Priya Jaiswal is a distinguished authority in the banking and finance sectors, renowned for her deep expertise in market analysis and the evolution of international business trends. With a career spanning decades at the intersection of traditional banking and emerging financial technologies, she has become a leading voice on how digital transformation impacts human capital. In this conversation, we explore the shift from viewing staff as “resources” to “potential,” the practical application of the Triple I strategy, and how leaders can navigate the cultural hurdles of AI integration while maintaining the human element at the core of their operations.
Many organizations are moving away from treating staff as interchangeable resources. How does the formula of Intelligence times Information times Ideas change the way value is calculated, and what specific behaviors should a leader prioritize to ensure the “Ideas” variable doesn’t fall to zero?
The shift from addition to multiplication in the value formula—Intelligence times Information times Ideas—is revolutionary because it means if any single element is zero, the total value produced is zero. When we treat people as “human resources,” we view them like water or electricity to be consumed, but this formula demands we view them as the multiplier of the technology we provide. To ensure the “Ideas” variable remains robust, a leader must prioritize psychological safety and the “fail fast” mentality, where employees feel empowered to try something “crazy” without fear of retribution. I recall a strategy session where I spent 30 minutes using AI to synthesize hundreds of pages of documents, which initially felt like a threat to my role; however, the real value emerged only when I added the human judgment and customer context the data missed. Leaders must actively reward curiosity and the “what if” questions, shifting the staff’s focus from being encyclopedias of facts to being curators of solutions.
When restructuring roles around the Doughnut Principle, how do you define the boundary between “Core” responsibilities and the “Ring” of discretionary space? What steps can managers take to protect that scheduled time for creative experimentation without compromising daily operations?
The boundary is defined by separating the “Core,” which includes the essential, repetitive tasks that AI can now handle—like writing standard code or processing basic transactions—from the “Ring,” which represents the discretionary space for human empathy and strategy. In a real-world application, this means rewriting job descriptions to explicitly state that a portion of the week is reserved for the “Ring,” making it a formal requirement rather than a luxury. Managers can protect this time by using AI to offload the “boring” work, effectively clearing the schedule for experimentation. For example, if a team member is scheduled for 40 hours, a manager might mandate that 10 of those hours are “protected Ring time” where they are unreachable for routine tickets and are instead tasked with solving a specific customer frustration through a novel lens. This isn’t just “free time”; it is scheduled, protected, and monitored to ensure the creative muscles of the organization do not atrophy.
Breaking down internal silos is often a goal, but achieving a “Glass Box” culture is difficult in practice. What are the primary technological and cultural hurdles to democratizing data, and how does providing a single view of information specifically empower non-technical employees?
The primary technological hurdle is that information is often locked in departmental vaults—Marketing doesn’t speak to Sales, and Engineering is isolated from Product—creating a fragmented view of the customer. Culturally, the hurdle is the “knowledge is power” mindset where teams hoard data to maintain relevance, but a “Glass Box” culture requires transparency where every employee has a single, democratized view of the data. When we provide this single view, a non-technical employee in customer service, for instance, can see the full 100% of the puzzle rather than just a 10% sliver, allowing them to spot trends that an AI might miss. This empowerment allows them to make informed decisions and offer better “Ideas” because they finally have the “Information” fuel required by the Triple I formula. By giving everyone enterprise AI tools starting Monday, we bridge the technical gap, allowing a branch manager to analyze complex data sets as easily as a data scientist.
Implementing a shamrock organization involves shifting some staff to portfolio work or reduced hours to prioritize learning and flexibility. What are the logistical challenges of managing this type of workforce, and how do you ensure these employees remain connected to the company’s core purpose?
The logistical challenge lies in coordinating a “Leaf Two” workforce, where individuals might contribute 10 hours to a specific project, 10 hours to learning, and 10 hours to other organizational needs, rather than a standard 40-hour week. To manage this, we are piloting this structure in specific departments like Payments, focusing on reducing overall monthly hours for everyone rather than reducing the headcount. Connectivity to the core purpose is maintained by ensuring that even portfolio workers are involved in the “Ring” activities and town halls where the company’s vision is reinforced. We make it clear that this flexibility is not a demotion but a strategic reallocation of talent designed to prevent burnout and foster continuous reskilling. By prioritizing learning as a core part of their paid hours, we signal to the employees that their growth is synonymous with the company’s evolution.
Traditional metrics like tickets closed or transactions processed do not reflect human judgment. What new performance indicators should be introduced to measure the success of AI-human collaboration, and how do you reward staff who successfully override an AI’s recommendation?
We need to retire outdated metrics and introduce Key Performance Indicators (KPIs) that value human intervention, such as the number of experiments run, novel problems solved, and specifically, instances where a human successfully overrode an AI recommendation. Rewarding an override is crucial because it highlights the “Judgment” and “Empathy” that machines lack; for instance, if an AI suggests a loan rejection based on data, but a staff member approves it based on a deep understanding of the customer’s character and long-term potential, that is a victory for the “Intelligence” side of our formula. We should celebrate these moments in team meetings to show that we value the “person inside the raincoat” more than the algorithm. Performance reviews should now focus on how effectively an employee uses AI to amplify their output, rather than how well they perform tasks that the AI could do alone. By shifting our focus to “Ideas tested” rather than “transactions processed,” we align our incentives with the future of banking.
What is your forecast for the future of AI-driven banking?
I believe we are entering an era where the “empty raincoat” of sterile, automated banking will be replaced by a more “human-centric” model where technology handles the complexity and humans provide the meaning. In the next few years, the banks that survive will not be the ones with the most advanced algorithms, but those that have successfully transitioned their staff from “resources” to “curators” of financial well-being. We will see a significant shift toward the shamrock organization, where flexible, high-intelligence roles become the norm, and the “Glass Box” approach to data becomes a regulatory and competitive requirement. Ultimately, the future of banking isn’t “You vs. The Machine,” but a collaborative partnership where human judgment is the ultimate premium, and our value is measured by the creativity we bring to the data. Those who fail to prepare their people for this shift starting now will find themselves with efficient processes but no one inside them to lead.
