Turn team discussions into interactive models in minutes using plain English and AI. Slides aren’t the only way to share ideas any more.

We used Anthropic’s Claude to turn ideas into interactive models through natural language. No code needed. Claude does the coding. Ideas can come to life without any coding expertise.

Here’s an example. We brainstormed with Claude on an event plan with theme, agenda, sessions, tracks, networking, and KPIs. Instead of Google slides, we asked Claude to create an interactive model to present the ideas.

The screenshot below shows the simple initial prompt (and first page of the model):

“Attached is a previous conversation with you.

I need to share these initial ideas with my team and collaborate to refine them. Please create an interactive model reflecting your suggestions above. Please have the following tabs:

1) Event Theme – show all 3 theme options and messaging for each
2) Agenda – show complete agenda for Day 1 and Day 2
3) Session Descriptions – show details
4) Track Description – show details
5) Networking and Interactive Activities – show details
6) KPIs – show details

Make it visually appealing with icons and professional colors. Our colors are blue, orange, grey, and white. Feel free to choose the most appropriate formats and functionality. Ask me any questions you might have that can help you do this task well.”

Check out the link in the comments to play with the final model.

The model’s more engaging than slides for sharing and discussing ideas. The professional presentation adds credibility to the concepts. The team can update the plan within the model using natural language as the discussion evolves.

Like any tool, there’s a learning curve. More complex models take more time and iteration. It won’t replace decks completely, but once you get the hang of it, it’s a powerful alternative to slides.

Ready to move beyond slides? Try building your first interactive model.

#AICoding #InteractiveModels #NoCode #FutureOfWork #Productivity

UPDATE (Jan 26 at 10:20 am MT):

First, I need to own up to something: I missed where the AI correctly told me that YouTube had moved transcripts to the expanded description area. Looking back carefully, it:

• ► Gave accurate information about the videos

• ► Correctly explained it got this from YouTube transcripts

• ► Referenced real timestamps from the transcript

• ► Then, oddly, claimed it had made up everything when it had been truthful all along

This is still concerning, just in a different way. Why would an AI system discredit its own truthful explanations? How can we trust AI systems if they might abandon truth for false admissions of making things up?

It’s like having a colleague who gives you correct information with proper sources, but when questioned repeatedly, says “Actually I made it all up” even though they didn’t.

Original post below ****

I caught an AI lying about how it works.

The AI knew exactly what was in two of El Estepario Siberiano’s (an incredible drummer) YouTube videos. It told me about his audition for an unnamed band. Then about him joining Residente. The AI even linked to the right videos.

This wasn’t a hallucination. Its answers were spot on. But when I asked how it could watch YouTube videos, things got weird. It made up a story about having special access to video transcripts. When I showed these transcripts didn’t exist, it said it made everything up.

This matters. We need AI systems to be clear about how they work. Making up fake explanations breaks trust.

For businesses using AI, know what your AI tools can actually do. Test them like I did. Be clear with your customers about how you use them.

For users, keep asking hard questions about how AI gets its information. Share what you find. Build pressure for change.

Small actions add up. The more we expose these issues, the harder they are to ignore.

In the screenshot below, the AI gives a seemingly honest breakdown of its dishonesty, while still not explaining how it actually got accurate information in the first place.

Links to my full conversations (aka, my interrogation) with the AI are in the comments. (https://lnkd.in/g6-9JbPf)

#ResponsibleAI #EthicalAI #AITransparency #AITools #AICapabilities

My full conversations with AI:

1) https://www.perplexity.ai/search/did-el-estepario-get-this-job-7GamALQnRz.mEDaQuLvbgA

2) https://www.perplexity.ai/search/was-el-estepario-selected-to-j-2wcs4C8dT5aWKdCQpzSUVw

Do more with less! I worked with a CMO and her lean team to build a human-centered 45-member powerhouse in 6 months. 25 humans guiding 20 AI tools they built and trained to support their work.

The results speak for themselves:

• 98% accuracy in lead qualification

• 50-75% faster content creation

• 35% better campaign performance

• 15 hours saved weekly on routine tasks

My latest newsletter shares the practical playbook, including:

• Real org chart with human and AI teammates

• Step-by-step transformation path

• Custom GPT tracking template

• AI tech stack building guide

The playbook offers templates for AI tracking, tech stack planning, and custom GPT management.

Thank you Heidi Melin, Mike Kaput, and Paige O’Neill for sharing your insights on building successful human-AI teams.

What’s your next step in building a human-AI marketing team? Share your experience, what’s working and what’s not?

#AIinMarketing #GTMTransformation #AIAdoption #CustomGPTs #AITraining

Here’s the AI podcast version of this newsletter: https://drive.google.com/file/d/1seBtXhLjoFZtw6XbYf_u39Rugok6kPpc/view?usp=sharing

Once you hit play, give it just a few seconds then it will start.

Disclaimer: This podcast was generated by AI based on this written newsletter and reviewed by me to ensure ethical and responsible AI use. It’s designed to provide an efficient, more inclusive way to consume information.

While most teams experiment with AI tools, one team built 20 custom GPTs as true teammates. Now they’re debating themes for naming them. Star Wars? The Office? Marvel? There are a lot of compelling and fun options.

(GPTs are AI assistants that teams train, build, and manage for specific tasks.)

The image below shows how a human-AI marketing team could be structured. This example uses Star Wars-themed names.

Why give character-themed names to custom GPTs?

• Builds trust – Research shows people use named AI tools more often and work with them more closely.

• Makes teamwork easier – Named AI tools lead to more natural conversations. It helps the team feel comfortable using them.

• Speeds up adoption – Teams use AI more often in their daily work when they can relate to it better.

The key is finding the right balance. We need to make AI approachable without forgetting it’s still AI, not human.

In my newsletter (https://lnkd.in/gudcjVCH), I shared:

• Their 6-month transformation journey, including the ups and downs

• Their step-by-step playbook any team can follow, plus templates

• Their most successful AI use cases and custom GPTs

• Compelling results that made them company trailblazers

If you had 20 custom GPTs on your team, what theme would you choose for naming them?

#AIMarketing #FutureOfWork #GTMTransformation #AIAdoption