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The rapid progress in AI is intensifying uncertainties for startups and their founders. Each model release from big AI players poses a challenge, potentially rendering thousands of startups obsolete, including those who believed they had a defensible tech stack. Similarly, the release of new open-source models can negate years of effort by startups overnight. This evolving landscape underscores the critical need for careful ideation and business model formulation for AI entrepreneurs.
To aid in this endeavor, I offer four key pitfalls to avoid, along with strategic recommendations, drawing from my extensive academic and industrial research.
Related: 6 Ways AI Is Revolutionizing Startup Ecosystems
1. Develop an AI-embedded product with organic workflow integrations and strong user experience
Imagine you’ve launched a startup that creates game assets for gaming companies using AI. Users upload images, specifying styles and providing textual descriptions for new designs, which your AI then brings to life, aligning with the users’ visions and the initial style cues. However, this AI isn’t integrated into the designers’ daily workflows or fine-tuned based on their evolving needs, rendering it only an external aid that shines as long as its outputs surpass industry standards. Then the following question arises: what will keep your clients from switching to a competitor offering a superior solution?
Therefore, your AI should integrate seamlessly into clients’ workflows, adapt over time and provide an engaging experience. Consider Notion as an illustrative example. It may not be a giant player in AI, but its users like the intuitive note-taking experience enhanced by an AI assistant. Even with superior models available, users stick with Notion for its smooth, integrated AI experience, demonstrating the value of user-friendly design over raw power.
Related: 5 AI Hacks You Need to Know About in 2024
2. Ensure your AI product is finely tailored for niche markets
If you are not creating the high-tech infrastructure from scratch yourself, then it might be overambitious to create an AI product with a focus that is too broad. There are mainly two reasons for this: Firstly, market leaders in these broad areas are swiftly incorporating state-of-the-art AI into their products, driven by the need to stay competitive and the ease of using foundational model APIs when developing in-house solutions isn’t viable.
Take, for instance, the initial rollout of APIs by OpenAI. Numerous ambitious entrepreneurs aimed to leverage these AI capabilities to challenge established players across various sectors. However, OpenAI’s subsequent partnerships, through ChatGPT Plugins, with industry giants like Expedia, Instacart and Zapier showcased the rapid integration of AI into leading businesses, helping them preserve their positions. Notably, OpenAI’s collaboration with Zapier presented a significant challenge to Adept AI, a startup formed by prominent AI researchers, since both companies aim to ease computer workflow automation via natural language commands. This scenario illustrates that opting for a broad focus in AI can be risky even for highly technical teams.
Second, despite major AI firms’ commitments to foundational technologies, they’re branching into application layers to boost revenue, targeting areas where minimal effort yields a broad impact. This shift towards products with expansive objectives suggests a strategic pivot for smaller AI startups: focusing on a highly specialized niche. By crafting an exceptional AI experience in a specific domain, an emerging AI startup can establish a competitive edge, leveraging specialization as a strong strategy in a market dominated by broader initiatives.
Related: How to Find Your Startup’s Niche
3. Avoid confining your AI product solely as a plugin for existing software — opt for a standalone solution instead
The emergence of generative AI APIs inspired numerous entrepreneurs to enhance everyday tools like Excel, PowerPoint and various software development platforms using AI. They crafted AI-enhanced plugins to augment user experiences within these applications. For instance, innovative tools enabled users to automate routine Excel tasks, significantly boosting productivity, particularly for finance professionals. Initially, these AI-enhanced solutions saw a surge in demand.
However, the landscape shifted when major platforms began integrating their own AI solutions, such as Microsoft Copilot for Finance or Google’s AI features in Gmail and Docs. These in-house developments rendered many third-party plugins nearly redundant. This evolution underscores a critical lesson for startups: relying excessively on a single platform can be risky. Ensuring your business’s resilience means diversifying your dependency and continuously innovating to stay relevant in a rapidly evolving tech environment.
4. Develop solutions that receive natural support from the AI ecosystem
A strategic approach to selecting an AI startup idea is to focus on areas likely to receive ecosystem support. Major AI firms are continually advancing models with the capacity to revolutionize various industries and businesses of differing scales. Yet, integrating these models is not without its challenges. Businesses often hesitate to fully deploy these models in customer-facing applications due to uncertainties about outcomes’ safety and concerns about data privacy, which could lead to the exposure of sensitive information.
Recognizing these hurdles, large AI corporations are particularly encouraging of startups dedicated to addressing these integration issues. These new ventures are working on solutions like conducting model evaluations, establishing data privacy safeguards and developing innovative security protocols. For example, OpenAI started grant programs to promote AI Safety and Security efforts. This support underscores the opportunities for startups to contribute value by facilitating the safe and effective adoption of AI technologies across various sectors.
Related: The White House’s AI Executive Order
