Author: Ross Stevenson

My latest experiments with AI to rewire learning have taken me back to playing with AI Avatars.

The market has many AI avatar tools with varying degrees of quality (I have seen some horror shows). I’ve played around with a few but never really found a good use case for them.

The quality level has mostly put me off sharing anything built with them, but times change, and the tech has advanced…a lot.

In L&D, I’ve often seen Avatars used as a way to elevate bad practices.

Too many turn that already crap course/powerpoint/pdf into a droning avatar and think that’s ‘job done’. In reality, its just crap in → crap out, and that’s not the fault of the tech – its ours.

We’re falling to the limits of our ideas.

Faster and more might feel like the only option on the table with AI avatars, but that’s not the case.

We have to rewire how we do what we do.

Converting an ‘average’ static piece of content into something AI-enhanced is probably not your best call.

Grand Theft Avatars

I must admit, watching several deepfake-themed thrillers like the superb British drama The Capture has put me in a constant state of suspicion when it comes to using my likeness with any of these tools.

Sadly, this choice has been taken away from me in the last few months, as I’ve dealt with continued instances of my video content being stolen, translated into different languages, and reused across social platforms.

Yes, they even target little old me.

Apparently, I’m a small hit with Japanese audiences.

Those reservations aside, I always believe every tool has a time and place.

Stand-alone vs built-in

While I like stand-alone AI tools, having everything in one place for video editing is an advantage.

So, when I saw Descript, a video editing tool I use daily, introduce their v1 of AI avatars to their platform, I decided to jump back in and see where the tech is at today.

Let me clarify something here: I’m aware that better AI avatar tools exist, but I’m not looking to use several different apps chained together.

If I get a lot of value out of an existing product and they bring AI features that work well within that product, I’m all for that.

The ability to do everything I need with existing features I’m comfortable with, and have that additional AI layer in-app, is what gets more of my attention these days.

Plus, I don’t have all the money in the world for a bazillion app subscriptions.

As the feature is a v1, I’m not expecting the level of something like HeyGen or Synthesia.

I’d give my experience and the result a 7/10 — and there’s nothing wrong with a 7/10 in my book.

Here’s what I liked:

• The lip-sync looks pretty damn good
• Body and face movements didn’t feel distracting
• The ease of writing my script and the transcription quality
• How effortlessly the avatar worked with existing features — I did expect breaks, but had none

What could be better:

• Avatar selection: currently, you can only pick from animated, otherworldly or digital-type characters. It’s a v1, so this is fine, and I didn’t want to provide my own avatar
• Avatar visual quality: You can probably see this in the video. Although the video is output in 4K, the avatar looks pixelated at times
• Over-exaggeration of words: I see this in a lot of these avatars, and you’ll see it in the video too. The pitch when pronouncing some words can be off.

In sum: I’m not assessing this as an AI avatar feature on its own. That 7/10 rating takes into account how it complements the existing product and the ease of use for me as a daily user.

→ What do you think? Check out the finished video and hit ‘reply’ to let me know.

Oh, I nearly forgot — I need to tell you about the use case.

The problem I’m solving

I binge (with AI) a lot of reports.

A lot of the time, I don’t want to keep writing another article or shoot another talking head video about new research, even if I’ve found it valuable.

A new report from Microsoft, covering how they think teams will evolve with AI, fell into this category.

It’s a great report, and I will write in more detail about it, but I wanted to get some initial thoughts out there, which I think you (yes, gentle reader — you) could benefit from, until I drink copious amounts of tea to distil those thoughts.

So that scenario, and Descript launching this feature, collided, and here we are.

I spent 15 minutes skim-reading the report (yes, I still do that), jotting down the key insights and ideas that stood out for me in my little notebook (and yes, I still do that too), before I spoke to my good AI friend in NotebookLM.

That chat involved NLM reviewing the report with my notes and crafting a narrative together on what I felt was most useful to share. This became the script for the video, which I tweaked a lot in the editing phase. I felt it needed more of my infused sarcastic British tone.

All the transitions and media in the video were done within Descript’s editor.

3 hours later, you have the below.

I feel like it turned out ok.

So, will I be using these all the time? No.

But will I be more open to experimentation with Avatars depending on the context of my goals? 100%.

Bonus: How I created this video in Descript (step by step tutorial)

I imagine you might want to know how the above was created.

Fret not, I filmed a second video to show you how I used Descript to build the end-to-end production, including the avatar creation and editing.

Don’t say I never treat you!

AI avatar tools to test

I always stick to the best tool being one that best fits your goals and priorities.

For me, Descript ticks this box right now as I already pay for the core product, but that doesn’t mean it’ll be right for you.

Other avatar focused platforms include:

• HeyGen
• Synthesia
• Elai
• Coloyssan

If you’re already a power user of these, let me know how you use them.

Final thoughts

There are 3 points to take away today:

1. Think about how you’re rewiring how we craft learning experiences with AI – don’t do what you’ve always done (death to PDFs and PowerPoint)
2. Getting quality features in a product you love using already can be more powerful than using several different tools combined. This is contextual, though
3. The best way to ‘figure out’ how useful AI could be, is to use AI

→ If you’ve found this helpful, please consider sharing it wherever you hang out online, tag me in and share your thoughts.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

I got an out of the blue message recently.

“I just saw this video where an AI agent completed a test for an employee, are you worried about this?”

There was no “Hey, Ross”, “How are you, Ross?”, “Hope life is good, Ross?” – just the question. I also find it incredibly unnerving to write about myself in the third person. This is how you know I write all of this and not AI. It’s way too polished to do any of that madness.

Lack of the sender seeming to care about me as a human aside, I have an answer.

I imagine it’s not the one you’d expect.

My take will be a little bit ranty, but nearly 20 years in the field has made me somewhat numb to dumb education and learning practices.

What was shown in the video (fyi, can’t find the video now, must have rage deleted the message) doesn’t worry me as much as the fact that the education system is broken.

Yes, that sounds like some lame statement to say to court attention, but this isn’t an algorithm and I’ll go down with this ship.

The problem with tests and quizzes in courses

If we confirm ‘skills’ and ‘understanding’ through quizzes, then it’s all just a game of who has the best memory, not who understands how to do ‘x’.

I know millions of institutions and corporate learning experiences worship at the altar of the almighty multiple choice exam as the measurement stick for human intelligence, skills and expertise.

Doesn’t mean it’s right.

Agentic AI thrives here because it’s a pretty easy process to tick a box of multiple-choice answers. There’s nothing inherently hard about that. It might be one of the easiest use cases of AI agents to date.

That is not the problem.

The actual problem is how we’ve shaped the process of measuring intelligence, skills and expertise.

What we should be doing is assessing critical thinking and how students/people approach and solve problems.

This is where AI tutors will play a big role. Instead of taking some stupid quiz or exam, you’ll talk with a tutor to explain concepts, provide analogies and break down your human chain of thought.

Thus testing your problem-solving capabilities and helping you identify blindspots.

The latest AI tech finally enables us to do something about this.

But let’s not stay in the rant too long…

So, instead of me shaking my tea cup at the world, let me share the enormity of the possible and how you can experiment with this in your learning experiences.

Why AI Coaches beat any test or quiz for real learning measurement

A few weeks back, I finished a Machine Learning Cert with Google.

And I really enjoyed it.

Yes, you read that right, someone actually enjoyed learning something. But what triggered this emotion? Simple, they treated me like an adult throughout the entire process, not just the stereotypical category of a ‘student’.

The most impactful way they did this was by removing all those worthless and kinda insulting tests and quizzes that pollute too many education and workplace learning products.

Instead, I was assessed based on what I understood from the course by talking with an AI assistant, which acted as a semi-assessor and coach.

No multiple-choice questions for me to get ChatGPT to answer here.

At the end of each module, I had to face what we could call the final boss in Google’s AI coach.

After hours spent learning about the wonderful components of machine learning, I’d talk with the AI coach to share what I learnt, answer its probing questions and explain how I would convey these learnings with others through examples and analogies.

It was unique compared to the industry standard, and that made it very rewarding.

Even as I write these words, my recollection is so positive, and it helped me cement my understanding of many of the core concepts that I know a multiple-choice test couldn’t have.

Now, you could read this and think, “This guy is crazy, isn’t a test better?” — NO!

The memory game is not a measure of intelligence

The problem we’ve created as a society is this odd ‘test your memory game’ with which we use to assess an individual’s skills and expertise.

Completing a course on ‘x’ doesn’t mean you know how to do ‘x’ for real.

That goes for this very course I’ve shared with you.

I took the cert as a way to expand my understanding and capabilities in the ML realm, but don’t expect me to be building algorithms anytime soon.

Knowing how to do ‘x’ requires me to demonstrate that I can do it and explain the critical thinking behind it.

We need to spend more time prioritising how we solve problems, thinking critically and nurturing our human chain of thought, not being the top memory champion.

And I think AI can help us shape this.

How to rewire courses with AI to measure real impact

Even before the Google cert, I’ve had AI coaches in two of my online courses.

One in my performance consulting course and the other for my AI prompting masterclass.

Both are vital, imo, in helping students to take action on what they learnt and for me to validate whether they actually understand any of what they’ve done.

They’ve been transformative for my students.

Both coaches are used daily by new and returning students. They extend the value of the courses, make sure students get real-time support and keep making the content applicable for years to come.

This is the power AI can bring to education and learning if you go beyond content creation.

Why do this instead of quizzes, tests, etc.?

Easy:

• I think they’re utterly pointless, but I assume you figured that out already.
• Because I want to develop each student’s Human Chain of Thought (HCOT).

I need to do a bit of explaining with Human Chain of Thought.

Early iterations of Large Language Models (LLMs) from all the big AI names you know today weren’t great at thinking through problems or explaining how they got to an answer.

That ability to break down problems and display its thinking is called a Chain of Thought technique.

This was comically exposed with any maths problem you’d throw at these early-stage LLMs.

They would struggle with even the most basic requests.

It’s a little different today, as we have reasoning models. These have been trained to specifically showcase how they solve your problems and present that information in a step-by-step fashion.

We now expect all the big conversational AI tools to do this, so why don’t we value the same in humans?

Providing AI coaches that help my students contextualise, apply and truly understand what they’ve learnt amplifies this Human Chain of Thought.

So next time you design a learning experience, maybe ditch the test and quizzes for a personalised AI coach/assessor/I’ll think of a better name in the future.

I plan to cover more on the ‘How to build’ these type of solutions in my next AI for solving real business problems boot camp. I only do these once or twice a year, you can join the waitlist to be first in line for the next one.

AI Tutor and Coach examples

I’ve used the word ‘coach’ a lot in this one.

We can throw AI tutors into that mix, too.

Here’s how I see the difference btw:

• AI Tutor = Breaks down concepts and works in more of a professor style
• AI Coach = Works with you in a live environment to solve challenges together. Basically, the new “Learning in the flow” but with AI.

Of course, these terms are interchangeable, and the capabilities can be merged.

Often, I find it’s easier to show you what I’m talking about with AI than try to describe it to you, so here’s some examples:

How I use AI coaches in my online courses

Using AI as a Tutor with Google AI Studio

Using AI as a Real-Time Coach with Google AI Studio

Final thoughts

I get that tests and quizzes are an easy way to measure “learning”, but that doesn’t make them useful.

AI now lets us reshape this.

You can create a similar AI coach to the one I had with Google, or the ones I use across my own courses.

Saying goodbye to completions as the measure of success is the new reality. Now, you can actually see whether people truly ‘get it’, not just if they finished something and passed the test.

→ If you’ve found this helpful, please consider sharing it wherever you hang out online, tag me in and share your thoughts.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

I believe this is one of the most underrated skills you can have in today’s world:

→ The ability to distil complex information, humanise it, and make it useful for others.

Might sound simple, but it’s not easy to do

I’ve built a career out of this.

Whether it’s analysing long reports, navigating those sleep-inducing corporate strategy decks, or breaking down that latest ‘think piece’ into something that actually makes sense.

This is the skill that makes you invaluable.

And now, with AI, this ability becomes even more powerful and sort of accessible. If you’re smart about it.

That’s what I’m sharing today.

(Btw, you can watch everything I share today in the video below. If you prefer visual and audio thoughts, this is for you).

If you’re thinking “Tell me more”, fret not.

Here’s a mini step by step guide to enhancing your human insights with AI that you can use immediately.

Step 1: Do a Human review

Yes…use your brain.

Shocking, I know.

Modern business life finds us drowning in white papers from McKinsey, HBR articles saved in 27 open tabs (yes, I have a problem), and PDFs full of charts no one understands.

There’s value in all of it, somewhere.

If you’re anything like me, you save some screenshots and highlight notes to share with your audience. Wether that reaches them is another question.

AI can help you to collate, build on and refine what you want to get across.

The problem is most people just use AI tools to craft basic summaries that parrot the original text without any real context for your audience.

That’s not the move!

Content is worth very little without context. Something AI is teaching us more by the day.

What you want to focus on before even touching AI is:

• What topics are relevant to your audience?
• What is your (yes your) key take on the topic?
• What do you want your audience to walk away with?

Use your human intelligence here.

That means (at least partially) reading the thing, digest it and decide what matters.

Then (and only then) we can go talk to your local neighbourhood AI tool about the ideas, insights and messages you want to solidify.

Step 2: Use AI after you’ve done the thinking to enhance your insights

Once you’ve got clarity on the topic and your key take, that’s the time to bring AI into the mix.

Personally, I’m a big fan of NotebookLM for this job.

I chuck in all the stuff I’ve been reading, add my own notes, and use it to help me cross-check ideas, open up different perspectives, and challenge my thinking.

But here’s the rule: AI is the co-pilot, not the captain.

This is the whole point of doing the human review first. You go in with clarity on your POV and use AI to help shape that.

So, that means working together to crystallise your insights, not relying on AI to reveal all, cause it won’t

There are loads of other tools you can use too:

• Gemini
• ChatGPT
• DeepSeek
• Claude

Take your pick.

The point is to use them to elevate your thinking not outsource it.

Step 3: Refine your insight for humans, not AI

Before you share any of those freshly generated AI takes on your notes – hold fire!

Now is the time to refine your information into a structure that makes sense for your audience

This is where most people drop the ball.

They’ll share a wall of text or copy-paste what a tool gave them.

Please, don’t do that.

Instead, ask:

What format serves this best? (A one-pager? A short email? A voice note for the team Slack?)

Once you’ve defined this, you can use AI tools to help structure your writing but your brain needs to lead the way.

Ask yourself:

• Why should people care about this?
• What’s my core point?
• What’s the value in this for them?
• What will they learn and how can that be used?

If you can answer that, you’re already 10 steps ahead of most.

This is still what makes this very much a human powered process with AI support.

Final Thoughts

This is one of the ways I get real value with AI.

Don’t get it twisted, AI doesn’t replace human thinking.

The best insights come from your unique perspective, and AI can help you refine, structure, and elevate that perspective.

That’s the person everyone wants to work with.

Let’s recap on what we’ve covered:

1. Identify a useful topic, establish your key take = your human review
2. Gather all that context with your sources and share with AI to validate, challenge and expand on your ideas
3. Bring all that together in a format that makes sense for your audience

→ If you’ve found this helpful, please consider sharing it wherever you hang out online, tag me in and share your thoughts.

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Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

One of the most common questions I’m asked by teams is: Where can AI REALLY help me at work?

That’s a question I like to hear.

Many people make the mistake of jumping to tools first instead of defining a problem.

I totally understand when a transformational technology like generative AI emerges, everyone wants to try it. And that’s great! One of the best ways to discover how these tools can be useful is through experimentation.

But, what we can do is apply a decision-making framework to structure that experimentation and ensure you’re using AI effectively.

The goal? Not bloody finding more use cases to create content – stop it!

Instead, I’ll focus on saving time, improving quality, and focus on more human things that matter. I like to class this as the boring and basic but HUGELY effective applications of AI in your work.

Not everything needs to be a huge multi-workflow project that takes more time to build and maintain than the ROI it provides.

Start with tasks

Everything starts with tasks.

We use our skills to complete tasks. With AI tools, we can identify specific areas where they can provide meaningful support.

In relation to tasks, we should ask:

• How long do they take to complete?
• What is the output, and does it justify the effort required?
• What does ‘x’ task enable you to do?
• Is it repeatable and low margin for error?

This is by no means a complete list.

If we assess these and add the lens of AI assistance, we can find potential opportunities to work smarter.

Identifying opportunities

I’m certainly not the first to propose this.

LinkedIn CEO, Ryan Roslansky proposed a similar model back in early 2023.

This idea originates from Ryan’s Redefining Work article, where he explores how AI will accelerate workforce learning and amplify the importance of skills.

Ryan suggests moving away from viewing jobs as titles, and instead, seeing them as a collection of tasks. These tasks will inevitably evolve alongside AI and other technological advancements. He recommends breaking your job down into its primary daily tasks.

You can bucket those tasks in this format:

1. Tasks AI can fully take on for you, like summarising meeting notes or email chains.
2. Tasks AI can help improve your work and efficiency, like help writing code or content.
3. Tasks that require your unique skills – your people skills – like creativity and collaboration.

This sets the stage for how I currently recommend working with AI.

You could say my framework is the process before Ryan’s.

Without understanding your tasks, it’s hard to say if/how AI can support.

Let’s unpack three real-world examples from my own work where I’ve identified tasks that fall into this category, how I use AI to support me and why.

1/ AI in Meetings

I spend a significant part of my week talking to different teams and individuals about how AI and technology can solve their problems, especially in learning and development (L&D).

Many teams still think of AI primarily in terms of content creation, but there’s so much more it can do.

Most of my meetings range from 15 to 45 minutes. Like many of us, I scribble quick notes in a doc or on a notepad, but they’re often fragmented and hard to revisit later.

But I really want to focus on the conversation rather than taking detailed notes.

It’s super distracting for me when I’m like “I should write down what they just said” yet, that means breaking off mid-convo, too unnatural for me. By the time there is a break, I’ve usually forgotten what I was supposed to write down.

To solve this, I use an AI meeting assistant to join calls, record transcripts, and summarise key insights (if participants are cool with that of course – always ask!). This might seem basic, but as I always say, boring and basic is sexy because it’s efficient.

We all go to meetings, we all need notes and we all forget important insights and useful ideas.

It’s a shared pain.

Instead of spending hours sifting through notes, I can quickly review key takeaways and action points compield by my AI friend. Nothing gets lost too because I can see the original transcript if I sense AI is off base with anything. Like that one time it thought I recommended unicorns as a solution – another story for another time.

This allows me to focus on human conversations without worrying about missing important details.

It genuinely improves my life.

A few tools to explore:

• Read AI
• SANA (as part of their AI platform)
• Granola (a new one I’ve been testing)

If your organisation already has approved AI tools, use those. Microsoft Teams and Google Workspace both have built-in meeting assistant functionalities. Never use anything outside your companies infrastructure.

TL;DR:

Not only does this save time, but it enables better human connection and helps keep track of discussions from weeks or months ago without relying on memory or notebook doodles (why do I always draw skulls? Question for a therapist 🤔).

You won’t find a billboard that reads “My AI meeting assistant saves me time, enables greater social connection and improves my work” but maybe it should.

2/ AI for Insights & Summaries

For most of us, reports are part of our jobs.

You might read short articles, while others (like me) deal with 250+ page research papers.

But let’s be honest – who has time to read all that in depth?

What we actually need is a way to synthesis key insights quickly while maintaining depth where it matters. Kinda like being your own Harvard Business Review.

A lot reports and research are a lot of fluff, an underrated skill is to distill, humanise and share the best bits for action with your audience. No one needs you to regurgitate what the report said.

Always ask, how do I serve my team/audience with this info?

One tool I really like for analysis is Notebook LM, I have a separate ‘How to’ video of NotebookLM, which you can check out if you’re interested.

It allows me to upload multiple documents (up to 300 🤯), analyse them together, and extract meaningful summaries.

Other AI tools help provide quick summaries, surface key insights, and even answer questions about reports and research too. NotebookLM is just my preference.

To be clear this isn’t about replacing critical thinking.

Instead, AI helps get that first layer of understanding:

• What is this report about?
• Why is it important?
• How can it help me?

This approach allows me to decide which sections deserve deeper human analysis rather than blindly committing hours to reading everything.

Something I find incredibly useful as these tasks eat up about 10 hours + of my week.

TL;DR:

AI summaries prevent wasted time on unnecessary deep dives, ensuring you focus only on the most impactful parts, and become better-informed vs overwhelmed with useless insights.

3/ AI in Data Analysis

We all deal with data in some form.

You don’t have to be an analyst to benefit from AI-powered support.

Whether it’s customer trends, sales numbers, content engagement, or HR reports, AI can help surface patterns that might otherwise take hours to uncover.

You can fire up literally any tools like ChatGPT, Claude, and Copilot to ask:

• Explain this data to me?
• How can I use it?
• What are the key insights and what are the actions I can explore?

Beyond data crunching, AI can even help you think more broadly by highlighting blind spots.

Again, helping you think critically in the process by working with AI to uncover:

• What you’ve missed
• What else could I consider (aka devils advocate)?

TL;DR:

Mostly, we look at AI to provide answers, but it can unearth better questions too. As always, the tools are only as good as the human using them.

Final Thoughts

If you’re sitting there thinking, I’d love to start using AI, but I don’t know where to begin, start with your tasks.

• ⏰ What tasks take up the most time?
• 🥊 Which ones require effort disproportionate to the value they create?
• 🤖 What processes could be improved with AI-assisted support?

Never forget, AI isn’t about automating everything.

It’s about enhancing your work so you can focus on higher-value, human-centred work.

→ If you’ve found this helpful, please consider sharing it wherever you hang out online, tag me in and share your thoughts.

---

Before you go… 👋

If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.

You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.

AI has been promising a lot these last few years.

Previously, we looked at how Google is attempting to evolve the education system with its LearnLM model, specifically trained on learning science principles.

While I’m keen to see how that develops, I know a lot of us are in workplace L&D teams.

In my opinion, it’s less about learning at work and more about performance.

I’m sure there’ll be a few eye rolls at that sentence, but most organisations are looking to L&D to build capability and performance, not help people learn.

So, how can new tech support this?

Josh Bersin coined the phrase “learning in the flow of work” back in 2018, yet we never really had the tech to realise that vision. In 2025, it’s a different story.

Today, we’re exploring how AI is reshaping performance support at work with multimodal tools that can see and speak.

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Since generative AI tools crashed into our lives, everyone from tech bros to social media gurus has declared that it’s going to reshape education and learning.

We’ve seen AI models built specifically for education, changing how students receive support in a previous edition covering when AI is trained on learning science a few weeks back.

But what about the workplace?

Performance is bigger than learning at work

Workplace learning is a multi-billion-dollar industry, and everyone wants a slice of that pie.

Unlike traditional education, a lot of workplace learning is focused on performance. It’s getting people the right support exactly when they need it.

I’m not talking about learning deep concepts or broad skills for life. That’s a different game entirely. Performance support is about immediate problem-solving.

It’s resources, not courses.

The reality is we face a series of small – mid-level performance blockers daily

Stuff like:

• How do I use VLOOKUP in Excel?
• How can I convert this PowerPoint into Google Slides?

Yes, basic things, I’m aware.

These micro-challenges fill our workdays, but the answers are often buried deep in Google searches or trapped in some long-forgotten company SharePoint.

Now this might not be what you class as ‘learning’ but they stop people from performing, and if we agree that workplace learning is actually enhancing performance, then these are big problems.

Multimodal AI Tools are reshaping our experience

For those unfamiliar with the term “Multimodal” it just means multiple inputs and outputs.

So you can have:

• A text input and output
• A text input and visual output
• An audio input and text output

We’re no longer limited to just asking AI how to do X.

It can now see, hear, and respond in real time.

Voice and vision capabilities mean we can talk to AI tools, show them what we’re working on, and get on-demand help. Instead of scrolling through pages of search results, we can (actually) solve problems in the flow of work.

How you can try this at zero cost

I’m not going to assume everyone has a paid license to top of the range AI tools.

Instead, I’ll show you how this type of performance support can work with a zero-cost tool courtesy of those folks at Google.

In Google AI Studio, you can test multimodal features for free. You can talk to the AI, show it your screen, and work together to solve problems in real-time.

With this, you can:

1. Talk with Gemini Live.
2. Show it what you’re working on.
3. Share your screen for real-time support.

It’s simple.

Head over to Google AI Studio.

Select your AI model (Gemini 2.0), choose your output format (audio, text, etc.), and start collaborating.

And…of course, here’s a step-by-step video on how to do that ↓

Note: AI isn’t perfect. You, the human, still need to apply critical thinking and validate results.

AI as a Support Tool, Not a Replacement

This is where on-demand performance support is heading.

We’re not talking about replacing human expertise, but rather evolving traditional job aids, FAQs, and knowledge bases into dynamic, AI-powered conversational support systems.

We can:

• Help employees understand new concepts.
• Troubleshoot technical issues in real time.
• Set up hardware and software.

Not everything needs a full-blown training course.

Sometimes, we just need an answer now.

Plus, solutions like this can only help us focus more on the human stuff that matters. I mean, do you really want to keep buying and running Excel courses for teams in 2025? I’ll leave you to ponder that.

Final Thoughts

This is still in its infancy.

ChatGPT, Google Gemini, and other AI tools are already capable of vision and real-time interaction. More will follow.

The key question is: How will organisations (and you) use this?

Perhaps. it’s time, once again to rethink how we provide support with today’s technology. It’s not a case of either/or. We have an opportunity to shape how this plays out.

This is just the beginning.

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