Enhancing Workforce Management with AI: Transforming Branch Analytics with Intelligence and Insight
In Seven Habits, Stephen Covey told us to “look at the lens before looking through it”. With all the technological advances, we are at the high noon on workforce management. It’s time to trim away any deficiencies. It’s time to look at the lens!
What’s wrong with WFM analytics?
The largest problem with analytics is – there is too much of it
The constant talk and repeated mention of labor cost savings, productivity and ROI has made us a paranoid lot. Somehow, we want to quantify and improve everything about the branch workforce. I call it The Great Multiplier Syndrome. We locate a tiniest potential issue in the branch operations, put a unit cost to it, multiply it by 365 days and multiply again by several branches. Result is a multi-million dollar ‘inefficiency’. Next leadership meeting will be all about how we can save a gazillion dollars by improving the way our branch team breaths or walks. Before anyone notices, out comes another batch of SOPs and KPIs to track the new found absurdity.
So IT and business teams are nicely caught up in a rut. We make IT get us data for analysis. Then we make some near-sighted decisions using the data. And then we make IT get us even more data to evaluate our decisions. To worsen the matters, pundits have us convinced that this is the ‘nature of the beast’.
A more pragmatic approach would be to make a long list of all the KPIs that we usually feast upon and reason each of them out to be in Santa’s good or naughty list. But for the moment, these are a few random thoughts on the subject of analyzing workforce performance and role of AI.
Where can AI help
In workforce management, AI offers the potential to bring nuanced, contextual, and adaptive measurement—a far cry from traditional one-size-fits-all approaches. Here’s how an AI-driven system can elevate analytics and insights for bank and credit union branches:
- Proportional and Contextualized Measurement
Not all branches require the same level of scrutiny or action, and AI-based systems can bring proportionality to branch analytics. By learning from historical data and patterns, an AI-driven solution dynamically assesses which branches need attention and to what degree, shifting focus only where it’s most impactful.
2. Understanding Relative Performance, Not Absolutes
In my decades in human capital management, I’ve seen the danger of viewing performance metrics as absolutes. AI-based systems can recognize that branch performance is fluid, evolving over time. Instead of labeling branches as “good” or “bad,” AI tracks performance relative to each branch’s unique journey, adjusting to changes and identifying true growth areas without prematurely judging or discouraging teams.
3. Reducing Unnecessary Monitoring with Smart Interventions
Traditional analytics often mean constant surveillance and directives that distract rather than empower. AI’s power lies in its ability to determine when intervention is actually needed, avoiding unnecessary disruptions to branch operations. With AI, branches can focus on making profits while corporate focuses on providing intelligent support where it truly matters.
4. Intelligent Metrics that Evolve with Real-World Scenarios
AI can help us develop more sophisticated and adaptive metrics, allowing room for real-life variables that branch managers face daily. Rather than rigidly applying corporate KPIs, an AI-based system can recognize when measurements fail to capture the full context and adjust accordingly, improving accuracy and fairness in evaluations.
5. Establishing a Corporate KPI Governance Framework
AI-driven systems are uniquely suited for a Corporate KPI Recycle Center—a governance system to regularly evaluate and update metrics. By using machine learning, AI can detect outdated KPIs, refine them based on real-world results, and ensure they remain relevant and supportive of branch success.
6. Focusing on Solutions, Not Just Problems
Finding a problem is easy; solving it and measuring success is challenging. AI assists by not only identifying potential issues but also suggesting context-aware solutions. Through continuous learning, AI provides actionable insights and tracks solution implementation, giving a real measure of effectiveness.
7. Rethinking Visual Representations: Beyond Red, Green, and Orange
Instead of simply labeling deviations in performance as “bad” with red indicators, AI can consider the complexity of each deviation, discerning between critical alerts and benign variances. AI’s data visualization can present trends and insights that encourage positive action without causing undue stress or bias from color-coding.
8. Seeing the Big Picture with a Net Effect Model
Corporate offices have the advantage of a panoramic view, and AI can harness this by aggregating data from multiple metrics to generate a holistic, net effect score. By consolidating these metrics, AI allows corporate to understand branch performance in a more nuanced way, without overemphasizing single metrics.
9. User-Friendly Analytics with Modern Design
As we move into an era of friendlier, more socially-oriented software, AI-based systems can enhance user experience through clean, intuitive, and insightful reports. By applying AI to data presentation, we can bring fresh, visually appealing reports that communicate essential insights without overwhelming managers.
With these AI-driven improvements, banks and credit unions can enjoy analytics that support, rather than hinder, branch performance. An AI-based workforce management system respects the expertise of branches while providing insights that add real value.
