From Insight to Foresight: Talent Intelligence reshaping HR with Sam Fletcher

Nilesh: Welcome everyone to a new episode of the business resilience series of the Zinnov Podcast. I’m your host for today’s session, Nilesh Thakkar, the president of Zinnov. Today we are speaking to Sam the Head of Talent Intelligence at PayPal. Sam established the Talent Intelligence Function for PayPal and has been instrumental in driving talent engagement strategies, workforce analytics, location strategy, and innovative approaches to attracting and retaining top talent.
A global expert on talent intelligence and workforce planning. Sam has developed cutting edge solutions to organizations around the world, leveraging data to gain competitive advantages. Welcome, Sam. We are thrilled to have you here.

Sam: Thank you Nilesh. Thank you for having me.

Nilesh: That’s great. Let’s dive in. So, Sam, as remote and hybrid work models become more prevalent across industries, how are leading organizations adapting their talent acquisition, engagement, and retention strategies to build a productive and engaged distributed workforce? What are the unique talent related challenges and opportunities companies are navigating in this context?

Sam: Companies are focused on two main things when it comes to remote work:

• Deciding where having employees work remotely or in a hybrid model is most advantageous. Many companies adopted these approaches in the last 2-3 years to access high-demand talent and stay competitive, as other firms were doing the same.
• Ensuring remote work fits with the company culture. For example, it aligns well with Dropbox since their products enable virtual productivity.

Additionally, companies want to adapt their HR processes like hiring, training, compensation, and management practices to better suit having an offsite, decentralized workforce. This is seen as beneficial, so HR teams are working closer together to develop data-driven strategies for successfully managing remote employees – a very different setup than traditional office-based work.

Nilesh: Okay. So I was actually talking to a CHRO for a global organization just a couple of days ago, and she was saying that the executive team feels that there’s a loss of productivity because of the hybrid and remote. Have you seen any data to substantiate that? And how are companies overcoming that?

Sam: Whether remote work impacts productivity really depends on the type of work and teams involved. For certain functions and workloads, there may be a lack of productivity working remotely. However, we should rethink how we define productivity.
Take software engineering as an example – traditionally, two engineers would collaborate in-person, sharing a screen and ideas. But now, many are using AI co-pilot technologies to effectively pair program with an AI assistant to develop code and get questions answered. It’s not the same as talking to a human expert beside you, but it presents new opportunities.
That said, for young professionals entering the workforce, the lack of face-to-face interaction and those casual water cooler conversations at the office is a real downside of remote work that companies shouldn’t overlook. It’s very difficult to replicate that experience virtually.
Ultimately, the data on productivity will vary based on the specific work, workforce demographics, and how you analyze it. There are trade-offs to consider when it comes to remote versus in-office setups.

Nilesh: Okay. Do you ever see companies going back to five days at work? Or that’s unlikely to happen.

Sam: Many companies will likely go back to the traditional 5-day work week in the office, since that has been the standard approach for a long time, especially in industries like banking where employees need to be available for client-facing roles.
However, we’ll also see different models emerge at various companies. Some might make having employees in the office 5 days a week a competitive advantage to attract certain workers. Others might make allowing 4 remote days a perk to appeal to employees who prefer that flexibility.
The only sure thing is that there will be variety and change in the coming years across different companies. It remains to be seen if we’ll slowly shift back to everyone being in the office 5 days per week as the norm, or if a 3 or 4 day in-office schedule with Mondays/Fridays remote becomes the new standard practice. How this plays out at different organizations and industries will be interesting to watch unfold.

Essentially, companies will experiment with different in-office versus remote approaches as they try to gain advantages in hiring and retention. There won’t be a one-size-fits-all model.

Nilesh: Okay, great. Thank you. so, you know, with constantly evolving skill demands in today’s economy, it seems like we need to learn a new skill almost every other week. How are talent teams utilizing data analytics to identify critical skill gaps within their own workforce and develop targeted upskilling, reskilling, and hiring programs?

Sam: The definition and approach to skills is highly variable across companies. Most organizations still heavily rely on traditional job titles and families rather than a true skills-first model. However, as HR systems become more sophisticated in tracking skills data, there’s an opportunity for companies to proactively integrate skills into their workforce planning.
In the past, if a company needed more data scientists, they’d simply hire for that job title. But with a skills-first lens, they may realize they already have software engineers, developers, or analysts who possess 80% of the required data science skills. This allows companies to identify internal skills gaps, design tailored upskilling programs, and maximize their existing talent pool.

While building comprehensive skills taxonomies is extremely difficult, companies that can accurately map skills adjacencies and understand which skills are correlated have a real advantage. They can be more strategic about reskilling versus hiring, using empirical data on skill acquisition timelines.

The proliferation of skills data provides an opportunity to move beyond legacy job structures toward a more dynamic, skills-focused workforce model. But it requires a fundamental shift in how organizations view and leverage their human capital.

Nilesh: Are you using any tools or programs which help you with that? Or what would you recommend an HR professional do for this?

Sam: To understand your workforce’s skills, start by building a skills taxonomy – even if it’s high-level initially. Map out key capabilities and skills needed for particular job families and workloads. This gives you a picture of your internal skills landscape.

Additionally, look at external labor market data, which forces a more pragmatic, skills-based approach. Many markets don’t use standardized job titles, so you have to break roles down into skill combinations. Analyze professional profiles, resumes, and datasets to identify relevant skills.
This external talent intelligence lens prompts a skills-first mindset. Instead of solely looking at previous job titles, you can assess candidates based on their skills and fit for the role’s requirements.
Using external data is a helpful entry point for shifting toward a skills focus, rather than trying to build an internal taxonomy from scratch. It allows you to align internal skills with market realities and calibrate your hiring based on actual skill proficiencies.

So leverage both internal workforce mapping and external labor insights to progressively build out a robust skills strategy. The two perspectives reinforce each other in making a true skills-based model attainable.

Nilesh: Great. Can you share some specific examples of how Talent Insights are enhancing the quality of your hiring decisions, improving engagement and retention and driving overall performance of companies?  Talent intelligence is a pretty important function.

Sam: In many organizations, talent intelligence still resides within talent acquisition teams. This allows them to leverage data like quality of hire metrics to objectively evaluate candidates beyond hiring manager biases. For example, data may show that hires from perceived top companies like Apple or Google don’t actually achieve the highest performance or retention at your organization due to cultural or technical mismatches.

However, talent intelligence needs to be integrated with broader people analytics and workforce data to truly be effective. It requires a holistic, 360-degree view tying external talent market insights together with internal workforce analytics focused on retention, development, and aligning talent to business objectives.
Traditionally, HR analytics teams are inwardly focused on their own employee data, while talent intelligence primarily looks outward at external hiring markets. But combining these internal and external data sources unlocks powerful capabilities. You get a robust data team that can connect disparate data silos – from real estate costs to benchmarks – to comprehensively inform workforce strategies at an enterprise scale.

Bringing talent intelligence together with people analytics creates a potent force for driving talent decisions with rich, multidimensional data insights aligned to core business needs.

Nilesh: Great. So, the role of a Chief Talent Officer or Head of Talent Intelligence, is becoming increasingly strategic as you just mentioned. How has the function evolved? You talked about internal, external data. If an organization is looking to hire a Chief Talent Officer or Head of Talent Intelligence, how do you align the business objectives and cultivate a future ready workforce? What would you be looking for in a Chief Talent Officer?

Sam: Data enablement and support is now critical for companies addressing talent strategies. Traditionally, talent acquisition teams have been execution-focused, simply carrying out hiring plans handed down from the business strategy. However, there needs to be closer collaboration where talent intelligence plays a more strategic advisory role.
Chief Talent Officers or Heads of Talent Intelligence should proactively involve talent acquisition’s data expertise in upfront strategic conversations and workforce planning initiatives. Instead of just supporting open reqs reactively, talent intelligence can provide data-driven insights on labor market dynamics, skill availability, competitive hiring activity, and talent motivations across different locations.

These talent data perspectives need to be integrated earlier when businesses are mapping out where and how to grow their headcounts and skill mixes. Talent availability should be a top agenda item alongside real estate, economic, and government considerations when making strategic decisions about workforce distribution.

By bringing talent intelligence to the forefront with other key functions like real estate and economists, companies can holistically align their business strategies with a comprehensive view of the talent landscape. This allows them to proactively model workforce plans for success from the start, rather than just executing job requisitions after the fact.

Nilesh: So Sam, I think we already spoke about the skills required in the digital economy are constantly changing. So how are talent intelligence teams working to identify and address the emerging skill gap? Like, how do you determine that here is what we need, as an organization, to focus on? Because sometimes those skills are maybe six months or a year ahead of what you need. And obviously you can’t start looking for talent when you need them. You need to be looking before. So what have you done? And what have others done? How do you go about figuring out what’s the company going to need in the future?

Sam: Yeah, I think the first step in that process is always connecting with the business leaders internally and understanding, you know, what are their changing capabilities?
Talent intelligence teams can be proactive in understanding the changing needs their organization faces based on evolving customer demands and market conditions – that’s always the kicking off point, if you like. They can bring an outside-in view by looking at how competitors are restructuring their org structures or the skills they’re hiring for. Even a small increase in demand for certain skills at competitors can signal a broader change happening that we need to adapt to. Having that initial conversation around what the business sees as its changing needs is critical. Then you can tie it back to skills, labor, and capabilities.

One massively impactful data source talent teams have is competitor hiring data – looking at which skills are increasingly in demand in similar markets or locations. If all our big tech rivals are hiring specific skillsets more than before, or hiring them for the first time, those are really telling signals about changes in the business environment and workforce needs. It can even help identify future competitors. If you’re a traditional bank and see a tech firm hiring banking pros, that could mean they’re gunning to get into your space. We saw this with automakers hiring EV experts years before launching electric vehicles – a clear early sign of their strategic pivot.

So I think analyzing competitors’ hiring is super rich and insightful for understanding how market demand for skills is evolving. Are we ahead of those trends? Keeping pace by attracting the skills that are hot in the market? Looking at what’s happening out there, proactively, is key for ensuring we have the capabilities to stay competitive as things change.

Nilesh: Okay. That’s great. Are you using tools again to identify competitive skills, hiring and all that? Or are you just looking at LinkedIn? What, what do you, what do you normally know?

Sam: Yeah, there’s a number of different platforms that will aggregate a kind of data from, from job adverts. And I think traditionally in research and executive search and recruiting, you might look at one job advert at a time. But now there’s platforms and tools like Draup that we’re a customer of where, uh, you know, they will aggregate that data, they will compile skills, they will compile data from job adverts kind of, you know, and that’s hundreds of thousands, if not millions of job adverts that have been passed in kind of different languages and extracting skills.

So, suddenly taking intelligence that you could get from one job advert at a time and then taking that to a data set of millions of job adverts, you start to see some really compelling trends in there as well. So there is there is tools and platforms that will do that for you, thankfully, and start to scrape that data, which speeds things up a little bit.

Nilesh: Now, one of the, obviously the other trend of which we’ve experienced it and we’ve actually worked with companies is on harnessing global talent, right? Earlier, people used to go where the jobs are. Now companies are going where the people are. So how are companies harnessing global talent to drive innovation and competitiveness? One of the things they were doing was cost arbitrage, but now I’m seeing more and more they’re doing it for innovation and growth and competitiveness. Have you seen that trend? And what do you have to say about that?

Sam: How companies approach global talent depends on their size and status. Large multinationals tend to be very hub-focused, concentrating hiring in major tech centers like the San Francisco Bay Area, Bangalore, or Beijing. However, for smaller firms or scale-ups, there are advantages to a more distributed hiring approach across non-hub locations with strong talent pools.
In hypercompetitive hubs, especially for AI talent, you have to vastly overpay to secure good people. But in well-developed, smaller markets, the labor pool is tight but not as cutthroat. You can access sufficient talent without paying premiums to compete with big corporations.

That’s why many startups and scale-ups capitalize on hiring in robust, but less hyper-competitive tech hubs across EMEA, Southeast Asia, and Latin America. The talent pools meet their needs without the intense competition of megalopolises.
Going this distributed route allows smaller firms to harness global talent cost-effectively, especially if hiring remotely. They can be more talent-focused versus location-focused, and avoid having to wildly overpay in the major tech hubs just to staff their teams.

Nilesh: You know, it’s great to have access to talent, but now you’re increasing the need for collaboration and communication. Have you seen what has worked well in these cases?

Sam: Companies need to set very clear expectations upfront when engaging remote talent – especially around onboarding processes. One major challenge is the reduced “stickiness” or loyalty of remote employees. When working from home, it’s much easier for people to continually interview and job hop since companies are no longer just competing locally, but globally.
This makes retention strategies for remote workers crucial. Firms also must be cognizant that remote work opens up diversity opportunities by broadening the talent pool, but it can also close off opportunities for some demographics who prefer in-person collaboration and the social benefits of an office.

There’s a misconception that all employees want remote work, when in reality, it may not meet the needs of certain age groups or those who learn and engage better face-to-face. Companies face a big challenge balancing these contrasting employee experiences and preferences.
They need to actively listen through surveys, social listening, and engagement data to ensure their remote/in-office model appeals to their entire workforce. A one-size-fits-all approach won’t work – they must be thoughtful in catering to the diverse needs of their employee populations when it comes to remote vs. in-office work.

Nilesh: Sounds good. And before we wrap up, could you share a piece of advice for HR professionals looking to leverage data, AI, and latest technologies and tools.

Sam: Many HR teams view data transformation as an intimidating mountain to climb, thinking they need perfect technology infrastructure and access to large language models first. But there’s opportunity in just doing good data work with what’s currently available.
HR leaders often think they need complete, standardized datasets before they can derive value from data. However, the best approach is to simply get started – any effort to utilize data is beneficial initially. It also helps build data awareness across the HR function by showing practical applications.

There’s still a lack of data literacy and comfort with data concepts in many HR organizations. An important first step is fostering that data familiarity and fluency among HR teams through exposure and hands-on experience.
Instead of seeking perfection upfront, HR should leverage existing data capabilities while engaging data-savvy talent across the organization, even outside HR. Look for inroads to initiate data efforts through cross-functional collaboration.

The key is starting with what’s available, getting hands-on with data work, and steadily increasing data skills – not waiting for unattainable perfect conditions before dipping into data transformation efforts.

Nilesh: Thank you so much. Um, just to kind of summarize what Sam you told us today, um, data, like in everything we do today is pretty important in HR to get started with data.

Don’t look for perfection. And you will see that your talent acquisition, your talent retention, your talent management, your location strategy will just get better with that. Also, you mentioned, uh, rescaling and upscaling, uh, look at, don’t necessarily have to always go and hire from outside. Look at your skills adjacencies and see whether you can upscale the team, which you already have, because that’s obviously they already know the company well, and now just giving them the extra skills can get you a lot more productivity and a lot more engagement from that, uh, from that exercise. Also, the role of talent officer or head of talent intelligence is becoming increasingly important.

It’s increasingly critical. It’s sometimes hidden under the talent acquisition, but considering that how critical talent is for every company, it really needs to be thought of as a very important role where they are talking to business proactively rather than as a reaction to what the business needs are, and working in business to figure out where the company is going, what kind of talent needs are.
And one other important aspect which you mentioned was looking at competitive data and see that would be a very. You know, the intelligence which you may be able to get from what other companies in your field or even adjacent field are doing that may even help you not from a talent perspective, but just from an oral business perspective to be a lot more proactive.