Across workplaces, staff are already using AI tools to write emails, summarise meetings, generate reports and answer questions. But inside learning and development teams, a bigger question is emerging:

What happens when AI is used not just to consume information, but to create and share knowledge across an organisation?

That was the focus of a recent webinar hosted by Totara where speakers explored how AI functionality inside the platform could help organisations move beyond traditional top-down learning models.

The message was clear: the future of workplace learning may depend less on creating more courses, and more on unlocking the expertise that already exists inside businesses.

The bottleneck facing learning teams

For years, most workplace learning has followed the same pattern.

A central L&D team creates courses. Staff complete them. Updates are requested. Compliance changes arrive. New training needs appear. The cycle repeats.

The problem is scale.

Many learning teams are small, stretched and constantly reacting to new demands. Valuable expertise often sits elsewhere across the organisation – with engineers, managers, sales teams, healthcare professionals or frontline staff – but turning that knowledge into structured learning has traditionally taken time.

As one speaker explained during the session:

“We don’t really have a knowledge problem. We have a capture and sharing problem.”

That challenge has become even more visible as organisations move faster and information changes more frequently.

Instead of waiting for formal training, employees often turn to colleagues directly through messaging platforms, informal chats or shared documents. While effective in the short term, this creates new pressures on experienced staff and makes knowledge difficult to scale consistently.

AI as a “co-pilot”, not a replacement

One of the strongest themes throughout the webinar was that AI is not being positioned as a replacement for learning teams.

Instead, the technology is being presented as a support tool – helping subject matter experts contribute knowledge more quickly and confidently.

Inside the Totara Learn LMS, AI tools can assist users in generating draft content, summarising long passages, creating knowledge checks and even producing supporting imagery.

The idea is simple.

Someone with expertise may not have the time, confidence or writing ability to build polished learning materials from scratch. AI can reduce that friction by turning a blank page into a starting point within seconds.

Rather than expecting L&D teams to create everything themselves, organisations can begin enabling contribution at scale.

Turning learners into creators

The webinar repeatedly returned to a broader shift in mindset.

Traditionally, learners consume content.

But AI opens the possibility for learners to become contributors as well.

That could mean:

• Sharing practical insights
• Creating quick resources
• Building draft learning materials
• Improving existing content
• Generating summaries or quizzes

In practice, this changes the role of the learning team.

Instead of acting solely as content creators, L&D professionals increasingly become facilitators, reviewers and quality managers – helping shape and govern learning created across the organisation.

The speakers argued that this approach could lead to faster knowledge sharing and more relevant learning experiences because the information comes directly from people applying it in real-world situations.

Faster content creation

One demonstration during the webinar showed how AI tools inside Totara can generate draft learning content from short prompts.

Users can ask the platform to create introductory material, expand ideas, summarise text or rewrite sections in different styles.

The aim is not necessarily to publish AI-generated text untouched.

Instead, the generated content acts as a first draft – something experts can refine, improve and personalise.

For many organisations, that could significantly reduce the time required to create or update learning resources.

This becomes particularly important in areas such as:

• Compliance training
• Policy updates
• Product knowledge
• Operational procedures
• Healthcare guidance
• Technical documentation

Where information changes regularly, speed matters.

Knowledge checks and engagement

Another feature highlighted was AI-assisted “knowledge checks”.

Users can upload documents or text-based resources, and the platform can automatically generate quizzes or understanding checks based on the material.

The purpose is less about formal assessment and more about reinforcing learning and encouraging engagement.

It also provides organisations with a lightweight way of confirming whether employees have interacted with key information – something increasingly important in regulated industries.

A wider change in workplace learning

Perhaps the most significant takeaway from the session was not the technology itself, but what it represents.

For years, organisations have struggled to scale internal knowledge effectively.

AI may finally offer a practical way to lower the barrier between expertise and learning.

If implemented carefully, that could help businesses:

• Share knowledge faster
• Reduce pressure on L&D teams
• Capture expertise before it is lost
• Encourage collaborative learning
• Keep training more up to date
• Increase learner engagement

There are still questions around governance, quality control, privacy and accuracy. Even the webinar speakers stressed the importance of human oversight.

But the direction of travel is becoming increasingly clear.

The organisations that succeed with AI learning tools may not simply be the ones creating more content.

They may be the ones that make it easiest for people to share what they already know.

Want to leverage AI for workplace learning? Contact us at Webanywhere for an informal discussion about your needs and goals.

The post How AI is Changing Workplace Learning on Totara appeared first on Webanywhere.

In 2026, artificial intelligence is no longer a future concept. It is embedded in everyday life, shaping how we learn, work, and interact with the world.

Yet while AI adoption has accelerated, understanding has not kept pace. A significant gap exists between using AI tools and truly understanding the principles, ethics, and implications behind them.

Forward-thinking organisations recognise that closing this gap is not just an educational challenge. It is a strategic responsibility.

This initiative addresses that challenge directly, equipping the next generation with the knowledge and critical thinking skills required to engage with AI responsibly, ethically, and confidently.

The Challenge: From AI Usage to AI Understanding

Across schools and learning environments, AI is increasingly visible but rarely explained in depth. Young learners are interacting with intelligent systems without the frameworks needed to question, interpret, or evaluate them.

Without early intervention:

• AI becomes a “black box” rather than a tool to be understood
• Ethical considerations are overlooked or misunderstood
• Critical thinking skills are underdeveloped in relation to emerging technologies

To build a future workforce that can use AI responsibly, education must evolve now.

The Initiative

This project bridges the gap between advanced academic research and real-world classroom delivery.

It is a university research-led initiative designed to future-proof AI literacy for primary-age learners, combining rigorous research with engaging, accessible learning experiences.

The focus is on active learning, ensuring that children do not just consume information, but interact with concepts, question outcomes, and develop their own understanding of how AI works and why it matters.

The Three Pillars of the Project

1. Research-Backed Frameworks

At the core of the initiative is a robust academic foundation.

Developed in collaboration with a leading UK university known for its excellence in computing, data science, and human-centred technology research, the programme is grounded in:

• Proven pedagogical methodologies
• Ethical neutrality and balanced perspectives
• Current research into AI systems and societal impact

This ensures that learning is not only engaging, but accurate, credible, and future-facing.

2. Scalable Classroom Delivery

Translating complex AI concepts into formats suitable for young learners is a critical challenge.

This initiative solves that through:

• Interactive, scenario-based learning experiences
• Age-appropriate storytelling and simulations
• Digital-first delivery designed for modern classrooms

The result is a scalable model that can be deployed across schools, regions, and education systems without compromising quality or consistency.

3. Verifiable Impact

Unlike many education initiatives that rely on anecdotal success, this project is built around measurable outcomes.

Sponsors and stakeholders benefit from:

• Quantifiable improvements in ethical literacy
• Data-driven insights into learner engagement and comprehension
• Clear evidence of behavioural and cognitive development

This focus on impact ensures accountability and supports continuous improvement.

Academic Excellence Behind the Initiative

The university partner behind this project is recognised as one of the UK’s leading institutions, with a strong reputation for innovation in technology and interdisciplinary research.

Its credentials include:

• International recognition for computing and data science programmes
• Pioneering research in artificial intelligence, ethics, and digital society
• A track record of translating academic insight into real-world applications
• Strong links with industry, ensuring relevance beyond academia

This combination of academic rigour and practical relevance provides a powerful foundation for the initiative, ensuring that learners benefit from insights at the forefront of technological advancement.

Why Partner with Webanywhere

As a leading provider of digital learning solutions, Webanywhere plays a critical role in transforming research into impactful learning experiences.

Expertise in Digital Learning Innovation

Webanywhere brings deep expertise in:

• Learning management systems, including Totara Learn
• Scalable eLearning delivery across sectors
• User-centred design for engaging, accessible learning

This ensures that complex ideas are delivered in ways that resonate with modern learners.

Bridging Research and Real-World Application

Where academic institutions generate insight, Webanywhere ensures it can be applied at scale.

Their role includes:

• Designing interactive learning experiences based on research frameworks
• Ensuring usability and accessibility for diverse learner groups
• Supporting deployment across educational environments

This bridge between theory and practice is essential for meaningful impact.

Proven Track Record with Large-Scale Learning Initiatives

Webanywhere has delivered learning solutions for global organisations, educational institutions, and public sector bodies.

Their experience includes:

• Managing large user populations across multiple regions
• Delivering measurable learning outcomes
• Supporting long-term digital transformation strategies

This makes us an ideal partner for initiatives that require both innovation and reliability.

A Collaborative Vision for the Future

This initiative represents more than a single project. It is a model for how academia, technology providers, and forward-thinking organisations can work together to shape the future of education.

By combining:

• Academic excellence
• Innovative learning design
• Scalable technology

it creates a pathway for embedding AI literacy at the earliest stages of learning.

Get Involved

Organisations that lead in AI have an opportunity to shape how it is understood by the next generation.

By supporting initiatives like this, you can:

• Contribute to responsible AI adoption
• Support measurable improvements in education
• Align your organisation with innovation and ethical leadership

To explore partnership opportunities or learn more about how this initiative can be delivered at scale, contact Webanywhere today.

The post Shaping the Future of AI Literacy in Education appeared first on Webanywhere.

In the latest episode of The Learning Summit Podcast, Sean Gilligan, founder of Webanywhere, explores how multimodal learning and AI are transforming education. Platforms like Watch and Learn, Gemini, and NotebookLM are creating personalized, flexible, and engaging learning experiences by integrating text, audio, video, and live interaction.

“Multimodal learning supports different styles—whether you prefer listening, watching, or interacting live,” Sean explains. “It’s about offering flexibility while keeping learners engaged.”

Sean also highlights how AI enhances real-time engagement: “Imagine live quizzes and polls during a session, giving presenters instant feedback to adapt and meet learner needs on the spot.”

These advancements don’t just enhance engagement—they empower educators with insights to refine their content and strategies. “With tools like NotebookLM, learners can even revisit sessions, generate podcasts, and ask AI hosts questions, creating a truly adaptive learning journey,” Sean adds.

The post Multimodal Learning and AI appeared first on Webanywhere.

Unlocking Learning Potential: Totara Learn, Engage, and Perform Features Explained

Alex (Host):

Hello, and welcome back to the Learning Summit Podcast! I’m Alex, and today’s episode is all about one of the most flexible, powerful learning platforms out there: Totara LMS.

Joining me is Sean Gilligan, founder of Webanywhere and an expert in learning technology solutions. Sean, welcome back!

Sean Gilligan:

Thanks, Alex. Always great to chat with you, and I’m excited to dive into Totara today. There’s so much happening with the platform—especially with the upcoming Totara 19 release.

Alex:

We’re going to unpack a lot here, but let’s start at the top. Sean, Totara offers three core products: Totara Learn, Totara Engage, and Totara Perform. What are they, and how do they work together?

Sean:

Good question, Alex. Totara is essentially a suite of tools built to manage learning, collaboration, and performance within organizations.

Totara Learn is the LMS side of things—focused on delivering formal training programs like onboarding, compliance training, and professional development.

Totara Engage takes things further by enabling social learning and collaboration—it’s all about peer-to-peer knowledge sharing, content curation, and driving engagement outside of traditional courses.

Totara Perform focuses on performance management, bringing goal-setting, competency tracking, appraisals, and continuous feedback into one platform.

Together, these tools cover everything from structured learning to informal collaboration and real-time performance tracking.

Alex:

So it’s a full ecosystem for learning and development. Let’s start with Totara Learn—the LMS that most people are familiar with. What makes it stand out?

Sean:

Totara Learn is incredibly flexible, which is its biggest advantage. You can tailor everything—from learning pathways to branding—to fit your organization’s needs.

With Totara Learn, you can:

•Create custom learning pathways for different roles or departments.

•Track and manage compliance training, ensuring everyone is up to date with required certifications.

•Integrate with external systems like HR tools or CRMs.

•Use detailed reporting to track learner progress and training ROI.

It’s ideal for organizations that need to deliver structured, formal learning at scale—whether that’s a global enterprise or a small team growing rapidly.

Alex:

It sounds robust and very adaptable. Now, you mentioned Totara Engage, which adds collaboration and social learning. What does that bring to the table?

Sean:

Totara Engage is all about making learning more collaborative and dynamic. Not everything an employee learns comes from a formal training course—people learn by sharing knowledge, reading articles, watching videos, and connecting with peers. Engage helps facilitate all of that.

Key features include:

• Content Curation: Employees can find, share, and curate resources—like videos, podcasts, and articles—to support their learning.

• Social Collaboration: You get tools like forums, discussions, and peer-to-peer feedback to create a real learning culture.

• Workspaces: Teams or projects can create dedicated spaces to share resources and collaborate on learning goals.

Alex:

So it’s great for organizations trying to promote continuous, informal learning alongside traditional training.

Sean:

Exactly! Engage helps organizations unlock the knowledge that already exists within their teams and puts it to good use.

Alex:

And then there’s Totara Perform—the performance management piece. How does that tie in?

Sean:

Totara Perform is all about connecting learning with employee performance. It helps organizations identify skill gaps, track progress, and align training with individual and business goals.

Some of the standout features include:

• Goal Setting: Managers can set performance goals that are tied directly to learning objectives.

• Competency Management: You can map learning outcomes to specific skills and competencies.

• Continuous Feedback: Employees and managers can track progress with regular check-ins and real-time feedback loops.

• Performance Reviews: It centralizes appraisals, making it easier to evaluate employees while aligning their development with company goals.

The benefit here is that performance management isn’t treated as a separate function. It becomes integrated with learning, so employees are constantly growing and contributing to the company’s success.

Alex:

That’s huge. So instead of annual performance reviews feeling like a chore, they’re part of an ongoing, dynamic process.

Sean:

Exactly! And it’s driven by real data from both learning progress and performance metrics.

Alex:

Now, something big is happening with Totara 19. Totara Learn and Totara Engage are being merged into one platform. Why is that such a significant update?

Sean:

This is a big deal, Alex. Traditionally, Learn handled formal training, while Engage handled informal learning and collaboration. By merging them into one platform, Totara 19 makes it seamless for organizations to combine structured learning with social and peer-driven experiences.

Imagine an employee completing a formal course in Totara Learn, but immediately sharing insights or related resources in a Totara Engage workspace. It’s a more integrated learning experience that reflects how people actually learn on the job.

For administrators, it simplifies things too—you don’t need to manage two separate systems. It’s one unified platform with a clean, user-friendly experience.

Alex:

It really seems like Totara is doubling down on the idea of blended learning—formal, informal, and performance management all in one ecosystem.

Sean:

That’s exactly it. Totara isn’t just a learning platform—it’s a complete solution for developing people. Whether you’re delivering compliance training, fostering collaboration, or improving employee performance, Totara brings it all together.

Alex:

If someone’s listening and thinking, “This sounds perfect for my organization,” what’s the best way to get started?

Sean:

The first step is to explore your goals. Are you focused on compliance? Do you want to foster social learning? Or are you looking to align learning with performance? Totara can handle all of it, but it helps to identify where you want to start.

At Webanywhere, we help organizations implement Totara, customize it to their needs, and train teams to make the most of it. You can visit webanywhere.com to set up a demo and see how Totara can transform your learning and performance strategy.

Alex:

Sean, this has been such a great conversation. Totara Learn, Engage, and Perform really offer a complete package for organizations that care about developing their people. And with Totara 19 merging Learn and Engage, it’s only getting better.

Sean:

Absolutely. Thanks for having me, Alex—I always enjoy talking about how technology can unlock learning potential.

Alex:

Thanks, Sean. And to our listeners, if you’re looking for a flexible, powerful solution to manage learning, collaboration, and performance, visit webanywhere.com to learn more about Totara LMS and how it can help your organization thrive.

Until next time—keep learning, keep growing!

The post What is Totara? appeared first on Webanywhere.

How AI Can Improve Employee Productivity: AI Agents, LLMs, and Multimodal Tools

Businesses face constant pressure to boost employee productivity. Enter Artificial Intelligence (AI)—the ultimate tool to streamline workflows, reduce wasted time, and empower employees to focus on what matters most.

In this Learning Summit Podcast episode, Sean Gilligan, founder of Webanywhere, explains how AI agents, Large Language Models (LLMs), and multimodal AI are transforming workplace productivity. Below is the full transcript of this insightful conversation.

Alex (Host):

Welcome back to the Learning Summit Podcast. Today we’re diving into a topic that’s transforming the future of work: how AI can enhance productivity for employees.

I’m joined by Sean Gilligan, founder of Webanywhere and a leading voice in AI and education technology. Sean, great to have you here.

Sean Gilligan:

Thanks, Alex. It’s a pleasure to be here. AI is unlocking so much potential for employees and businesses, so I’m excited to share some insights today.

AI Agents: Connecting the Dots Across Systems

Alex:

Let’s start with the basics. Productivity often breaks down when employees spend time searching for information across different platforms—like Salesforce, Jira, and Google Workspace. AI Agents seem like the solution. Can you explain what they are?

Sean:

Absolutely. AI agents—like Google’s Agentspace—act as smart assistants that connect data across systems. Instead of toggling between apps, employees can simply ask an AI agent to pull the information they need.

For example, you could ask, “What’s the status of Project X?” and the AI agent will fetch Jira tickets, updates from Salesforce, and notes from Google Docs. It provides a single, instant summary, eliminating the need to search manually.

Alex:

That’s a huge time-saver. I’ve read that employees spend up to 30% of their day just searching for information.

Sean:

Exactly, Alex. AI agents give that time back, allowing employees to focus on strategic tasks—like decision-making, problem-solving, and innovation.

Training LLMs on Your Own Data

Alex:

The next game-changer is Large Language Models (LLMs). Businesses are now training models like OpenAI’s ChatGPT, Claude by Anthropic, or Google Gemini on their own company data. How does this improve productivity?

Sean:

Great question. Off-the-shelf LLMs are already powerful, but when you train them on your specific data—like company policies, internal knowledge bases, or product details—they become far more tailored and relevant.

For instance, a customized LLM can:

• Generate highly accurate reports.

• Answer employee questions about workflows and protocols.

• Streamline customer support with instant, data-specific responses.

Alex:

That sounds like a win for security too.

Sean:

Absolutely. Training LLMs on your data in a secure environment means sensitive information stays in-house. Businesses are essentially creating their own private AI ecosystems that are tailored, secure, and incredibly useful.

Alex:

And when businesses are choosing between tools like ChatGPT, Claude, or Gemini, what factors should they consider?

Sean:

It depends on their use case.

• ChatGPT is versatile and well-rounded.

• Google Gemini integrates seamlessly with Google Workspace, so it’s great for businesses already using Google tools.

The right model depends on your needs and existing systems.

Multimodal AI: Voice, Video, and Live Collaboration

Alex:

You also mentioned multimodal AI, which seems to be a big trend. Can you explain how it helps employees?

Sean:

Of course. Multimodal AI means models don’t just process text—they handle voice, video, and images too. This opens up new ways to improve collaboration and productivity.

Imagine you’re in a virtual meeting. An AI tool can:

• Analyze video feeds in real-time.

• Generate instant meeting summaries and action points.

• Process shared screens to extract key information.

It’s like having an AI assistant watching, learning, and summarizing everything for you.

Alex:

So AI could watch a live presentation, generate notes, and even pull insights?

Sean:

Exactly! This is especially useful for remote teams. Platforms like Watch and Learn already enable these capabilities—creating automatic transcripts, summaries, and action plans after meetings.

This ensures nothing is missed, and employees can focus on discussions instead of note-taking.

Getting Started with AI

Alex:

Sean, you’ve shared so many powerful insights: AI agents connecting systems, LLMs tailored to company data, and multimodal tools enhancing collaboration. For businesses ready to get started, where should they begin?

Sean:

Start small and practical. Identify bottlenecks in workflows—like time wasted searching for information or summarizing meetings. Pilot AI tools to solve those problems.

Once you see the impact, you can expand AI across other processes. The goal is to empower employees, not replace them. AI gives them the tools to be more efficient, creative, and strategic.

Alex:

Fantastic advice. For our listeners who want to explore AI solutions for their teams, visit webanywhere.com—Sean’s team is doing incredible work in this space.

Sean, thanks for joining us.

Sean:

Thanks, Alex. It’s been great.

Alex:

And that’s a wrap for this episode of the Learning Summit Podcast. Stay tuned for more insights on the future of work and technology. See you next time!

AI Agents streamline workflows by connecting data across tools like Jira, Salesforce, and Google Workspace, saving valuable time. Custom-trained LLMs deliver tailored, secure, and highly relevant outputs by leveraging your company’s data. Multimodal AI enhances collaboration through live video analysis, automatic transcripts, and real-time summaries. To get started, focus on solving specific productivity challenges with AI and scale gradually for maximum impact.

The post How AI Can Boost Employee Productivity? appeared first on Webanywhere.

In this episode of The Learning Summit Podcast, Alex interviews Sean Gilligan, founder of Webanywhere, to explore how AI agents are transforming learning and development. Discover how these virtual assistants are automating repetitive tasks, personalizing learning experiences, and boosting productivity across industries like healthcare and retail.

Learn practical examples of how AI agents can:

• Simplify compliance tracking

• Streamline onboarding

• Personalize training for employees

• Enhance collaboration for remote teams

Sean also shares insights into Webanywhere’s innovative use of AI agents and what the future holds for L&D technology.

Alex (Host): Welcome to The Learning Summit Podcast, where we explore the ideas shaping the future of learning and development. I’m your host, Alex, and today, we’re diving into a transformative technology that’s changing how we work and learn—AI agents. Joining me to discuss this fascinating topic is Sean Gilligan, founder of Webanywhere, a trailblazer in L&D technology. Sean, welcome to the podcast.

Sean (Guest): Thanks, Alex. Excited to be here.

Alex: Let’s get right into it. AI agents—what are they, and why are they such a big deal?

Sean: At a high level, AI agents are software programs that act on behalf of a user to complete tasks and achieve goals. What’s revolutionary now is their ability to control computers, automate workflows, and handle repetitive tasks that would otherwise take up valuable human time. This allows people to focus on more meaningful, higher-level work.

Alex: That’s a great introduction. What kinds of tasks can AI agents take off our plates, especially in the context of learning and development?

Sean: Think about all the administrative work involved in L&D—creating reports, scheduling training, sending reminders, or collating feedback. AI agents can handle these tasks seamlessly. For example, they can automatically pull progress data from an LMS, generate performance summaries, and notify managers, all without any manual effort. It’s like having an extra team member dedicated to the mundane so your team can focus on strategy and creativity.

Alex: That’s a game-changer. On the learner side, how can AI agents improve the experience?

Sean: AI agents make learning more personalized. Imagine a scenario where an employee struggles with data analysis. The AI agent can identify this gap, recommend specific training modules, and even schedule a session with a coach. It’s about delivering the right content to the right person at the right time. This not only enhances the learner’s experience but also boosts retention and application of skills.

Alex: And for teams working across different locations or time zones? How do AI agents support collaboration in learning?

Sean: Remote teams often face challenges in staying aligned. AI agents can act as virtual facilitators, capturing live meeting discussions, summarizing key points, and sharing them with absent team members. They can also prioritize tasks on platforms like Slack or Microsoft Teams, ensuring everyone knows what’s next without having to sift through endless messages.

Alex: That sounds powerful. But there’s always a concern about job displacement. Should we be worried about AI agents replacing human roles?

Sean: It’s a valid concern, but I see AI agents as augmenting, not replacing, human roles. By taking over repetitive tasks, they free people to focus on high-impact activities like problem-solving, creative thinking, and relationship building. For example, L&D professionals can spend less time managing compliance workflows and more time designing impactful learning journeys.

Alex: That’s a positive way to frame it. Can you give us examples from industries like healthcare or retail where AI agents are already making a difference?

Sean: Sure. In healthcare, compliance is a massive task—certifications and renewals can be a logistical nightmare. AI agents can track renewal dates, send reminders, and even enroll employees in the right courses. In retail, onboarding is a constant need due to high turnover. AI agents can deliver tailored training to new hires, helping them get up to speed faster and more efficiently.

Alex: Scalability is another big question, especially for global organizations. How do AI agents make scaling L&D programs easier?

Sean: AI agents are perfect for global operations. They can localize content for different regions, ensuring relevance across cultural and regulatory contexts. For instance, an agent might deliver GDPR training in Europe and OSHA training in the U.S., all while maintaining a consistent learner experience. They can also analyze learning data globally, helping organizations identify and address skill gaps proactively.

Alex: That’s impressive. How is Webanywhere leveraging AI agents in its platforms?

Sean: At Webanywhere, we’re integrating AI agents to make learning smarter and more accessible. For example, we’re building features that allow users to ask natural language questions like, “How do I deal with a difficult customer?” and instantly get relevant training materials. We’re also exploring voice and chat interfaces to make interactions even more intuitive.

Alex: That sounds cutting-edge. Where do you see AI agents taking the L&D industry in the next five years?

Sean: The future is all about hyper-personalized learning. AI agents will act like personal tutors, continuously adapting to each learner’s needs. They’ll also become more proactive, identifying skills employees will need before they realize it themselves. It’s about making learning not just reactive but predictive.

Alex: Before we wrap up, Sean, what advice would you give to organizations thinking about adopting AI agents for learning and development?

Sean: Start small. Pick one repetitive task—like compliance tracking or onboarding—and test how an AI agent can optimize it. Measure the results, get feedback, and then scale up. Also, involve your team from the start to build trust and get buy-in.

Alex: Fantastic advice. Sean, this has been a fascinating conversation. Thanks so much for joining us on The Learning Summit Podcast.

Sean: Thanks, Alex. It’s been a pleasure.

The post How AI Agents Are Changing Learning and Development appeared first on Webanywhere.

Imagine a scenario: a large organization relies on its L&D department to design and deliver training, manage an LMS, create content, and measure success through assessments and analytics. Traditionally, these tasks required a significant investment of time, often split between designing learning programs, developing content, providing learner support, and running assessments. Now, with AI in the mix, that time equation is shifting.

Take instructional design, for example. AI can now provide frameworks, templates, and initial drafts for learning materials. Instead of spending 20 hours per week meticulously crafting each course, instructional designers may only need 15 hours to refine what AI has already started. The process becomes about curating rather than creating from scratch. Content development, long a time-consuming task, is reduced even more dramatically. Where once professionals might spend 15 hours per week writing content, they may now spend just 5, with AI generating initial drafts and outlines. The work becomes about fine-tuning, personalizing, and ensuring the tone is right.

Conversely, areas like learning analytics are expanding. AI is providing more data than ever before, but interpreting this data—translating it into actionable insights—requires human intuition and expertise. Time spent in learning analytics will increase, with professionals now needing to focus on what the data means for the organization’s strategy and long-term goals.

Current Time Spent Per Job Type in L&D (Pre-AI)

Projected Time Spent Per Job Type in L&D (Post-AI Adoption)

Similarly, L&D leaders will need to spend more time on planning and strategy. AI can generate mountains of data and automate routine tasks, but human insight is critical for connecting those dots. What is the future of learning in this organization? How do we use AI’s insights to shape long-term strategies? As AI frees up time elsewhere, this becomes the crux of the L&D professional’s role—translating machine-driven insights into human-centered outcomes.

So what does this mean for the future of L&D professionals? It’s simple: adapt or fall behind. As AI handles the more repetitive aspects of content creation, coordination, and assessment, L&D professionals need to shift their focus. The future is about data interpretation, strategic thinking, and human-centered leadership.

Before AI: Traditional L&D Process Flow

1. Needs Analysis
[L&D Team] –> Conduct manual surveys, interviews, and feedback analysis

2. Program Design
[L&D Team] –> Manual design of learning objectives and curriculum

3. Content Creation
[L&D Team] –> Develop training materials, presentations, and courses from scratch

4. Training Delivery
[Instructor-Led Training] –> In-person or virtual sessions, mostly standardized

5. Assessment & Evaluation
[L&D Team] –> Collect feedback, conduct manual assessments/tests

6. Reporting & Improvement
[L&D Team] –> Analyze data manually and make adjustments for future programs

After AI: AI-Enhanced L&D Process Flow

1. Needs Analysis
[AI-Powered Analytics] –> Automated analysis of performance data and skill gaps
↓
[L&D Team] –> Interpret insights and align business goals

2. Program Design
[AI Recommendations] –> AI suggests learning paths and training objectives based on data
↓
[L&D Team] –> Validate and adjust program design

3. Content Creation
[AI-Generated Content] –> AI creates customized training modules, quizzes, and learning materials
↓
[L&D Team] –> Curate and refine AI-generated content

4. Training Delivery
[AI Chatbots & Virtual Tutors] –> AI delivers personalized learning through chatbots and virtual platforms
↓
[L&D Team] –> Focus on coaching and handling complex queries

5. Assessment & Evaluation
[AI-Based Adaptive Assessments] –> Real-time assessments with instant feedback loops
↓
[L&D Team] –> Monitor AI-generated insights and learner progress

6. Reporting & Improvement
[AI Predictive Analytics] –> Continuous feedback and predictive data to optimize learning
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[L&D Team] –> Proactively adjust learning strategies

Imagine the L&D department as a finely tuned orchestra. In the past, each musician—the L&D team—was responsible for not only playing the instruments but also writing the sheet music, setting the tempo, and even analyzing the acoustics of the room. Every step, from data collection to content creation, was laborious and manual, leaving little room for the big picture. Enter AI, and the entire dynamic shifts. Automation steps in, taking over the tedious tasks like data collection and content generation. Personalization now happens in real-time, with AI crafting tailored learning paths and adaptive assessments for each individual. As a result, the L&D team is liberated from the minutiae, their focus now on strategy, validation, and guiding the larger, more complex conversations. It’s a transformation, where the orchestra still plays, but the conductor now has the freedom to lead with creativity and precision, rather than getting lost in the mechanics of the music.

What Skills Should L&D Professionals Focus On?

1. Data Literacy: With AI generating more learning data than ever before, L&D professionals need to become fluent in interpreting this data. The ability to derive actionable insights from complex data sets will be invaluable. This isn’t just about understanding what the numbers say, but also what they mean for learner success, engagement, and future strategy.
2. Strategic Thinking: As routine tasks become automated, L&D professionals will need to think more like business leaders. The ability to align learning initiatives with organizational goals, forecast future needs, and adjust strategies based on data insights will become key to thriving in an AI-enhanced environment.
3. Emotional Intelligence: AI may be able to create learning content, but it cannot replace the human touch. Understanding learners’ needs, building rapport, and fostering an inclusive learning culture will remain critical. Professionals who excel in these areas will be the ones who guide their teams through the AI transformation, ensuring that the human side of learning doesn’t get lost in the technological shuffle.
4. Continuous Learning: In an AI-driven world, the pace of change is relentless. L&D professionals must continuously update their skills, not only in technology but in leadership, strategy, and the softer skills that AI cannot replicate. Staying curious and committed to learning will be essential.
5. Creativity and Problem Solving: While AI can handle tasks like generating content or analyzing data, it lacks the creativity and innovation that humans bring to problem-solving. L&D professionals should focus on creative ways to use AI tools, enhance learner experiences, and design new learning solutions that go beyond what the algorithms can generate.

The bottom line is that AI will change the way L&D professionals work—but it won’t replace them. The skills that machines can’t replicate—insight, empathy, strategic thinking, and creativity—are the ones that will matter most in the AI-enhanced L&D landscape. Those who embrace this shift and adapt their skill sets accordingly will thrive. The rest may find themselves left behind in a world that is moving forward at an unprecedented pace.

In this new era, AI isn’t the end of human-driven L&D—it’s the beginning of a new kind of partnership between humans and machines. And the future of learning will be defined not by the technology itself, but by those who know how to harness it.

The post How AI is Redefining Roles and Skills in L&D appeared first on Webanywhere.

Take, for instance, the new voice mode introduced with ChatGPT’s latest models. It’s not merely a feature; it’s a reimagining of how we interact with information. You speak, the AI listens, and what follows is a dialogue, not a monologue. You can interrupt, question, and redirect the conversation. Gone are the days of waiting for lengthy responses or struggling with unclear instructions. The responsiveness, the back-and-forth nature, mimics human conversation but with the precision and speed of a machine. This makes the process of learning not only faster but more intuitive. It feels less like a session with a tutor and more like a conversation with a well-versed colleague.

Now, think about the implications for translators, coaches, and educators. Traditionally, these roles have relied on human expertise—empathy, nuance, and adaptability. But AI has begun to step into this space, not as a replacement, but as an enhancement. A translator powered by AI doesn’t just convert languages; it understands context, tone, and culture, offering near-instantaneous translations with accuracy that was unimaginable just a few years ago. Similarly, a coach using AI can provide feedback tailored to a learner’s unique pace, strengths, and weaknesses in real time. It’s personalized learning at a scale never before possible.

The efficiency gains are clear. Imagine an organization rolling out a global training program. Instead of hiring a cadre of trainers, an AI could lead those sessions, translating materials into multiple languages on the fly, answering questions instantly, and adapting the delivery to the needs of each learner. This isn’t a dream of the future; it’s happening now. And with these advancements, the role of the trainer shifts. Trainers are no longer the sole gatekeepers of knowledge but become facilitators of AI-driven learning experiences.

But efficiency isn’t the only outcome. There’s also the profound impact on the learning experience itself. Traditional models of education have long been criticized for their rigidity. Everyone learns at a different pace, yet we’ve often applied a one-size-fits-all approach. With AI, this is no longer necessary. Learners can move at their own pace, revisit materials as needed, and engage with the AI in ways that cater to their individual learning styles. This transforms learning from a passive activity into an active, dynamic engagement.

Still, these advancements raise important questions about the future of work. If AI can deliver personalized training, offer instant translations, and serve as a coach, what happens to the roles traditionally filled by people? For learning and development professionals, this means a shift in focus. The value will no longer be in the delivery of content but in curating, designing, and optimizing the learning experience. The human touch will still be needed, but it will be less about the act of teaching and more about understanding how people learn best—and how to use AI to facilitate that.

AI is not here to replace jobs; it’s here to change them. The demand for empathy, creativity, and strategic thinking will grow as routine tasks are automated. Trainers, educators, and coaches will become experience designers, tasked with crafting learning journeys that integrate both human and machine expertise. Those who embrace these tools will find themselves not diminished, but empowered.

We are at the cusp of a new era in learning and development. The advancements in AI, from voice interaction to real-time translation, are not just technological milestones—they are changing the way we think about education and work. And as these tools become more integrated into our daily lives, they will redefine what it means to learn, teach, and grow in ways we are only beginning to understand.

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The Imperative for Retraining

AI’s impact on the workforce is multifaceted. It automates routine tasks, augments human capabilities, and creates new opportunities that we couldn’t have imagined a few years ago. However, this shift also means that some jobs will become obsolete, while others will require a new set of skills. The challenge is not about fighting the change but navigating it effectively.

As Erik Brynjolfsson, Director of the MIT Initiative on the Digital Economy, asserts, “AI is not simply a matter of having machines do what people used to do. It’s a matter of reinventing work itself.”

Retraining is not merely an option; it is an imperative. Companies that invest in the continuous development of their workforce are not just preparing for the future—they are building it. This proactive approach can mitigate the risks of job displacement and unlock new avenues for growth and innovation.

Cultivating a Growth Mindset

The first step in retraining workers for an AI-driven world is fostering a growth mindset. This mindset is the belief that abilities and intelligence can be developed through dedication and hard work. It is the foundation for embracing new challenges and persevering in the face of setbacks.

Carol Dweck, a renowned psychologist, explains, “The view you adopt for yourself profoundly affects the way you lead your life.” Encouraging a growth mindset is crucial for adapting to technological changes.

Employees should be encouraged to view AI not as a threat but as a tool that can enhance their roles. This perspective shift can be achieved through transparent communication about the benefits of AI, coupled with real-world examples of how AI can make work more efficient and rewarding.

Identifying the Skills of the Future

What skills will be in demand in an AI-powered world? While technical skills related to AI, such as machine learning, data analysis, and programming, are crucial, they are not the only skills needed. Soft skills, such as critical thinking, creativity, emotional intelligence, and adaptability, will become even more valuable. These skills enable workers to collaborate effectively with AI technologies and leverage them to solve complex problems.

As Satya Nadella, CEO of Microsoft, states, “The ability to learn is the most important quality a leader can have.” This sentiment underscores the importance of continuous skill development.

Organizations should conduct thorough assessments to identify skill gaps and create tailored training programs. This approach ensures that employees acquire both the technical and soft skills needed to thrive in their evolving roles.

Embracing Lifelong Learning

Lifelong learning should be the cornerstone of any retraining strategy. The rapid pace of technological change means that static skill sets quickly become obsolete. Continuous learning ensures that workers remain agile and capable of adapting to new tools and processes as they emerge.

Jeff Weiner, Executive Chairman of LinkedIn, emphasizes, “One of the most important things we can do is to teach people how to learn.” This highlights the necessity of fostering a culture that values ongoing education.

To foster a culture of lifelong learning, companies can offer various educational opportunities, such as online courses, workshops, and certifications. Encouraging employees to take ownership of their professional development and providing them with the resources to do so can create a more resilient and innovative workforce.

Leveraging Technology for Training

Ironically, the same technologies driving the need for retraining can also facilitate it. AI and machine learning can be leveraged to create personalized learning experiences. These technologies can analyze individual learning patterns and tailor training programs to suit each employee’s needs, making the learning process more efficient and effective.

Virtual reality (VR) and augmented reality (AR) can also play a significant role in training. These immersive technologies can simulate real-world scenarios, allowing employees to practice new skills in a risk-free environment. This hands-on approach can accelerate learning and improve retention.

As Andrew Ng, a leading AI expert, points out, “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.” This transformation includes the way we train and educate our workforce.

The Role of Leadership

Leadership plays a crucial role in the successful retraining of workers. Leaders must be champions of change, demonstrating a commitment to continuous learning and adaptability. They should lead by example, participating in training programs and encouraging their teams to do the same.

Effective communication is also key. Leaders should articulate a clear vision for how AI will integrate into the organization and the benefits it will bring. By fostering a positive and inclusive environment, leaders can alleviate fears and resistance to change, making the transition smoother for everyone involved.

Tim Cook, CEO of Apple, says, “The most important thing is, you need to believe in your vision.” Leaders must communicate and embody this belief to inspire their teams during transitions.

The rapid advancement of AI necessitates a proactive approach to retraining the workforce. Embracing a growth mindset, identifying future skills, fostering lifelong learning, leveraging technology for training, and strong leadership are all critical components of this strategy. By committing to these principles, organizations can not only navigate the challenges posed by AI but also harness its potential to drive innovation and growth. The future of work is not just about surviving technological change; it’s about thriving in an era of continuous transformation.

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There has been a lot of buzz recently about voice social media platforms and two platforms have carved out their own unique spaces—Airchat and Sound Branch. Both aim to revolutionize how we interact online, but they do so in distinct ways that cater to different user needs and experiences.

What Is Airchat?

Focused on simplicity and the power of voice, Airchat offers a platform reminiscent of social media but with a twist—everything is voice-based. It positions itself as an intuitive tool where users can engage in conversations using their voices instead of text. This approach not only humanizes digital interactions but also speeds up the exchange of ideas. A user described the experience as akin to “Twitter but with my voice,” highlighting its distinctive, vocal-driven interface​.

The platform allows users to easily join conversations, reflecting a design philosophy that emphasizes user-friendly interfaces and accessibility. With its push notifications and personalized updates, Airchat ensures users stay connected and informed about conversations that matter to them. Privacy and user consent are also central, with clear options for managing communications and data privacy preferences​.

What Is Sound Branch?

On the other hand, Sound Branch takes a slightly different approach by integrating features that cater to both personal and professional communication needs. It allows users to create short audio clips (“sound branches”) that can be used for everything from team updates to family messages. This versatility makes it a useful tool for quick updates or detailed discussions without the formality of written emails or the time commitment of meetings.

Sound Branch’s structure encourages a tree-like conversation flow, where each audio clip can branch out into more detailed discussions, allowing for a depth and breadth of dialogue that’s both organized and engaging. It’s a format that particularly suits collaborative environments where ideas evolve over time.

Key Differences and Considerations: Airchat vs Sound Branch

• Formality vs. Spontaneity: While Airchat promotes more spontaneous and casual voice interactions​, Sound Branch provides a structured environment that could be more suited for formal or semi-formal settings due to its branching conversation style.
• User Interface and Accessibility: Airchat’s interface is straightforward, focusing on ease of use and quick access to voice chats, which can be particularly appealing to users who prefer simplicity and speedy interactions​​. Sound Branch, with its unique branching conversations, offers a different type of clarity—organizational clarity, which is crucial for tracking complex discussions.
• Application in Daily Use: Airchat seems ideal for users looking for a social experience that mimics real-life interactions through voice. In contrast, Sound Branch’s design serves well for those who need to manage detailed projects or maintain clear records of evolving discussions.

In-Depth Comparison: Airchat vs. Sound Branch Features

In the digital realm, communication tools like Airchat and Sound Branch have revolutionized the way we interact. Both platforms are dedicated to voice-based networking, but they offer different features and experiences tailored to distinct user needs. Here’s a deep dive into their features, highlighting where they align and where they diverge, providing unique functionalities.

Common Features

Both Airchat and Sound Branch facilitate voice-based social networking, and each has several common features that align them closely in functionality:

• All Voice Notes Transcribed: Both platforms offer transcription of voice notes, making content accessible and searchable in text form.
• Voice Only Social Network: They strictly focus on voice content, distinguishing them from multimedia social platforms.
• iOS and Android Apps: Ensuring accessibility, both have applications available on major mobile operating systems.
• Direct Messaging: Users can send private voice messages to one another, enhancing personal communication.
• Likes and Voice Note Replies: Interaction mechanisms like liking and replying to voice notes are integral, fostering community engagement.
• Categories and Subcategories: Both organize content into categories, making navigation and content discovery easier.
• Notifications: They keep users engaged by notifying them about new content and interactions.
• Moderation and Reporting: Tools to report inappropriate content and moderate discussions are available, maintaining platform integrity.

Exclusive Features of Sound Branch

Sound Branch differentiates itself by incorporating features that cater to both personal and professional environments, which are not available on Airchat:

• Editable Transcription Text: Unlike Airchat, Sound Branch allows users to edit the transcriptions of their voice notes, providing greater accuracy and flexibility in how content is presented.
• Web App: Sound Branch is accessible via a web browser, offering users flexibility to access the platform from various devices without the need for a mobile app.
• Voice Assistants Integration: Sound Branch integrates with Alexa and Google Assistant, enabling users to interact with the platform through voice commands on supported devices.
• Podcast Creation Tools: It provides tools for users to create and distribute podcasts directly from the platform, appealing to content creators.
• Custom Branded Walled Garden Site: Perhaps the most significant feature is the ability to create custom-branded sites. This allows organizations to maintain their own private, branded Sound Branch community, offering control over content and user interaction, unlike Airchat’s single global community approach.
• Sentiment Analysis, Audio Profiles & Testimonials: Advanced features like sentiment analysis of voice notes, and the ability to create audio profiles and testimonials, cater to professional users seeking deeper insights and personal branding.
• Search Voice with Voice: Enhancing accessibility and ease of use, users can perform searches using voice commands.
• Private Groups: This feature enables creating private conversation spaces, ideal for focused discussions or confidential matters.
• See Who Has Listened to a Message and Export Full Transcript: These features are particularly valuable for content creators and professionals who need to track engagement and maintain records.
• Analytics Visible to Users: Offering insights like the number of plays, which can be critical for users who use the platform for public speaking or marketing purposes.

While both Airchat and Sound Branch serve the voice-only social network niche, their target audiences and the depth of their features vary. Airchat appeals to users who favor simplicity and the novel experience of a voice-based social media platform. In contrast, Sound Branch caters more to professional users and organizations with its suite of advanced features, particularly the custom branded sites, which provide a controlled environment for tailored community interactions.

These platforms exemplify how voice communication technology is being leveraged to create more engaging and meaningful online interactions, each adapting its features to meet specific user demands and scenarios.

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