HSBC: How to build skills intelligence across 250,000 employees

Overview

Do we have the right skills to do the things your future organisation needs to do to keep winning?”

For Rob Etheridge, Group Head of Workforce Strategy and Insights at HSBC, this isn't just an HR question. It is the definition of future competitive advantage.

HSBC is undergoing a massive transformation to become a technology-driven, engineering-led organization. To get there, they didn't just need a headcount number; they needed a precise understanding of the skills they have and the skills they lack across 250,000 employees globally.

But as Rob found, you cannot answer strategic questions with operational guesswork.

Based on our conversation with Rob on the TechWolf Podcast, this is how one of the world's largest banks stopped guessing and started measuring.

1. Business problems first, skills second

The most successful skills transformations don't start with "skills." They start with a business headache.

For HSBC, that headache was visible in Wealth Management in Asia. The bank needed thousands of additional advisors to meet growth targets. The traditional answer would be to look externally.

But Rob and the team asked a harder question: “We have to know what skills we have internally and what adjacency there is between wealth management advisors and other types of roles that we have in the bank.”

They quickly realized they were making billion-dollar decisions with fragmented, inconsistent data scattered across HRIS and LMS tools. They were reacting, not preparing.

To solve this for Wealth Management, and eventually for the entire bank, they needed a repeatable way to view talent supply.

2. The end of the "one-off" project

The most annoying problem in HR is still data. Organizations often try to solve this by running massive manual consulting audits, but the moment the audit finishes, the data is out of date.

HSBC avoided this trap by choosing perpetual intelligence over static inventories.

You may do a one-off exercise... but essentially, what you'll be doing is resetting each time,” Rob explains. “If you embed this agenda into AI, then what you have is a perpetual way of keeping on top of that data.”

By integrating TechWolf to infer skills from live work data, HSBC achieved scale at a speed that manual audits can’t match:

• Speed: Mapped skills of 250k+ employees in 1 structured, unified taxonomy
• Depth: Generated skill profiles for 30,000+ technology employees.
• Granularity: Provided full visibility across 2.5k+ distinct job roles.

This created a source of truth resistant to data decay, powering talent acquisition, learning, and planning with real-time reality.

3. Data, not another dashboard

One of the most pragmatic decisions HSBC made was to stop looking for new software for employees to master. Rob was clear:

We don’t need another employee portal. We’ve got plenty of those”

The bank possessed an extensive tech ecosystem, including learning and talent acquisition systems, all of which had the capability to hold skill data.

The issue was not the interface; it was that different vendors used inconsistent taxonomies and siloed data, which prevented a strategic view.

HSBC needed a source of data that became the "truth" across the entire stack. By treating TechWolf as a data provider rather than a destination, they ensured that high-quality skills intelligence flowed directly into the platforms they were already using.

4. The “boring” work that unlocks speed

In the age of AI hype, it is tempting to skip straight to the magic. But Rob is clear about what actually drives transformation.

It starts with taxonomy.

To make skills data usable for decision-making, you have to clean up the mess. You need a structured model that business leaders can actually understand.

That sounds incredibly boring,” Rob admits. “But it is so fundamental now to how we are going to do things going forward.”

TechWolf helped HSBC build a unified skills data foundation that brought order. This wasn't just a list of tags; it was a structured hierarchy covering 18 skill domains, 48 subdomains, and 398 skill clusters.

This "boring" foundation is exactly what allowed them to scale from a pilot to a bank-wide capability.

5. From insight to the "fulfilment playbook."

The real breakthrough happened when HSBC connected this new data foundation to its strategic timeline.

In a pilot within their Data Analytics Office, they compared their 2028 strategic blueprint against their live TechWolf supply data. The gap was clear.

But the data also exposed a challenging reality: knowing the gap isn't the same as closing it.

The intelligence is great. Actually, what do we do about it?”

This realization forced the creation of a "Fulfilment Playbook". Instead of vague HR support, the business now has a decision engine where the data dictates exactly when to trigger external hiring, when to deploy internal mobility, and when to launch upskilling.

The results are already tangible:

• Reskilling at scale: The bank is uncovering the full potential of deploying hundreds of digital & tech employees.
• Targeted interventions: Accelerating Cloud adoption by identifying hidden cloud-ready talent across the bank.
• Expanding scope: The model is now poised to scale to many thousands of colleagues across the bank to help with future career progression and internal mobility.

Closing thoughts

Rob Etheridge and HSBC have proven that skills intelligence is not a theoretical exercise. It is a rigorous, data-first discipline.

By ignoring the distraction of "shiny new tools" and focusing on perpetual data, they have built a machine that doesn't just describe the workforce of today, but models the needs of 2028.

The question is no longer "do we have the right skills?" It is "What are we doing about it?"

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-> Listen to the full story on the TechWolf Podcast:

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