Developments in AI are moving at breakneck speed. From generative AI to Large Language Models to Transformers, a new golden age of AI research is powering some of today’s most innovative technologies.
Not surprisingly, AI-first companies, where AI is integral to the company’s product or platform, are increasingly coming to market and outstripping their competition. Product-led growth companies are also taking advantage of this enormous opportunity by embedding AI into powerful features that increase adoption and drive growth.
Google, for example, recently acquired the AI avatar startup Alter for $100 million in order to offer more creative content for end users. Navina raised $22 million in Series B funding for its AI-powered assistant software for physicians. Jasper uses AI to generate automated marketing copy and was able to raise a $125 million Series A funding round at a $1.5 billion valuation.
As companies race to take advantage of these new opportunities for AI-powered products, many product teams are left pondering the best approach to embedding the right AI into their product roadmap.
This guide serves to examine the best practices for building better products with AI to demystify the process, and to smooth and accelerate the path to deployment.
In the guide, you'll learn:
- Detailed best practices for product teams to consider before building an AI-first product
- Potential blockers and risks, and how to successfully overcome them
- How to choose the best AI partner for your use case
- Real AI-first product and feature use cases