Hi both, thanks for joining. Really interested to get into the topic. Just from my perspective, thinking about AI and reward, what I’ve really not seen clients lean toward is wanting to transform their whole HR or reward function through AI. I’m certainly seeing more of a desire to add value — or unlock value — from the foundations of reward elements: job architecture, job levelling, skills, benchmarking, all of that good stuff. And clients are really taking a moment to pause and reflect, and maybe conclude that what they have in place at the moment isn’t really working for them. Maybe it was built for stability, and when things are changing so much, that doesn’t really work anymore. It’s not necessarily about just introducing new tools, and everyone is on a bit of a journey with this — either dipping their toe in or really pushing ahead. So, Ruchi, I’m going to come to you first. We have always seen real value in thinking about the foundations: levelling, architecture, skills. How are you seeing AI play into that? What are our clients doing, and how are they prioritizing?
RUCHI ARORA: The first place I’ve seen organizations move to — or think about introducing AI — is in the foundations, and what I would define as tasks in reward that take the most amount of time and are repetitive. As you mentioned, job levelling with AI — and I hope everyone has seen and heard about our tool by now — one of our clients said it is saving them 90 percent of job evaluation time. That is a significant proportion of time saved. Job architecture is another area where we’re seeing organizations wanting to refresh or evolve their frameworks to be ready for the next generation of organizational design. Job architecture health checks are really helpful: you can put your existing job architecture into our tool and it gives you insights and recommendations quite quickly, so you can make changes swiftly. Job architecture projects are not necessarily the long, resource-intensive projects they used to be. And to design new job architecture, AI is being used to identify what the typical job families look like in the market, what the skills are for those jobs, how skills are defined, what the accountabilities are. No one is starting from scratch anymore — we’re getting AI to give us that first pass, and that is really helpful because it gives you a market view to take into your organization..
If I look at some of the other areas where organizations are really using AI, it’s survey submissions, benchmarking and pay structure design. I’m still surprised by the number of organizations I speak to who say they have spreadsheets full of pay structure modelling — the risk that holds is quite high. Using AI and technology to streamline those processes is probably the most obvious area where reward is investing right now. Interestingly, I’ve been talking to clients who are saying they now need to get to the next level of detail. Those are the obvious places where AI can be applied — but what comes next? I’ll come on to that as the conversation evolves..
CHARLOTTE WHEELER: What you’re mentioning there is really interesting, because some of those are actually quite small steps. It’s not the huge leap that some organizations are concerned about taking..
RUCHI ARORA: Exactly — and there’s a spectrum. Some organizations haven’t done anything, so even a small first step feels significant. Others have already done that and are ready to think about what’s next. It really depends on where you are in terms of readiness and adoption. There’s no right or wrong here — everyone is learning. We’re in a new phase of this..
JASON KARMODY: I just want to build on that. I think meeting the organization where it is is also really critical. When I was in-house as an HR Director, the question was always: which function needs the most support? Where are we seeing the most demand on job levelling or benchmarking? Or if we’re doing some M&A or launching a new product or going to a new market — well, then we have to pivot our resources, and the time saving is really multiplied at that point in time. So there is a big part to play in terms of where we meet the organization internally..
RUCHI ARORA: Absolutely. And the one thing I would add — we cannot have this conversation without using the word agents. Every organization, every reward team I meet, is wanting to build an agent. We are as well. But I think you have to build agents with intent. There are lots of dos and don’ts, and that’s something to watch carefully.
JASON KARMODY: Just to add to that — I think the secret agent is actually the reward team itself. Part of what AI enables us to do as reward professionals is to come out of the shadows and be more frontline in some cases, partnering differently with business leaders to inform them about benchmarking, about the how behind reward decisions, about how they can be future-proofing their hiring plans and their workforce plans. The reward team is another secret agent in the mix..
CHARLOTTE WHEELER: I think you’ve both been talking about how AI is being integrated, but also you’re focusing a lot on the human in the loop and the interactions. Jason, based on your in-house experience and now working with us, where do you see human involvement in the AI process as being fundamental?.
JASON KARMODY: Three things come to mind. First, humans will still be necessary at the next stage of evolution in technology — AI is just one step. If we think about the advent of the internet, reward professionals could then gather information online, look at competitors’ job postings. The internet unlocked so much value for organizations and leaders, but it didn’t mean you would just copy and paste what you found there. It didn’t mean you would self-serve and hope for the best. There has to be human judgment in the mix..
Second, the employee experience and the manager connection remain critical. When we look at teams inside organizations using AI outputs — a reward statement, or our auto job levelling solution, for example — we need to communicate person to person internally. Especially in global organizations, our clients are navigating culture, language and time zone. AI is helpful, but it doesn’t bridge all of that entirely..
And the last piece — which is critical when it comes to the growth agenda for organizations — is: who is making the decisions? AI should be informing us but not deciding for us. A strong compensation philosophy, a strong compensation agenda and a strong directive from the business will really drive those decisions. AI is just there to inform. Humans are still necessary, as far as I’m concerned..
CHARLOTTE WHEELER: I’m pleased to hear it. We’ve all been really fortunate to speak with organizations that are pushing ahead with AI, as well as those that are dipping their toe in and beginning to think about it. For organizations that want to learn from who is doing this really well, Jason — what are forward-focused organizations doing to prepare themselves?.
JASON KARMODY: To be honest, in some ways it’s less about the tooling, the AI, the technology. I’m a technologist at heart and I love technology — but it’s about the decision-making frameworks and the governance behind it. I had a conversation with a client this morning and they were talking about implementing an AI solution as a no-brainer, but their concern was about the access the tool would have to their compensation repository, about colleagues wondering who made the decision about their pay bands, about the remuneration committee at board level wondering how precise everything was and whether the AI had hallucinated. The brightest minds in the organizations we work with are really looking at: what is the governance, what is the framework and what are the priorities? Where do we start and where do we want to evolve to? There are so many ways to leverage these tools, and the sharpest organizations are really asking the how and the why. Why are we doing this? AI is a great topic — but the reason to do it is to unlock additional value..
RUCHI ARORA: Yes — and is it efficiency? Is it an experience you want to enhance? Is it saving cost? Defining that purpose is really important..
CHARLOTTE WHEELER: It is so interesting to hear what the very best organizations are doing. I do think there is value in talking about how other organizations have started their journey, and what sticking points they’ve found. Ruchi, have you been working with organizations where they’ve shared those challenges?.
RUCHI ARORA: Yes. I’ve spoken to many organizations who simply don’t know where to start. And that’s where the frameworks we’ve been sharing come in — you’ve really got to think about what the use cases are that you want to apply AI to, and what outcome you’re trying to achieve. Is it cost saving? Is it efficiency? Is it time? Is it a better experience? There are so many different reasons why an organization would want to use AI. The sticking points I would identify are around capability — by which I mean internal knowledge about how AI can be used. There’s a lot of experimentation and piloting going on. The narrative in 2025 was about experimenting. Now, organizations are looking for a return in 2026 on those pilots and starting to embed AI more for the long term. I would argue we are still quite early on that journey and there is still more experimentation to come — but the pressure to move from pilots to embedded practice is real..
CHARLOTTE WHEELER: We were having those exact conversations with clients at events in 2025 — that you’d be experimenting in 2025 and ready to launch in 2026. I don’t think all organizations are there yet. And your point about return on investment is important — this is about impact, not doing it for the sake of it. Jason, what do you think is going to create the biggest impact for reward leaders getting the most out of AI in the future — and you can define the future as one year, three years or five years?.
JASON KARMODY: It might be a controversial take, but I actually think it’s about the infusion of AI into reward strategy and compensation philosophy. If we think big picture — and that’s one of the pleasures we get as consultants working with clients — how do we step back? The day-to-day demands are always there: a compensation request, mediating a candidate discussion, whatever it may be. But how do we actually say that AI is going to enable time savings, drive cost evaluation and also change the shape of our workforce? What roles are we looking for and how are we pricing roles — like the machine learning engineers we didn’t necessarily have a few years ago but critically need? Fusing AI into how we envision compensation at a leading organization is critical. And I think some of the brightest clients we’re working with are also thinking about what they are not doing with AI — because sometimes we’ve looked at AI as a one-stop shop. There are things it maybe shouldn’t be leveraged for, where we’d rather use a task force, run a pilot or trust a partner. AI has certain use cases and those need to be built into the philosophy and the strategy..
RUCHI ARORA: Jason, I want to add to that, because I think that second point is really important. Many reward and HR teams are thinking about how to use AI within their own teams — to be better or to improve processes. But actually there is a huge opportunity for reward teams to partner with the business and help leaders think strategically about how AI is going to impact the organization: what new jobs are needed, how jobs will change, how that impacts skills, what levels those roles would sit at, how work and jobs will be defined going forward, where AI will augment work and where we need to upskill or reskill. It starts so many conversations that reward professionals know a great deal about. Yes, it’s exciting to improve processes and core foundations — but the even more exciting opportunity is to partner with the business to help them think about how AI is going to shift the people agenda in a completely different way..
CHARLOTTE WHEELER: Can I put you on the spot for our last question. If you had one recommendation for HR and reward leaders getting themselves ready for AI — whatever that means for them — what would it be?.
RUCHI ARORA: The experimentation piece is key. Experience it, play around with it, see what benefits you get. Don’t stay away from it out of fear — but do it in a way that is well-governed, ethical and works within your organizational culture and boundaries. Do it thoughtfully, but get on with it quickly. Don’t get left behind.
JASON KARMODY: And there are really quick ways to act on that. It can be a workshop. It can be setting up a task force. There are so many quick wins available — for reward professionals, for business leaders and for colleagues who may have concerns about AI. And you know we’re only a conversation away..
RUCHI ARORA: Can I add one more point before we finish? One thing we haven’t talked about is the power that AI gives leaders, reward teams and HR teams — the power to analyze and use data insights. How do we use insights on people and on reward to help business leaders make the right decisions when they’re thinking about AI or about how their organization is changing? That data insight piece is going to be hugely valuable and important in the future..
CHARLOTTE WHEELER: Thank you. I know we could talk about this for a lot longer, but I’m going to wrap up there. I’ve taken so much from this conversation: the importance of making sure AI is integrated and tactical, that it aligns with your strategy and isn’t being used for the sake of it, and that it needs to be embedded with people. I didn’t expect the people element to come up so much in a conversation focused on AI. Thanks so much for joining us and please do check out the rest of our Turbocharging HR series. Thanks everyone..
