Finding Market Gaps to Fill with AI

Recently, I’ve been knee-deep in the AI hype, just like many of you. It’s been all about the next big thing—AI for this, AI for that. I sat down one night, buzzing with ideas, ready to draft up the blueprint for my next project. I spent the following weeks building projects that sounded amazing in theory. But alas, launch after launch, no one cared. No one used any of my apps. That's when it hit me. I was thinking about ideation all wrong. It’s easy to get carried away with the tech and the potential, but at the core it must be solving someones pain.

So I decided to search Reddit for pain points. I tried to find areas where AI is enabling problems to be solved that previously were unable.

What I found was super insightful. So much so that I decided to scale up and analyze thousands of Reddit discussions, extracting the pain points from each and analyzing the trends across industries.

That exercise led me to my newest project, IndiePulse - where I continue to optimize my data pipeline to extract valuable insights across thousands of Reddit posts!

Below are some trends in the AI space that i've picked up on

Trends in AI Generating Revenue

In the current market, I believe AI apps can be placed into one of three categories:

• Automation and Efficiency: AI’s ability to streamline operations and automate mundane tasks is universally valued. Projects in this category are likely to be newly enabled by AI due to the necessity of novel decision making, and taking advantage of things like summarizing, transcribing, etc
  Examples: Viable, Fireflies
• Data-Driven Decision Making: Leveraging AI to parse through vast amounts of data helps businesses make informed decisions quickly and accurately. Previously, custom machine learning models and lots of training data was needed to enable data driven decisions, but now LLMs can intuit and drive decisions with little to no additional training
  Examples: Obviously AI, IndiePulse (😛)
• Personalization: From marketing to media, AI’s ability to tailor experiences to individual preferences is a game-changer. There is lots of surface area for innovation here. For example, just think of any professional service that can now be (at least to some degree) automated with AI
  Examples: HeadshotPro, FounderPal

If you have any AI project ideas, try to map them to one of these categories, if it feels like it belongs in one of these, theres a good chance its solving a pain point unaddressed before the AI era.

So how can you find problems like these to solve?

Find Your Niche

Start by identifying industries or sectors where you have personal interest or experience. Understanding the specific challenges and needs of these areas can guide you to where AI applications could be most beneficial.

Engage with the Community

Dive into forums, attend industry meetups, and participate in online discussions to get a feel for the pain points others are experiencing. This direct engagement can uncover nuanced issues that are ripe for AI solutions.

Identify Overarching Pain Points

Look for niche, but impactful problems common across your chosen area. Solving these can provide substantial benefits to a small user base, but can then grow outwards as you add more features to widen your target audience. Think about those three AI app categories when looking for your niche's pain points.

Explore Opportunities at Task Boundaries

Often, the potential for innovation lies at the intersections of routine tasks. For example, content creation involves multiple outputs—creating a video might also necessitate a blog post, a tweet, and a Reddit post to maximize reach. Automating this process can significantly increase productivity and ensure a cohesive content strategy. In this case, AI could be employed to:

• Automatically generate written content from video transcripts, tailoring the language and format for blogs or social media.
• Create and schedule social media posts that highlight key points or quotes from the video, optimizing engagement across platforms.
  Analyze feedback from these various channels to refine content strategies continuously.

In this case, the boundaries around content creation expose surface area for AI assistance. Identifying and automating these interconnected tasks can unlock substantial efficiencies, across many niches. (Of course, don't take my contrived example as gospel, ideas still need validation, and should come directly from your target niche's pains)

Conclusion

As we dive deeper into what AI can do, it's crucial to keep our eyes on the real prize: solving actual problems that improve everyday life. LLMs are already changing the game, showing us that the right application can turn a cool tech idea into something that really sticks and makes a difference. So, as we keep pushing the boundaries of AI, let's make sure we're not just chasing the next big thing, but actually building tools that people want to use, that make their lives easier, and maybe even a little better. That’s how we’ll make AI truly matter in the long run.