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What was it that set IBM on the path to include skills in compensation decisions?

Let me start with the external factor what we were experiencing. And what we were seeing was the emergence of new technologies that was redefining the talent needs. We were seeing new professions emerging, higher skill levels needed for those new professions. That was a disruption we were seeing in the industry that was helping us understand the need for skills.

Second thing that was happening within IBM was we were changing how we pay for performance. We were making a fundamental shift in our pay philosophy. We were moving away from considering performance as the only factor to consider when looking at salary decisions. And we were moving towards using multiple factors.

And we believed that to assess performance, we don't need to necessarily use base pay, but we can use a variable pay program. So that shift we were also experiencing internally. And the final thing was we felt that our employees were demanding greater pay transparency, and we needed to make sure that as we talk about base pay, we needed to explain to our employees what could they do in order to have a more competitive base pay.

This is part of a larger shift we undertook at IBM. We knew that the half life of skills was five years, so half of what's learned gets obsolete or forgotten in five years. And so we really needed to rethink how we embed skills across the employee journey, all the way from who we hire, how our employees learn, how we grow people in terms of mentoring and other experiential learning promotions. And pay was just a natural evolution of that journey, as well embedding skills across the lifecycle.

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Who were the critical stakeholders that you had to get on board, and I'm sure our listeners would appreciate hearing how did you build a business case to make this type of change happen?

So like all good ideas, they're best when they come from somebody in the field, and this was actually not an idea that germinated from our corporate headquarters or some senior leader. It was actually a practitioner in the field. Actually, somebody in compensation out in one of the markets who saw the need, and the need that the practitioners saw was, how do I help managers make much more fact-based decisions?

Managers still have the final decision rights, but how do we equip them with the information so that they can make the right decisions. And if you think about a company like IBM, 350,000 employees in multiple business segments from software to services to hardware, and you take a manager in a market. How can he or she better understand what's happening in another city, in similar skills?

How do they see the more macro lens at their fingertips? Otherwise, he or she is just seeing their own world view of their 10 employees and making decisions based on that. But if you can bring the information of 350,000 IBMers, as well as what's happening in the wider industry at their fingertips so that they're making fact-based decisions. That was the idea of this compensation practitioner.

And so when we started, we didn't just go whole hog and deploy this worldwide. We started with a very small pilot of 30 managers in the small market where we beta tested this concept. And obviously, to take it to the next level, we needed investments. This is one of those areas where actually the investment case is a no brainer.

For any company of our size, compensation spend is such a huge portion of our expense that even if we spend our money 1% better in terms of efficiency as well as better quality outcomes in terms of retention of key skills, the payback is huge. So this is a win-win for the company, for IBM in terms of business, but also for the [? IBMer ?] because they transparently can understand what they're being rewarded with. What pay investments are getting, as well as for managers who can now allocate investments in the right areas.

Very interesting.

[INAUDIBLE] add to that, Jean. I mean, I'm in compensation, and I often hear from managers or business leaders. They're saying, look, I want to pay everyone competitively. That's our goal, but I also want to differentiate. So I want to make sure that my investment is not only going to the right people but even the increases are in the right size.

So I think that was something that, initially, when we didn't have the AI tool, we were hoping the managers were able to do that differentiation.

But I think with the help of the AI tool, we were able to help put together the recommendation, and that saved managers a lot of time as well. They don't want to spend a lot of time on HR transactions.

So we were able to meet both the goals. We were able to show the business leaders that here is our recommendation, which helps us or helps your businesses to differentiate as well as pay competitively where we think we have those gaps. And at the same time, your managers don't have to spend hours and hours doing those analysis themselves.

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What were the short, medium, long-term impacts that you saw from moving to pay for skills? How did it affect recruiting say, your retention? Did it change the culture or the demographics of the workforce?

So certainly, Jean. A variety of different aspects of business impact. The primary one being, retention of key skills. Did we see that? And we're definitely seeing that. We see that when managers follow the recommendations, the retention of their teams is one third higher than when they don't.

So we're talking a significant-- it's not a 5% improvement, it's a one third, 33% improvement in retention of key skills. So that's huge in terms of retaining our key talent. And making sure that our talent is paid competitively.

Then obviously, there are broader market shifts occurring with transparency and Binny talked about that. This helps us get ahead of that in terms of pay equity and other aspects that are-- there are broader compliance shifts as well as-- being thought leaders in the space. So this brings us much closer to setting the standards and the bar by which we lay out our pay philosophy and are seen as market leaders in the space.

And then finally, our own HR organizations, as I said earlier, we have been on a journey to move away from administrative work to more value-added work. This helps our HR practitioners-- are freed up, get freed up from that administrative work and focus on more higher value work.

This actually helps the practitioners as well, because then they can get upbanded and promoted. We have fewer number of those roles but they're doing higher value work. And that's a significant part of their journey, as well.

Between short term and medium term, that was clearly-- we saw the shift in people's thinking. And they started asking, OK, what do I do and what skill set am I in? So a lot more conversation around skills started happening when we introduced the skill-based compensation.

And I think long term, some of the things that Anshul has talked about, it has clearly helped us to reduce attrition of our high scarcity skills because we do see that the market pays differently. And there is a differentiation in pay for them. So that really has helped us to look at that group and make sure that we are not lagging the market. And we are able to prioritize investments in those areas.

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One of the other question is whether pay-for-skills can be applied in roles that are much more centered on soft skills, such as communications or leadership. When you had rolled out the system, did the program bring an actual pay change across all the job families in your organization or were there certain jobs for which managers noticed a significant change more so than other job families because of pay-for-skills?

So certainly, we rolled this out enterprise-wide. So this is applied enterprise-wide across all job families and skills. And ultimately, we do see differences wherever it shows up in our data. So we're not-- it's not like we're going and asking people, all right. You're a subject matter expert. Tell us whether the skill is high scarcity or not.

We infer whether a skill is high scarcity or not by looking at things as like our time to hire, the offer rejection ratio on the hire, the new hire salary of the hire, as well as the number of jobs in the market for that particular job category. So as long as you have differentiation along any of these data points, it automatically gets picked up in our data.

Having said that, I do think the point that you make, Jean, is a natural one, and it's easier for us to have a conversation around more technical jobs and validate those. On the softer skills, we do see less differentiation, but we do see differentiation in those job categories as well.

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How did employees react to this transition? I know as you mentioned, Binny and Anshul, there was a lot of preparation work done. Did it change the employee experience, were there risks to that you had to manage or get out ahead of?

No, we got very positive feedback from employees because at the time we were shifting towards a skill based compensation, and explaining the reason why we were doing it. We were also bringing more transparency around pay. So the conversation that, and again this was part of our enablement with the managers that the part of the conversation on pay shouldn't be that you're not getting an increase or your salary remains the same because of budget, right.

I mean that's sometimes you would hear that I am unable to give you an increase because I don't have the budget. So it became more around the reason the salary increase is x or it's not increasing is because of various factors. And the manager was able to lay out to the individual that, OK, on a skill perspective, this is how we assess you. From the competitiveness, this is where you are. On your performance, this is where you are, and the potential.

So the conversation had much more evidence in terms of how they landed on a pay recommendation. And the employee could see the, we had also introduced the pay statements to include the employee's salary and his Compa Ratio and the salary range. So I think kind of bringing the whole thing together. It also helped us to kind of create confidence in the employees that OK, I see the data, I understand why my increase is, what it is?

They may not necessarily all be happy. But at least they had, they were given better explanation as to why they were position where they were from a compensation perspective. And we got a feedback also from managers that the conversation became a lot easier for them because they had everything in one place to go and have that conversation. All the data points that are most relevant in a pay conversation, they had it everything in front of them at an individual level. So the point actually was saying that it was a lot more personalized with evidence.

The other thing that helped us, Jean, on this journey to manage this change was actually doing it in phases, right. As we did pilots, we were intentional which employee segments to go after, which geographies to go after? So we're learning, what's working and what's not working from these pilots? As an example, we learned sometimes some managers actually saw skills data for the first time during the pay conversation.

So we recognize that some of the things, even though as part of the design, we wanted it all to come together, it was not happening. So we were much more intentional than in making sure that as we scale further, it's actually happening in advance. And managers have the ability to make changes and give feedback during the cycle. So that's just one example of learning as you go through pilots. So that you're more confident as you scale scale your solution.

Looking forward then into the future having accomplished so much with this global rollout of a pay-for-skills system, how do you see this developing or what's next in your roadmap, what are your plans to get to the next horizon in terms of this vision?

Definitely, I mean we'll continue to have the ITIL run during our pay cycles. What we want to do is kind of go beyond our annual salary cycle, and see how we can bring the concept of infusing AI, and also looking at scale in other pay programs. So when we are doing for instance, promotions, right how the skills play into that, and how can AI help us or help managers make better decisions on promotion using similar concepts like using these four factors.

So that one thing is about scaling this current program, and using it in other programs for sure. I think the second area would be how we can kind of even get more crisper on skill. And Anshul and I, we were just talking about that we started on journey for skill. It is hard work even for us to kind of keep up to date, right. How are the skills evolving or how can we kind of get more into the depth of the skill? You talked about the soft skill part right, how can we bring that into the decision making? So I mean, the world of skill and specialties, this is just waiting to be explored a lot more.

So I'm hoping that we will add a lot more sophistication on the skill side, and then scale this program into other compensation areas. If you think about where employees are on their own skill development journey, and what clients are asking of us. Five years ago, employees wanted to be data scientists, clients were asking, I want a data scientist. Nobody asks for that anymore. Some people want to develop a skill in Hadoop and Python, that's what clients ask for. So client needs as well as employee needs have gotten so much more granular, and as our pay philosophy evolves to address those needs, that's going to require a significant shift as well. So that's going to be exciting as we go forward.