The Future of Learning & Development is AI-Powered

Artificial intelligence (AI) is transforming nearly every industry, and corporate learning is no exception. As financial institutions seek to remain competitive, leveraging AI in learning and development (L&D) has become a strategic imperative. Integrating AI can simplify L&D processes, enhance the learning experience, and provide data-driven insights to improve programs.?

While some may view AI as a threat that could potentially replace human jobs and roles, the reality is that AI is best leveraged to augment and enhance human capabilities. According to a recent article by Forbes, “Artificial intelligence helps with streamlining the learning process, tailoring and personalization of learning experiences to specific employee needs (which is part of what adaptive learning is), and unpacking analytics.”

By taking an intentional approach and establishing responsible AI practices, financial institutions can benefit from the tremendous advantages of AI in L&D, without compromising their long-standing training relationships and partnerships.

You can effectively leverage AI in your L&D to:?

1. Personalize content recommendations
2. Deliver microlearning and spaced reinforcement
3. Enable voice-powered learning
4. Analyze data to continuously optimize your L&D needs?

Personalized Content Recommendations

The first and most powerful application of AI in Learning and Development is the ability to personalize content recommendations for each individual employee. With the right neural networks analyzing data like employee profiles, roles, skills, performance data, and content consumption patterns, AI systems can automatically determine knowledge gaps and recommend the optimal eLearning courses for each person.?

Providing a personalized learning experience tailored to the distinct needs and goals of each employee is imperative for improving engagement, retention, and on-the-job skill application. The Harvard Business Review recently described personalized learning by saying, “This bespoke learning approach is aligned closely with individuals’ learner profiles and career trajectories and increases engagement, effectiveness, and retention.”

Without personalization, employees may feel overwhelmed navigating a vast catalog of content. They can also waste time taking irrelevant courses that don’t address their specific skill gaps.

The process of personalization can be automated and optimized on an ongoing basis. Here are a few ways AI can power this process:?

• AI can assess information in employee profiles like job role, level, location, tenure, career goals, and interests. This data signifies key markers for targeting relevant content. For example, an AI model may determine that branch managers in a certain geographic location need to focus more heavily on customer advisory scenarios compared to other roles.?
• AI also helps diagnose gaps in both technical and soft skills by evaluating things like assessment scores, participation data, manager feedback, and demonstrated on-the-job capabilities. Diagnosing gaps at scale is extremely difficult without an AI assistant.?

Performance management data offers tangible insights into struggles that may be addressed by supplemental training. AI would be able to identify member service representatives who are struggling to overcome member objections by analyzing incidents logged of accounts closed or services canceled. Additional objection-handling course content could then be automatically prescribed based on similar scenarios across the employee base. Equipping staff to confidently navigate objections helps retain accounts and maximize service adoption.

By assessing variables like content topics completed, time spent in a course, or completion rates, AI models can discern usefulness and relevance at the individual level in refine recommendations. For example, microlearning consumption habits may indicate a preference for certain modalities, or learning by device type could redirect course development to alternate modalities.

Tailored learning interventions for individual employees was once a near-impossible task at scale. Applying AI makes it automated, adaptable, and intelligent. As employees’ needs evolve, AI consistently maps the most optimal learning pathways. Staff across the entire organization can self-direct their learning with content that efficiently addresses personal growth areas while accomplishing institutional objectives and driving productivity.

Microlearning and Spaced Reinforcement

The second way to leverage AI is through microlearning and spaced reinforcement. These two rising approaches in learning help employees better retain information and advance toward mastery over time. AI makes it viable to scale the delivery of these modern learning approaches across organizations.

Microlearning refers to consuming knowledge in small bites, usually spanning 3-5 minutes. It prevents cognitive overload that occurs in lengthy courses. The brain's natural attentional drift makes absorbing anything longer than 10-15 minutes very ineffective. Ever wonder why there are commercial breaks every 15 minutes when watching TV? AI excels at creating and continually updating microlearning content libraries on various topics. Algorithms can rapidly turn book summaries, research publications, reports and articles into condensed, consumable snippets.

Spaced reinforcement promotes long-term retention by re-exposing employees to key ideas over optimally timed intervals using machine learning algorithms. Knowledge that is reinforced in this way sticks. One-off courses often lead to quick knowledge decay when completed.

There are two models AI deploy to enable microlearning and spaced reinforcement at scale: personalized push model and intelligent pull model.?

• Personalized Push Model - The traditional push model is personalized when AI systems assess individual employees’ data. This can include previously consumed content, demonstrated competencies, role, and goals to predict optimal microlearning pieces to push to each person, along with the ideal times to reinforce previous content. This relieves L&D teams from huge manual overhead.
• Intelligent Pull Model - The intelligent pull model allows learners to actively seek out and engage with learning resources and experiences based on their specific needs, interests, and curiosities. Employees can access AI-curated microlearning content libraries spanning topics like leadership, software, communication, critical thinking, and specialized job skills to self-direct learning in the flow of work. The libraries surface bite-sized pieces most related to employee interests and needs as they evolve.

When L&D scales to larger organizations, AI handles complexity that human teams cannot, such as:?

• Rapid microcontent generation
• Personalized sequencing
• Optimized space reinforcement based on proven memory science
• Updated libraries with new in-demand content
• Accessibility across the organization.?

Employees also benefit from digestible, engaging content and the flexibility of just-in-time learning. The capability to deliver the right microlearning content to the right employee at the right time and continuously reinforce it is how AI improves learning outcomes.

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Voice-Powered Learning

The third, and arguably most entertaining modality, is voice-powered learning. Voice-powered interfaces represent a fast-emerging paradigm for delivering learning experiences. AI makes it possible to scale customized voice learning across organizations with smart assistants that feel natural and conversational. These smart assistants are often called animated pedagogical agents.?

Allowing employees to access training content and assessments hands-free by verbally asking questions or requesting information unlocks significant flexibility to learn in dispersed environments. Field technicians can learn on-site without pulling out a mobile device. Branch employees can confirm protocols with a quick voiced query. Call center reps can refine pitches through roleplaying with a voice coach.

AI delivers a range of voice-based learning experiences. Conversational learning is a natural means of query and response. Much like querying a human instructor, employees ask questions out loud to an AI assistant that understands natural language. It responds with structured responses answering the question or probing for clarity just like human dialogue. Over time, the network analyzes queries to strengthen its knowledge.

Voice-enabled assessments allow the completion of assessments via voice input. This empowers learners to verbalize responses as they would naturally explain concepts to colleagues or leaders. AI evaluates responses based on understanding, accuracy, and clarity compared to model answers. Feedback is provided verbally in real-time. This allows users to show what they know, rather than responding to questions proving what they don’t.

Roleplaying with voice agents is a way to practice scenarios with conversational agents that exhibit realistic emotions and responses. This is proven to accelerate skill development. Whether pitching a complex mortgage product or navigating an objection, interactive roleplaying via voice helps employees master delivery.

Voice activation enhances discoverability of available training resources. Voice-based learning offers employees flexibility to build skills anywhere, with the experience feeling more natural with AI powering conversational interactions. Thanks to the conversational capabilities of AI, scaling voice learning in your organization unlocks use cases once considered unfathomable across dispersed workforces thanks to the conversational capabilities of AI.

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Analyzing Data for Continual Optimization

The fourth way to effectively leverage AI in your L&D is to use it to analyze data to continuously optimize. An enormous advantage of integrating AI into learning programs comes by gaining visibility into exactly how content is being consumed. With this knowledge, effectiveness can be quantified and opportunities to optimize can be uncovered.?

AI analytics make learning organizations data-driven. By gathering inputs spanning content usage trends, employee surveys, manager feedback, and competency demonstrations, AI gives clarity into what’s working and what enhancements may improve outcomes. Continual optimization is how L&D programs remain relevant to ever-evolving roles.

Three ways AI analytics lead to continuous optimization include:

1. Pinpointing gaps in content effectiveness
2. Overhauling outdated learning resources
3. Prioritizing the highest-value content creation.

Pinpoint Content Effectiveness Gaps

By finding content effectiveness gaps, you will be able to revamp your L&D library strategically. Granular reporting reveals specific topics/courses where employees struggle to retain and apply knowledge. Low scores or negative manager feedback on related skills signifies ineffective content. AI can recommend supplemental content to boost outcomes.

Regularly Overhaul Outdated Learning Resources

Track when courses were last revised against their assignment frequency, especially for skills assessments. Courses untouched for over two years but still heavily used likely contain outdated content. Refreshing these resources ensures we teach current best practices rather than obsolete methods.

Prioritizing Highest-Value Content Creation

AI aggregates queries and keywords searched across the learning portal over time. Spikes around previously low-searched topics reveal rising demand. Quantifying search trends gives clear direction on the highest-value content to develop based on employee curiosity.

The capability to uncover specific enhancement opportunities at scale is unique to AI. The analytical horsepower required to deliver a truly optimized learning function has simply never been possible until now. With continual insights on precisely how employees consume resources, struggle with skills, and exhibit curiosity, L&D can now perpetually fine-tune programs around what will demonstrably drive maximum human capability.

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Best Practices for Integrating AI into L&D

While the opportunities and advantages are vast, integrating AI into L&D programs must be done thoughtfully to ensure success. There are several best practices to keep in mind when considering how to integrate AI into your L&D. Here are a few to consider:

• Start small and scale. Begin with pilot programs to test AI functionalities and gather feedback before rolling out on a larger scale.
• Focus on collaboration. AI should complement, not replace, your training partner. Use AI to enhance the capabilities of OnCourse Learning and other training resources.
• Prioritize data privacy. Ensure that AI tools comply with data protection regulations and that employee data is handled responsibly.
• Invest in training. Equip your L&D team with the knowledge to leverage AI tools effectively. Understanding the technology is key to maximizing its benefits.
• Monitor and adjust. Continuously monitor the performance of AI in your L&D programs and be ready to pivot as needed.

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As financial institutions navigate a complex landscape, AI-powered learning presents a strategic opportunity to leverage technology for more adaptive, personalized, and data-driven training experiences. With a thoughtful and responsible approach grounded in human-centric principles, you can use the powerful tool that is AI to simplify processes, boost program effectiveness, empower employees, and build capabilities without replacing essential human insight.?

The successful adoption of AI in learning and development requires financial institutions to complement capable training partners like OnCourse Learning, with AI’s strengths in order to transform programs for the modern era. This involves embracing innovation while ensuring fairness and transparency.

By taking an adaptable, accountable, and ethical approach to AI integration, banks and credit unions can drive the development of a skilled, knowledgeable workforce ready for the evolving financial world. AI-powered learning allows institutions to improve L&D outcomes, empower employees, and secure a competitive edge.