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  • Become an AI-fluent Leader in Learning and Development

    Updated: May 30, 2024 Written By: Mike Vaughan & Markus Bernhardt The Rise of AI in Learning and Development The infiltration of AI in Learning and Development (L&D) is accelerating, with a noteworthy 89.7% of organizations utilizing AI-based tools to enhance their operations. This surge has brought to light pressing concerns surrounding privacy and security, with 51.7% of professionals citing these as their primary apprehensions. Despite these worries, a surprising 60% of organizations are yet to implement a formal AI policy. It's imperative for L&D leaders to not only recognize the transformative potential of AI but also to advocate for robust governance frameworks that safeguard ethical use and data protection. Assessing AI Readiness: A Checklist for L&D Leaders For L&D leaders steering their teams towards AI adoption, readiness is key. Assessing AI readiness involves a multi-faceted approach: understanding the technological landscape, evaluating organizational infrastructure, and ensuring team competence. Leaders must ask probing questions: Is our data AI-ready? Do we have the necessary support systems? How AI-literate is our workforce? Answering these can be the difference between a seamless integration and a challenging implementation of AI in L&D initiatives. Strategies for Building AI Fluency in Your Team Building a team proficient in AI who can develop and deploy a mature AI Strategy requires an innovative and supportive approach. Start with training and educating your team, shifting mental models to embrace AI's potential, and setting the stage for a culture receptive to change. Draft a manifesto that reflects your organization's values and ethical stance on AI, ensuring alignment with broader goals. Engage your team in scenario-based exercises to brainstorm and evaluate AI's application in L&D, fostering a hands-on understanding of its capabilities. Codify your approach with a clear playbook detailing actionable processes for AI governance, providing a trustworthy guide for your team to follow. And finally, have communication systems in place that can surface signals and feedback, internal and external, effectively and efficiently. Integrating AI into L&D Programs: Best Practices As AI becomes integral to L&D, the focus shifts to best practices for integration. Embrace design and development processes that leverage AI for speed and efficiency, ensuring that programs are not only effective but also agile. The deployment of learning programs should aim for a global impact, accessible anytime and anywhere. This approach democratizes learning and underscores the importance of creating scalable, AI-powered L&D solutions that are as innovative as they are inclusive. Future-Proofing Your Organization with AI Competencies In an era where AI development is relentless, L&D leaders must be adept at identifying signals that indicate where to invest in AI competencies. It involves a balance of staying informed about the latest AI advancements and discerning which innovations align with your organization's strategic objectives. By equipping your team with the right competencies, you not only future-proof your workforce but also establish a culture of continuous learning and adaptability. This is essential for maintaining a competitive edge in a world where AI is reshaping industries, and key not only as part of your AI Strategy, but paramount much more generally in a fast-changing environment.

  • From Sandbox to Scale: Integrating Generative AI into Core Business FunctionsIntroduction

    Updated: July 1st, 2024 Written By: Mike Vaughan & Markus Bernhardt Introduction Generative AI is rapidly transitioning from experimental pilots to essential tools for business transformation . Moving beyond pilot projects and integrating generative AI into core business functions is crucial for achieving transformative growth and maintaining a competitive edge. For learning and development leaders, driving this transition within their organizations is vital to harness the full potential of AI. The Shift from Pilot Projects to Core Integration Pilot Projects as Innovation Playgrounds: Pilot projects have served as a sandbox for innovation, allowing businesses to explore generative AI with minimal risk. These projects often focus on peripheral tasks such as enhancing customer service with chatbots, automating IT operations, and diversifying marketing strategies through AI-driven content creation. According to IBM’s research, these pilots have been crucial stepping stones, demonstrating the potential and reliability of AI in business contexts​​​​. Transitioning to Core Integration The challenge and opportunity now lie in leveraging these learnings to integrate AI into core business functions, thereby unlocking its full transformative power. By moving AI from peripheral to essential operations, businesses can achieve new levels of efficiency and innovation. The Strategic Imperative of AI in Core Business Functions Integrating AI into core business operations is no longer a futuristic concept but a strategic necessity. Embedding AI into essential functions like supply chain management and product development can streamline complex processes and open new avenues for strategic decision-making. IBM’s data underscores this imperative, showing a leap in AI-related ROI from 13% in 2022 to 31% in 2023​​​​. This significant increase highlights AI integration as a fundamental driver of modern business success, providing substantial competitive advantages and operational efficiencies. Key Areas for AI Application Customer Service: Enhancing interactions, efficiency, and personalization. IT: Generating and testing code, automating documentation, improving cybersecurity. Sales and Marketing: Analyzing customer data, automating content creation, refining marketing strategies. Supply Chain: Identifying potential disruptions, optimizing logistics and operations​. Overcoming Challenges in AI Integration for Business Transformation We aren’t denying reality— AI adoption comes with its own set of unique challenges, and many organizations find themselves in need of a strategic AI framework for purposeful implementation. Consider the following areas of transition and how they may impact your teams and business. Data Governance and Ethics As organizations transition from pilot projects to full-scale AI integration, they must navigate several challenges, particularly around data governance and ethics. Ensuring data accuracy, privacy, and security is paramount. Establishing a robust governance framework is essential for overseeing AI deployments and addressing ethical considerations​. IBM’s practices and policies offer a blueprint for managing these challenges, emphasizing the importance of ethical AI use and comprehensive data governance to mitigate risks and ensure responsible AI integration​. Managing Change and Mitigating Risks Change Management: Addressing organizational resistance to change with clear communication strategies. Risk Mitigation: Techniques for phased implementation and continuous monitoring​. What This Means for Learning and Development Teams L&D teams play a critical role in fostering AI literacy across the organization. This involves designing and implementing training programs that cover the fundamentals of AI, its applications, and its impact on various business functions. By promoting a deep understanding of AI, L&D leaders can help demystify the technology and alleviate any apprehensions employees might have, fostering a culture of AI literacy. Here's what that may look like. Training Programs: Develop comprehensive AI training modules for different levels of expertise. Continuous Learning: Encourage ongoing education opportunities, such as webinars, online courses, and certifications. Practical Applications: Integrate practical AI applications into training programs​​​​. Addressing Skills Gaps: The shift to integrating AI into core business functions necessitates new skill sets. L&D teams must identify and address these skills gaps to ensure a smooth transition. Skills Assessment: Conduct regular skills assessments to identify gaps and tailor training programs accordingly. Targeted Upskilling: Focus on upskilling employees in areas critical to AI integration, such as data analysis, machine learning, and ethical AI practices​​. Supporting Change Management: AI integration can be disruptive, and managing this change effectively is crucial. L&D teams are instrumental in supporting employees through this transition. Communication Strategies: Develop clear communication strategies to inform employees about AI initiatives. Change Management Training: Offer training on change management techniques to help employees adapt to new workflows and technologies. Feedback Mechanisms: Implement feedback mechanisms to gather employee input and address concerns related to AI integration​. Promoting Ethical AI Use: As AI becomes more integrated into business operations, ethical considerations become increasingly important. L&D teams must ensure that employees are aware of and adhere to ethical guidelines. Ethics Training: Include modules on AI ethics in training programs, covering topics such as data privacy, bias mitigation, and responsible AI use. Ethical Frameworks: Work with leadership to develop and enforce ethical frameworks for AI deployment within the organization. Fostering a Culture of Innovation: Finally, L&D teams should cultivate a culture of innovation where employees feel empowered to explore and leverage AI in creative ways. Innovation Labs: Establish innovation labs or centers of excellence for AI projects. Incentivizing Innovation: Create incentives for employees to develop and implement AI-driven solutions. Cross-Functional Collaboration: Encourage cross-functional collaboration to leverage diverse perspectives and expertise in AI initiatives​. Future-Proofing Your Business with Generative AI Strategies In an era where change is constant, future-proofing your business with generative AI strategies is essential. This involves an organization-wide commitment to upskilling employees and fostering AI literacy, ensuring that everyone understands AI’s capabilities and potential. Learning and development leaders play a critical role in guiding organizations through this change, addressing resistance, and implementing risk mitigation strategies. Drawing on IBM's phased implementation approach, businesses can continuously monitor progress and adjust strategies to ensure successful and sustainable AI integration. Conclusion The strategic integration of generative AI into core business functions represents a pivotal moment in corporate innovation. Transitioning from pilot projects to full-scale implementation is not just a technological upgrade but a fundamental shift in business processes. Learning and development leaders are crucial in this transformation, ensuring their teams are equipped to navigate and capitalize on the AI-driven landscape. BONUS: Pave the way for a future where your team not only adapts to the tidal wave of AI but excels with its use. The Brandon Hall Group’s Strategy Brief , " Bigger Business Impact: Using AI to Create Skill-Based Learning—Fast " is available for you to download now!

  • The Future of Learning Development: Where the Puck Is Going

    Updated: April 30, 2024 By: Mike Vaughan & Markus Bernhardt As we navigate the exciting terrain of learning and development, infused with the transformative power of artificial intelligence, it's essential to anticipate where the industry is headed, not just where it currently stands. Today, many companies have embraced AI tools like chatbots, tutors, and large language model interfaces, making significant strides in the educational landscape. While these advancements are commendable, they merely scratch the surface of what's possible to enhance learning efficiency and efficacy. The Present: An Inflection Point in AI Utilization Currently, AI in learning largely focuses on providing interactions via chatbots and AI tutors. These tools wrap around existing large language models to offer a semblance of personalized learning. However, they often deliver standardized content that fails to deeply engage with individual learner needs and preferences. While these technologies represent a leap forward, they are preliminary steps towards a more revolutionary approach to learning. The Future: Personalization and Speed in Skill Acquisition The future of learning development is not about consuming more information; it’s about refining how we acquire and apply new skills swiftly and efficiently. Imagine a future where learning is as dynamic and personalized as your daily interactions with your favorite digital assistant, but focused on professional and personal development. This is where we see the significant pivot—towards truly personalized learning experiences that adapt in real-time to the learner's progress, preferences, and performance. Dynamic Learning Environments In the future, AI-driven platforms will not just respond to user inputs but will proactively challenge learners by dynamically generating new content and interactive challenges based on their learning history and future goals. This approach will allow learners to absorb knowledge and, more importantly, apply new skills in various contexts, thereby accelerating skill acquisition and application—perhaps not as instantaneously as downloading skills in "The Matrix," but significantly faster than today's methods. Data-Driven Personalization The cornerstone of this evolution is the sophisticated use of data. AI systems will become adept at analyzing vast amounts of data regarding individual learning patterns and outcomes. This data won’t just inform content delivery; it will enhance it, creating a feedback loop where every interaction enriches the system’s understanding of the learner. As these systems become more attuned to individual learners, they can anticipate needs and adapt challenges to optimize learning efficiency. The Impact of Enhanced Learning With these advancements, the implications for professional development are profound. Employees can learn new skills more quickly, responding to changing job requirements with agility. Organizations can foster a more adaptable and skilled workforce, ready to meet the demands of rapid technological change. Conclusion: Embracing the Future As futurists and innovators in the field of AI and learning development, we must focus on these advanced capabilities to transcend the current limitations. By prioritizing personalization and efficient learning, we can revolutionize how skills are developed and applied, ensuring that individuals and organizations are prepared for whatever the future holds. Let's skate to where the puck is going, not where it has been.

  • How to Select AI Vendors and Tools

    Navigating the AI landscape is becoming increasingly challenging, not only due to the constant hype and noise surrounding it, but also due to fast progress in model capabilities, paired with new products and vendors emerging onto markets at a high pace. These trends show no sign of slowing down, at least not in the near future; in fact, it seems the noise surrounding AI is only growing louder, making it more difficult to distinguish what is genuine, significant, and practical. This complexity is additionally exacerbated by numerous marketing efforts and articles from companies that have only recently begun to engage with AI. These companies often suggest that their AI innovations are groundbreaking, adding to the confusion for those seeking to gain an overview of the market and product capabilities. As L&D professionals, we need a clear path to choose the right tools that empower our learners and prepare them for the future, equipped with the right skills. This guide aims to do just that, and for simplicity,  we have organized what to look for in a vendor into three overarching categories. At The Thinking Effect, our goal is to get the conversation started, and we hope this will lead to more refined thinking that will help us all navigate the future of learning and AI. Choose Vendors Who are Human First Ethical Guardians: Look for vendors with transparent AI governance and ethical practices. How do they ensure responsible development and avoid crossing boundaries? An effective and simple way to approach and judge this is to simply ask: Can they articulate their "red lines"? Open Box:  Choose vendors who can explain how their AI works, how decisions are made, what data is used, and who offer demos or trials for you to test drive. Remember, you should be able to use the tools yourself before trusting them with your learners. Privacy Sentinels: Your learners' data security is paramount. Choose vendors with robust security measures and clear data privacy policies. Choose Vendors/Tools with Growth Potential Extensibility:  Pick tools with a robust API to integrate with your learning ecosystem. If they don’t already have a vision, then they may not be thinking systemically. Constant Evolution:  Choose vendors who actively develop and update their AI, releasing new features and improvements regularly (monthly if possible).  We noticed vendors who released compelling updates monthly last year are still leading the pack. A good thing to ask is also what a vendor’s road map looks like for the next 3 months. Collaborative Spirit: Look for vendors who value partnerships and feedback. Choose those who work with other AI players and are open to co-creating with you to refine their tools. Data Analytics: Look for tools that can capture extensive learner data. This data can be used to improve the learner’s skills and experience, and help learning leaders determine where best to invest future budgets and resources. Of course, also ensure their data privacy policies are vetted. Choose Tools that Meet Your Learner and Business Needs Remember the three tool categories we discussed at the beginning of 2023? They're still relevant and easy to remember when evaluating tools: Asset Creation Tools - Speed up the design and development of engaging learning materials like images, videos,  animations, audio, music, and scripts. These tools can be real time-savers for busy L&D teams. They can also significantly cut design and development costs, which stakeholders are keen to achieve this year. Optimization Tools - Analyze data to personalize and improve the learner experience. Think of them as behind-the-scenes wizards, crunching numbers to deliver insights and recommendations. These tools are crucial for creating real personalized and adaptive learning experiences, which optimize learner time and potentially accelerate speed to proficiency. Experience Tools - Make learning interactive and engaging. Imagine bots offering coaching, feedback, or additional resources on demand. These tools enhance learner motivation, improve learning effectiveness, and make learning feel experiential. Now, let's be honest, L&D pros love sparkly things.  Shiny does not typically equate to learning efficacy, however.  So, as you evaluate tools in each of these categories, ask how the tool will: cut design and development costs reduce review cycles and time of subject matter experts accelerate upskilling and reskilling Remember, AI is and will continue to replace knowledge-level learning. So, focus on tools that will develop higher cognitive skills.  In other words, let AI teach what people need to know while you guide them in applying what they know to real-world situations. Final thought. AI is a Tool, Not a Magic Wand! The most sophisticated AI tool is powerless without a well-defined learning strategy and human expertise. Simply put, AI should augment your existing efforts, not replace them. Focus on creating a holistic learning ecosystem where AI and human ingenuity work together. Bonus Tip: Stay curious! The AI landscape is constantly evolving. Attend industry events, network with fellow CLOs, and subscribe to The Thinking Effect to learn from other L&D professionals. Authors Mike Vaughan & Markus Bernhardt

  • 2024 Predictions: 5 ways AI Will Impact L&D

    Looking back at the mindblowing advancements in AI during 2023, we anticipate a range of impacts AI will have on Learning and Development (L&D) in the coming year. We aim to support the L&D community and welcome your thoughts, suggestions for additions or changes, and points we might have overlooked. From Solo Innovations to AI Synergy: The Future of Collaborative Tools 2023 was a wild ride in the AI world, with an astronomical 10,000 new tools launched! While the innovation shows no signs of slowing down, the future might not be about standalone solutions, but instead a “toolbox” of powerful AI,leading to seamless user experiences. 2023's tools were laser-focused on specific tasks. This helped refine functionalities and train and improve the output, but for users, it meant juggling multiple tools like an air traffic controller. Now, imagine the power of a unified AI toolbox. One tool that generates avatars, adds voices, and crafts contextualized background images – an instructional designer's dream! This user-friendliness will be key to wider adoption. In 2024, seamlessness will be the survival currency. We may enter the age of the "Island of Misfit Tools," where isolated, incompatible options struggle to find a place. Meanwhile, tools that combine their specialties into streamlined experiences will empower users like a master mechanic wielding a well-organized, versatile toolbox, where every tool fits perfectly and serves a specific, crucial purpose." Here's the call to action for L&D leaders: AI vendors: Look beyond your own tool. Explore partnerships, integrations, and collaborations. Build bridges, not walls, in the AI landscape. Users: Let your voice be heard. Demand seamless experiences. Opt for tools that understand your workflow and speak your language, not just ones with the fanciest tech. Remember, the future of AI isn't a solo act; it's a dazzling team performance. By working together, we can create an AI ecosystem where everything clicks seamlessly, empowering instructional designers to achieve their goals while meeting the goals of the business to do things faster, better, and cheaper. A Burning Question for 2024: L&D's Mindshift Revolution As 2023 drew to a close, a recurring theme buzzed through The Thinking Effect team's inbox: "What radical changes in mindset do we need to make to secure L&D's future in 2024?" It's a powerful question that deserves a collective brainstorm from the L&D community. Here's our forecast and some ideas to chew on. By the end of 2024, expect a significant shakeup in processes, people, and technology. But before we get too grandiose, let's zoom in on the nitty-gritty: Many organizations are bogged down for months creating training – some for a whopping 4-6 months! –to build eLearning or workshops. Why? A tangle of inefficiencies: instructional designers grappling with unfamiliar topics, subject matter experts drowning in writing and reviews, stakeholders holding up approvals, and tools requiring too much manual work. In 2024, L&D professionals will need to foster a mindset of – speed. Speed to Product: Design, build, and deploy faster using efficient tools and collaborative workflows. Speed to Proficiency: Craft experiences that accelerate skill development and ensure learners are job-ready sooner. Speed to Performance: Capture data to measure impact, adapt programs, and continuously improve learning efficacy. Resistance is futile. Remember the Cloud and SaaS resistance? Just a decade ago, the idea of storing data in the cloud or using SaaS applications sent shivers down many IT spines. "Never gonna happen!" was the common refrain. Fast forward – everyone's in the Cloud now, using SaaS tools daily. Get ready for another paradigm shift, L&D professionals. 2024 is the year AI explodes in the workplace. With its AI co-pilot, Microsoft Office hits the market in January, followed by the next release of Google's Duet AI. This means the tools you use daily – email, presentations, spreadsheets, word processors – will all have AI built in. And that's just the beginning. By mid-late 2024, expect everything from AI phones and smart home devices (think beyond Alexa and Siri) to AI-powered wearables to flood the market. Resistance is futile, and preparation is essential. This is your wake-up call, L&D professionals. Now's the time to craft your L&D AI manifesto and AI advocacy plan. If you don't, non-L&D professionals might think they can create training and probably will.  That's a recipe for chaos and headaches for everyone, so let’s mitigate the impact. From Content Creators to Experience Architects – The Rise of the Instructional Alchemist Here at The Thinking Effect, our mission is clear: we're on a crusade to equip L&D professionals with the knowledge and tools they need to thrive in the age of AI. We believe every L&D professional deserves a seat in this revolution, but here's the catch: you must practice what you preach! Embrace the need for upskilling, reskilling, and adaptation – it's your advice! With AI handling the content and asset creation side of your job, your focus should shift to crafting experiences that cultivate higher cognitive skills – critical thinking, creativity, problem-solving, and the like. These are the skills the World Economic Forum and other experts highlight as crucial for future success. Forget dusty old eLearning modules and forget-me-not PowerPoint presentations. 2024 is the year instructional designers morph into something far more fascinating: experiential designers. Imagine crafting gamified journeys where learners solve puzzles and level up their skills. Think micro-learning bursts tailor-made to individual needs, igniting the sparks of critical and creative thinking. Picture immersive simulations mirroring real-world scenarios, putting decision-making skills to the test. This isn't just about flipping content on its head; it's about a fundamental shift in design mindset. Forget churning out generic modules. Instead, start with the "aha!" moment in mind. Reverse engineer the experience, building toward that transformative moment where deep understanding and its real-world consequences click into place. Think of yourself as an alchemist of learning, turning knowledge into transformative experiences. You'll need to master the art of: Storytelling and engagement: Hook learners with narratives that captivate and challenge them. Adaptive learning: Personalize the journey, cater to individual needs, and keep everyone on the edge of their seats. Active learning: Ditch passive lectures for immersive games, simulations, and real-world applications. Data-driven design: Track progress, refine experiences, and ensure every element fosters meaningful learning. Embrace the challenge, instructional designers! The future of learning is calling, and you hold the key to unlocking its potential. Become the architect of impactful experiences, not just the curator of content. 2024 awaits, and your learners will enjoy a new journey into experiential learning. Ditch the Catalog, Embrace the Learning Journey: AI Personalized Your Way Imagine AI tools that read your learning style, adjust the pace to your rhythm, and adapt content based on your understanding – in real time! This isn't science fiction; it's happening now. These aren't just tweaks to the old model; this is a revolution. Learning will be: More efficient: No more wading through irrelevant modules. Dive straight into what you need when you need it. More engaging: Forget monotonous eLearning page-turners. Get hooked by content that speaks directly to your interests and challenges. More effective: Master skills faster by practicing the skills across multiple, evolving situations. So, what does this mean for those static course catalogs? They're relics of the past. Instead, imagine: You are creating a course on any topic, industry, or situation – on the fly! Need to train your sales team on the latest product? Boom, in an instant AI-powered course tailored to their specific needs. You are stepping into immersive simulations that mirror real-world scenarios. Practice leadership skills by making decisions as a leader and seeing the impact, or hone your business acumen skills by running an aspect of your business. The possibilities are endless. The best part? This isn't some distant dream. The tools are here, and they're getting better every day. The extensive catalogs with minimum completion ratings and heavy price tags will become part of the L&D folklore ‘Do you remember when we watched hours of videos or had eLearning on one screen while I did work on another.’ The AI Platform Revolution: Why 40% of Companies Will Be Ditching Their Old Systems Remember those clunky, outdated software tools and platforms that look like they were created 20 years ago? Fosway Group predicts 40% of companies will ditch them within two years for a game-changer: AI-centric platforms. 2024 is the year of the smart platform.  Remember all those scattered tools causing chaos? New AI platforms will put them out to pasture. One platform handles everything – authoring simulations, eLearning, micro-learning, and assessments. Deploying self-guided, team-based, and in-person.  Capturing data and generating practical insights.   And yes, AI will do much of the heavy lifting, generating content and assets and slicing and dicing data. Here's the deal: these new AI-powered platforms blow the older ones out of the water. They do it by: Harnessing AI's power to deliver better data and insights. Imagine uncovering hidden trends, optimizing learning, and making better recommendations to the learner and stakeholders. Streamlining your course creation like never before. Say goodbye to lengthy design and development processes and scattered tools. AI platforms will integrate various AI tools, accelerating design, development, deployment, and maintenance. Making technology and learning feel more natural and intuitive. AI will not only make use technology use easier, but it will also significantly change the learner experience. But what about your existing content? No worries! These platforms are designed to leverage what you already have. Ingest your eLearning, PDFs, PowerPoints, and knowledge bases – everything comes along for the ride. Authors Mike Vaughan & Markus Bernhardt

  • Navigating the AI Wave: Transforming Learning and Development for a Smarter Tomorrow

    By Mike Vaughan and Markus Bernhardt Welcome to our Q&A blog, where we dive into the evolving landscape of Learning and Development in the age of artificial intelligence. As questions about the impact of AI on future jobs and workflows loom large, we explore the Thinking Effect approach, envisioning a future where AI relieves us of mundane tasks, and allows us to focus on critical thinking skills. How is AI affecting future jobs and workflows? The way we see it here at The Thinking Effect, AI will enter the job market by automating mundane tasks and workflows, especially those activities that have traditionally been labeled for knowledge workers. This will benefit both employees as well as clients in a number of ways. First and foremost, these tasks will be automated and take place instantly, 24/7. Secondly, the AI-supported processes will reduce ‘human error’ in bureaucratic tasks. From a learning and development (L&D) standpoint, AI will enhance the creation of knowledge-based resources and content. This will enable designers to dedicate more time to crafting experiences that cultivate higher cognitive skills such as critical thinking, creativity, and systems thinking. As AI technologies advance, their impact on the job market is expected to grow significantly. However, predicting the specific effects and their sequence remains challenging and largely speculative at this point. What should L&D, HR, and Talent Leaders be doing right now? In our view, there has been a lot of interest in AI, and early adopters have been exploring how to use AI tools to accelerate time-consuming tasks, and for ideation. Organizations have largely adopted a cautious approach, closely observing the evolving landscape and pinpointing potential opportunities. While some have proactively engaged, many are holding off until their 2024 budgets are finalized. Consequently, we anticipate a significant increase in requests for proposals (RFPs), practical applications, and projects. These initiatives are expected to be more strategic in nature, going beyond individuals using standalone tools for isolated tasks. This implies that organizations will soon start rolling out new business strategies, with communication flowing from top leadership through various departments, teams, and smaller units within the organization. However, the presence of a budget and a vision doesn't automatically guarantee that these efforts at departmental and unit levels will be effective or impactful. In fact, we anticipate many organizations may find themselves grappling with more questions than answers. Therefore, it's crucial to develop a well-defined, actionable, and communicable AI strategy. This strategy should cover everything from ethical considerations to balancing risks and rewards, and the decision-making process regarding the selection and application of tools and use-cases. At The Thinking Effect, we have long advocated for such a strategic approach. For more insights, explore our AI Advocate resources. If you are interested in getting support to develop your business strategy, be it at the leadership level or within a business department or unit, do contact us for an explorative chat: What recommendations do you have for training programs to learn how to use AI responsibly and effectively? In our experience supporting organizations and teams with AI training and implementing an AI strategy, good facilitation, ideally in person and in small to medium groups works best. This approach enables teams to create a common language and mutual understanding, constructively discuss concerns and opportunities, and unite around a well-defined AI strategy. A key challenge, however, will be maintaining the relevance of the AI strategy, given the rapid emergence of new tools and breakthroughs. The AI strategy should be a consensus-driven, clearly communicated, and actionable guide to effectively navigate rapidly evolving technology. It must be tailored to each organization's unique structure, focusing specifically on its product, service, and client strategy. If you are interested in delivering training within your organization, do feel free to get in touch with us for an explorative chat: What are the fastest-growing and fastest-declining jobs? We don’t feel like we have the data and global perspective to answer this question, so we recommend checking out resources from McKinsey, IBM, the World Economic Forum, and BCG. How can AI be used to make their jobs easier? AI tools like predictive text have been part of our devices and applications for some time. Despite significant attention in the past year and the availability of many new generative AI tools (over 400 featured for L&D professionals on The Thinking Effect), their integration into the workforce is just beginning. A notable development is Microsoft's launch of Microsoft Copilot on November 1, an optional feature for enterprise clients within the Office 365 package. The extent and timing of its adoption and visibility in the public sphere are yet to be determined. It is possible that significant uptake may not occur until after 2024 budgets are allocated. However, a major shift is on the horizon: Until now, individuals had to actively seek out AI tools like ChatGPT, signing up for a login or a free trial to experiment on their own. With AI tools becoming integrated into the everyday tools we depend on, we expect a rapid increase in their adoption. So how will this make jobs easier? We could give you a full overview here, in writing, but we’ve provided the link to Microsoft’s intro video for Copilot above, which we think will give you a much better picture of what is already possible. Just imagine the possibilities when capabilities improve further over the next rounds of releases. How will AI shape the future of corporate training and learning as a whole? At The Thinking Effect, we say content is no longer king - experience is. What we mean is that AI is getting better, and at the moment weekly, at creating and packaging content. We anticipate AI will replace eLearning, micro-learning, personalized and just-in-time learning, knowledge and performance management, and other knowledge-level learning modalities. Therefore, we suggest letting AI do what it is good at and equipping humans to do what we’re good at - creating experiences that develop higher cognitive skills, and with a human touch.

  • L&D Professionals: Watch for these innovations in AI

    Hey, L&D professionals! Here at The Thinking Effect, we're all about keeping you ahead of the curve, so here are some significant innovations in AI that will impact L&D later this year and into 2025. Multimodal Mania: Get ready for learning experiences that go beyond text prompts. AI's evolution now includes sound, images, and even physical interactions. These new capabilities will redefine just-in-time learning, performance support, and knowledge management.   In addition, the responses chatbots provide extend beyond text, ensuring more nuanced, human-centered learning experiences. Context Explosion: Remember feeding AI crumbs of information? Well, that’s a thing of the past. Get ready for a feast! AI's understanding of context is about to expand significantly. Echoing the need for nuanced AI applications in L&D will make learning experiences more adaptive and responsive, catering to individual needs and preferences. All in line with the personalized approaches we've emphasized previously. Wearable Wonderland: AI innovations bring us smaller, smarter, and cheaper language models, perfect for powering tiny AI brains in wearables and devices. Think earbuds whispering personalized language lessons, smartwatches providing coaching, or even VR glasses creating immersive learning simulations – all controlled by smaller language models. Speedy Training, Stellar Results: Buckle up for lightning-fast AI training! This means quicker development of new models, lower costs, and ultimately, sharper, more effective AI powering your learning experiences.  Think about L&D having their models trained on your leadership model, aligned with your cultural norms and organizational vision, providing just-in-time learning. Specialization Station: Dedicated AI models for specific tasks are on the rise. Think automated grading, personalized feedback bots, and targeted content creation – all handled by AI specialists trained to do one thing exceptionally well. We're here to help you navigate this exciting journey, so let's build a future where learning is as diverse and dynamic as the human mind itself! Leave us your feedback in the comments on what you would add or modify to the list above. Authors Mike Vaughan & Markus Bernhardt

  • The AI Advocate's Role in Unlocking Its Potential in Learning and Development

    by Mike Vaughan, Brian Hackett, and Markus Bernhardt Why are Learning and Development (L&D) professionals struggling to see the biggest opportunity in L&D? As one CLO recently shared, “I get it; people outside L&D teams already utilize AI to create training. Why wouldn't they? The tools are user-friendly and efficient… [it] is adequate for immediate business requirements. It seems like a significant opportunity to step up. Instead, L&D professionals are sitting on the bench. We need to get in the game!" Does this sentiment resonate with you? For half a century, technological innovations have been shifting the learning landscape. Today, with the introduction of AI, we stand at a pivotal juncture in the journey as L&D professionals. AI is the unexpected Swiss Army knife revolutionizing the processes of creating, developing, and delivering training. It's akin to injecting nitrous into a race car, propelling it to newfound speed and excitement. From a business standpoint, this translates to substantial cost efficiencies, time savings, and better learning—an unequivocal win-win scenario. What does it take to get into the game? L&D needs AI advocates. No, we’re not saying grab your most enthusiastic technophile on the team to sing the praises of AI publicly. Imagine the strategic addition of an AI advocate who plays a crucial role in promoting the understanding, acceptance, and responsible integration of AI technologies while skillfully championing the adoption of these solutions. AI advocates can actively ensure L&D has a seat at the table. Put in direct terms, the sooner you get an AI advocate in place, the more L&D jobs you’ll save. AI will not replace jobs, but L&D will need to learn to work alongside AI to automate and accelerate the creation of training. We believe AI can be a unifying force across organizational ‘silos’, and L&D can be instrumental in making this happen. What does an AI Advocate do? An AI Advocate demonstrates how AI can significantly cut design and development costs, accelerate the time required to produce high-quality learning, and transform L&D into a data-driven culture. Here is a summary of the key benefits the Advocate can demonstrate (not just talk about): Speed: From manual to automated. Many of the AI tools created this year can help L&D professionals cut costs by upwards of 90%. There are tools to create images, audio, videos, avatars, animations, and 3D graphics within minutes. Some tools rapidly create eLearning, microlearning, and simulations. Other tools are revolutionizing just-in-time and in-the-flow of working learning. There are even tools for securely ingesting previously created learning materials and converting them into experiential learning. Experience: From what-to-think to how-to-think learning. What-to-think learning entails teaching people the processes, procedures, and methodologies they need to perform their jobs. How-to-think learning entails applying what people know to new and emerging situations. AI is optimized for teaching the what while humans are great at teaching the how. Analytics: From smiles to insights. Many organizations still evaluate the efficacy of learning using smile sheets. There’s a good reason: most traditional technologies do not capture useful data, and making sense of what little data is available is challenging. With modern learning platforms and AI, behavioral analytics (how an individual’s thinking and behavior changes over time) is now possible and applicable. What is your AI perspective? Undoubtedly, AI is a game-changer, but it also raises new questions from stakeholders. Each organization has unique attributes, so we’ve posed the following questions to help you shape your perspective on AI’s transformative potential for your L&D team and organization. The lens in which we encapsulate our perspective is tied to one or more of the outcomes of speed, experience, and analytics. Most businesses are adopting AI to help them achieve a competitive advantage in getting products to market more quickly and providing higher-value services more effectively. So, if business leaders are thinking about speed, experience, and analytics, then L&D should be thinking about the same thing. What advantages can we expect from AI in L&D? Let's flip the script on this question and introduce a new concept: speed to performance. Rather than detailing a list of advantages, let’s focus on how AI accelerates speed to performance. The more quickly we can educate individuals and the more effectively they can apply their newfound knowledge, the better the organization will become. This reasoning will resonate with stakeholders, positioning L&D as a partner in helping the organization become more competitive. Is reducing staff a viable option if AI can satisfy all our training requirements? While some organizations might see this as an opportunity to ‘right-size,’ let’s consider it an opportunity for organizational growth and adaptation. Instead of cutting staff, consider reallocating resources to address a critical business imperative—upskilling and reskilling. Heavyweights like McKinsey, IBM, and the World Economic Forum agree that the world is on the brink of the most substantial training challenge in our history, with a staggering 1.4 billion people needing reskilling in the next five years. That's why it's vital to kickstart this process by upskilling instructional designers and turning them into experiential designers. While AI automates knowledge-level learning, L&D professionals can focus on crafting experiences that turbocharge skill development. You mention cost savings; what are training costs today compared to what we anticipate they will be once we start to see the benefits of AI tools? This is another budget-cutter question. Therefore, our recommendation is to frame the discussion regarding how L&D will accelerate the design and development of learning to meet the demand of upskilling and reskilling the workforce. Not to sound overly cliché, but the conversation needs to convey that learning can be done faster, better, cheaper, and at scale. Emphasize the better by talking about quality. This is critical because speed is often associated with a lack of quality. Many organizations, especially those focusing on heavily researched products, must emphasize quality. Here’s how: to achieve speed to performance–that is, to provide an individual with the skills they need to do their job better and faster- requires the content and learning experience to be high-quality. Thus, suggest using the savings from AI tools to enhance and elevate the quality of educational experiences to accelerate skill acquisition. Can we get rid of certain tools to cut costs? While this seems logical, costs for tools shift to newer and more advanced options rather than vanish. That’s why it’s prudent to concentrate efforts on enhancing the learner's experience. AI will soon outshine traditional tools for creating eLearning content, disseminating knowledge, facilitating just-in-time learning, and improving performance on the job. Since AI will become responsible for knowledge-level education, L&D professionals can shift their energy toward what they love doing: improving the skills of others to feel more confident and successful in their careers and their lives. This approach emphasizes AI's unique advantages and how it supercharges upskilling, making learning more efficient and effective. How are organizations ensuring the successful integration of AI to cut costs and improve learning? To establish credibility as an AI Advocate, you’ll want to consider drafting both an AI Manifesto and an AI Playbook. AI Manifesto – This document outlines how L&D will use AI ethically and responsibly to help the organization gain a competitive advantage through the upskilling and reskilling of the workforce. AI Playbook – If you’re playing to win, you’ll need a playbook that outlines various experiments to conduct to determine how to create scalable and better learning experiences faster and more cheaply. The playbook will serve three purposes. It will be: Your plan of initiatives and objectives to lead L&D teams in their adoption and use of AI tools. Your roadmap to help stakeholders see how you intentionally explore how AI can improve L&D and the business. Your data strategy to measure the efficacy of learning, inform learners of what they need to improve, and guide the organizations on where to invest time and resources. Tips: Embracing the concept of "fail fast and learn quickly" is crucial in today's rapidly evolving AI landscape. It's essential to act with urgency and adopt an experimental mindset. Avoid becoming overly dependent on any specific vendor's product as new tools frequently emerge and outpace older ones. Navigating this rapidly changing landscape can be challenging, but resources like The Thinking Effect can provide guidance. How will we gauge Return on Investment (ROI), and what does this entail? In L&D, we've traditionally relied heavily on smile sheets to gauge training success. It's high time we reframe our ROI conversations and focus on how learners' thoughts and behaviors align with organizational objectives. This is where Behavioral Analytics (BA) can help. BA can help determine efficacy, provide learners with more valuable and practical insights into their strengths and opportunities, and change conversations from “this is how people felt” to “this is what we recommend doing next.” How can we ensure the security of our data and individual privacy? Here's reassuring news: major professional service firms have recently invested billions of dollars to instill confidence in their clients regarding AI and data security. Much like the transition to cloud computing or the emergence of Software as a Service (SaaS) companies, organizations are progressively becoming more comfortable with data protection. Soon, organizations will be ingesting content into their private AI models to provide guidance, answer questions, and create training at mind-blowing speed. As a result, we recommend advocating for how L&D can guide this transformation. For example, the AI manifesto should encompass statements addressing data privacy, data protection, the secure use of company data and intellectual property, and the cautious use of unsupported AI tools. In addition, it should set the expectation that learning and content experts will diligently review all AI-generated materials for accuracy, bias, and quality. Finally, what can we anticipate the future of learning to be like? Not surprisingly, this question is top-of-mind for many people and organizations. Though no one can predict the future, here are some key points we're highly confident about, which may aid you in shaping your perspective: Personal AI training assistants: We foresee a future where each of us will have a personal AI training assistant and coach that will readily respond to commands like "create a 5-minute course on improving team performance," "craft a discussion guide with questions," or "schedule a team meeting for Friday morning." Widespread use of chatbots: Chatbots will become commonplace, offering on-the-spot, just-in-time learning and performance support. Integration of AI: AI will seamlessly integrate into various software tools, reducing the need for separate software training. Shift from knowledge-level learning: Traditional knowledge-level learning, including microlearning and eLearning, will continue to evolve toward AI tools that require minimal human intervention. Rise of Simulations and Games-Based Learning: Simulations and games-based learning will emerge as the dominant learning modalities, offering high levels of efficacy, cost-effectiveness, and scalability–through contextual learning and contextual practice. Practical and Valuable Training Data: Increasingly useful training data will become valuable, providing actionable insights and turning learning from an event into a continuous cycle alongside or interwoven with job performance. Focus on Learning New Skills: Learning initiatives will increasingly prioritize rapidly acquiring new skills and honing those distinctly human attributes. Performance Support: Learning at the moment of need and verifying that the employee has the knowledge to do a task will be enhanced with AI. User-Generated Content: Everyone throughout an organization will be able to research, innovate, solve problems, and collaborate with others to save time and money. This evolving landscape signifies a profound shift in our approach to learning, one where AI takes center stage in driving knowledge-level learning, allowing L&D professionals to focus on creating high-quality learning experiences. As we embark on this transformative path, it's vital to understand that AI doesn't replace human expertise; it enhances it. With an AI Advocate on board, you can fully leverage these advancements for more valuable, cost-effective learning. Remember to prioritize speed, experience, and analytics as key business goals, and let them guide your L&D strategies. CLO and L&D leaders occupy a special position in the organization: you have a business viewpoint and deeply understand the needs of learning professionals. We recommend you take on the role of AI Advocate and find a few others within your team, preferably across borders and departments, to assist you. If you need help thinking this through, don't hesitate to contact us at The Thinking Effect or Learning Forum. About the Authors Mike Vaughan is the Chief Editor at The Thinking Effect, a platform dedicated to helping L&D professionals learn about and use AI tools to advance their careers. He is also the CEO of The Regis Company, which specializes in providing AI-driven tools to accelerate the design and development of business simulations. Mike has been at the forefront of educational technology and adult learning for 25 years. He earned a master’s in cognitive neuroscience from Middlesex University, London, and degrees in psychology and computer science. In addition to speaking at TEDx on the power of questions, Mike authored The Thinking Effect, The End of Training, and Strategic Performance Learning. Brian Hackett is the founder of The Learning Forum. After a career at what is now Willis Towers Watson, and The Conference Board, Brian saw a need for senior executives to be more innovative and active in their own learning and professional development. The Learning Forum is a member-driven learning organization for senior executives from F500 firms and key government agencies. Members learn directly with each other, face-to-face in candid and confidential forums. The forums cover L&D, Talent Management, Workforce Analytics, HR Technology, HR Operations, Knowledge Management, Innovation, Foresight and Cyber Security. Markus Bernhardt is a Fellow of the Learning Performance Institute (LPI). He is affiliated with organizations such as the Forbes Technology Council, the HBR Advisory Council, and the Learning Development Accelerator (LDA). Markus is a recognized authority, author, panelist, and global learning and HR community speaker. Recently serving as chief evangelist at OBRIZUM on the vendor side, Markus has been at the forefront of combining cognitive science with digital learning transformation and AI. Previously, Markus held the CEO position at two educational institutions in the UK, backed by an extensive leadership background serving in executive and non-executive board roles.

  • The AI Advocate for Learning and Development

    AI is quickly becoming commonplace, and its pervasiveness will only grow. Boosted by AI, stores, transportation, and homes will become smarter, and most apps and applications will employ AI to some degree. It’s only a matter of time before kids' toys join the AI bandwagon and begin teaching us a thing or two. Resistance to AI in organizations prohibiting its use is temporary, as giants like Microsoft and Google lead the way in integrating AI into everyday productivity suites and tools. Following suit, every app and application developer is furiously looking for ways to integrate AI into their products and offerings - making its ubiquitous integration inevitable. Enter the need for an AI Advocate - the champion who supports and promotes AI’s responsible and productive use of AI technology throughout an organization. These AI ambassadors inspire and educate on AI's potential while keeping ethics and data privacy firmly in mind. Their mission is to harness the power of AI to elevate organizational performance and decision-making without compromising on standards, policies, and regulations. What does this role of the AI Advocate in Learning and Development? As an AI Advocate, key responsibilities include: Education: One primary responsibility is to clearly communicate the benefits, risks, ethics, and potential applications of AI technologies to a diverse range of stakeholders. AI Opportunity Identification and Support: Stay up-to-date with the latest trends and developments in AI technology in order to collaborate with various teams to identify opportunities for AI implementation, fostering innovation and problem-solving. Monitoring AI technology: Regularly monitor and assess the functionality and effectiveness of deployed AI systems, recommending any necessary improvements. Cross-Functional Collaboration: Work closely with departments such as IT, legal, and data privacy to ensure AI initiatives align with the organization's goals, policies, and values. Training and support: Facilitate AI-related training for teams across the organization on responsible and effective use of AI tools. Offer ongoing support and address inquiries or concerns that arise. Why is this role essential now? The IBM AI Adoption Index increased from around 31% in 2021 to 35% in 2022 and is expected to reach 52% in 2023. The global AI market is predicted to exceed $1.5 trillion by 2030, with a Compound Annual Growth Rate (CAGR) of 38.1% from 2022 to 2030 (SnapLogic). This growth is largely a result of organizations striving for a competitive edge, with productivity being a primary driver. By offloading tedious, repetitive, and routine tasks to AI, employees can focus on higher-value tasks, leading to higher satisfaction rates, with 68% wanting more AI-based technology in the workplace. AI is also being explored to enhance customer experience, drive sales growth, replace knowledge-level training, optimize supply chains, and reduce staff costs. Consequently, the urgent need for a dedicated AI advocate is critical to empowering organizations to harness the power of AI and maintain a competitive position in the market. Where does this role sit within the organization? The AI advocate role should report to a senior leader within the organization, such as the Chief Technology Officer (CTO) or Chief Information Officer (CIO), to ensure strategic alignment and access to comprehensive support and guidance. Key reasons for this reporting structure include: Technical expertise: Working closely with a senior leader with technical expertise, such as the CTO or CIO, will provide the AI Advocate with technical support and resources, aiding in the effectiveness of their role. Strategic alignment: Aligning the AI Advocate's responsibilities with the organization's strategic goals is crucial. Reporting to a senior leader guarantees that their work remains consistent with the company's vision and mission. Cross-functional collaboration: The implementation of AI technology calls for cooperation among various departments, such as IT, legal, and data privacy. The AI Advocate will benefit from the senior leader's oversight, fostering collaboration across these diverse functions. Budgetary support: Deploying AI technology can entail significant costs. Reporting to a high-level executive will help the AI Advocate secure the necessary budgetary support to effectively execute their role. Why is the AI advocate essential to L&D? An AI advocate is crucial for L&D teams, as they can lead the way in utilizing the vast, sophisticated AI tools at their disposal to accelerate the design and development of training programs, minimize costs connected to subject matter experts, elevate the learner's experience and efficacy, and transform L&D into a data-driven entity. In essence, the AI advocate can establish L&D as a beacon that directs the organization toward ethical AI usage, while simultaneously maintaining a competitive edge in the market. Here are some additional resources: Fast Company: Why every Fortune 500 business needs a chief AI officer: . Worklife: The rise of the chief AI officer . Venture Beat: How to choose the right Chief AI Officer.

  • The Future of Learning: Experience

    A 1997 report from Idaho claimed that thousands of Americans have died from accidental ingestion of dihydrogen monoxide (DHMO), a chemical compound that can cause severe burns and other unpleasant side effects. Asking what can be done about this dangerous substance, a 14-year-old student distributed the report to classmates, prompting a vote which resulted in overwhelming agreement to ban DHMO. The punch line? None of the students stopped to consider what dihydrogen monoxide was. Ironically enough, they chose to ban something that is critical to our survival! Turns out, DHMO is simply two molecules of hydrogen to one molecule of oxygen, also known as H2O or water (which can, of course, cause death from drowning, dehydration, or scalds!) The lesson? Trust, but verify. In the age of misinformation, social media overload, and an onslaught of endless news, people need more time, space, and increasingly sophisticated ‘thinking’ capabilities to critically consider the information they’re consuming. And, with AI bots generating content, creating deep fakes, and learning people’s behavior patterns, the noise we experience is only going to get louder. What can be done to help? Develop higher cognitive skills. The World Economic Forum (WEF) recognizes these skills as the most important for individuals to learn. L&Ds can play a crucial role in the essential upskilling of workers by prioritizing capability development and enabling individuals to better navigate the complexities of the future. And let's face it, the pressure to keep up with the latest skills and deliver value is not going away anytime soon. In today’s environment, focusing on developing higher cognitive skills is vital for two reasons. First, reasoning and decision-making are currently the least automated workplace tasks, accounting for just 26% of task automation. Second, higher cognitive skills help individuals cut through the noise of information overload. Noise refers to the constant influx of information and distractions that can cloud our judgment and hinder our ability to think and communicate clearly. In both our personal and professional lives, this can be observed in countless situations, whether it be overwhelm by choice at a supermarket, being bombarded by Slack messages, or mindlessly scrolling social media. By developing higher cognitive skills, we can be more discriminating, elevate decision-making, and better navigate this noisy world. Here comes the harsh reality…As AI advancement intensifies, so does the noise, stress, and pressure. Did you know that according to Nobel laureate Daniel Kahneman, our brain operates using two distinct systems? System 1 is like a fast and automatic brain, responsible for making quick judgments and decisions, while System 2 is like a slower and more analytical brain, which analyzes information and considers solutions. We tend to rely more on System 1 thinking, which can lead us to accept information at face value without much critical thinking. It's important to be aware of this tendency and to consciously engage, develop, and enhance our System 2 thinking so that we are prepared to make important decisions or evaluate information critically. Here comes a glimmer of hope. Decades of research in mental models have shown that the most effective way to cultivate higher cognitive skills is through practice and reflection. We can enhance our innate System 2 thinking abilities by practicing in a practical context that allows us to apply what we have learned. This helps craft new neural pathways and refine our abilities. Plus, it exposes us to different scenarios and challenges, enabling us to adapt and improve our problem-solving skills. And, when we take the time to reflect on our experiences, we gain valuable insights into what worked well and what needs improvement. Reflection enables us to analyze our performance, identify patterns, and recognize opportunities to enhance our understanding or approach, but it also helps us connect the dots, integrate new information, and extract meaningful lessons from our experiences. And, here comes the great news! Experiential learning provides an ideal framework for the practice and reflection individuals need to stay ahead of the game. By engaging directly in real-world experiences, learners apply their knowledge and skills, receive immediate feedback, and reflect on their actions. The combination of hands-on experience with thoughtful reflection maximizes the effectiveness of the learning process, promotes more profound understanding, and enhances the transferability of knowledge and skills to real-life situations. With experiential learning, education’s future is bright, and the key to giving individuals the skills they need to quiet the noise. And you, my L&D friends, play a vital role in making practice and reflection an everyday reality.

  • AI vs. Human: How do ‘we’ ensure our relevance

    As technology continues to transform industries and the human-AI convergence rapidly gains speed, it’s becoming increasingly apparent that we must hone in on what makes us individually unique and leverage innate abilities to maintain relevance. Luckily, there are several uniquely human skills that AI, hopefully, will never be allowed to develop: Emotional intelligence: Humans can understand, express, and regulate emotions, as well as perceive and respond to the feelings of others in ways that AI cannot. Complex problem-solving: Humans can solve complex and ambiguous problems that require reasoning, judgment, and decision-making based on context, intuition, and experience. AI is helpful in decision-making, but is limited by the availability of large amounts of referenceable data. Interpersonal communication: Humans can communicate with others in a nuanced and empathetic way, using body language, tone, and nonverbal cues, which AI cannot yet fully grasp. Adapting to new situations: Humans can adapt to new and unfamiliar situations by applying their knowledge, experience, and creativity in ways AI struggles to do. Judgment and decision-making: Humans can make judgments and decisions based on ethical, moral, and social considerations that are difficult for AI to ‘reason’. Empathy: Humans can empathize with others, showing understanding, compassion, and concern for their feelings and experiences, all emotions that are unattainable by AI. Situational awareness: Humans can understand the context of a situation and adjust their behavior accordingly, whereas AI struggles to understand the context. Leadership and teamwork: Humans can lead and work effectively in teams by building relationships, managing conflict, inspiring others, and adapting to the team's needs. AI is a solo show, incapable of leading or interpersonally connecting. Strategic thinking: Humans can analyze complex situations, make connections, and develop long-term strategies; AI is undoubtedly helpful in analyzing data and identifying patterns and trends, but it lacks the creativity and intuition needed for strategic thinking. Creativity: Humans can think creatively and develop original ideas; AI responds to a prompt and is only as creative as the data it's sourced from. Not only is this list great news but it points us directly to a “relevance recipe.” Essentially, if humans can build and enhance these imperative skills, they can ensure relevancy. Now that we have our list of ingredients, how do we go about sourcing and strengthening them? If you ask ChatGPT, it says the most effective way to learn new skills is to practice. However, humans know that not all practice is the same, nor equally productive. Effective practice requires a safe environment that gradually becomes more challenging and evolves based on the individual's decisions and results. Learners need to receive targeted feedback along the way, reflect on their performance, and then be given the chance to apply their learning. This is called Experiential Learning. Experiential learning is a process of learning by doing. It involves all the elements effective practice requires: taking action, reflecting on the experience, and receiving feedback, all within a contextually relevant, safe environment. Rather than just reading or listening to information, learners actively engage in the learning process. They can take risks, make mistakes, and experiment with new ways of thinking. Learners improve their skills and gain self-confidence by reflecting on their mistakes and adjusting their actions. This active engagement in learning creates "muscle memory," increasing the likelihood of applying the new skill effectively on the job and in daily life. The increasing prevalence of technology and AI has highlighted the importance of developing uniquely human skills. Ironically enough, thanks to AI, we now have a cost-effective way to develop experiential learning programs to strengthen and build these uniquely human-skills. AI has driven down development costs, making the most effective form of learning widely accessible. Previously, creating simulations for experiential learning was expensive, challenging to deploy, and difficult to adapt. But, with the help of AI and new authoring platforms, such as SimGate, experiential learning can now be created as quickly as eLearning and at a cost similar to VILT. Aiding humans in developing essential skills and confidence is critical to navigating the future of work. Amidst the excitement and uncertainty of the great human-AI convergence, it’s comforting to know that we have the key to the “relevance recipe”: experiential learning.

  • How AI and Simulations will change L&D.

    The learning industry is going through the most significant shift since the Netscape browser was introduced in 1994. Advances in AI natural language processing models, such as ChatGPT, gives “knowledge at your fingertips” an entirely new meaning. When you need to fix something, improve team performance, or understand a complex topic, AI-enabled digital personal agents will have it ready for you within minutes. AI is ready What’s different about AI now than a few years ago? Finally, AI models are trained on enough data to make what they generate useful. For example, GPT-3, a neural network machine learning model by OpenAI, is trained on 175 billion parameters, significantly outperforming prior models trained on only 10- 20 billion parameters. This massive amount of training helps make AI-enabled apps, like ChatGPT, smarter and produce more human-like text. Unlike a search engine, where you weed through links to articles, videos, and websites, the AI generates curated content. What’s even more remarkable? The AI is teachable, learning what you like, how you like it, and when you want it. Imagine any topic that you’re interested in learning more about, and waking up to an AI-generated course just for you. For example, suppose you want to take a deep dive into history, learn the steps to perform a specific job, or how to become more self-aware, customized learning is just a click away. AI and Simulations Simulations have long been the most effective way of learning. For decades, pilots, astronauts, and business leaders have used simulations to practice complex skills and navigate evolving situations. Despite their proven efficacy, the drawback has always been that simulations are expensive, time-consuming, and labor-intensive to create. Fortunately, this is no longer the case. With AI-enabled simulation authoring tools, simulations can be produced faster than eLearning. The marriage of AI content generation and simulation authoring tools is set to revolutionize the industry, making the most effective and fun forms of learning accessible to everyone. So, how will AI and simulations change adult learning? Here are ten predictions: The days of eLearning catalogs are numbered. Today, learners go to sites like LinkedIn Learning, Udemy, or Skillsoft to watch reams of videos, consume pages of eLearning, or are constrained to a predetermined agenda. This will rapidly change. AI apps that generate just-in-time learning and content will deal a significant blow to this traditional catalog model. Why pay for off-the-shelf when you can learn what you want, when you want, by asking an AI app? Skill practice (upskilling/reskilling) will continue to gain momentum. Similar to eLearning and just-in-time learning, most AI-enabled technologies are only able to provide knowledge-level content that adds to an individual’s knowledge base. While good for building foundational skills, the training is not enough to upskill a global workforce. Therefore, to upskill and reskill with greater impact and speed, organizations will look for more experiential ways of learning - providing practice and application that helps employees grow their skills and careers. Content is no longer king; experience is. Creating a seamless and engaging customer or user experience has been the central focus in many industries this last decade. It’s finally hitting the learning industry. When considering the efficacy that simulations have shown in studies, and combining that with AI, then learners have an experience that is far better than any other online learning - and that’s game-changing. Future of work, meets the future of learning. Imagine acquiring the skills you need without ever getting the ‘real-world’ experience. Similar to how an airline pilot practices (learn, fail, repeat) in different situations in a flight simulator, an aspiring employee or leader can log hours in a skill simulation to gain the practice and coaching they need to perform a function or role. Massive university disruption is here. AI-enabled learning apps, content, and instruction - add this to the discontent many young people feel toward costly universities and you have a university system poised for massive disruption. The saving grace will be experiential learning. The universities that truly ‘flip the classroom’ by leaving knowledge-level training to AI, and making in-person time an emotionally charged practice using skill simulations, will not only survive but thrive. Minimize biases, widen perspectives. We all have biases, and when designing courses or writing content, those biases filter and shape what is included. With AI, you’ll be able to ask it to provide multiple perspectives spanning several generations on any topic. This content can be loaded into a simulation to challenge the learner to explore alternative perspectives. And, it can point out where the learner may exhibit certain behavioral tendencies in certain situations - now that’s insightful. One tool covers all learning needs. It has been costly for organizations to learn, create, and maintain multiple platforms that generate eLearnings, just-in-time learnings, tutors, and skill simulations. Now, with AI-enabled simulation authoring tools, they can switch to one tool, which covers all of their experiential learning needs, eliminating the burdensome requirement of varied platforms and solutions. Democratization of experiential learning. Due to the cost, high-end experiential learning has only been available to large corporations. However, AI-enabled simulation tools will continue to drive the cost down, allowing any organization or university to have access to the best learning experiences available. In the near future, even high school students will learn through simulations (and their personal AI teacher). People will actually like compliance training. That may be a stretch… but the days of people clicking through boring eLearnings on one screen while working on another are ending. With AI-enabled skill simulations, compliance training will be engaging - learners will want to take the training as it will captivate their hearts and minds, not just feed them reams of content. Behavioral analytics finally join the party. Behavioral data provides insights into how people think, how their thinking changes, and how their thinking aligns with others. Yet, training organizations have been the slowest to embrace analytics. One of the main reasons is that most delivered training lacks the design and technology required to capture useful learner data. Fortunately, simulations capture a massive amount of data. By leveraging analytics tools and AI, the data becomes practical for the learner and invaluable for the organization.

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