Viewing the Market Through Your Own Lens!

Viewing the Market Through Your Own Lens!


Viewing Your Market Through Your Own Lens

"Sometimes, when I feel disconnected from my work, when things around me don’t go as I expected, or when new opportunities arise, I have a habit of stopping by a bookstore after work and randomly picking up a book to purchase.

(Of course, buying a book doesn’t always mean I read it cover to cover. 😅)

At some point, I started noticing a pattern—whenever I visited a bookstore, the books that caught my attention were about AI (its influence, transformation, and history), ML (Machine Learning), DL (Deep Learning), Python, and fundamental mathematics.

I also found myself drawn to autobiographies of AI pioneers who had made remarkable contributions to the field.

To me, this signified that the impact of AI on market trends had become something that professionals could directly feel in their daily work."



"The bus that has passed will not come back."

In light of these changes, I started to think about those who didn’t catch the wave in time or perceived it only as a passing breeze.
I believe many are now wondering what they should do next or what kind of work they can pursue moving forward.

Perhaps, just like my former self, they are finding comfort by picking up books on Python, basic data analysis, or similar topics at the bookstore, thinking that they are taking steps toward the future.

I don’t think the attempt itself was wrong, but as a professional, I believe that depending on your industry, role, and position, it’s important to take a step-by-step approach tailored to your specific context.
I hope that others will also consider this and take action thoughtfully.



If there are professionals in companies who are tasked with introducing AI or acquiring technologies, solutions, and services, and are required to paint an ideal picture and write reports under the direction of higher-ups (i.e., fingers pointing), then...

We should reflect on what this means for us.
We need to think about what kind of results such actions could bring, especially from an opportunity cost perspective.

For a company, driven by the pursuit of profit, and for an employee, receiving a limited salary in return for work, how will such practices play out in the long run?

Shifting the Focus: Gaining Insight into AI

Instead of blindly following the current direction, what if we take responsibility for gaining insights into AI, starting from where we stand in our respective roles, industries, and positions?

Where to Start?

While learning Python basics might be a good starting point, as a busy professional with limited time but efficient work practices, how about we start by analyzing AI trends from our own market perspective?

Instead of diving straight into technical skills, let's take a moment to analyze the broader AI landscape within our specific market.

Actionable Steps:

  1. Leverage your professional status (your business card) to research AI solutions in your industry and others.
  2. Create a list of leading AI development cases and companies in both your industry and others.
  3. Identify the key AI-related terms, functionalities, and features mentioned in those solutions.
  4. Analyze how AI is being applied across different sectors and understand its real-world use cases.

By doing this, you'll be able to gain a broader perspective on AI and use your industry insights to make informed decisions about how AI can be effectively implemented in your own work environment. 🚀


Can you spot any common or notable words and features across solutions?
If we start with those, wouldn’t it lead to a more efficient approach?

After identifying these commonalities, it’s important to consider:

  • What do I want to achieve, and what must I achieve?
  • Do the reports I’m writing or the technologies and features I’m developing align with the same direction?

We need to seriously reflect on:

  • What is our unique competitive advantage?
  • Let’s share our thoughts and explore how we can differentiate ourselves!

Action Steps

  1. Organize keywords and related solution documentation, such as news articles or white papers.
  2. Review relevant research papers to gain a deeper understanding of industry trends and AI applications.

For me, my starting point in AI was in the field of Chatbots and Dialogue Systems, which I later worked with in NLP (Natural Language Processing), specifically NLU (Natural Language Understanding) and NLG (Natural Language Generation).
I also had the chance to be involved with IruDa (the controversial chatbot), which brought NLP into the social spotlight.

Given the opportunity, I hope to collaborate with others who are starting to organize and apply AI to their own industry areas (e.g., video, comics, music, healthcare) and engage in deep discussions about direction and competitive edge.

Let’s dive into this together and think critically about the AI-driven opportunities in our respective fields! 🚀





 



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