Webforge Development

"How do I optimize my product development strategy?"
"What if my product doesn't resonate with customers?"
"What if my competitors outshine me?"

These are legitimate concerns for any business.

Whether you're a startup or an established company...

How you present your product is everything.

- You want to be clear in your strategy
- You want to be strong in your execution
- You want to be remembered for your innovation

To do so, you need a robust product development approach and a data-driven strategy.

They trust me with their product vision. Will you?

Need to streamline your product management?
Need to integrate AI into your business processes?
Need to develop custom dashboards for data insights?

Say "Hello Jofu" in a message and let's discuss your product success journey.

Chief Product Officer

Freelance | Self-Employed

As a team, we harness the power of machine learning to decipher complex consumer behaviors and trends. This AI-enabled approach allows us to anticipate customer needs and deliver targeted, effective advertising campaigns. It's exciting to see how this technology-driven approach not only optimizes ad performance but also opens up new growth opportunities for businesses of all sizes.

We are a diverse group of professionals - marketers, data scientists, and technology enthusiasts - all working together to push the boundaries of what's possible in AdTech. Our commitment to innovation and impact defines who we are and what we strive to achieve.

Senior Product Manager

Powerful Nutrition

As a Product Manager at Powerful Nutrition, I optimized product development operations and implemented Agile methodologies, leading to a 25% increase in revenues and improved customer retention. My expertise in deploying data-driven tools like Tableau and leveraging project management software (JIRA, Confluence) contributed to enhancing user experience, streamlining collaboration, and driving business growth.

Product Manager

RAGE LOGISTICS LLC

At Rage Logistics, I developed efficient vehicle management systems and utilized analytical tools to optimize daily operations, while strategizing product pricing and market positioning. My achievements include generating over $1M in revenue, enhancing growth by 175%, and acquiring long-term partners through the successful implementation of B2B marketing strategies and communication plans.

Product Project Manager

I Help Businesses Thrive with Data-Driven Product Strategies | Senior Product Manager | Product Development Specialist | Chief Product Officer | LinkedIn Top Product Development Voice

Hey there, thanks for stopping by my profile!

I'm Jonathon Daigle, a seasoned Senior Product Manager with over 11 years of expertise in product management, specializing in innovative solutions that bridge businesses with their customers. I am currently a Senior Product Manager at The Cutting Edge.

In the trenches at The Cutting Edge, I lead a dynamic team comprising marketers, data scientists, and technologists. Together, we harness the power of machine learning to decode intricate consumer behaviors, crafting targeted advertising campaigns that drive substantial growth and value.

I've also co-founded WebForge, where we empower businesses to launch their dream products with confidence. Through agile development methodologies, we streamline internal tools, develop AI agents, and automate mundane tasks, enhancing business efficiency and productivity.

Services:

- Internal Agents Assistance
- AI Integration for Business
- Internal Tools Management
- Internal Automation Solutions
- Custom Dashboard Development

Schedule your personalized consultation call today to discuss how we can tailor our services to meet your specific business needs and goals: (Check Feature section)

Here's what I bring to the table:

Design sprint proficiency
Consumer research expertise
Seamless workday integration

As a team player, I thrive on collaboration and innovation, driving projects from ideation to execution with precision and agility. My passion lies in leveraging data and technology to solve complex business challenges and propel brands towards sustainable growth.

If you're looking to elevate your product strategies and drive impactful results, let's connect!

Thanks for reading up until here. You're amazing!

Cheers,
Johnathon
Website: https://www.webforgedevelopment.tech/

Johnathon Daigle

Manage AI risks with interconnected tips | Lawyer | AI GRC | DPO | Ambassador for Kapfou | Global AI Delegate | Lead Auditor ISO 27001 | Founder: RTivara Advisory|

Most organisations want to use AI with confidence. What slows them down is not ambition, but uncertainty. Who is responsible when AI influences decisions? What happens when something goes wrong? What will regulators, insurers, or courts expect to see?

That is where my work sits.

I help insurers and regulated organisations prepare for AI use in a way that can be explained, defended, and carried responsibly. My focus is AI assurance and liability readiness, before incidents occur, not after damage is done.

I work at the intersection of law, governance, and operational reality. The questions I help answer are practical ones:

Where does accountability sit?
What evidence supports your decisions?
What risks are being accepted quietly, without oversight?

I have spent over a decade working across law, data protection, cybersecurity, international legal work, and AI governance.

This background allows me to support organisations operating under EU and cross-border regulatory expectations, where mistakes carry financial, legal, and reputational consequences.

I also advise organisations expanding into or operating across African markets, where clear due diligence and legal structure often determine whether partnerships hold or fail.

What clients value:
Clear understanding of AI-related liability and responsibility
Reduced exposure through defensible governance and assurance structures
Stronger confidence from insurers, regulators, and senior leadership

Background: Lawyer, Certified Chief AI Officer, Data Protection Officer, and Lead Auditor for ISO 27001 and ISO 42001. I bring legal depth and careful judgement to decisions that carry long-term consequences.

How I support organisations:

AI assurance and liability readiness
AI risk and governance design
Data protection and regulatory compliance
Cross-border legal and contractual advisory

I have worked with organisations across Belgium, Cameroon, India, and the EU.

If your organisation is introducing AI into regulated or high-impact environments and needs clarity on responsibility and exposure, you are welcome to message me.

Remy Takang (CAPA, LLM, MSc, CAIO).

Stress isn’t the problem. Holding onto it is.


I came across a short video that left a lasting impression.

𝐈𝐭 𝐬𝐚𝐲𝐬:
→ Think about something stressful for a few minutes, no big deal.

→ Hold it in your mind for an hour, it starts to weigh on you.

→ Carry it all day and it paralyses you.

That’s exactly how it feels.

Stress is like holding a glass of water. The glass doesn’t change. But the longer you hold it, the heavier it feels.

And yet, so many professionals do this daily:

💼 Carrying pressure from meeting to meeting

💬 Replaying conversations in their heads

🕒 Holding problems overnight, and into the next day

Sometimes the most productive thing you can do is put the glass down.

Take a break. Breathe. Step away.
Then return with strength, not strain.

What is that thing you have been holding on to for far too long? 

Put it down!

-----------------------
👉For actionable insights on meaningful AI implementation for business leaders and individuals who want value beyond the hype, 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/eRe8VFtz

The Liability Challenges of Automated Systems


This week felt like a natural progression.

We talked about AI assurance and AI insurance,

Then we attempted an answer to the question, “Is AI insurance a risk allocation mechanism”, or is it quietly becoming a signal of governance maturity?

That question led directly to this week’s issue.

In Issue #31 of Meaningful AI, I focus on the liability challenges of automated systems.

Not from a technical angle, but from a responsibility one. When systems act, learn, and change after deployment, liability no longer sits neatly in one place. It moves across designers, deployers, operators, and decision-makers.

This issue looks at how responsibility travels through automated systems, why traditional liability frameworks strain under that movement, and what this means for boards, insurers, and regulators.

Automation does not remove responsibility. It tests whether we are willing to stay present when decisions scale.

That question feels harder to ignore now.

I invite you all to explore this issue and share your thoughts. Is there something I missed? Let's connect the dots together.

Introduction There is a quiet moment after something goes wrong. A machine makes a decision. A plane reroutes. A vehicle brakes too late. A credit app

Open article: Issue #31: The Liability Challenges of Automated Systems by Remy Takang (CAPA, LLM, MSc, CAIO). • 7 min read

Issue #31: The Liability Challenges of Automated Systems

Is AI insurance a risk allocation mechanism?


We started yesterday with AI assurance and AI insurance. Today, let's answer the question of ‘if AI insurance is a risk allocation  mechanism.’

AI insurance does not remove risk.

It forces a decision about where that risk sits.

Every automated system already distributes responsibility across many hands. Designers shape behaviour. Deployers decide context. Operators choose when to trust outputs. Leaders decide how much oversight is “enough.”
𝐈𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐬𝐢𝐦𝐩𝐥𝐲 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐚𝐭 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐞𝐱𝐩𝐥𝐢𝐜𝐢𝐭.

When insurers ask questions, they are not only pricing harm. They are testing:
 
》Whether responsibility has been consciously assigned or quietly assumed. 

》Whether someone is accountable for monitoring drift. 

》Whether escalation paths exist beyond theory. 

》Whether failure has been rehearsed.

Where answers are vague, risk pools awkwardly. It lingers between teams, vendors, and contracts, waiting for an incident to expose it.

Seen this way, insurance becomes less about compensation and more about structure. It reveals how organisations think about consequences before they arrive.

𝐈𝐟 𝐫𝐢𝐬𝐤 𝐜𝐚𝐧𝐧𝐨𝐭 𝐛𝐞 𝐜𝐥𝐞𝐚𝐫𝐥𝐲 𝐚𝐥𝐥𝐨𝐜𝐚𝐭𝐞𝐝, 𝐢𝐭 𝐢𝐬 𝐮𝐬𝐮𝐚𝐥𝐥𝐲 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐰𝐚𝐬 𝐧𝐞𝐯𝐞𝐫 𝐩𝐫𝐨𝐩𝐞𝐫𝐥𝐲 𝐬𝐞𝐭𝐭𝐥𝐞𝐝.

And automated systems are unforgiving of that kind of ambiguity.

If this resonates, then drop your thoughts in the comments.

List three to five accomplishments that reflect your best work.


That is the question Amazon’s leadership is now asking employees to answer. 

On the surface, it appears to be a routine performance exercise. In context, it says something deeper about how relevance is being judged as AI reshapes work.

Senior executives are no longer impressed by activity, hours logged, or proximity to tools. They are asking people to point to outcomes. 

What changed because you were there?

What moved forward because of your judgment, not just your access to systems?

In an AI-shaped workplace, many tasks can be assisted, sped up, or drafted by machines. What cannot be replaced so easily is responsibility—choosing what matters. Seeing second-order effects. Improving a process that others depend on. Holding a problem when the answer is unclear.

This kind of question forces a quiet reckoning. It asks employees to articulate value in human terms, not technical ones. It also asks leaders to listen carefully, because not all meaningful work is loud or easily counted.

Relevance today seems less about how advanced your tools are, and more about whether you can clearly explain why your work mattered at all.

Most AI failures are advisory failures.


Not because advisers lacked knowledge.

But because they did not ask the right questions early enough.

In AI assurance work, the first risk is not the model.

It is a misunderstanding of what the client thinks assurance actually means.

Many organisations believe assurance will tell them their AI is “safe”.

What they really need is clarity about where responsibilities reside, who is responsible for them, and what happens when things go wrong.

That is why serious AI assurance starts before any system review.

It starts with questions like:

>Who will rely on this advice to make decisions?

>What harm is foreseeable if this system fails quietly rather than dramatically?

>Where does accountability blur once the system is live?

>What confidence level is being implied, even if not stated?

If these questions are skipped, everything that follows becomes fragile.

The report may look polished, but it will not protect the organisation, or the adviser.

Good AI assurance does not remove risk.

It makes risk visible enough for leaders to take responsibility for it.

If you are deploying AI, procuring it, or being asked to sign off on it, this distinction matters.

If you want help asking the right questions before assurance becomes a liability, DM me.

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The Battle

Johnathon Daigle

Remy Takang (CAPA, LLM, MSc, CAIO).

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The Battle

Johnathon Daigle

Remy Takang (CAPA, LLM, MSc, CAIO).

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Remy Takang (CAPA, LLM, MSc, CAIO).'s LinkedIn Performance

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Performance ScoreRemy Takang (CAPA, LLM, MSc, CAIO).'s LinkedIn Performance