A Year Inside the AI Conversation

A year ago, as I listened to family, friends, and colleagues talk about AI, I noticed a wide range of reactions.

Some were embracing the opportunity wholeheartedly. Others were genuinely scared. Many were cynical. But the most common response I heard was something else entirely: overwhelm.

People didn’t know where to start, what mattered, or what to do next. The pace of change felt relentless and the language intimidating.

Having worked in technology and AI for many years, and seeing AI adoption challenges play out firsthand I felt there was an opportunity to help demystify the conversation. By sharing practical perspectives, interesting developments, and thoughtful explanations that made AI feel more understandable and less intimidating.

So I started writing, experimenting and sharing

From LinkedIn Posts to a Platform

I began with LinkedIn posts. Short reflections, explanations of emerging ideas, and occasional deep dives into topics I was already spending time thinking about.

As the ideas grew, I realised I wanted a space where I could explore concepts in more depth, that’s when I created AIwithShehara.com.

The site became a place to explain what sits underneath the models. To explore why infrastructure, networks, data, and organisational design matter just as much as prompts and applications.

It has also become a space for me to experiment, build and create.

Writing regularly about AI has been one of the most effective learning tools I’ve ever used.

It forces clarity. If I can’t explain something simply, I usually don’t understand it well enough. It exposes weak assumptions. And it surfaces the questions people are actually asking, not the ones we assume they should ask.

Some of the most meaningful conversations happened quietly, in messages from people saying things like:

• “I would like to learn more about...”

• “I didn’t realise AI could be approached this way.”

• “This gave me the confidence to start experimenting.”

The feedback mattered thank you

The Topics That Resonated Most

One of the unexpected insights from running the website has been seeing which topics people return to.

Some of the most-read articles over the past year have focused on:

• Foundational AI concepts, explained in plain language

• What sits underneath AI, especially infrastructure, data, and networks

• AI agents, systems and emerging AI models, framed practically

• Human-centred perspectives, looking at how people and organisations actually work with AI

A few examples that continue to resonate:

• AI’s Fact-Checker: Why RAG Matters

  
  

  https://www.aiwithshehara.com/post/ai-s-fact-checker-why-rag-matters

  
  

• Quantum AI: What It Is and Why It Matters

  
  

  https://www.aiwithshehara.com/post/quantum-ai-what-it-is-and-why-it-matters

  
  

• Managing Humans, Agents, and Bots

  
  

  https://www.aiwithshehara.com/post/managing-humans-agents-and-bots

Learning Along the Way

Writing about AI also pushed me to deepen my own learning and practice.

Over the past year, alongside publishing regularly, I:

• Completed the UC Berkeley “Artificial Intelligence: Business Strategies and Applications” course, grounding my thinking in real-world AI strategy, organisational design, and execution

• Built and experimented with chatbots, agents, and no-code AI applications, testing ideas rather than just describing them

• Ran my first hands-on chatbot workshops, focused on showing people how approachable AI can be

• Spent time exploring areas like AI infrastructure, quantum AI, agentic systems, and AI-native business models, and sharing those learnings openly

The combination of learning, building, and writing created a reinforcing loop. Each made the others better.

Looking Ahead

In the year ahead, I’d like to:

• Have the site professionally designed so it better supports the content

• Continue writing across a broad range of AI topics, from infrastructure to creativity

• Keep sharing perspectives that are practical, thoughtful, and useful

• Run a small number of workshops connected to the projects I’ve been building

And continue to focus on the democratisation of AI

Thank You

If you’ve read an article, shared a post, asked a question, or quietly followed along, thank you.

The most encouraging thing I’ve learned this year is that people want space to understand, question, experiment and build confidence.