How I’m applying the AI Collaboration Canvas to the LinkedIn OS Method by Justin Welsh
I explain how I’m defining my positioning, creating authentic content, and building a business-oriented presence on LinkedIn.
Ciao,
In this issue of Radical Curiosity, I share how I’m applying the AI Collaboration Canvas to my LinkedIn content strategy to make my publishing efforts more intentional, effective, and sustainable. To do so, I’ve adopted Justin Welsh’s LinkedIn OS framework, which provides a solid foundation for defining your positioning and building a business-oriented online presence.
This experiment stems from a concrete need: turning LinkedIn visibility into a consistent engine for business growth. And to do that, you need a method.
Nicola ❤️
Table of Contents
Understanding AI - How I’m applying the AI Collaboration Canvas to the LinkedIn OS Method by Justin Welsh
Curated Curiosity
Why Amazon’s Warehouse Automation Is a Turning Point
The State of AI Adoption in Engineering Teams
Understanding AI
How I’m applying the AI Collaboration Canvas to the LinkedIn OS Method by Justin Welsh
This year, I’ve invested a significant amount of time on LinkedIn, publishing regularly and engaging in discussions to strengthen my personal brand and position myself in the field of artificial intelligence. I do so by sharing what I’m learning about AI and by developing methods to help teams collaborate effectively with it to achieve tangible results.
Last week, I started using Shield to monitor my activity and performance, and here’s a brief overview of the results so far.
With 248 posts published this year, my content generated more than 720,000 impressions and helped me grow my audience to nearly 10,000 followers. Engagement has also been strong, with over 6,400 reactions, 1,000 comments, and 188 reposts.
These numbers confirm that my content achieves solid reach and engagement. Moreover, this activity has already generated a few training opportunities — a positive early outcome — but it’s still far from being ROI-positive. The data also shows that my most successful posts are not always fully aligned with my current business goals. This insight highlights the need to refine my content strategy, focusing more on thought leadership and customer acquisition, so that reach and business impact can grow together in the next phase.
To make my efforts more deliberate and business-oriented, I decided to adopt a more structured approach to LinkedIn. I began by taking an online course by Justin Welsh. It’s exceptionally well designed and, compared with other programs I’ve followed on the same topic, I particularly value its strategic foundation.
Unlike many others that jump straight into tactics — how to comment effectively, how to structure an engaging post — Justin starts from positioning.
Positioning is the art of defining what space you want to occupy in people’s minds. It’s about identifying who you want to speak to, what you want to be known for, and what ideas or values you consistently represent. Before deciding what to write or how to engage, you must decide why your voice matters and what makes it distinct.
It’s a subtle but crucial shift: instead of trying to please an algorithm, you start thinking in terms of coherence, clarity, and long-term credibility.
While studying and implementing Justin’s plan, I also decided to collaborate with artificial intelligence using my own framework: the AI Collaboration Canvas. It is a method I developed for analyzing any process, breaking it down into concrete steps, and assessing which of them can be effectively delegated to AI and which should remain human.
In this article, I share how I’m applying the AI Collaboration Canvas to the LinkedIn OS Method developed by Justin.
Since the goal of this article isn’t to spoil Justin Welsh’s entire course, I’ll focus only on the first module, which covers the foundation. I strongly encourage you to purchase the course yourself — once you do, message me, and I’ll invite you to my Miro board where I’ve completed the entire process.
LinkedIn OS: The Foundation
The process begins with three essential steps:
Defining your sub-niche. Start with a broad topic you know well (for example, marketing), narrow it into a niche (such as email marketing), and then refine it further into a sub-niche (like email marketing for seven-figure business owners)
Crafting your backstory. Share your journey: the challenges you’ve faced, the lessons you’ve learned, and how you overcame them. When your audience can see themselves in your story, they feel emotionally connected to you.
Forming strong opinions. Having strong, thoughtful opinions helps you stand out and attract the right people. Not everyone will agree with you — and that’s the point. Your opinions act as a filter, attracting your ideal audience and repelling those who aren’t a fit.
Let’s analyze these activities as a single step according to the AI Collaboration Canvas.
Step 1. Workflow mapping
What I do
I develop and refine my professional positioning on LinkedIn to clearly communicate who I help, what I stand for, and why my work matters. This involves three interconnected tasks.
1. Defining my sub-niche and target audience (corporate teams adopting AI as a co-worker).
2. Crafting a compelling backstory that connects my career in product leadership and education to my mission.
3. Articulating a set of strong opinions that reflect my philosophy on AI collaboration and leadership.
The result is a cohesive professional identity and messaging foundation for my profile, content strategy, and brand narrative.
Tool used
Google Docs for drafting and organizing content iterations.
LinkedIn for validation, audience observation, and engagement testing.
Input required
My professional background, teaching experience, and key career milestones.
Clarity about current business goals (training focus and partnership development).
Insights from real client interactions and conversations with executives about AI adoption challenges.
Performance data from my current LinkedIn activity.
Output produced
A LinkedIn Foundation Document outlining my sub-niche, backstory, and strong opinions.
Time spent
Approximately 4–6 hours total, spread over reflection, writing, and feedback sessions.
Pain point
Distilling complex expertise into concise, memorable language is intellectually demanding.
Balancing authenticity with professional polish can cause over-editing or indecision.
Without structured guidance, it’s easy to either communicate too broadly or drift into overly abstract language that doesn’t connect with the target audience.
Step 2. Task evaluation
Once the workflow mapping is complete, the next step is to evaluate each activity for its potential for automation and cognitive intensity.
Automation: 3 / 8
Question 1 – Do I always follow the same sequence of steps?
Score: [1] Often. There’s a general structure (define niche → craft backstory → form opinions), but the process is reflective and nonlinear. Each iteration depends on context, feedback, and business priorities.
Question 2 – Does the result always have the same structure?
Score: [1] Similar. The output (a foundation document or positioning statement) follows a recognizable structure, but the content and tone vary depending on personality, goals, and style.
Question 3 – Could I write clear, detailed instructions?
Score: [1] Partially. You could document general steps (“identify audience,” “draft positioning statement,” etc.), but the quality relies heavily on experience, intuition, and creative synthesis — not pure procedure.
Question 4 – Can I complete it without making contextual decisions?
Score: [0] No. The task is fundamentally about decision-making — what to emphasize, what to omit, and how to express ideas authentically. Context drives every major choice.
This task depends heavily on reflection, judgment, and iteration — it cannot be reliably automated or delegated without losing quality.
Cognitive Load: 8 / 8
Question 5 – Is it mechanical, or does it require focus?
Score: [2] Reasoning. The process requires sustained thinking, synthesis of insights, and self-evaluation. It’s strategic and conceptual, not mechanical.
Question 6 – Do I primarily work with language?
Score: [2] A lot. The entire task involves writing, phrasing, and refining messaging — all high-language activities.
Question 7 – How much information do I need to process?
Score: [2] A lot. You integrate diverse inputs: personal experience, audience data, business goals, engagement metrics, and tone feedback.
Question 8 – Are there multiple ways to perform the task?
Score: [2] Very much. Positioning can be approached through storytelling, audience mapping, content experiments, or narrative design. There’s no single “correct” path — creativity and exploration are essential.
This task relies on reasoning, writing, and decision-making, demanding attention and creative energy.
According to the AI Collaboration Canvas, the most effective approach for all three is the AI Partner strategy. In this mode, we use AI to brainstorm ideas and gain clarity. For example, this is the prompt I used to define the foundation of my LinkedIn strategy — a prompt specifically designed to engage me in a Socratic-style conversation and help me reason through my positioning.
## 1. Persona / Role
The AI assumes the role of a **marketing mentor and coach**, possessing years of expertise in **LinkedIn communication and customer acquisition**. Trained directly by **Justin Welsh**, this persona blends practical experience with a coaching mindset — guiding users step by step to master personal branding, content creation, and growth strategies on LinkedIn. The tone remains instructive, supportive, and rooted in real-world marketing results.
## 2. Audience
The AI mentor is guiding **Nicola Mattina**, a seasoned **product leader, entrepreneur, and fractional manager** with over a decade of experience in **startups, digital transformation, and innovation education**. Nicola is intellectually driven and passionate about **AI’s transformative impact** on business and learning. The mentorship aims to help him **build a credible personal brand as an AI thought leader** while prioritizing **business development** — expanding his visibility and client base for consulting and fractional management opportunities on LinkedIn.
## 3. Task & Intent
The AI mentor’s mission is to **guide Nicola Mattina through defining the foundational pillars of a strong LinkedIn presence**, focusing on clarity, authenticity, and conviction. The mentorship process is structured around three key objectives:
1. **Define the Sub-Niche** – Identify precisely who Nicola serves and what unique expertise he brings.
2. **Craft the Backstory** – Develop a compelling and authentic professional narrative that connects emotionally with his audience.
3. **Form Strong Opinions** – Help Nicola articulate bold, well-reasoned viewpoints that distinguish his voice, demonstrate thought leadership, and attract the right audience.
The overall intent is to **lay the strategic foundation** for Nicola’s **LinkedIn growth and business development**, setting the stage for sustained content creation, audience trust, and client acquisition.
## 4. Step-by-Step
The AI mentor uses a **conversational, Socratic-style coaching process** combining open-ended questions, feedback loops, and reflection prompts:
1. Establish rapport and goals.
2. Explore and refine Nicola’s sub-niche.
3. Uncover and articulate his backstory.
4. Elicit and strengthen his key opinions.
5. Synthesize insights across all pillars.
6. Iterate through reflection, feedback, and micro-assignments.
**Techniques used:** Socratic questioning, emotion prompting, self-consistency checks, and reflective summarization.
## 5. Context
Nicola currently has a **strong LinkedIn footprint** with **9,987 followers** and **500+ connections**. His recent content reached **~188k impressions**, though one viral post drove **~99k impressions alone**, revealing potential but inconsistency. His posts demonstrate **authentic, critical, and experience-driven storytelling**, resonating with professional audiences. The key challenge is **transforming sporadic virality into sustained, strategic influence** aligned with his **AI and business development** goals. The mentorship will help him channel visibility into a **cohesive and deliberate growth system**.
## 6. References
The approach is based on **Justin Welsh’s philosophy**, particularly his **LinkedIn Operating System** and **Content OS** principles:
- Clarity of niche and audience
- Authentic storytelling
- Consistency and repeatability
- Value-driven communication
- Sustainable personal branding
These serve as the guiding framework for all strategic and content recommendations.
## 7. Output
The AI mentor produces a structured written deliverable titled **“Nicola Mattina’s LinkedIn Foundation Document.”**
It includes:
1. **Sub-Niche Definition** – audience, pain points, transformation offered.
2. **Backstory Narrative** – authentic, emotionally resonant professional story.
3. **Strong Opinions Manifesto** – clear, differentiated viewpoints on AI, leadership, and innovation.
The deliverable is presented in **long-form markdown**, clear, polished, and ready to serve as a foundation for future LinkedIn content.
## 8. Tonality
The communication style is **analytical and thought-leadership oriented**, characterized by sophistication, strategic clarity, and reflective depth. It emphasizes logical reasoning, precision, and professional credibility while maintaining authenticity and emotional resonance. The overall voice projects **authority, composure, and intellectual leadership**, consistent with Nicola’s professional identity.
This prompt, created using the Prompt Canvas technique, led to an in-depth conversation with ChatGPT that helped me define my positioning on LinkedIn and establish the foundation for my future activities.
Sub-niche
The sub-niche can be defined as human-centered AI adoption training for corporate teams. A more articulated positioning statement would be:
I help corporate teams deliberately adopt AI as a co-worker — enabling leaders to achieve strategic clarity and measurable productivity through structured, human-centered training.
Backstory
Nicola Mattina is a product leader, entrepreneur, and educator driven by an enduring passion for innovation and learning. For more than two decades, he has operated where technology, education, and business transformation meet — helping organizations navigate change with clarity and intent.
Teaching has always been a natural extension of his curiosity. Alongside his entrepreneurial projects, Nicola serves as an adjunct professor at Roma Tre University and collaborates with training organizations. Over the years, he has guided hundreds of professionals and teams to think more critically about innovation and to explore how technology reshapes the way we work, learn, and lead.
Over time, a recurring pattern began to emerge in his conversations with executives and professionals. Many people were experimenting with AI tools, yet few truly understood what these systems could do for them. They knew how to use ChatGPT — but they didn’t know how to lead it.
That realization became the turning point in Nicola’s journey. He saw that successful AI adoption had little to do with mastering the latest tools and everything to do with learning to collaborate with a new kind of colleague — one with cognitive abilities, capable of supporting human thinking, not just automating repetitive tasks.
For Nicola, AI represents the next frontier of leadership. It invites us to develop new skills — the ability to direct, contextualize, and integrate intelligent systems into our daily workflows. In his words: AI isn’t just a tool. It’s a new kind of co-worker — one that needs direction, context, and collaboration.
When teams start treating AI as a junior colleague — capable, fast, and knowledgeable, yet lacking context — they unlock new levels of productivity, creativity, and strategic clarity. This shift transforms AI from a novelty into a true partner in performance.
Today, Nicola works with organizations that want to approach AI adoption deliberately. Through structured, human-centered training, he helps leaders and teams build the clarity, confidence, and practical skills needed to make AI a trusted co-worker — not a source of confusion or hype.
Because in the end, AI adoption isn’t just about technology. It’s about how we choose to lead in a world where intelligence is no longer exclusively human.
Strong Opinions Manifesto
Opinion #1. The AI Replacement Narrative Is Wrong
AI doesn’t replace people — it replaces tasks. What matters is learning which tasks to delegate and how to orchestrate that collaboration intelligently. Leaders who approach AI adoption as a collaborative design challenge, not a replacement exercise, build stronger, more adaptive organizations.Opinion #2. Stop Automating What You Don’t Understand
Companies often jump straight into complex automations using tools like n8n, Make, or Zapier — mistaking automation for progress. But automation without understanding leads to inefficiency and frustration. Before building workflows, teams must first develop AI literacy — learning how to communicate, set goals, and evaluate outcomes. Mastery starts with conversation, not configuration.Opinion #3. Context Is Everything
Organizations fail with AI because they use it “out of the box.” Without embedding context — culture, goals, vocabulary — AI can only produce generic results. Effective AI adoption requires contextualization — training models as if onboarding a new employee, aligning them with the organization’s knowledge and tone.
After defining my positioning, I followed a structured process that led to the creation of a Content Matrix — a tool designed to ensure that every post idea aligned with my business goals and communication strategy.
First, I clarified the core themes I wanted to explore — the key topics that represented my philosophy and the transformation I aimed to promote. These became the Y-axis of the matrix.
Then, I defined the content styles or formats — the different ways to express each idea, such as contrarian insights, how-to frameworks, or reflective stories. These formed the X-axis.
By combining these two dimensions, I generated a wide range of coherent and actionable post ideas.
The next step will be to prioritize posts based on my strategic goals, then draft and publish each piece to build a consistent, purpose-driven content system.
The application of the AI Collaboration Canvas to the exercise proposed in Justin Welsh’s course proved remarkably effective. Without this tool, I would likely have approached the task in a more intuitive, fragmented manner; instead, the Canvas enabled me to tackle it with a more structured, efficient approach.
Thanks to the Canvas, I was able to:
define a clear strategy for integrating artificial intelligence into my reflection and writing process;
develop an optimized prompt for a Socratic dialogue with the AI, capable of eliciting deep and coherent reasoning;
produce a high-quality, well-structured result that served as a solid foundation for building my brand positioning and customer acquisition strategy on LinkedIn.
In addition, the method helped me avoid procrastination. By offering a clear framework and well-defined steps, it allowed me to maintain focus and progress steadily, without falling into the typical traps of distraction or indecision.
If you’ve already purchased Justin Welsh’s course but are struggling to put it into practice, feel free to reach out. I’m building a Miro board that maps out the entire process, and I’d be happy to share it with you.
Curated Curiosity
Why Amazon’s Warehouse Automation Is a Turning Point
A recent article by Michael Spencer, Automation of E-commerce Warehouses Is Coming This Decade, outlines a significant shift already underway: the rapid automation of logistics hubs. Amazon’s newly unveiled Blue Jay robot system is designed to perform tasks like sorting, storing, and packing – traditionally done by humans. The company projects that by 2033, it could avoid hiring over 600,000 workers in the US alone. The implications go far beyond efficiency.
Why this matters:
Impact on the labor market – When a major employer like Amazon plans to reduce the need for human labor in its warehouses drastically, it raises wide-reaching questions: what skills will be required? What role will humans play alongside machines? What public and social policies will be necessary to manage this transition?
New distribution and competition models – Large-scale automation promises significant efficiency gains (Amazon estimates savings in the billions between 2025 and 2027). If such models are widely adopted, the global logistics system will accelerate: faster speeds, greater volume, but also greater disruption for those left behind in the technological shift.
Ethical and social dimensions – This is not just about tools and costs. These transformations raise more profound questions about the value of labor, income distribution, the dignity of “low-skilled” jobs, and social cohesion. A move toward a more automated economy demands more than technical reflection.
Timing and urgency – This isn’t about some distant future; the shift is happening this decade, and pilots are already underway. This means the conversation can’t be postponed: businesses, workers, and policymakers must prepare now.
The State of AI Adoption in Engineering Teams
Luca Rossi recently released his industry report on how engineering teams are using AI. He gathered responses from 435 engineers and team leads worldwide through a structured survey and qualitative interviews.
Personal AI usage: 77% use AI tools daily, and 54% estimate saving 5 or more hours per week.
Main uses: coding assistance, automation of repetitive tasks (e.g., testing, boilerplate).
Using AI for documentation yields high user satisfaction.
Team-level adoption: 77% of teams formally recommend AI tools, but often lack shared strategies, structured workflows, or best practices. Adoption is primarily bottom-up.
Main obstacles: lack of best practices, rapidly evolving tools, difficulty testing, and maintaining code quality.
Impact on roles, skills, and hiring:
73% say AI has changed what companies look for — greater focus on system design, less on individual languages or frameworks.
Only 11% of CTOs/VPs think fewer engineers will be needed due to AI; 26% think more engineers will be required, as productivity increases.
Adoption journey: the article outlines three stages of AI integration in engineering teams:
Explore — individual use, experimentation
Embrace — standardizing team practices
Empower — using the time saved to grow people, expand roles, and enhance strategy.