capacity” to the ability to expand the human plus machine capacity to drive value in some new and different ways? So we’ll launch a poll here and get some perspectives from you all.

Nav Singh 14:12

People are pretty quick in responding to the polls. That’s great.

Kyle Forrest 14:17

And both times so far, there’s like a clear one that jumps out there, although here, here’s a little bit of maybe a change about to happen.

Nav Singh 14:29

Yeah. And this is about, you know, the area of the talent lifecycle, right? So it could be high volume screening, it could be skills-based matching. And I’m seeing, you know, there is a clear winner, but there are some changes happening. Yeah. Yeah. And then automating transactional inquiries and service delivery. It is true, if we look at even the startup ecosystem, automating transactional inquiries and service delivery is where we are seeing a lot of investment in terms of the startup ecosystem as well. So… very similar to what we are seeing.

Kyle Forrest 15:02

Yeah, yeah. And I think oftentimes folks use this one as a common place to start, especially if there are cross-functional efforts, right? Like with colleagues in IT, procurement, others who may also be wanting to pursue a similar thing, right? “How do you address tier zero, tier one, and certain inquiry and transactions? Because if we can build ourselves some capacity there, that allows us to now go tackle other parts of the function.” Okay, great. So let’s… let’s close this poll here, and we’ll kind of keep it going from here, right? So again, everything that we’ve framed thus far, and hopefully what you all are kind of seeing at your organizations as well, is that to derive value… to derive value from all of the investments in AI and automation, it’s not a technology problem. It’s not, “Do I have the latest tools? Do I have enough of the tools?” Right? It’s a human problem. And so want to share a few things now for what we’ve seen as tools that are helpful in getting the workforce engaged to think about working differently and getting involved on that journey. And so I’ll start with this framework, which is: at Deloitte, when we work with organizations, we think about how the work of today is changing into the work of tomorrow. And we use this “AI Assisted, AI Augmented, AI Powered” framework, which really acknowledges that “AI Assisted” means the work of today that’s largely done by humans, tomorrow is still going to have a significant percentage of the work done by humans, but they will use AI to do the work faster, better, different, right? You think about a sales account executive meeting with customers. You think about a recruiter meeting with candidates. You think about an HR business partner meeting with managers and leaders, right? That’s still going to require a lot of time and engagement with humans, right? But AI can help make that a much more quality, impactful time. Other end of the spectrum, “AI Powered”: work of today that’s largely done by humans, in the future might be largely done via AI and automation capabilities, and so the role of the human will change in relation to that work. We see a lot of organizations focused in that transactions and inquiry resolution space, you know, interestingly, based on the poll that we just did. And so we expect, and we see companies changing the work of people who are in shared services types of environments right now, being responsible for managing teams of agents as they think about deploying, being responsible for owning the knowledge and content curation and creation and updates that feed and train those teams of agents, right? And so every talk that I give, and I’m saying this to you all now, if you take nothing from today, take this slide. Map your workforce against this slide and tell them where they fall, right? Because if I, Kyle, know that I’m going to be in an AI Powered role, I’ve got a significant learning journey over the next couple of years to reskill, upskill, redeploy into something else, right? If the work of today is going to be done by AI and automation that I’m used to doing, right? And so that really helps workers understand what might their journey look like along the next couple of years. And so then companies compare their AI and automation investments with the investments in the people to navigate that journey. And so, yeah, Nav, I think one of the things that… as we think about this, like you’re seeing this play out as well, right? Like, how do organizations make this type of shift? And so we’d love to kind of hear you share some examples of how companies are really bringing that to life and making that shift occur. Yeah.

Nav Singh 19:05

And when I think of the audience, right, you might be from different areas, different verticals, different industries. I want to share different types of examples. So in this case, what comes to mind is a public sector example. So we’ve been working with state government, and we saw that their hiring was on an average… it took six to eight months. That’s ridiculous, taking so long to hire a person. That’s the average. Some of the candidates take longer than that, right? So it’s not because of a lack of trying. They are all smart individuals. They all have their heart in the right place. They want to do the right thing, but it is just running up against archaic processes. So we were able to partner with them, and saw a tremendous turnaround. Today, their hiring process, their average is 13 days. And you don’t get to that kind of a transformation by just adding AI onto existing processing, right? So they, just like how you were mentioning, they said, “What are the areas in hiring that we can do AI Powered?” For example, they started the screening round. It can probably be used… they can use an agent for the screening round. And then some of our early adopter customers are actually saying that “Why don’t I use this agent for functional interviews, for coding interviews and coding assessments?” So that part of the process could potentially be AI Powered. And then some of that could be AI Augmented. For example, if I’m doing a hiring manager interview with the top candidates, then I could have an AI companion who is always there to guide the interviewer, to make sure that we are asking questions in compliance, we are asking the right questions, there are not some areas that I’ve left uncovered. The AI interviewer companion can actually prompt the interviewer. And then there are some areas which would be AI Assisted. For example, rolling… actually making the decision is human in control, and then maybe rolling out the offer could be potentially AI Assisted, right? Or if the customer—if the candidate, in this case—has many questions like, “What is our leave policy in Ireland, for example?” right? So some of those could be AI Assisted, where a human is in charge, but AI is assisting. So they were able to think about this framework very similar to… without even having this slide, right? They potentially just thought about the framework in this way, and that’s how they were able to make that transformation.

Kyle Forrest 21:34

Yeah. And Nav, I think that ties to something that you all have been seeing, and then you think about, like, what is the shift from human-only, and what can humans scale into? What is that scale when it’s at an agentic perspective—human and agentic? So do you want to share more around kind of how Eightfold is now seeing that piece of it play out, and maybe some of the early indicators, whether from how you all are doing it yourselves or also with customers?

Nav Singh 22:03

Customers are asking us, like, what does the role of humans and AI look like in the future? What does that future workforce look like? What we are calling that is the Infinite Workforce. Which is, in the past, there was a linear relationship between headcount and output. If you needed to increase output, you needed to increase headcount. Today, customers are seeing that they can increase output with a combination of humans and agents. And that’s why we’re calling it the Infinite Workforce. It will fundamentally transform work as we know it today. If I look at my own work, I do a lot of repetitive things, like, for example, just looking at my meetings, attending meetings, doing weekly reports, doing… maybe I have a board deck meeting every quarter, creating slide decks for them. All of that are repetitive tasks. Then I do some intelligent work. For example, last week, I listened to customers and they described the impediments that they were seeing in terms of agent adoption. They also saw the successes that they were seeing in agent adoption, right? That gives me insights on what they are doing, and that allows me to advise my Chief Product Officer on what enhancements should be rolled out into the product to align with the customer journey, and also gives me insight about the marketing campaigns my team should run. That insights-based work is intelligent work. And what we see is that in the future, agents will be able to do a lot of that repetitive and intelligent work. And what that leaves me to do is a lot of the other work which is uniquely human. For example: what is the vision for my team? What goals should we be targeting? What is that big, you know, that hill that we should climb? What is, you know… I can show empathy, which is working shoulder to shoulder with the team and seeing what bottlenecks exist. How can I remove those bottlenecks? How can I inspire the team to achieve bigger, bolder, better things? Those are uniquely human capabilities that will become even more important, and AI and agents will give me time to invest in those skills.

Kyle Forrest 24:06

Yeah, and what I think as folks think about all of that, that has implications for how professionals within a function spend their time on certain activities, and those activities being ones which can drive certain amounts of value for the business, right? And so we have a perspective for the HR function here, but you can take this and do a similar exercise with any business function. We’re on the left-hand side of the page, you’ll see kind of estimates of time allocation around some buckets of activities. And a lot of those activities are on things like the annual performance cycle, merit cycles, you know, certain activities that are the programmatic delivery of ongoing HR activities, right? But not as much time spent with the business thinking about real-time and proactive workforce planning, organization design. What’s the right mix of on-balance sheet headcount, contingent worker, or now, you know, machines, AI automation, AI agents, etc., to help drive M&A and restructuring, or new market entry, new product launch, right? So as you think about… as the work begins to shift right in this middle with this AI Assisted, Augmented, Powered framework, where could, or should, the humans be spending their time in the future, right? So maybe it’s less time on those kind of programmatic delivery of certain activities throughout the year, because that’s where some of the AI and automation capabilities really help kind of free up capacity for humans to then focus on activities at the top right. And there are a couple of things that I always say with this being a particular slide, which is: one, whatever shape of the pyramid, so to speak, occurs in the future for your organization needs to depend on business strategy, right? Maybe it’s a diamond, maybe it’s a… maybe it’s a square, maybe it’s something, right? But it’s presumably people spending time in different ways than they are today, right? Because I think every organization would acknowledge that there are certain activities that, in the right conditions, they would like to stop having humans spending time on, right? And then secondly, is wherever that is needs to be grounded on what makes it uniquely you as an organization, right? You shouldn’t be chasing something because peers are doing it, right? Just like there needs to be the linkage to the business strategy, it’s what’s uniquely right for your organization and the workforce segments that you have, right? It’s going to look different for a company with a frontline workforce in healthcare versus retail versus manufacturing, right? So, sharing that, Nav, I know you’ve seen, like, customers begin to really drive value as they make some of this shift. So do you want to share kind of another customer example to articulate how you’ve been seeing some of this play out?

Nav Singh 27:01

Yeah, so let me share the example of a tech customer we’re very familiar with and one of the most iconic companies of our time, Salesforce, which we’re proud to call them a customer, a joint customer, and proud to be working with them. So they, as you know, is a large customer, and they were looking at what skills should their employees have for the workforce of the future? So they looked at all the talent inside their organization, what skills are required, and how do we reskill the organization? So they partnered with us and implemented our talent management solution, or Talent Marketplace, and inside there, they had AI courses that they rolled out, and they had, you know, badges and stars and certifications that they rolled out to their organization. And instead of just having internal mobility in terms of just advertising the roles that are available, they really rethought the whole exercise. Did some gamification, as I was saying, around badging, certification, and so on, and they really rethought how to reskill workers and how to advertise jobs to workers from within. They’ve achieved something phenomenal. Today, 50% of their jobs are filled from workers within. So imagine 50% of the time, right? They don’t have to go through attrition or onboard new employees, see if they fit the culture of the company, see if they perform well. These are employees that have already been there, and 50% of the roles are just filled from within. And all of that was possible because they had a year-long exercise working with us to see, how do we look at talent in the future? What skills are needed, rolling out those programs, and then applying internal mobility on top of that.

Kyle Forrest 28:51

Salesforce has shared some of their journey publicly with what they call a 4R playbook, which is, how do you redesign the work, reskill the people, redeploy the people, and then continue to rebalance the work that’s done between humans and, you know, machines as those technology capabilities continue to evolve within the organization? So if folks haven’t seen something like that before, I know there are a number of other organizations who have kind of published their own playbooks for how they’re navigating this to really help share out into the market, you know, examples that might be of help for folks. So let’s… let’s take that and move into a next polling question, and really kind of get a perspective from you all. What is potentially the primary bottleneck preventing your HR team from moving forward to more strategic work? Capacity, data silos, operating model, or skills visibility?

Kyle Forrest 29:59

And just like the other two, one answer immediately kind of jumps to the top. But let’s see how that one kind of evolves in the next moment or two.

Nav Singh 30:12

And I know that, you know, data silos are 0% here. We do see some of that, maybe not directly from the HR teams, but from IT teams. We do see that as a bottleneck as well in the overall adoption. Maybe this question is specific to HR, right? But we do see that as an impediment in the overall AI adoption and journey.

Kyle Forrest 30:37

Yeah. Having spoken with a number of CHROs, Chief People Officers, HR teams, I think not surprising that capacity maybe jumped to the top. Because I think sometimes folks feel like capacity first has to change to be able to address the other three, right? Almost as a precursor, so to speak. And I also know many leaders feel like they continue to be pressured to do more with the same, or more with less, which also gets into a challenge from a capacity perspective, especially depending on where those organizations are with their AI/automation journey, and if the organizations are seeing those investments beginning to provide some of that capacity relief to their people.

Nav Singh 31:29

You’re also seeing that in many cases, CEOs are saying that they are more paranoid than ever because there is the risk of a startup just completely disrupting their work and fundamentally disrupting how things are done in their industry. So they have this vision, and many times they ask the CHROs, “What are you doing to get to that vision?” But as we are seeing in this question, moving to that vision is difficult because of all of these constraints, like rigid roles and workflows, skills visibility, not having enough visibility, and, of course, the transactional work that completely buries many times the HR leaders.

Kyle Forrest 32:10

Okay, so let’s move on from the poll here. And what I’m going to share next is going to be a little bit of an eye chart, folks, but let me just orient you to this. So this is Deloitte’s perspective, kind of across the talent lifecycle. Essentially, the black bars you see are key process areas. The boxes within them are key sub-processes. And the shading is a mapping to where an organization could see work moving into AI Powered, AI Augmented, and AI Assisted, right? So a couple of things that I always say to this slide as well: Number one, just like with the flip of the pyramid, every organization is going to aim for a different color scheme of this map because certain work that one company might want to become AI Powered, someone else says, “I don’t, because that’s some activity that is uniquely human work that we want happening at our company aligned to our business strategy and the customers that we serve.” The second, I’ve been very deliberate to say AI and automation, you know, throughout. This is not an agentic AI heat map. This is not a Gen AI heat map. There are many AI and automation capabilities that have to get brought forward, right? And I’ve seen many organizations who have underinvested in or under-optimized the HCM workflows that exist. They don’t have systems integrated, so someone on a Friday pulls down a file from one system to load it to another system, right? And to really take advantage of some of the AI and automation capabilities and get to a truly agentic workflow perspective, you need systems to be able to connect. You need the agents to be able to have access to different databases, right? To the… you know, one of the questions around fragmented data silos, because without those things in place, you’re going to be limiting the potential of what the technology investment can look like. So Nav, you know, I know this is an example of why sometimes organizations can feel overwhelmed. They look at this and be like, “There’s so much I could be doing. You know, where do I start?” So how does… you know, Eightfold team with some of your customers to identify what are some of those highest value opportunities and where you could potentially activate them?

Nav Singh 34:31

Yeah. So, and if you move to this slide… so this is where what we are seeing is, you know, as we move into this workforce of the future we were talking about, right? Agents are going to be able to do intelligent work, repetitive work, and the humans will be able to focus on work that is uniquely human. So which is about making trade-offs: how much risk should we be taking, and so on. Let me just start with an analogy here. So growing up, I saw lots of workers working on farms. So paddy fields, you know, hundreds of workers are working on it. If one person makes either a mistake or doesn’t perform the work as well as they should, the impact of that is not huge because there are many other workers there. But fast forward to today: if the same farm is being managed by, or even a larger farm is being managed by, a couple of people with mechanized equipment and AI and agents, the decisions that those couple of people are making, which are uniquely human decisions, the impact is far wider. And customers are looking at that and saying, “In this world, when the impact of human decision is far more, humans are actually more important. It’s not… the humans are not going away. And the uniquely human capabilities, I actually need to pay more attention to it. I want to see whether those skills are in my workforce, and if not, then how do I reskill my workforce?” So that’s where I think we help our customers as well. So we help customers build our shared foundation, which is skills data intelligence, so that every use case they activate then becomes a compounding effect, right? So that’s… and then we work with customers to identify the highest value opportunities. It could be across the lifecycle. It could be like AI Interviewer and hiring, like I mentioned. Or it could be enabling internal mobility, like we mentioned in the example of Salesforce. Or it could be improving workforce planning for the future. So it is really important for us to work with customers to identify a use case and then make that successful. Do pilots, clear ROI measurements, and then move on to the other use cases, right?

Kyle Forrest 36:47

And I think, Nav, one of those things that’s important with that use case identification is being clear: where within your technology stack are you aligning them? Whoops. And jumped forward a couple quickly. So this is something that I think is important for people to understand. Right on the left-hand side, you see an Experience layer, which folks are probably familiar with those concepts because that’s been around for several years, and many, many workers engage through things like Teams, or Slack, or, you know, other things from an experience like a browser, right? Depending on what type of workforce you have. Newer is the AI Orchestration layer, right? And how are you having AI capabilities orchestrated across a set of applications? Right? Because I think every technology provider is bringing various types of AI and automation capabilities forward, right? Which then gets into your Systems of Record and your Data layer. So Nav, would love to kind of have you share a little bit about, you know, as you all talk with customers from an Eightfold perspective, how Eightfold fits within kind of this architecture and how the power of Eightfold plus then some other applications really helps from an end-to-end perspective.

Nav Singh 38:08

The customers that we talk to, they don’t have a lack of technology. In fact, they have an abundance of technology, right? And in those systems, unfortunately, they were not designed to work together in a coordinated way. So if you just layer on AI on top of these isolated systems—just embed AI or try to add AI to these isolated systems—the result is actually more complexity, and people are not able to get ROI. And that’s one of the reasons we believe many of the projects fail and don’t achieve the ROI that was intended. And that’s where we come in. And you can work with an orchestration layer like Eightfold, and we come in and we say, “You don’t have to rip and replace your existing ATS, HRIS, etc. systems.” We look at all of that data, and we are the AI and orchestration layer on top of that. And then what we do is we apply AI to understand the workforce holistically to help drive actions across the talent lifecycle. And that’s why, instead of disconnected workflows, you have to get something much more seamless, where hiring informs mobility, right? Mobility informs learning, and then all of it ties back to workforce planning. So that’s where we partner with customers. And another customer example that comes to mind is Bayer. They expanded Talent Marketplace from 16,000 people to all their 100,000 people, and they integrated skills data across their entire ecosystem. And they said, “Now we’ve enabled the talent flow at scale, connecting the right people to the right opportunities regardless of traditional hierarchy.” It could be a completely different role because the same skills could be applicable in different areas. One internal example that I can take from Eightfold is one of our recruiters. Our system identified that the recruiter had capable skills like stakeholder management, process design, and maintaining relationships. We said that similar skills are required in a Customer Success Manager role. So we were able to surface that role, she transitioned into that role, and is successful in that. So that’s why at Eightfold, we say we look at potential in terms of skills: what the person is capable of doing, not just what the person has done in the past. Awesome.

Kyle Forrest 40:32

And so one of the things I want folks to kind of understand, to Nav’s point about how there’s the orchestration across, it really comes down to the expectation that organizations do not want their workers, their leaders, to have to go to 20, 30 different agents, right? And so we use this example here from an onboarding perspective to show, on the left-hand side, a new hire comes in, and they might have an agent, and it could be an “employee agent” persona or however an organization might tackle it. But it’s not the new hire saying, “I go to my benefits agent. I go to my IT agent to pick a laptop. I go to a procurement agent or real estate to kind of navigate, you know, getting a desk or other,” right? It’s just one. And as you kind of move to the right across the screen, you’ll see there’s a series of questions that typically get asked during onboarding: enrolling in benefits, taking compliance training. And you’ll see very targeted agents that sit within, right, tied to specific things like a learning provider, or a benefits module, or a third-party benefits provider, or other, right? And it is truly that ability to orchestrate across to then have that experience for the new hire be much more seamless, natural, that leads to impact in new and different ways than technology was able to deliver on before.

And that has implications as well for what does that mean for the HR team, the HR operating model, right? And so at Deloitte, we’ve also done some thinking about how do components of the operating model start to change if you say, “What is AI Assisted, Augmented, and Powered?” right? And so in the core here, in the kind of color shading of AI Assisted, is still going to be the CHRO, the HR leadership team owning, “What is that HR function strategy? What is that workforce strategy for the business?” working with business leaders to make decisions, of course, using AI capabilities to help with that decision making, with the analysis, with other things going on. Then the kind of wrapper around that: some different components, with some intentionality with not using “Business Partner” or “COE” or “Shared Services” to articulate differences between the world of today and the world of tomorrow. And those components most likely being where AI Augmentation is happening, right? The split between human and machine work maybe reaching more towards 50/50. And that workforce engagement kind of wrapping the entire thing turning into, from an AI Powered perspective… and that’s where you hear concepts that have been introduced into the market over the last several months like a “digital front door” or other things that are taking browsers and simple chatbots from maybe a search bar into a solution bar, right? The ability to do more than just search and find things. And I think it’s really important for folks to kind of have a sense of where might the world be going. And so to share then one example here of what that from/to might look like is what we kind of call the Workforce Solution Architect. And so you’ll see in the bottom left-hand corner, the skills and the type of work that are done within the COE and HR Business Partner role today might map into this role in the future, right? Because now, as a Workforce Solution Architect, I could be sitting with the business with my own AI/automation capabilities, or kind of a Workforce Solution Architect Agent supporting me, and I can work through a wide range of talent topics more real-time with the business, right? Versus today, I might go in as a Business Partner, and I might leave the meeting with a bunch of action items to go connect with multiple of my COE colleagues to then bring the answer back, right? Or if I’m a COE meeting with the business, I might have to go to the Business Partner later, or I’ve got to have multiple people show up at one go, right? So the way in which the work will occur will begin to shape or will begin to evolve, right? And Nav, I know that, like with your customers, like, you’re seeing this role evolution beginning to happen. So I’d love kind of for you to share some perspective on where you’re seeing that begin to play itself out.

Nav Singh 44:57

Let me here share an example which is more personal, which is my own team’s transformation. So I lead the marketing team, as you know, at Eightfold, about 40, 45 people, right? So I have multiple OKRs—Objectives and Key Results. One of the OKRs is to become an AI-native marketing organization. And to get there, I partner very closely with our HR Business Partner. And what we’ve done is, working with the Business Partner, we’ve done some mapping of roles as they will evolve into the future. For example, a Content Manager role is evolving to a Content Engineer role. So we were able to do that, and said that “Content Engineer, it requires XYZ skills.” We were able to reskill an existing Content Manager into a Content Engineer role. And this requires the very close partnership with the HR Business Partner, right? So we also looked at what other things we can provide to the team for that reskilling in the future. So what we’ve implemented… a few things we’ve implemented are, for example, one is monthly: we have what we call a “Marketing Prompt Call.” In that Marketing Prompt Call, 30-minute call, there are three people who come in, 10 minutes each. They showcase what they have achieved with AI, some successful projects. Then I have a weekly Agentic Office Hours where you can ask the most dumb question, the most advanced question about AI. And completely optional for the team, but still the team joins and asks the questions, and I get the answers, whether it’s from within the team or outside the team. And then weekly reports, we have a section which says, “What did you accomplish?” and including AI experiments. And one of the drop-down fields says, “The result was a failure.” So failure is not only accepted; it’s actually rewarded, because with that failure, others can learn about what not to do. We are early in the innings of AI. We need to learn from each other, right? So with all of those things being part of workforce planning, reskilling, also in their evaluation criteria for performance evaluation every 6, 12 months, all of that is something that I’m working very closely with the HR Business Partner on. And we are seeing early successes: a few people who have already transitioned into new roles. And we are seeing a lot of success in terms of people being very enthusiastic and learning from each other. Rather than being a top-down mandate, people just learn from each other and say, “If X person can achieve this using this tool, what else can we?” So this all is in addition to the tools adoption, right? Tools adoption is on one hand, and then I’m working with the HR Business Partner to make sure we have the environment, the people process redesign, to enable our AI journey.

Kyle Forrest 47:52

I think it’s back to exactly where we started at the beginning: everything that you’re doing there is not just throwing technology at people, right? You are role modeling. You are making space for people to learn to work differently, to celebrate failure, to be inspired by their peers who are working differently and sharing those learnings, right? And I think it’s that type of environment for every role, every team, every function that’s truly what’s going to be really critical. And I wanted to share one more tool that folks… this might be a helpful thing for you all to kind of take back to your teams, to your organizations, that we call a Human and Machine Blueprint. So the most important part of this slide is going to be the right-hand side, right? But we’ve created this to just give… on the left-hand side, what might the Business Partner role start to shift into? Because I will say, when I talk with many organizations and I show them something like a Workforce Solution Architect, the next question is, “Love it. How do I get there?” right? Well, step one is you’ve got to begin to evolve certain of the roles that you have today by pairing those investments in technology and the capabilities of the people to shift over time into what that future might hold. But I think what’s important here is, on the right-hand side, you can take a scenario—and I have organizations who have said they want everyone in their individual teams to do it for their role. So you can do this at the role level, individual level, team level, and start to say, “If here’s the work that I do today, in the future, what work do I still want to be done by a human, versus what work do I want to be done by a machine or AI and automation?” right? And so in this scenario, right, you still have the human owning the workforce strategy, right, in collaboration with the business, right? Well, AI and automation capabilities… and there, of which, there are multiple, right? Again, this is why we’re not just saying this is agentic or generative by itself. You need to bring the multiple capabilities together.

Kyle Forrest 55:00

Yeah. I also think many organizations—and why there’s been the demand and growth in things like Talent Marketplaces—are like, “I want to understand what are my career paths and options at organizations,” right? So the ability to go, point it there and say, “You have these skills, here’s matches to careers and jobs and roles. What’s a learning path?” you know. “How does that all come together?” So I think all of these are based on, like, demand from the workforce relative to pain points that they would really find benefit of, and we think about delivering on that benefit, right? Workers should be happier, more engaged, more productive, excited about their futures at a company, right? Which should then help contribute to driving business value as well.

HCI Moderator 55:43

Well, great. Well, we have one question here that if we can just answer briefly, and then again, if you needed an answer that extends, we can follow up afterwards. But are there any companies looking into how utilizing AI can address the burnout epidemic and increase work-life balance?

Kyle Forrest 56:03

Yeah. And I think the short answer I’ll say is yes. Longer answer that I will say is, I think organizations are still also grappling with how to navigate all that split of time back, right? If I as a worker give back or I’m eight hours more productive, who gets that, right? Is the company saying, “Okay, now you can help us do more,” or, “Worker, now you can, you know, navigate time”? But the short answer is yes. And Nav, I know you were about to say more there as well, so go ahead briefly.

Nav Singh 56:35

What I would also say is, what I’ve seen… where the burnout problem happens is that if you don’t start with the outcome in mind, and if you don’t start with the task in mind, you just say, “Just experiment,” right? And customers and employees end up saying, “Oh, AI, I can use it for this. I can use it for that.” And ultimately, that results in scope creep, that results in things that you were not going to do as part of your job, and now you think that you are able to do just because you have AI as part of the process. And that results in actually extending the hours rather than reducing. So what I always advise clients is to look at the tasks that they want… start with the outcome, define the outcome, define the metrics, and then use AI to augment that work and that task. Great.

HCI Moderator 57:20

Well, thank you very much for that answer. And we are at the end of the hour now, so unfortunately, we do have to wrap this up today, even though this has been an extremely informative and very thought-provoking, in-depth presentation. So thank you, Kyle and Nav, for that. And I also want to remind our HCI members that today’s webcast has been approved for HRCI and SHRM credit as well as for HCI recertification. Your credits for attending this webcast will soon show up in your My HCI profile, under the transcript tab. And while you’re there, don’t forget to check out hci.org for even more insights, as well as information on our certifications, live conferences, premium membership, and more. And I’d like to say just a huge thank you again for this conversation, and to the good people at Eightfold and Deloitte. And I’d like to also thank you, our webcast viewer. Thanks for spending the hour with us, and we will see you all next time. Thank you.