In the pursuit of reducing risk and fuelling innovation via AI, it's clear that success depends as much on the people driving the technology as on the technology itself. The AI tools and models are evolving so quickly, but the human capacity to lead, adapt, and learn remains the ultimate differentiator. As discussed in our previous blog on The Human-In-The-Loop Approach to Building Real AI Skills, the increased need for upskilling for AI success also means that there is a renewed pressure to demonstrate the tangible impact of learning.
Organisations should be turning towards models that offer a more robust framework for measuring real outcomes. The Kirkpatrick Model is one such model and it evaluates reaction, learning, behaviour and results.
The Kirkpatrick Model remains one of the most reliable frameworks for evaluating training impact. It focuses on four levels:
While levels 1 and 2 provide immediate feedback, it is levels 3 and 4 that indicate true learning ROI. For financial services organisations, this could mean:
Reduced error rates in financial modelling
Faster compliance and risk assessment processes
More strategic decision-making
Improved client retention through better advisory skills
By focusing on behavioural change and measurable business impact, L&D teams can ensure their efforts actively support organisational priorities rather than just ticking boxes.
Purposeful Learning: The P-U-R-P-O-S-E Framework
The new learning imperative for AI drives a requirement for individuals who are adaptable, digitally savvy, and strategically minded. To support this new need, L&D programmes must:
Address key skills gaps
Enable ethical and effective AI adoption
Support ongoing digital transformation
Demonstrate clear, measurable business value
Learning initiatives must fuel the capabilities that deliver organisational purpose and competitive advantage. To ensure that these initiatives are closely aligned with broader business goals, many L&D leaders are adopting structured models like the P-U-R-P-O-S-E framework. This model fosters collaborative, actionable, and measurable learning, directly tied to strategic outcomes like innovation, adaptability, and responsible leadership.
Tactics that bring this framework to life include:
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Peer Mentoring: Senior staff sharing expertise with junior team members to build resilience and retain knowledge. -
AI Literacy Programmes: Empowering finance professionals with foundational AI knowledge to ensure confident, ethical use of new technologies. -
Internal Knowledge Sharing: Setting up collaborative learning circles where teams exchange practical AI use cases, such as fraud detection or portfolio analysis.
The increased significance of AI integration within the banking and finance industry, and upskilling teams to be able to react to this new normal is essential. Organisations are under growing pressure to ensure that AI-related learning and development efforts don’t just tick boxes but actively support their core purpose and strategic priorities.
This means shifting the focus beyond traditional metrics like course completion rates or learner satisfaction. While these indicators still matter, to justify investment and drive meaningful transformation, training initiatives must deliver clear, measurable business outcomes such as innovation enablement, increased productivity, improved decision-making, or enhanced risk management, all the while empowering teams with the capabilities to harness AI in a way that delivers real organisational value.
Measuring Adaptability
Adaptability has become a critical skill in the age of AI because the pace of technological change is rapid, constant, and often unpredictable. As AI reshapes roles, automates tasks, professionals must be ready to shift their thinking, adopt new tools, and respond effectively to evolving business needs. Adaptable professionals are better equipped to navigate this AI ambiguity and because of this, it’s an important skill that should be measured.
One of the most effective tools that is emerging for this purpose is the Adaptability Quotient (AQ) Assessment. Grounded in research, and unlike traditional intelligence or emotional intelligence metrics, AQ specifically measures an individual’s ability to adjust to change, unlearn outdated approaches, and thrive in uncertain or evolving environments.
The AQ assessment evaluates key dimensions such as mindset, ability, and character - providing a multi-dimensional view of adaptability that goes beyond gut feeling or anecdotal observation. For example, it can highlight who is likely to embrace new AI tools, who needs additional support to build confidence, and which teams may struggle with rapid change. When integrated into L&D and talent management strategies, AQ data can guide more targeted training, support strategic workforce planning, and help identify adaptability champions who can lead others through transformation. This ensures that as AI evolves, human capability evolves with it - making adaptability not just a soft skill, but a measurable and strategic business asset.
Any type of business investment must demonstrate value. Business outcomes - not just engagement metrics - must guide L&D strategy. By integrating structured models, fostering continuous learning, and embedding AI-ready skills into day-to-day operations, L&D teams can drive not only productivity, but resilience and long-term growth.
Whether it’s through the Kirkpatrick Model, the P-U-R-P-O-S-E framework, or tools like the Adaptability Quotient, the goal is clear: make learning work harder and measure what matters.