Deloitte: Financial Firms Lag in Talent-focused AI

Financial services organisations continue to underutilise artificial intelligence in talent management, with only 18% of industry executives reporting implementation in this function, compared to 47% in marketing, sales and customer service, according to Deloitte’s latest report Scaling AI across talent management in financial services organisations. 

A focus group conducted by the Deloitte Center for Financial Services with 38 senior executives from major US financial institutions reveals that most firms are at early stages of applying AI to enhance talent functions, with current deployments concentrated in recruitment rather than employee retention or development.

“I have seen the most significant return on investment in the areas of talent acquisition and learning and development,” says a Head of Business, HR/Talent Management at an investment management firm. 

“For example, by implementing AI-driven recruitment tools, we have reduced our time-to-hire by 18%.”

Predictive Analytics for Employee Retention

Generative AI is enabling a shift from traditional “pull” analytics, where executives manually gather and analyse information, to “push” analytics that monitor data automatically and generate insights with minimal prompting.

Participants identified early detection of disengagement and burnout as a valuable application of AI technology. 

By analysing multiple data points, including job satisfaction metrics, engagement scores, performance indicators and wellbeing data (with permission), talent leaders can develop comprehensive risk profiles for employees.

“Gen AI can predict the likelihood of an employee leaving by identifying predictive factors such as employee burnout, declining performance, decreasing engagement. 

“Also, a predictive model could assign a flight risk score to flag employees that are most at risk [of leaving],” explains a director of HR/talent management at a bank.

This analysis can incorporate demographics and external factors such as labour market trends to create a more nuanced picture of retention risk, potentially enabling targeted interventions before staff departure.

Performance Management and Skill Development

Performance evaluation represents another area with significant potential for AI application. 

Tools can analyse historical career trajectory data to identify factors correlating with high performance across the organisation.

A Chief Information Officer from an investment management firm notes: “AI can be used to identify high performers and identify key performance attributes and characteristics of those performers and then apply those attributes to other performers for advancement.”

This approach builds on established methodologies such as Google's Project Oxygen, which identified key behaviours of effective managers through data mining. Modern AI can scale such initiatives across organisations.

Focus group participants highlighted the need to mitigate bias in performance evaluations and career advancement decisions. 

“Often when performance is not metrics-based and requires soft skills, or if the skills do not match that of the manager, it can be misjudged. 

“Given gender, age and neurodivergent and cultural differences, this can create bias and [lead to] unfair, poor ratings of just 'different' skill sets,” says a Vice President for HR/Talent Management from a bank.

Some institutions have begun implementing AI-powered solutions for career development. DBS Bank uses an AI platform called iGrow to help employees identify necessary skills for career advancement and recommend relevant development opportunities.

AI can also facilitate mentorship connections, similar to Mastercard's internal talent marketplace called Unlocked, which matches mentors and mentees based on skills and experience profiles.

Leadership Pipeline Development

Succession planning remains largely manual in many financial services organisations, potentially overlooking qualified candidates and focusing too narrowly on traditional career paths rather than skills-based assessment.

“Succession planning is still done quite manually, which leads to under recognition of potential internal talent,” notes a Vice President of HR/Talent Management from a wealth management firm. 

“Applying skill-based criteria should expand the internal pool beyond the traditional selection criteria (that is, who you know).”

AI analytics could transform succession planning from an annual exercise to a dynamic process by identifying leadership gaps, expanding candidate pools and suggesting tailored development paths.

Implementation Challenges

Despite potential benefits, organisations face challenges in employee trust and data privacy. 

“The biggest challenge I see would be getting workers to trust the [AI-based] tool and the process and getting them to understand how the tool works,” another participants states.

Deloitte's 2024 Global Human Capital Trends research indicates that 86% of workers believe greater organisational transparency leads to higher workforce trust, suggesting firms must clearly communicate how employee data is collected, used and protected.

Organisations need robust data management practices to identify and mitigate algorithmic bias, particularly as regulatory oversight of workplace AI tools increases. 

Human oversight remains essential to ensure algorithms do not miss nuances in individual development trajectories.

The talent function's approach to AI implementation may determine competitive positioning. 

As one participant states, talent leaders can either “hinder adoption due to a lack of understanding or trust in the technology, or they can facilitate it by finding ways to enhance the work experience for both employees and managers”. 

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