In research done by the Digital Banking Report, financial organizations of all sizes indicate a low level of data maturity despite an increasing array of AI solutions offered by third-party vendors. In the research, only 12% of organizations believed they were “very effective” or “extremely effective” at using data and advanced analytics. This is lower than prior to the onset of COVID-19. While legacy systems are cited as the primary reason for the shortfall, the second most cited challenge is the lack of expertise within the organization to deploy AI technology effectively.
In other words, while banks and credit unions can purchase sophisticated AI solutions, there usually isn’t a defined path to achieving strategic goals or increasing business value. This creates a digital transformation paradox; where decision makers and employees believe in the power of data and AI, yet the appropriate actions are not being taken to leverage these tools for the benefit of digital transformation solutions.
As discussed in the book, The Technology Fallacy: How People Are the Real Key to Digital Transformation, it was found that human and organizational aspects of digital transformation are often more important than the technological ones. These human aspects of digital maturity include:
- Forward-looking leadership that is open to change.
- Development of talent.
- Innovative, collaborative and risk-tolerant culture.
- Cross-functional organization structure.
- Clearly communicated digital strategy.
This is the reason that, despite increasing investments in data and AI capabilities, financial institutions still do not report the business gains from AI that are promised or anticipated. Success requires much more than data infrastructure and AI technology alone – it requires the development of talent, a rethinking of the supporting leadership and culture, and an organization structure that will deploy AI solutions to the marketplace.
- Banking Innovation Push without Cultural Shift is a Failing Formula
- Digital Banking Transformation Begins With Quality Data
Speed of AI Technology Outpaces AI Skills
The ability to collect, analyze and make decisions based on AI and machine learning has improved exponentially in a very short amount of time. The challenge is that while the banking industry is faced with an increasingly data-driven environment, people at all levels of an organizations must make decisions based on data, analytics and models that they may not fully embrace or understand.
How can we expect managers and senior level executives with legacy expertise to become adept at using the tools and insights at their disposal without adequate training? How can these employees optimize the use of data, AI and machine learning to create positive business results as the technologies are outpacing the capabilities?
As mentioned by an article in the MIT Sloan Management Review, “The problem for managers is less about managing the technology itself and more about managing the skills and processes needed by people and teams. Compounding the difficulty, as the organization matures, the skill levels among employee groups develop at different rates.”
In many cases, the expertise of those who work directly with data and AI technologies far outpaces the expertise of those who consume and deploy the technologies. This gap tends to widen even more as financial institutions enlist the help of outside organizations to produce even better AI-related results. The business context of these improved results is needed.
Building AI Skill Sets For the Future
Using outside talent to improve productivity and results with data and AI technology is definitely a valid path in the short-run, especially as most banks and credit unions play catch-up in the race to leverage data insights across the organization. But, as mentioned, the ability to deploy the insights to specific product and service needs requires the experience of those who have known the business for years. Without the involvement of the users of the data and AI results, technology is deployed in a vacuum.
Marketing managers need to understand the targeting and personalization methodology the models create. Product managers must understand the changes to processes and procedures that is recommended by AI technologies to ensure all of the required steps are in place for compliance purposes. And, risk managers need to feel comfortable that the assumptions made by models continue to reflect the cybersecurity requirements of the organization.
The need to upgrade the skills of the consumers of data and AI solutions usually is done by training existing employees of the organization. This is usually a much more efficient and less disruptive process than trying to train technology people the internal intricacies of an organization. In fact, research from the Digital Banking Report found that while very few organizations currently had the skills in place (17%), more than half the organizations (53%) were looking to reskill and retrain current employees.
Reflecting the significant need to improve current skills around data, machine learning and AI, financial institutions are also looking to hire from outside the organization (33%), and contract with current and new outside providers (50% and 43% respectively).
In an exclusive Banking Transformed interview of Gerald C. (Jerry) Kane, Professor of Information Systems at Boston College’s Carroll School of Management and co-author of the book The Technology Fallacy, there is a massive disparity between how well firms are developing talent within. “Only 10% of less digitally mature companies said they were doing enough to develop their employees to get the digital skills needed,” says Kane. “On the other hand, about 80% of the maturing companies said they were developing their talent within. That’s a huge jump there that distinguishes the leaders from the laggards.”
Similarly, according to the 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report, organizations that actively help their existing teams gain AI skills are more likely, by 40 percentage points, to generate value from AI than companies that have not focused on reskilling. “AI winners are focused on organization-wide alignment, investment, and integration,” stated the research. This includes ongoing training and management support of personalized learning that goes beyond traditional skills training.
Embracing Change is Step One
Financial institutions must realize that change has never happened this fast … and will never happen this slowly again. With the onset of the pandemic, financial institutions globally needed to reset their strategic plans to reflect a world that will be using data, AI and machine learning in ways that was only imagined in the past. Not only are the technologies in place, but consumers and business clients expect experiences that reflect a much greater understanding of their relationships and needs.
According to Kane, adapting to this change is the business challenge of the 21st century. “We asked our respondents an open-ended survey question, giving them a blank box and said, ‘What is the biggest difference between doing business in a digital age versus a traditional one?’ 25% of survey respondents said the pace of change was the biggest challenge.”
In a world impacted by the digital transformation changes brought on by the COVID crisis, change itself is the defining business challenge of the 21st century, and how companies deal with that – how they address that – is going to be the key differentiator.
At the core of this will be how prepared employees, leaders and organizations are to embrace these changes.