AI isn’t slowing down. If anything, it’s accelerating. Every week there’s a new tool, a new update, or a new opinion about how artificial intelligence is changing the way we work. Some of it is genuinely useful. A lot of it is just noise.
Most people don’t have the time to test hundreds of AI tools. Even figuring out which ones are worth attention can feel like a job on its own.
That tension is exactly why we created People & AI Month.
From November 17th, 2025 to December 16th, 2025, we ran People & AI Month 2025: When Purpose Meets Prompt, a month-long AI learning program focused on real experimentation. Instead of reacting to hype, we slowed things down, tried tools properly, and learned together in the context of actual work.
No pressure to become an AI expert. No buzzwords. Just curiosity, hands-on testing, and honest reflection.
What is People & AI Month?
People & AI Month is a structured but lightweight AI learning program that we designed for our teams, community, as well as external participants. It combines live virtual sessions with a four-week community learning challenge where people explore AI tools, pressure testing them on their own workflows.
This wasn’t designed as a heavy training initiative. Most participants spent between 30 minutes and a few hours per week. The idea was to make AI learning realistic alongside everyday work, not something that required stepping away from it.
We anchored the program around four clear goals:
- Make sense of AI without jargon or fear-based narratives
- Learn new AI tools through hands-on experimentation
- Use AI to support more human work, not replace it
- Learn together as a team and community, openly and honestly
Instead of talking about artificial intelligence in abstract terms, we focused on one question: does this actually help in practice?
The People & AI Learning Challenge (Slack-based AI learning)
At the center of the program was the People & AI Learning Community Challenge, which ran in Slack over four weeks. This is where most of the real learning happened.
Each week, participants chose one AI tool to explore. They used it for something real in their work, then shared a short video explaining what they tried, how they used it, and whether they’d keep using it. Along the way, people shared practical AI nuggets like prompts, workflows, shortcuts, or small wins.
The structure stayed intentionally simple:
Committ to try one tool per week.
Use it in real work.
Reflect honestly.
Help others if you can.
There was no expectation that every experiment would be successful. Saying “this didn’t work for me” was just as valuable as saying “this saved me hours.”
AI tools we explored and how we used them
Rather than prescribing a fixed list, we encouraged participants to follow their curiosity and choose tools that made sense for their role. That gave us a much more realistic picture of how AI tools perform in everyday work.
Research and sense-making AI tools
| Tool | How we used it | What stood out |
|---|---|---|
| Perplexity | Fast research, summaries, and sense-checking information. | Especially helpful at the start of a task when we needed a quick overview before diving deeper. Treated as a research assistant, not a final authority. |
| NotebookLM | A thinking partner for working through documents, notes, and links. | Helped pull structure out of messy inputs. Particularly useful for long or complex materials. |
Agentic AI tools and AI-first workflows
| Tool | How we used it | What stood out |
|---|---|---|
| Manus.ai | Research, planning, and multi-step workflows. | Powerful but best used selectively. Most useful for specific use cases rather than everyday tasks. |
| Comet browser | Exploring AI-first browsing with built-in AI assistance. | Promising glimpse into AI-native workflows, but still evolving. |
Data analysis and AI insights tools
| Tool | How we used it | What stood out |
|---|---|---|
| Julius AI | Cleaning messy spreadsheets, generating summaries and charts, and surfacing insights. | Clear time-saver for operations and marketing work by reducing manual calculations. |
Documentation, operations, and internal AI workflows
| Tool | How we used it | What stood out |
|---|---|---|
| Guidde AI | Turning screen recordings into step-by-step guides and SOPs. | Worked well for internal documentation, with human review still playing an important role. |
| Miro AI | Summarizing boards, turning sticky notes into structured outputs, and speeding up workshop follow-ups. | Reduced friction between collaboration and action. |
| Monday.com Sidekick | Summarizing documents and answering questions from internal files. | Especially useful when quick answers were needed without rereading everything. |
Design and creative AI tools
| Tool | How we used it | What stood out |
|---|---|---|
| Leonardo AI | Generating early visual ideas for presentations and content. | Best suited for inspiration and exploration rather than final assets. |
| Gemini | Visual generation and technical explanations. | Useful for clarifying visuals from rough sketches and understanding technical details from images. |
| Dreamina | Image and video generation for creative exploration. | Easy to use with generous free limits; mostly used for experimentation. |
Productivity and AI thinking tools
| Tool | How we used it | What stood out |
|---|---|---|
| Goblin.tools | Breaking down overwhelming tasks, rewriting text in different tones, and estimating effort. | Quietly useful for getting unstuck and reducing mental friction. |
| dartai.com | Project planning and task breakdowns. | Showed strong potential for structuring goals into clear, actionable steps. |
Video, audio, and AI content tools
| Tool | How we used it | What stood out |
|---|---|---|
| Pippit | Video editing and content ideation. | Helpful for some users, confusing for others, depending on familiarity with video workflows. |
| Kapwing | Quick video edits and subtitle creation. | Useful for simple tasks, but not always a replacement for existing tools. |
| Suno | Generating AI music for internal and creative experiments. | A reminder that not all AI value is about productivity. |
The AI Maturity Index assessment
Another key part of the challenge was the AI Maturity Index assessment.
This took the form of a 15 to 20 minute conversation with an AI agent, designed to help participants reflect on where they are in their AI learning journey. It wasn’t about scoring or ranking. It was about awareness.
The assessment was required once, anytime before the challenge ended. Many participants treated it as a pause point to think about habits, confidence, and next steps.
Find out your AI Maturity score.
Live AI sessions that anchored the program
Alongside the learning challenge, we hosted three virtual speaking sessions to give context and structure.
November 27th, 2025
AI Trends and Updates for Non-Techies (Virtual)
Focused on what’s actually changing in AI, from new models to agentic systems and AI-first browsers, and what that means for real careers and businesses. The goal was clarity, not overwhelm.
Led by Lavinia Iosub, CEO of Livit International and Founder of Remote Skills Academy and Livit Hub Bali.
December 4th, 2025
Build Smarter Sales Funnels with AI (Virtual)
Explored how AI is changing sales and marketing without requiring a technical background. Led by Ilia Maksimenka, founder of iSales, with a focus on practical systems, chatbots, AI-powered content, and “Vibe Marketing.”
December 15th, 2025
Purpose Driven AI: How to Implement Intentional AI Solutions (Virtual)
Zoomed out to examine why many AI projects fail and how teams can slow down and build AI solutions that actually make sense. Led by Maaria Tiensivu, Co-founder of Purpose Driven AI.
Wrapping up People & AI Month 2025
We closed the program on December 16th, 2025 with a live wrap-up session. We reflected on what we explored, shared participant takeaways, announced the challenge winners, and talked about how to keep building intentional, human-first AI practices.
During the wrap-up, we announced the challenge winners: Akira M. took first place with 138 points, Chalsie came in second with 120 points, and Bronze medals were awarded to Mitha Wunikha (110 points) and Yaya. We also gave honorable mentions to Ingrid Janice, Ayu Juwita, Nathan Tew, and Raymond for their consistent contributions throughout the challenge.
As participant Mitha Wunikha shared afterward:
“This challenge was super fun and insightful. Big thanks to the Livit team for organizing it. I really appreciate the opportunity and the support throughout the challenge. I learned a lot and truly enjoyed the journey with everyone here.”
That sense of shared learning was exactly what we hoped to create.
Thinking about running a similar AI learning program?
People & AI Month reinforced something we strongly believe at Livit. AI learning works best when it’s practical, social, lightweight, and human-first.
If you’re exploring how to design an AI learning program for your team or community, we’re happy to help. We support organizations in creating People & AI Month-style learning experiences that fit their goals, culture, and pace.
Get in touch with us if you’d like to explore what this could look like for your organization in 2026.
