In 2023, Verizon had an interesting year. Seven of nine direct reports to the CEO changed positions, which was unusual for a Fortune 500 company. The changes were expected and driven by retirement and transitions. Verizon embraced this opportunity to promote internal talent and hire for critical capabilities and difference of perspective.
However, these significant shifts revealed that succession plans had become exhausted and needed to be replenished or that some slates had candidates whose C-suite capabilities Verizon wasn’t quite sure about. Having weathered these successive transitions, Verizon wanted to revisit how they conduct succession planning and apply data science and AI to steer the direction of their decisions.
They started their journey with the top 300 employees in the organization, the pool for succession, and set out to understand the depth of capability as it relates to business strategy, as well as the depth and strength compared to the competition and world-class corporate benchmark partners. This new strategy focused on understanding how to set Verizon up to win based on what other companies do and optimize their top executives’ strengths against their competitors.
Enter Censia: A Data-Driven Solution
Verizon turned to Censia, a leader in AI-driven talent intelligence, to overhaul its approach.
Censia’s approach included a broad spectrum of industry benchmarks and competitor analyses that allowed Verizon to understand not just the strengths of its executives but also how they stacked up against the best companies in the world.
This was possible because of Censia’s structured talent data, which maps out and categorizes all aspects of talent information, from job families to skills families and capabilities to information about whole companies and industries. This allows recruiters and executive search companies to match up company events (such as growth stages, product launches, and more) to those who did the heavy lifting during these periods. This allows the software to predict who is most capable of success and how they compare to their competitors, best-in-class peers, and smaller players who are quickly rising.
Verizon embraced this opportunity to understand hiring trends (internal vs. external), what industries to look at for disruptors, what diversity looked like, and how other companies were building or buying capabilities compared to them. They found that they fared well on diversity and internal talent development and promotion and have significant depth in deep networking engineering core spaces – but needed to improve their internal hiring of differentiating capabilities.
Censia’s search was effective because it goes beyond the two most common factors predictive of talent success: keyword search and networking. Keyword search is inefficient because it requires a direct match of words, so if you don’t say VP of Enterprise Sales or the numerous other titles used to describe the position, you won’t get a complete list of candidates available for that role. Networking is equally problematic, limiting users to a small set of possible candidates.
Once you remove those two methods and introduce Censia’s multidimensional talent data and search, the picture shifts quickly, revealing more qualified and diverse candidates.
While AI-powered talent search can never replace a human, the hard data can shed light on personal characteristics, such as the ability to build a following, promotability, and more.
An example of a slate at Censia is as follows:
- worked at a Fortune 100 company,
- that doubled revenue, AND
- went into double-digit billions, AND
- in the past ten years, AND
- has a significantly consumer-based organization
- Is diverse
Using this multidimensional search and our talent data, that’s precisely what we delivered more than 100 times for our customer Verizon.
If you want to see Censia’s powerful search in action, please click the Request Demo button above.
