AI products have become an integral part of both business and everyday life. Given the increasing demand, an AI startup has the potential to take off quickly and generate substantial revenue. However, the industry comes with its own set of challenges. Maxim Tsygankov, an expert in AI product development and marketing, shares insights on creating a relevant AI product and the obstacles entrepreneurs may face in this competitive field.
Why AI Products Are More Relevant Than Ever
Neural networks have been evolving since the 20th century, but their potential was limited by the computing power available at the time. A significant breakthrough in AI development occurred in the 2020s, driven by a combination of factors: the growing volume of data, advancements in machine learning algorithms, and increased computational power. One of the most groundbreaking events was the emergence of cutting-edge generative AI models, including transformers and diffusion models. These innovations unlocked new possibilities in natural language processing, image generation, and business process automation.
Today, two crucial factors have aligned—an abundance of machine learning research and the availability of powerful computing infrastructure. The only major constraint on AI product development remains hardware capabilities, but manufacturers are rapidly adapting to market demands by producing increasingly powerful solutions.
Now is the perfect time to develop AI services, as they are widely used across industries and in daily life. Entrepreneurs looking to launch AI-driven products have a strong chance of success.
However, competition is fierce due to the high demand for AI solutions. If you have an AI product idea, chances are someone else is already working on it. This leaves two viable paths to success: creating an exceptionally high-quality product or addressing a precise and pressing customer pain point.
How to Build a High-Demand AI Product
I recommend tackling this challenge in several key steps.
Step 1: Choosing a Niche. First, determine whether you’re targeting the B2B or B2C market. The key is to focus on real demand and audience pain points. In B2B, acquiring clients is more challenging, but companies are willing to pay more for solutions that effectively address their needs. In B2C, market entry is easier, but you’ll need to scale quickly and attract thousands of users since the average revenue per user is lower. Choose a niche where there is a genuine need rather than just an interesting technology.
Step 2: Selecting the Right Tools. You can leverage any open-source neural network. For example, AI avatar generation services often use Stable Diffusion, which enables model fine-tuning with DreamBooth to create personalized images.
Step 3: Building an MVP. To avoid investing in an idea that doesn’t work in practice, start by testing it as a Minimum Viable Product (MVP). You can develop a pilot version using no-code platforms or even Telegram. For instance, we launched DreamPics this way. Afterward, gather feedback from early users and refine your solution based on their insights.
Step 4: Planning Monetization. You can choose between a one-time payment model or a subscription-based approach. The choice depends on the product’s pricing, features, and target audience. For instance, if you’re offering a complex B2B product, a subscription model makes sense—companies can try your service without committing to a large upfront cost, making it easier to close deals. On the other hand, if you’re providing low-cost, one-time services, a pay-per-use model may be more effective.
Step 5: Scaling the Product. When developing your product, you should have a clear vision of how to expand it—otherwise, users may lose interest over time. Consider adding new features, targeting additional audience segments, or expanding into different industries.
Another key factor in making your product successful is accessibility. While neural networks and machine learning are complex technologies, your customers don’t need to understand the intricacies—they only care about getting fast and accurate results. A great way to stand out from competitors is by ensuring seamless implementation. If users can set up your product easily without requiring IT specialists, it will significantly boost adoption and market appeal.
How to Determine the Right Industry for Your AI Startup
There are several methods for analyzing the market—ideally, you should use all of them:
Monitoring Competitors. Start by examining existing solutions in your target industry. Analyze how they work, identify their shortcomings, and find opportunities for improvement. You don’t always need to invent something entirely new—sometimes, refining an in-demand idea can lead to success. If your product solves a problem that competitors haven’t addressed—whether through better performance or easier implementation—you’ll have a significant advantage.
Research Studies. Numerous publicly available sociological and market research studies provide valuable insights into people’s pain points and needs. If you can effectively address a critical issue, your chances of success increase significantly.
Customer Development (CustDev) Interviews. The best way to understand what people need is to ask them directly. Conducting around 50 customer development interviews can provide meaningful insights. You can find respondents through Telegram surveys or hire a specialized agency to gather feedback. CustDev is also useful for validating your ideas or testing an MVP before scaling.
One more crucial factor: when developing an AI product, it’s beneficial to have at least a basic understanding of the industry you’re targeting. Alternatively, having a team member with relevant expertise will help guide product development in the right direction.
Challenges in AI Product Development
AI products can be profitable, but their development comes with several challenges:
Choosing the Right Model. The AI model at the core of your product should be tested and proven in other applications to avoid unexpected failures. It’s best to use well-established neural networks, such as Stable Diffusion. Additionally, ensure that the model is open-source or can be seamlessly integrated with other platforms.
Computing Power. Processing large volumes of data efficiently is crucial for AI products. If your solution runs too slowly, users will become frustrated. One way to address this challenge is by leveraging cloud computing to host and scale your resources.
Data Volume. Sufficient data is required to train the model effectively. If your product targets a highly specific niche with limited publicly available data, training the model to a high standard may be difficult. Additionally, training AI models takes time, which must be factored into your release schedule.
Ethical Considerations. AI models often face skepticism and misinformation. For example, some users fear that AI systems secretly send confidential data to developers for malicious purposes. Addressing these concerns and reassuring users about the security of your solution is essential for gaining trust.
As with any product, before developing an AI-based solution, it’s crucial to create a business plan and outline a clear marketing strategy.
Which AI Technologies Will Be in Demand in the Future?
AI-powered products are emerging across various industries, but based on my experience, I see the following areas as particularly promising:
AI-Powered Video Creation. Video content attracts more engagement than images or text—just look at the explosive popularity of vertical video clips on social media. AI is already being used to generate videos, animations, and even custom avatars that can narrate pre-written scripts, making it an ideal tool for content creators. However, producing high-quality AI-generated videos is still challenging, making this field highly in demand.
AI in Healthcare. Doctors deal with vast amounts of data to diagnose conditions and prescribe treatments. AI can help by quickly analyzing test results or predicting disease progression. Additionally, neural networks can be leveraged in pharmaceuticals to assist in developing new drug compounds.
Personal AI Assistants. Smart assistants in smartphones can already set timers, provide weather updates, and schedule meetings. The next generation of AI assistants will be even more intuitive, remembering user habits and anticipating their needs. For example, if a user typically watches movies on Friday nights, the assistant could suggest a tailored movie selection before the weekend.
AI in Education. Today, AI-powered tools assist teachers in creating assignments and tests. In the future, AI could be used to grade work or generate personalized learning plans—either for entire classrooms or individual students based on their unique abilities.
AI in Marketing. AI-driven tools will become indispensable wherever large-scale data analysis and behavioral predictions are required. From shaping marketing strategies and analyzing ad campaign performance to selecting influencers for specific campaigns, AI will help create targeted ads and promotions that adapt to constantly shifting consumer interests.
These fields offer great opportunities for launching AI products—of course, after conducting thorough market research.
Final Thoughts
AI-powered products have immense potential, but their development still faces three major challenges:
Quality of Neural Networks. Even the most advanced AI models—such as ChatGPT—can still make mistakes or generate irrelevant responses. Building a high-quality AI product requires significant time and effort to train the model effectively.
Computing Power. Developing and deploying AI solutions demands substantial resources. It’s crucial to plan the infrastructure and architecture of your service in advance to ensure efficiency and scalability.
Ethical Standards and Trust. AI regulations remain unclear, and debates continue over which data can be legally used for training. Additionally, many users remain skeptical about AI products, fearing potential data breaches or misuse.
Despite these challenges, AI has already become deeply integrated into our daily lives and various industries. Developing high-quality, user-friendly AI solutions can be both impactful and highly profitable.
