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By 2025, the World Economic Forum estimates that 97 million new jobs may emerge as artificial intelligence (AI) changes the nature of work and influences the new division of labor between humans, machines and algorithms. Specifically in banking, a recent McKinsey survey found that AI technologies could deliver up to $1 trillion of additional value each year. AI is continuing its steady rise and starting to have a sweeping impact on the financial services industry, but its potential is still far from fully realized.
The transformative power of AI is already impacting a range of functions in financial services including risk management, personalization, fraud detection and ESG analytics. The problem is that advances in AI are slowed down by a global shortage of workers with the skills and experience in areas such as deep learning, natural language processing and robotic process automation. So with AI technology opening new opportunities, financial services workers are eager to gain the skills they need in order to leverage AI tools and advance their careers.
Today, 87% of employees consider retraining and upskilling options at workplaces very important, and at the same time, more companies ranked upskilling their workforce as a top-5 business priority now than pre-pandemic. Companies that don’t focus on powering AI training will fall behind in a tight hiring market. Below are some key takeaways for business leaders looking to prioritize reskilling efforts at their organization.
Build data literacy with customizable learning paths
Any digital transformation requires leaders to focus their investments on two modern sources of competitive advantage: data and people. First, boosting data literacy across the organization helps line of business and domain experts (Sales, HR, Marketing, Financial Analysts, etc.) collaborate with AI and machine learning experts, which is critical to move beyond proof of concepts and experimentation.
For AI tools to be deployed at scale, those employees whose jobs involve interactions with AI systems need to understand how those systems work and what the constraints and limitations might be. Reskilling these individuals may include how to interpret the results of the AI/ML models or how to intervene with AI/ML experts when the results seem off.
A recent McKinsey study found that effective reskilling is 20% more cost-effective than a “hiring and firing” approach, and utilizing the right tools and technology can help companies accomplish their reskilling goals.
Importantly, before taking on any AI reskilling efforts, banks and financial services organizations need to first understand what outcome they’re driving towards and what skills are required. An employee self-assessment survey that focuses on necessary skills can help companies determine a customized curriculum and plan based on the existing skills gaps.
The notion of a one-size-fits-all training program or that employees need to take significant time away from the office to attend courses is no longer relevant. Utilizing digital learning platforms like Skillsoft, Udacity, or Udemy, or integrating content into mainstream work systems can make employees’ reskilling experiences more user-friendly. Platforms like WalkMe can help employees learn complex software systems quickly, and Axonify can deliver 5- to 10-minute microlearning sessions to employees within their daily workflow. For an even more customized approach, companies may opt to build their own programs with the help of industry consultants and professors who are experts in their field.
Turn to in-house, existing tools and groups for AI reskilling
A Deloitte survey found that 94% of employees would stay at a company if it helped them develop and learn new skills, but only 15% can access learning opportunities directly related to their jobs. AI reskilling offers an immense opportunity for both financial services companies and their employees, but it can be daunting to consider monetary and time investments needed with reskilling efforts. The good news is that businesses can often utilize existing company tools instead of purchasing all new software.
Here are three excellent sources to help accelerate AI/ML training and implementation:
- Industry consortiums: You might also consider joining industry consortiums that support your team’s progress and encourage employee growth through collaborative groups. For example, FINOS (fintech open source consortium under Linux Foundation) helps facilitate the processing and exchange of financial data throughout the entire banking ecosystem.
- Cloud Service Providers (CSP) Training and Certification Programs: Many of the CSPs, such as AWS, Google Cloud and Microsoft, offer ML training and certification programs for free or subsidized prices. These self-guided programs vary in topics and tracks from understanding conversational AI to machine learning for business and technical decision-makers and are designed for those looking to learn new skills or to build or switch careers.
- Technology Enablers’ AI-powered Solution Accelerators: Additionally, many companies like IBM, AWS, PwC and Databricks offer easily deployable tools and solutions accelerators for common data analytics and machine learning use cases that organizations can utilize. Instead of enduring the weeks of development time, technical practitioners like data scientists, solutions architects and developers (from novice to experts) can leverage these accelerators to enable faster time to modernization and help talent upskilling. At Databricks, our financial services solutions accelerators help companies capitalize on the open banking paradigm, providing free code and training that helps with front-to-back-end automation. This includes free SAS to Python training to help technical and non-technical teams combine AI and rules-based fraud algorithms.
Recognize the cultural benefits of offering AI reskilling opportunities
Investing in employees’ skills and knowledge can build a positive company culture and reduce turnover by boosting employees’ confidence and productivity, and it creates a more well-rounded workforce that increases teams’ effectiveness.
AI reskilling efforts can also help financial services organizations make better progress on their diversity, equity and inclusion methods by making learning more accessible to individuals who have faced barriers to higher education. To address this and the skills gap, banks including Bank of America, BBVA, Capital One, CIBC and JPMorgan Chase have invested in job training and reskilling efforts for their employees.
Bank of America’s career tools and resources have helped more than 21,000 employees find new roles at the company. Consistent training of new technologies and certifications are an investment in shaping the workforce of the future and will help to ensure that employees stay ahead of current trends and industry demands.
Look to data and employee metrics
As a leader at an organization focused on data and AI, we always look to the data to show what we should prioritize internally – and this includes what we should focus on in our AI reskilling efforts. When measuring the success of reskilling programs and initiatives, a recent LinkedIn study found that today’s measures assessing the impact of training programs relied primarily on soft metrics, including completion rates, satisfaction scores and employee feedback.
This is a missed opportunity as company leaders can – and should – consider utilizing harder metrics that measure business value including increases in employee retention, productivity or revenue, to gain the most helpful insights from their reskilling initiatives. If it’s not working well, companies can consider bringing in new technologies or tools, or adjusting their program and overall experience to make it successful in the future, and by doing so, continue to stay ahead in the competitive war for talent.
Future-proofing starts now
In Jamie Dimon’s latest shareholder letter to JPMorgan investors, he points out: “Our most important asset — far more important than capital — is the quality of our people.” He continues, “technology always drives change, but now the waves of technological innovation come in faster and faster.”
Since companies that reskill their employees are more productive, produce positive economic returns and see increased employee satisfaction, there’s no better time to start than now.
Junta Nakai, RVP and global industry leader of financial services at Databricks.
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