The pace of change isn’t slowing down, and most organizations aren’t keeping up. According to Deloitte’s 2026 Global Human Capital Trends, one-third of workers experienced 15 or more major changes in the past year, yet only 27% of organizations believe they manage change effectively. The result is a workforce stretched thin, disengaged, and at risk of falling behind.
The organizations pulling ahead aren’t managing change better. They’re building systems that make adaptability part of how work gets done every day.
Watch this executive conversation between Eightfold AI and Deloitte on moving from reactive change management to dynamic workforce orchestration. We have explored how leading organizations are rethinking how they access, deploy, and develop talent, and how AI is shifting from a back-office automation tool to an exponential multiplier of human performance.
In this session, we have covered:
Mahima Sharma and Sue Cantrell discussed the 2026 human capital trends, highlighting the shift from static planning to dynamic orchestration driven by AI. The study, based on data from 9,000 respondents in 76 countries, revealed a significant gap between leaders’ awareness of trends and their operationalization. Key trends include the expansion of HR’s scope, the impact of AI on workforce planning, and the need for continuous adaptiveness. Examples like Levi Strauss and Deutsche Telekom illustrated the benefits of orchestration and adaptiveness. The discussion emphasized the importance of integrating AI with human capabilities to enhance organizational agility and efficiency.
Mahima Sharma 00:09
Thank you so much. As the introduction says, I am Mahima Sharma, Senior Director HR for R&D at Eightfold, and we’re very happy to be behind this session. Before we get started, a quick word on the “why” behind this session: all of us spend our days with talent teams trying to actually operationalize ideas like the ones in this report. So an hour of this research firsthand from someone who wrote it is a good use of everybody’s time. I’m excited to have Sue Cantrell join us today, who leads human capital eminence at Deloitte. She has spent about 25 years on the future of work, and she’s an author of both of the trends we are covering today. What you’re getting today is firsthand, not somebody’s summary of a summary.
Moving on to the next slide. Sue, before we get into the trends themselves, this is the 15th year of the study, with over 9,000 respondents in 76 countries. My understanding is you deliberately survey workers and managers as well to catch where leaders and their people see things differently. So I want to ask you two things upfront: when you put this year’s data next to last year’s, what actually moved? And when you step back from the nine trends, what do they add up to?
Sue Cantrell 01:45
Well, first of all, thank you so much for having me. It’s a pleasure to do this with you today, Mahima, and we always enjoy our partnership with Eightfold.
Yes, so you can see some of the data on the slides here. This is a massive report we do every year; we’re actually the largest longitudinal research study on human capital globally, spanning all these countries. In addition to surveys with about 9,000 respondents, we interviewed a lot of organizations across different levels—managers and CXOs—with over 50 interviews. As you mentioned, it’s really grounded in what’s happening today.
To answer your question, Mahima: what has changed from last year to this year? There are a couple of things I might mention. One thing that has not moved—and let me start with that—is what we call, in some ways jokingly, the “knowing versus doing” gap. Generally speaking—and this should come as no surprise—leaders and organizations often know about these trends and know they are important. Almost every year, we get high responses indicating “yes, this is a critical thing for our competitive success.” But where the gap lies is in the doing, or as you said, in the operationalization of it. There is usually a big gap: about 85% say it’s important, but usually only around 8% are really moving the dial on a specific trend. That is what has not changed.
What has moved is that we ask a few of the same survey questions every year. One interesting change we found from last year to this year comes from asking about the scope of HR as a function. The exact question we ask is: “To what extent has the HR function expanded its scope and influence over the last three years to materially impact how work is done?” We are seeing, year over year (and from last year to this year in particular), a radical expansion of HR’s scope. We’ll talk about why today, but in many respects, a lot of it is due to AI. HR is a cross-functional function; HR has to lean into redesigning roles, redesigning work, and reskilling folks for an AI-enabled world. So that’s what has changed most importantly.
Okay, let’s move on to introducing the 2026 trends. This is our full set of trends. We’re not going to go through all of them today—we actually have eight trends, but we’re going to focus on two (the ones labeled in green for this session). Those two are focused on keeping up with the pace of change and how to unleash organizational agility by being nimble. We’re going to talk today about how HR can help workers stay relevant in a world that will not sit still. We know the pace of change is increasing, but we really need to reinvent traditional change management, training, and learning practices so that adaptiveness becomes a daily muscle embedded in the flow of work.
We’re also going to talk about how organizations can fluidly orchestrate capability and capacity at speed. When I say capability and capacity: capability is the ability for humans or AI to do things (how effectively they can get things done), and capacity is how much they can get done. We’re going to zero in on those two trends today, but I encourage people to check out the full trends report. I’ll give a super brief overview of the other trends, and we’ll have a link or QR code at the end of the session for you to check out the report.
Primarily, these focus on the impact of AI on organizations and the workforce, including the need to stay nimble and adapt. On the left, we look at intentionally designing how workers work with AI—we call it getting human-and-machine relationships right. In reality, we often design human-to-human relationships and are increasingly designing machine-to-machine workflows, yet rarely are we designing for people and technology together. We typically do them separately, so that’s what that trend is about.
Sue Cantrell 06:47
Fact or fabrication: AI is blurring the line between what is fact and what is fabrication, or what is real. It makes it harder to trust what is real at work. Just one example is that more than half of new web articles were generated primarily by AI. When it comes to workers, a third say they use AI to embellish their personal profiles and resumes. So, what is real in today’s world? We tackle that.
Then there’s AI and the future of decision-making: who’s accountable when both AI and humans are making decisions?
Next, we go into what we call cultural debt. In reality, AI is changing the way people interact with one another, not just how they interact with technology. It’s often silently changing culture in negative ways. So we talk about cultural debt, which is the compounding cost of neglected culture when it starts to drift in unexpected ways due to AI—a bit like financial debt accruing interest.
On the right, we talk about how organizational functions are evolving and ask: have they outlived their function? Functions are under pressure to reduce costs and collaborate across other functions, so we ask if it’s time to reinvent them, and how.
Finally, we close with an epilogue primarily written for boards, where we explore how boards need to think not just about business outcomes, but about how every choice becomes a societal choice that echoes into communities, families, and labor markets, impacting civic trust, not just balance sheets.
That’s a quick overview, and I’m excited to talk to you today about these two green trends regarding change and adaptiveness.
Mahima Sharma 08:45
Yeah, and that was going to be my question, Sue. We’ve particularly chosen orchestration and adaptiveness as the two topics. Looking at it from my lens, it looks like it’s the same problem viewed from two different ends. Tell me, why were these two particularly chosen, and what are your thoughts on it?
Sue Cantrell 09:06
Yeah, absolutely. They both tackle the pressing need for speed and agility, which our trends research found is becoming an increasing pressure on leaders today. Orchestration—you’re right—is kind of two sides of the same thing. Orchestration is really about being able to move resources (people, AI) to where they’re needed as business conditions change. The second one—helping people stay relevant in a world that won’t sit still—is less about moving people around and more about helping people continuously learn and adapt to change wherever they sit in the organization or wherever they’re working. So, very good point.
Before we get into those two trends, I might mention one of the big themes running throughout our entire Human Capital Trends report: what we call the “human × machine” difference. We talk about this very intentionally because a lot of people think about “humans + machines,” where humans work in parallel with AI, but not necessarily together. When we put humans working iteratively together with intention and skill, working back and forth, that’s “human × machines.” We found that makes an enormous difference in outcomes. It’s really that human edge—not just tech investment—that enables organizations to continue to innovate and jump those S-curves that are getting compressed over time.
One interesting data point we have is that organizations typically spend 93% of their AI transformation budget on tech and only 7% on people and organization. We need to make that a more balanced equation. Our research shows that when you even it out a bit, you get better business outcomes.
So, shall we jump into our first trend? The orchestration advantage.
Typically, the way we think in organizations is through planning: we plan, lock the plan, and then execute the plan—whether that’s workforce planning, strategic planning, or what have you. But the issue is that those plan-lock-execute strategies can no longer keep pace. What do we do instead? When the only thing known about the future is that it’s unknown, you shift from static plans to dynamic orchestration of resources—both humans and AI/machines. When you think about it, AI in a lot of ways is completely rewiring the organization’s nervous system. It enables capabilities and capacity to be ported to different areas as needed so workflows can flex and adapt in the moment.
Orchestrating talent, skills, and AI to deliver outcomes means continuously sensing where they’re needed, then assembling, reassembling, and recombining the elements as needs evolve. We use the word “orchestration” very intentionally. It’s like the analogy of a conductor: resource allocation might assign a musician a part, but orchestration enables organizations to adjust in real time.
Before I go further, let me give a real company example of what this means. One of my favorite examples is Levi Strauss. They wanted to jump that S-curve and boost sales—a target many of us have. They ported resources from across functions to create a cross-functional team across marketing, sales, product design, and other areas, and they added AI to the team. So it was a human, cross-functional, AI-enabled team. First, they sensed customer demand and found a trend toward baggy jeans. (I have three teenage boys, and all they wear is baggy jeans!) They sensed this customer demand, and then cross-functionally, together with AI, they designed, delivered, and marketed them in three months. That created a 15% sales boost in three months. That’s what I mean by orchestration of skills and AI.
Mahima Sharma 14:11
I love the analogy around the conductor, and I want to push on it a little bit. A conductor can hear every section of the orchestra at once and is reading from the score. When you sit with organizations trying to move from allocation to orchestration, do they have anything like that score? Where does this analogy start to break down in a real company?
Sue Cantrell 14:39
You’re right to push on it, Mahima. In some ways, a “score” could be outcomes—that is the guiding force. For Levi Strauss, their outcome was creating a big uplift in sales within a very short timeframe, and that was the guiding force that brought everybody together. That created the symphony or ability to work in harmony because everybody was on the same path.
Where the analogy breaks down is that it’s never a standard score you rehearse over and over again. It’s more like jazz improvisation, where people come together and adapt in real time.
I mentioned the knowing versus doing gap earlier. Here are some stats specific to the orchestration advantage: 88% say that it’s really important to orchestrate capability and capacity at speed—that was the highest-ranked item across all our trends this year. Yet, only 7% say they’re making great progress. The good news is 77% are doing something and trying to move in this direction.
A couple of other interesting stats on the right: I used the word “resource” because it could be humans or AI, yet only 51% of leaders look at both humans and AI when thinking about workforce composition or workforce planning. Only 11% of managers strongly agree that their organization provides them with the right data and tools to make good decisions about allocating and distributing work and deciding who does what. There is work to be done here.
Mahima Sharma 16:34
On this slide, Sue, I would like to sit on that 11% for a second. If anyone listening writes down one number today, this should be it: only 11% of managers strongly agree that they have the data and tools to make good decisions about who does what.
I don’t think that’s a willingness problem. Every manager I talk to right now wants to make that call well. They are currently making decisions from memory, headcount reports, or whatever else they can get. The good news hiding behind a bad-looking slide is that this gap is addressable. You can close the data and tooling gap.
An orchestration strategy set in the boardroom does not reach the actual work until the manager on a Tuesday morning can see more than they can see today. For example, NTT DATA was hiring externally for roles that internal people could fill, simply because they had no view of in-house skills. As soon as they had that visibility, they reported a 52% improvement in internal placement and a 128% increase in self-nomination. That’s a great example and good data to share.
Sue Cantrell 18:10
I love that, Mahima. It’s so important. In fact, our research showed that organizations leading the way (like the example you provided) are about twice as likely as their peers to report both better financial results and more meaningful work for workers.
I mentioned how important speed and agility are, and here is data to back it up: 67% of leaders say their primary competitive advantage over the next three years will come from being fast and nimble, whereas only 28% believe scale will be their main differentiator. Scale is never going away, but this need to be fast and nimble is increasingly what differentiates organizations.
A fun side note from our research: with AI, we are increasingly able to upend the traditional speed-quality-cost triangle. Traditionally, you could only pick two out of three: speed and cost (without quality) or speed and quality (without low cost). AI is starting to bend this matrix so that, for the first time, we can achieve all three simultaneously.
Mahima Sharma 19:50
On the previous slide, there was one thing, Sue, that I wanted to talk about: this quietly redraws the org chart.
Mahima Sharma 20:00
The advantage doesn’t come from what you own. The workforce isn’t just the headcount you’re paying for. When talking to CHROs, should they be planning for the people they employ, or for everyone doing the work? Those are two very different planning exercises, and most CHROs currently come from the first mindset. That’s an interesting split you talked about, and I love the slide on the speed-quality-cost triangle. It’s one of the most important conversations everyone is trying to have right now. We can jump to some of the critical actions you wanted to share as well.
Sue Cantrell 20:42
I love that you brought that up, Mahima. Increasingly, we see organizations planning for everyone doing work—whether contingent workers, outsourcing partners, or AI. Even with tokens, a big conversation we’re having recently is: are we planning for the total cost of work? Tokens are a cost we have to plan for. This is why we see functional areas coming together to orchestrate.
How do you orchestrate? First, you need to identify where capability and capacity exist in the organization, and sometimes create them in new ways.
Traditionally, we think about the “Four Bs” when identifying capability and capacity:
There are extenders or multipliers as well:
Mahima Sharma 23:13
You’ve covered most of them, but I’d like to chime in on Bridge and Boost. Both depend on reading adjacent skills—not what someone’s title says they’ve done, but what they could credibly do next. Keyword and resume matching cannot see that, which is why Bridge and Boost stayed aspirational at many organizations while Build and Buy got all the money. Sue, your examples help illustrate this well, so let me turn it back to you.
Sue Cantrell 24:00
Great point. Earlier, I mentioned needing to orchestrate across functional areas to make decisions: right people, right decision, right time. Who makes orchestration decisions? Tech leaders, HR leaders, finance, procurement (for external workers), and business strategy/leaders all need to come together.
Walmart exemplifies this: they created a cross-functional team composed of senior leaders from all those areas, intentionally designed to orchestrate skills, work design, strategic workforce planning, job architecture, org design, and human/machine performance optimization.
Hewlett Packard Enterprise is another good example: it recently integrated its strategic workforce planning capability with its org design teams. That joint team works closely with finance, business ops, and IT to figure out how roles can be automated or augmented.
Mahima Sharma 25:38
Beautiful example. I’d like to share an example from Eightfold’s perspective with Softtek, a global software engineering firm. Before Eightfold, their talent team worked in silos. Now, 90% of their workforce is integrated into one platform: their time-to-candidate improved by 25%, and time-to-fill dropped by 30%.
David Rafael, their Chief Talent Officer, doesn’t describe the win merely as recruiting speed. He describes it as having the right ecosystem to integrate talent acquisition with real insights for learning and development teams. Two functions that solved the same problems from opposite ends of the building are now working off the same picture with Eightfold. That represents the shift from allocation (one team filling one role) to orchestration (the whole system seeing the same talent at the same time and deciding together where it should go).
With that, let’s jump to the next slide on cost efficiency, Sue.
Sue Cantrell 27:11
Love that, Mahima. To finish up on “right people, right decision, right time”: in addition to senior leadership, we must empower managers. Very few managers currently have the data and tools to distribute work properly.
Seagate overcame this with technology: they have a platform where people type in the work they need done and their objectives. The technology surfaces relevant workstreams, identifies AI agents that can help, and shows internal/external people with the right skills who can be borrowed.
To cover the third and fourth points on this slide: when moving people around, plugging into new areas can be difficult. We can borrow the concept of modularity from product design—creating plug-and-play interfaces to integrate people into new teams seamlessly. You do this by establishing a shared mission based on outcomes rather than outputs. AI can also create a shared body of knowledge to get people up to speed quickly. (I interviewed one leader who trained AI agents on different stakeholder personas—CHRO, CFO, CIO—and had them dialogue with each other so he could learn their context and language before meetings!)
Finally, using AI to orchestrate capabilities and capacity: digital twins of the organization and workforce are now available. These live, AI-powered models let organizations simulate scenarios, such as the effects of increased AI investment, changes in outsourcing, or shifts in location strategy. About half of leaders say organizational digital twins will be critical to success in the next three years. AI agents can also monitor signals for shifts—such as potential skill attrition or labor supply changes—and prompt leaders to adjust workforce plans or redesign work.
To conclude this trend: leaders traditionally focused on the best ways to organize resources through formal structures. Now, the question is how fast we can move resources around using adaptive orchestration. In an ideal world, human × machine teams work iteratively toward shared outcomes, roles become flexible skill sets, and people have agency.
Mahima Sharma 32:03
With technology, tools often arrive and people use them, but 18 months later, nobody knows what changed. Eightfold Workforce Readiness gives CHROs defensible metrics tracked quarter-over-quarter:
Regarding cost efficiency: for one of our global biotech customers, cost efficiency and value creation stopped being a trade-off. They saw a 30% increase in internal mobility and avoided millions of dollars in potential severance costs by redeploying people instead of exiting them. You retain internal context and avoid severance expenses simultaneously.
With that, let’s jump into our first poll question.
When you plan your organization, what is the primary unit you reason about?
Mahima Sharma 35:12
While we wait for answers: Sue, you’ve written directly on the move from jobs to skills to outcomes. When you’re with Deloitte clients, where are they really? I suspect there’s a gap between what organizations say their unit of analysis is and what they can genuinely plan against on a Monday morning.
Sue Cantrell 35:12
It’s such a good question. Realistically, most organizations are never going to completely let go of jobs—jobs work well for stable organizations with work that doesn’t change often. Clean job architecture provides a strong foundation to layer skills on top of.
These choices are not mutually exclusive; there is room for all three. You can layer skills and tasks on top of jobs for a nuanced approach. However, task-based approaches are often backward-looking rather than forward-looking. Increasingly, organizations are moving toward broader definitions of work around outcomes and human capabilities (like critical thinking and communication), which allows for flexible adaptation.
Mahima Sharma 37:05
I agree. Skills are the operational unit today, outcomes are where we are heading, and jobs are how current systems are built. That’s not a strategy failure; it’s what a transition looks like.
Let’s move to the second half of our session: whether people can adapt at the pace the organization moves.
Sue Cantrell 38:36
This is about helping people develop adaptive capacity to continually learn and evolve, rather than just moving people around. The knowing-versus-doing gap is stark here too: 85% say adaptiveness is important, but only 7% are making progress.
Traditional top-down change management (pushing communications and training as a separate bolt-on exercise) no longer works when change is continuous. AI is upending both change management and training to make adaptiveness an ongoing muscle.
Mahima Sharma 39:35
The metrics here mirror orchestration (88% to 7% vs. 85% to 7%). When the same gap appears across two trends, it points to a shared root cause. Orchestration needs a live picture of what people can do to route work; adaptiveness needs that same picture to route development.
Most organizations fund these as two separate programs competing for budget, while starving for the same missing input. If you take one thing away today, it’s that these two concepts rely on the exact same core data.
Sue Cantrell 40:48
Spot on, Mahima. Only 27% of leaders feel they manage change effectively, and only 8% believe their organization meets continuous learning needs. A third of workers experienced over 15 major changes last year! Uncoordinated change leads to burnout and reduced engagement. But when organizations adopt an adaptive approach, they see better financial results and more meaningful work. We need to enable individuals to sense, respond, and grow in real time in the flow of work.
Mahima Sharma 42:27
To give an example: Deutsche Telekom had dozens of subsidiaries running separate staffing tools, making talent across companies invisible. Someone two countries away with the exact right skills sat underutilized while the company hired externally.
A simple test for operations: does the change signal reach people inside the tools they already use, or does it live in another portal they have to remember to log into? Integration into daily workflow is critical.
Sue Cantrell 43:41
100%. The top changes workers experience are changes in the work itself and changes in required skills.
Mahima Sharma 44:19
Those top two are really one event described twice. Do workers experience them as one thing or as separate issues?
Sue Cantrell 44:46
Unfortunately, they experience them as separate because role redesign usually happens first, followed later by a separate reskilling program. We need to close that gap.
To build adaptiveness, we recommend creating a surround sound system—borrowing from omni-channel marketing to surround workers with experiences in the flow of work:
Mahima Sharma 46:44
Underneath all surround-sound experiences, the system must know what people can actually do right now.
Sue Cantrell 49:54
Other key approaches include hyper-personalizing change and learning at the unit of one:
Finally, establish continuous feedback loops and empower workers to sense and respond to changes directly.
Mahima Sharma 52:03
Appetite isn’t the constraint—trust is. People move at the speed of trust. They want to know what the system monitors, where data is stored, and how it’s used. Hold your vendors accountable: Eightfold publishes bias results and is ISO-certified and FedRAMP-authorized. Transparency builds lasting willingness.
Sue, given the time, let’s look at the final resources.
Sue Cantrell 53:49
Here are QR codes for our Human Capital Trends report, our AI adaptation article, our six-part workforce planning series, and our 2027 trends survey. We welcome your input!
Mahima Sharma 54:28
I encourage everyone to read our customer case studies on Deutsche Telekom, Softtek, and STMicroelectronics to see how this works inside real organizations. Thank you, Sue—this was a fabulous conversation!
HCI Moderator 55:11
Thank you, Sue and Mahima, for a fantastic presentation, and thank you to Eightfold and our viewers. Today’s webcast is approved for HRCI, SHRM, and HCI recertification credits. Check your My HCI profile and visit hci.org for more insights. Have a great afternoon, everyone!
By submitting this form, I consent to Eightfold processing my personal data in accordance with its Privacy Notice and agree to receive marketing emails from Eightfold about its products and events. I acknowledge that I can unsubscribe or update my preferences at any time.
