The early moments of artificial intelligence (AI) were colored with a sense of wonder and excitement about its potential, leading to inflated expectations. However, organizations are now more readily embracing the integration of AI in L&D and into workflows, and we have gained a better understanding of its strengths and weaknesses.
AI is having a transformative impact on talent development and is reshaping our approach to acquiring knowledge and enhancing skills. AI’s capabilities reach beyond minor efficiency gains and are beginning to address complex challenges with depth and sophistication through content rationalization, content personalization, and workflow integration.
AI Applications for Learning and Efficiency
#1: Content Rationalization
Organizing and maintaining hastily created content, which often lacks comprehensive metadata such as titles, descriptions, and learning objectives, is a longstanding challenge for all learning organizations. Having a lot of content means resources can quickly become lost within an organization’s learning ecosystem.
AI’s capacity for content analysis and usage pattern identification can provide invaluable insights into their content inventory’s efficacy and relevance. This enables informed decisions regarding the need for additional content, potential modifications, or enhancements to existing materials, like streamlining the learning experience and maximizing resource utilization.
Moreover, AI’s role in content rationalization extends to adaptability in dynamic environments. Imagine a scenario in which an operational workflow is changing across an entire enterprise. This necessitates updates in all related learning materials. AI-powered tools can swiftly identify all instances in which the outdated workflow is referenced within a content library, facilitating updates for all content, and ensuring alignment with the new practice.
#2: Content Personalization
Learning and development (L&D) is shifting toward providing as many personalized learning experiences as possible. This personalized approach marks a departure from traditional one-size-fits-all methods, empowering learners to engage with content that is relevant and resonant with their learning journey. While personalizing learning journeys has been notoriously difficult in the past, AI is now making it easier than ever. AI can be leveraged to deliver tailored learning content to individuals, catering to their unique needs and preferences.
One of the earliest ways that L&D personalized content was through adaptive learning platforms. Learners would answer questions, and based on each learner’s performance, the learning platform would direct the learners to learning content that best matched their needs. These platforms can now utilize AI algorithms to analyze individual performance and mastery levels and to use comparative data from peers with similar profiles. This AI application specifically enables these platforms to curate a highly personalized learning path for each learner, optimizing their learning experience and facilitating continuous improvement. Moreover, the synergy between human expertise and AI capabilities allows organizations to capitalize on each person’s strengths, ultimately enhancing the overall efficacy and impact of other L&D initiatives.
#3: Workflow Integration
Unlike past attempts in which AI was merely layered over existing workflows, the current focus lies in a seamless integration that transforms how humans collaborate with AI to achieve business objectives. This shift is a result of lessons learned from earlier attempts in which AI’s true potential was not fully realized.
One of the critical aspects driving this change is the concept of bounded versus boundaryless learning. Traditional approaches confined learning resources within fixed courses and modalities, limiting their adaptability to changing needs. However, with AI-enabled workflow integration, organizations can create content and resources in an atomic structure, allowing dynamic reconfigurations to meet diverse learning needs and business requirements. This approach optimizes efficiency and fosters a culture of continuous innovation and adaptability, paving the way for enhanced productivity and meaningful outcomes.
Enterprise Software
Moreover, the vision of AI for workflow integration extends beyond isolated platforms or tools. Major players such as Microsoft with its Viva suite, Google Workspace, and Salesforce are spearheading efforts to create seamless AI-driven environments in which users can access AI resources within their existing workflows. This integration enhances productivity by leveraging AI’s ability to track data and anticipate user needs, ultimately empowering individuals to work smarter and achieve more significant results without constantly switching between tools or systems.
Productivity Bots
The initial excitement surrounding AI productivity bots, such as meeting bots, has given way to a more nuanced understanding of their utility. The consensus seems to lean toward leveraging meeting bots in the context of structured conversations rather than indiscriminately deploying them in all meetings. The value of bots in meetings is their ability to take notes and create action items, so structured conversations can provide more valuable outputs when users want more than a simple transcription or meeting recording.
The evolution of how we use this AI functionality indicates a maturation in how we integrate AI into our daily workflows. As AI becomes an inherent part of our productivity apps and systems, the focus shifts from its novelty to its practical capabilities and impact on enhancing human performance.
Rising Comfort, Rising Responsibility
As AI has evolved and course-corrected over the last year or so, we see a noticeable rise in general comfort toward AI, which directly corresponds with an increase in user responsibility. Skepticism is giving way to more comfort (but not necessarily more trust) as AI-driven tools such as Bing and Google have demonstrated their ability to generatively summarize information accurately and efficiently. Still, users must remain mindful of AI’s limitations.
One of the key aspects driving this rising comfort is the transparency and accountability in AI interactions. The ability to fact-check and validate AI-generated content ensures that humans remain in control of the information they consume and act upon. This sentiment aligns with GP Strategies’ Human+AI perspective, emphasizing the symbiotic relationship between human expertise and creativity and AI capabilities. It’s not about replacing humans but leveraging AI to elevate human problem-solving and creativity.
Also, organizations are responsible for preparing and structuring data effectively for safe and accurate AI utilization. The adage “garbage in, garbage out” holds true, highlighting the importance of clean, structured data to derive meaningful insights and outputs from AI systems. Defining what constitutes “good” learning content and embedding this knowledge into AI systems is crucial to avoid generic or subpar content generation.
Unlocking the Potential of AI in L&D
The AI and L&D journey forward involves restructuring data, rethinking workflows, and reskilling employees to leverage AI effectively. While these efforts may initially result in a productivity gap as teams adapt to new ways of working, it sets the stage for a paradigm shift akin to moving from propeller planes to jet engines. Building a solid infrastructure paves the way for significant advancements and unlocks the true potential of scaling AI into everyday workflows.
For more on the topic of AI applications and their impact on learning and development, check out the “Transformative AI in L&D” conversation between Matt Donovan, GP Strategies Chief Learning and Innovation Officer, and Jeff Fissell, LTG Vice President of Solutions, on the award-winning Performance Matters Podcast.
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