The Digital Adoption Show | Upskilling the Future Digital Workforce

You'll have to keep up during this face-paced episode The Digital Adoption Show and our Partner Podcast Mini-Series featuring Jessica Franco, GEP's Senior Director of Alliances.

Jessica brings not only insights on how enterprises can get more out of their teams' and organization's Source to Pay processes and systems, but actionable advice on maximize your own ROI from S2P.

Tune in to hear Jessica share how scalable, measurable change management tactics can actually lead to frictionless user onboarding, and how pivoting your own S2P stack towards setting people up for success is the fastest way to adoption.

Don’t forget to like, share, and subscribe to The Digital Adoption Show for more real conversations on digital transformation, L&D, and enterprise tech! Subscribe to The Digital Adoption Show:

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Episode Highlights:

🤖 AI is revolutionizing Digital Adoption Platforms by enabling summarization, suggestions, authoring, and intelligent insights. 

🧩 Understanding your current organizational “box” is critical before unleashing AI to avoid unintended consequences and resistance. 

👥 People and culture are central to AI adoption; buy-in and clear communication empower employees for transformation success. 

🎯 Learning & Development must tailor AI strategies contextually, adapting methodologies alongside content to support evolving work. 

🚀 AI removes routine tasks, allowing humans to focus on higher-value, creative, and strategic activities. 

⏳ Short-term AI training adoption faces challenges like model hallucinations and cultural resistance, requiring patience and pragmatism. 

🌐 The long-term impact of AI on work will be as profound as the internet and personal computing, creating entirely new paradigms.
 

Key Insights From Our Experts:

• 🤖 AI’s Four-Fold Impact on Digital Adoption Platforms (Sharath Hari): AI is fundamentally reshaping DAPs by enhancing content delivery through summarization, providing real-time user suggestions, aiding content authors in workflow creation, and generating actionable insights via analytics. This multi-dimensional influence is reducing friction in user adoption and raising overall enterprise productivity. Implementing DAPs as AI facilitators helps organizations navigate complex policy landscapes and user adaptation challenges, critical for accelerating AI transformation from pilot phases to full-scale deployment.
   
• 🧠 The “Box” Analogy and Readiness for AI Adoption (Christopher Lind): Christopher Lind’s metaphor of being “in a box” encapsulates the crucial need for organizations to understand and critically assess their existing workflows before integrating AI. Without this awareness, AI-driven changes risk exacerbating resistance or creating operational mismatches. This insight highlights that AI adoption is not simply a technological upgrade but a fundamental re-examination of organizational processes and decision-making frameworks to ensure beneficial integration.
   
• ⚖️ Accountability and Decision-Making in AI (Christopher Lind): Lind also emphasizes the legal and ethical risks of allowing AI to make autonomous decisions. Accountability must remain with human leaders; delegating decision-making entirely to AI can result in legal liabilities and organizational failures. This insight stresses the importance of frameworks where AI functions as an assistive tool, enhancing human decisions rather than replacing human judgment.
   
• 🔄 Contextual and Adaptive L&D Strategies (David Kelly): David Kelly’s analysis reveals that AI adoption in workplace learning is not universal; it depends heavily on the organization’s culture, AI maturity, and risk tolerance. He stresses the importance of evolving support methodologies in L&D, not merely updating content but changing how learning is delivered, reflecting how work itself is being transformed by AI. This dynamic approach to L&D ensures that AI’s benefits are effectively realized by enabling employees to learn as their roles and tools evolve.
   
• ⏳ Opportunity over Risk in Automation (David Kelly): Kelly reframes AI-driven automation from a threat to human employment into an opportunity to reallocate human effort toward creative and strategic tasks. Rather than fearing job displacement, organizations should focus on leveraging the bandwidth freed by AI automation to pursue growth and innovation, fostering an optimistic viewpoint that underpins positive organizational change.
   
• 🎭 Limitations and Realistic Timeframes for AI in Training (Christy Tucker): Christy Tucker tempers AI enthusiasm with practical caution, especially regarding early applications in scenario-based and software training. Issues like AI hallucinations - the tendency to generate plausible but inaccurate outputs, pose risks that organizations must manage carefully. Moreover, cultural acceptance and organizational readiness for AI-assisted learning are slower-moving factors, requiring measured expectations and iterative development rather than immediate transformation.
   
• 🔮 Long-Term Vision: AI Ubiquity Comparable to Internet and PCs (Christy Tucker): Tucker projects that while short-term disruptions may be incremental, in the long term, over decades, AI will become deeply integrated across all facets of work and life. This vision frames AI as a foundational technology with widespread, subtle, and enduring influence analogous to the internet and personal computing revolutions, indicating the need for sustained strategic planning and investment.
   

Collectively, these perspectives offer a roadmap for organizations and learning professionals, underscoring the balanced integration of AI technology with human, cultural, and strategic considerations. This approach will be necessary to fully harness the transformative potential of AI while mitigating risks and resistance.

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In this episode of The Digital Adoption Show, host Riya Rathi dives into the world of HR transformation with Vikas Maheshwary, VP of HR Transformation. Known for his bold takes and practical insights, Vikas unpacks what transformation really means in today’s context, beyond buzzwords and tech rollouts.

We explore the myths that hold organizations back, the power of skills-based design, the rise of agentic AI, and what it takes to truly rethink performance management and change enablement from the inside out.

From real-life transformation stories to controversial takes on AI replacing jobs. This episode delivers an honest, refreshing, and strategic look at where HR is headed.

Vikas Maheshwary’s Journey & Approach to HR Transformation

• Why transformation is painful but necessary and what most leaders get wrong.
• HR’s biggest myth: starting with tech instead of people and processes.
• Skills, not roles: how skill-based orgs can reshape workforce strategy.

Rethinking Performance Management

• Why today’s performance management is outdated and needs reinvention.
• Introducing Enterprise Performance Management (EPM): a proactive, KPI-driven approach.
• How to use KPI trees to connect individual efforts to enterprise goals.

AI in the HR & Transformation Landscape

• The rise of agentic AI: augmentation vs. replacement.
• Why some jobs are already being replaced—and how leaders must rethink team design.
• Future-ready teams: why human skills like empathy will soon become premium.

Change Management, Simplified

• Vikas’ change formula: 4(M) + 3(V) + 2(I – F).
• Why motivation trumps communication in driving adoption.
• How to reduce friction and increase buy-in for enterprise-wide initiatives.

Organizational Design & Skills-First Thinking

• A case study in linking business KPIs to skills, not roles.
• Buy vs. build: how to decide whether to hire or develop new skills.
• Why success metrics look different in skills-first organizations.

Cultural Shifts: Gen Z, Authenticity & Politics

• Why generational labels are limiting and personas matter more.
• “Authenticity eats perfection for breakfast”—a new mantra for modern workplaces.
• Office politics in the age of AI: will agentic tech reduce workplace conflict?

If you're a transformation leader, HR professional, or just curious about the future of work, this episode is your masterclass in bold, human-centered change.

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In this episode of The Digital Adoption Show, host Rajesh Panina kicks off our Partner Podcast Mini-Series with a deep dive into the future of insurance with Ganesh Kumar, Principal Director of Insurance Solutions & Products at LTIMindtree.

With over 20 years in the industry, Ganesh has been at the forefront of helping global insurers modernize their core systems, navigate cloud transformation, embrace GenAI, and drive real-world results through better employee and customer adoption. This episode unpacks the pressures insurers are facing today—from regulatory demands and rising risks to evolving customer expectations—and explores how smart digital transformation is helping companies stay ahead.

Ganesh shares first-hand stories from the trenches, the real reasons core modernization projects succeed or fail, and why adoption is the most overlooked success metric in transformation programs.

Ganesh Kumar’s Journey Through Insurance Tech

• His early career working on 8 concurrent modernization projects at a regional carrier
• How tools like Duck Creek and Guidewire shaped his expertise
• Moving from delivery to solutioning, and now, ecosystem partnerships

Core Modernization & What’s Driving It in Insurance

• Customer expectations and the shift toward personalized, digital-native experiences
• The data-driven push behind transformation initiatives
• Reducing expense ratios and creating flexible systems for specialty markets
• The overlooked driver: employee experience

Top Challenges in Insurance Transformation

• Balancing configuration vs. customization to preserve upgrade paths
• The “heart transplant” problem—how core fits into IT landscapes
• The human factor: change management, stakeholder buy-in, and communication gaps

Cloud Adoption in Insurance: What’s Slowing It Down

• Why insurers have historically lagged behind other sectors
• Regulatory complexities and legacy mindset hurdles
• The shift to SaaS-based insurance platforms and rising cloud confidence

Solving the Talent Gap in a Changing Tech Landscape

• The dilemma: hire, upskill, or partner?
• Why upskilling internal teams creates long-term value
• How digital adoption platforms support both talent and transformation goals

Digital Adoption as a Transformation Game-Changer

• Why traditional LMS models are no longer enough
• How Whatfix helped reduce onboarding time by 50% during a catastrophe claims rollout
• Measuring the downstream impact of better in-app guidance—from IT to call centers

GenAI in Insurance: Real-World Use Cases

• AI for underwriting, claims triage, document summarization, and customer service
• Language translation, emotional tone detection, and personalization at scale
• Why GenAI needs user guidance and nudging to deliver ROI
• How platforms like Whatfix support responsible and productive AI adoption

Final Takeaways

• Adoption is the real unlock behind ROI
• Transformation isn’t just tech—it’s people, platforms, and behavior
• The future of insurance lies in agile, intelligent, user-first digital ecosystems

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In this episode of The Digital Adoption Show, host Neha Smriti dives deep into gamification in learning with Karl Kapp, a leading expert in instructional design and gamified learning. Karl is a full professor at Commonwealth University of Pennsylvania, an L&D practitioner, speaker, and the mastermind behind the L&D Mentor Academy. He has spent over two decades transforming traditional learning experiences into interactive, game-like experiences that engage learners and drive real-world results.

We explore the science of making learning addictive, why traditional training fails to engage, and how action-first learning changes the game. Karl shares real-life gamification success stories, common pitfalls companies make when adopting gamification, and how AI is shaping the future of instructional design.

His upcoming book, Action First Learning, is a must-read for anyone looking to move beyond passive learning models and create truly engaging and impactful learning experiences.

• Karl’s first experience in instructional design through a sixth-grade safety video.
• How theatrical training influenced his approach to learning and public speaking.
• His transition from traditional instructional design to innovative, gamified learning.

• Why most corporate training fails—the problem with passive learning.
• How to make learning addictive and engaging through gamification.
• The science of behavior change and why experience-driven learning works.

• Escape Room Onboarding: How a consulting firm improved teamwork and retention through an interactive airport simulation.
• Board Game for Business Strategy: Transforming siloed managers into strategic thinkers through gameplay.
• Fraud Investigation Simulation: Training professionals to conduct financial investigations through immersive storytelling.

• Why traditional training metrics (attendance, completion rates) are misleading.
• How to measure behavioral change and business impact effectively.
• The importance of pre-intervention and post-intervention assessments.

• The problem with passive learning models.
• How action-based learning improves retention and engagement.
• Step-by-step playbook for designing gamified experiences.
• Why every chapter is a standalone guide to implementing a different gamification strategy.

• How gamification can boost user adoption of digital platforms.
• The power of badges, leaderboards, and incentives in enterprise software.
• How companies can gamify onboarding & training experiences for better retention.

• How AI augments instructional designers, making content creation more efficient.
• AI’s role in brainstorming game mechanics, designing scenarios, and improving learning personalization.
• Why AI can’t replace human experience design but can be a powerful exoskeleton for instructional designers.

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• Started as a psychology lecturer with a deep interest in learning theory.
• Moved into corporate learning through roles at Siemens, BBC, BP, Deloitte, and PA Consulting.
• Developed groundbreaking approaches such as the Courses to Resources shift and the Affective Context Model.
• Published How People Learn and has since led consulting projects transforming corporate L&D strategies.

• Organizations spend millions on LMS platforms, courses, and training—but employees still rely on Google, peers, and self-discovery.
• Traditional training methods fail because they focus on content delivery rather than real performance outcomes.
• The overload of e-learning courses often creates passive learning experiences that lack engagement or relevance.

• Learning is not about information retention—it is about emotional engagement.
• The Affective Context Model explains that emotion drives memory formation—if there’s no emotional reaction, the content is forgotten.
• Real learning happens through experiences, challenges, and meaningful interactions—not through passive consumption of content.

• Instead of pushing courses, organizations should create resources that employees can access at the point of need.
• Example: A one-page checklist often outperforms a two-week training course because it’s immediately useful.
• Many organizations make the mistake of creating "resources" that are just repackaged courses, which don’t solve real problems.
• Performance support tools (like job aids, chatbots, and interactive guides) are often more effective than formal training programs.

• Traditional instructional design models like ADDIE are outdated and ineffective.
• 5Di (Define, Discover, Design, Develop, Deploy, Iterate) follows a human-centered design approach:
  1. Define business goals and expected behavior changes.
  2. Discover learner needs and real-world challenges.
  3. Design solutions that either build experience or provide performance support.
  4. Develop, Deploy, and Iterate based on feedback and performance impact.
• Companies that follow 5Di can prove ROI instead of just measuring course completion rates.

• L&D teams often struggle to show impact because they focus on the wrong metrics (e.g., course completion rates).
• ROI is not difficult to measure—it’s about tracking the real behavior changes defined at the beginning of the program.
• Examples of measurable learning outcomes:
  • Increase in sales conversions after a sales training program.
  • Reduction in safety incidents after a compliance program.
  • Faster time-to-productivity for new hires in an onboarding program.
• Without a clear business outcome, a training initiative is just content delivery, not learning.

• Most AI applications in learning today are focused on content generation, but that’s not the real problem L&D needs to solve.
• AI should be used for:
  1. Performance support (e.g., AI-powered assistants that provide instant answers).
  2. Simulated learning experiences (e.g., AI-based coaching for negotiation or leadership skills).
• The biggest mistake L&D teams make is dumping content into AI systems instead of designing AI-driven learning experiences.

• L&D should be structured like a high-performance engine, consisting of:
  1. Performance Support Team – Builds resources that employees pull when needed.
  2. Experience Design Team – Creates immersive learning experiences for skills that require practice.
  3. Business Consultants – Engage with leadership to define real business goals.
  4. Impact Measurement Team – Tracks behavioral change and ROI metrics.
• AI and automation should support this structure, not replace human-centered learning.

• L&D must move beyond "training programs" and focus on real-world learning solutions.
• Emotion and relevance are the key drivers of learning—not just information delivery.
• Companies that embrace performance support, experience-driven learning, and measurable outcomes will create real business impact.

How People Learn - https://bit.ly/4hpircj
5Di Learning Design Toolkit - https://bit.ly/4hn2JOS

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Will Thalheimer’s Background and Entry into L&D

• Started his career in business but shifted to instructional design after discovering an interest in learning science at Drexel University.
• Developed an early passion for simulations and learning effectiveness, leading to a career focused on research-based training strategies.
• Founded a consulting practice to bridge the gap between academic learning research and corporate training.

Common Misconceptions in Learning and Development

• Traditional training often overemphasizes content delivery rather than decision-making and skills application.
• Many corporate training programs do not incorporate research-backed techniques such as spaced learning, retrieval practice, and realistic practice.
• Energy and engagement in a training session do not necessarily indicate learning effectiveness.

The Problem with Traditional Learner Surveys

• Most learner surveys (often called smile sheets) fail to measure actual learning outcomes.
• Research shows that these surveys have little correlation with whether learners retain or apply knowledge.
• Many rely on vague Likert scale ratings, making the feedback difficult to interpret or act upon.

Introducing Performance-Focused Learner Surveys

• Uses specific, behavior-driven answer choices rather than generic ratings.
• Focuses on four key areas: understanding, motivation to apply, memory retention, and post-training support.
• Helps organizations make meaningful improvements to training based on structured feedback.

Learning Transfer Evaluation Model (LTEM) as an Alternative to Traditional Models

• Developed as a replacement for outdated evaluation models like Kirkpatrick’s Four Levels.
• Provides a structured way to measure training effectiveness, separating knowledge acquisition from real-world application.
• Includes levels such as knowledge retention, decision-making skills, task performance, and impact on business outcomes.

Shifting L&D from Compliance to Business Impact

• Organizations should integrate learning into daily workflows rather than relying on one-time training sessions.
• Reinforcement techniques such as spaced learning and manager-led coaching can improve retention and application.
• L&D should focus on contributing to measurable business results rather than just tracking completion rates.

The Role of Technology in Learning

• Technology alone does not improve learning outcomes; the right instructional methods are key.
• Virtual reality can enhance training when designed for realistic practice but is not always necessary.
• Generative AI and adaptive learning platforms have potential but must be implemented strategically to avoid ineffective applications.

Building a Competitive Advantage Through Learning

• Organizations that apply evidence-based learning methods can significantly improve employee retention and performance.
• CEOs and senior leaders should view L&D as a business driver rather than just an HR function.
• Continuous evaluation and refinement of training programs help create a culture of learning that supports long-term success.

Final Thoughts
Will Thalheimer emphasizes that effective L&D requires a shift from outdated practices to research-based strategies. By improving learning evaluation, aligning L&D with business goals, and strategically using technology, organizations can turn learning into a competitive advantage.

Will Thalheimer’s Books and Resources

• The CEO’s Guide to Training, E-Learning, and Work - https://amzn.to/3FhqryJ
• Performance-Focused Learner Surveys - https://bit.ly/3DGwfB5
• The NEW LTEM(The Learning-Transfer Evaluation Model): https://www.worklearning.com/ltem/
• Will's Blog: https://www.worklearning.com/wills-blog/

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In this insightful episode of The Digital Adoption Show, Neha Smriti welcomes David Kelly to discuss the rapidly evolving landscape of Learning & Development (L&D) and what leaders must focus on as we approach 2025.

David shares his journey into L&D, highlighting how his natural curiosity and problem-solving mindset led him to become a thought leader in the space. He emphasizes that as technology reshapes the way we work, L&D strategies must also evolve.

• The Shift in Learning: Traditional L&D models are being challenged as employees demand more flexible, real-time, and self-directed learning experiences. Organizations must adapt to these changing expectations.
• AI’s Impact on L&D: AI is not just about efficiency—it’s about transformation. L&D teams should explore how AI-driven tools can enhance learning experiences rather than simply making existing processes faster.
• The Rise of Super Workers: Inspired by a recent article from Josh Bersin, David discusses how employees who integrate technology seamlessly into their workflow are shaping the future. Organizations must learn from these individuals to foster adaptability and close skill gaps.
• Reframing ROI in L&D: Instead of focusing solely on financial returns, L&D leaders should shift their approach to problem-solving and demonstrating the value of training through improved business performance and employee effectiveness.
• Learning in the Digital Age: People naturally turn to tools like Google and AI assistants for instant learning. L&D should embrace and integrate these behaviors rather than competing with them.
• The Power of Experiential Learning: Hands-on practice, simulations, and continuous learning opportunities are key to making training more impactful. However, the right learning strategy depends on the context and business needs.

David encourages L&D professionals to expand their view beyond traditional education and training methods. He stresses the importance of observing how employees are already learning and using that insight to refine L&D strategies. Ultimately, success in 2025 will come from blending innovation with a human-centric approach to learning.

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Key Highlights of the Episode

1. Introduction to Christy Tucker and Her Journey in Learning & Development
   • Christy's journey started in K-12 music education and evolved into corporate training and instructional design.
   • Transitioned from classroom software training to creating engaging online learning experiences.
   • Expertise in scenario-based learning and branching scenarios, focusing on practical application and performance improvement.
2. What is Scenario-Based Learning? Why is it Effective?
   • Focuses on real-world practice and feedback, enabling learners to make job-related decisions in a simulated context.
   • Closes the gap between theoretical learning and practical application by mimicking workplace challenges.
   • Encourages relevant context and decision-making, avoiding generic training that fails to resonate with specific audiences.
3. Making Software Training Engaging Through Scenarios
   • Contextualized training with a "why" behind each task (e.g., using a pivot table to analyze data).
   • Moves beyond traditional "menu-driven" training to focus on how tasks are performed in real workflows.
   • Example: Teaching PowerPoint features within a scenario of updating an organizational chart after changes.
4. When to Use Scenario-Based Learning vs. Other Approaches
   • Best for skills requiring judgment, problem-solving, or application of knowledge.
   • For simpler tasks (e.g., filling forms), direct instructions may suffice.
   • Scenarios are ideal when addressing frequent user mistakes or exploring complex workflows.
5. Branching Scenarios: When Do They Work Best?
   • Complex branching scenarios are suitable for multi-layered decision-making.
   • Single decision-point scenarios are effective for focused skill development.
   • Example: Instead of one long scenario, multiple short ones can address varied challenges efficiently.
6. Scenario-Based Learning vs. Simulations: Complementary Tools
   • Simulations are ideal for procedural tasks, while scenarios are better for decision-making and thought processes.
   • Example: Combining simulations for basic navigation and scenarios for troubleshooting LMS issues enhances training outcomes.
7. Measuring Success of Scenario-Based Learning
   • Metrics to track include reduced support calls, increased self-help usage, and improved task efficiency.
   • Assessments like simulation-based evaluations are more effective than multiple-choice questions.
   • Learner surveys can capture emotional and cognitive engagement, focusing on perceived task value and confidence.
8. Emotional Engagement and Cognitive Impact
   • Emotional engagement fosters motivation and connection to learning objectives.
   • Tools like Will Thalheimer's Performance-Focused Learner Surveys can help measure motivation and self-efficacy.
9. The Role of AI in Scenario-Based Learning
   • AI has potential to personalize learning experiences by dynamically adapting scenarios in real-time.
   • Chat-based scenarios and voice recognition are emerging trends for customer service and training.
   • Long-term, AI may revolutionize software training by enabling real-time customization, though cultural and logistical shifts are required.

Final Thoughts
Christy's expertise highlights the transformative potential of scenario-based learning in software training. By focusing on decision-making, real-world application, and targeted practice, organizations can create meaningful and impactful learning experiences. The conversation also underscored the importance of integrating complementary approaches like simulations and leveraging emerging technologies like AI to shape the future of training.

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