#2 From DHM to Product Strategy: Generative AI Opens Up a Creative New World

2 From DHM to Product Strategy: Generative AI Opens Up a Creative New World

In today's rapidly evolving digital landscape, the intersection between product strategy and generative AI is paving the way for groundbreaking innovations and opportunities. Both concepts share a common goal of achieving high-level objectives that combine delight, hard to copy advantage, and margin. By leveraging generative AI, businesses can unlock new possibilities in personalization, social engagement, price and plans, entertainment, and more.

When it comes to personalization, Netflix stands as a prime example. While the platform wasn't initially simple at launch, it gradually evolved to become more user-friendly over time. A surprising move in 2018 was the decision to remove movie reviews. By allowing members to quickly hit "play" or "quit" at any time, Netflix eliminated the need for reviews. This decision not only simplified the user experience but also highlighted the importance of delivering delight and margin simultaneously.

Social engagement is another area where Netflix has experimented and excelled. For six years, the platform explored the inclusion of the TV show "Friends" in its offerings. By tapping into the power of social connections and popular content, Netflix successfully engaged its audience on a deeper level. Additionally, Netflix recognized the value of unique movie-finding tools. In 2005, the vision was to have personalized previews play on each member's homepage, providing both delight and margin.

Price and plans have always been significant considerations for businesses, and Netflix is no exception. The company explored various strategies, including ads, used DVD sales, and next-day DVD delivery. However, with the advent of streaming technology, Netflix found a hard-to-copy advantage. The technology employed by Netflix to encrypt and deliver video content sets it apart from competitors and enhances the overall user experience.

Entertainment has been at the core of Netflix's product strategy. The platform embraced open APIs in 2006, following the footsteps of Facebook, LinkedIn, and other major players. By opening their Application Programming Interfaces, these platforms enabled partners to innovate on their platforms. Netflix leveraged this opportunity, leading to the creation of an extensive device ecosystem. By 2012, Netflix had established critical mass with hardware partners, offering customers the ability to enjoy their favorite shows "anytime, anywhere."

Furthermore, Netflix recognized the importance of exclusive DVD content, paving the way for original content and interactive stories. These additions not only delighted users but also ensured high-quality video and sound experiences. From its early days, Netflix learned the significance of staying focused on improving the core product, resulting in continuous innovation and growth.

While product strategy has been a driving force for companies like Netflix, generative AI introduces a new dimension to the creative process. With the emergence of powerful language models, machines are now capable of writing, coding, drawing, and creating with impressive results. This marks a significant shift in the labor productivity and economic value of knowledge work and creative work.

Generative AI has the potential to make workers in these fields at least 10% more efficient and creative. By empowering them with superhuman capabilities, generative AI transforms how individuals approach their tasks. Text generation has emerged as the most advanced domain, but the challenges of getting natural language right and ensuring quality remain. However, the recent viral success of generated images on platforms like Twitter indicates a growing interest in visual creative applications.

Copywriting is one area where generative AI shows immense promise. The growing need for personalized web and email content to fuel sales, marketing strategies, and customer support aligns perfectly with the capabilities of language models. Additionally, there is a vast opportunity to develop vertical-specific writing assistants tailored to specific end markets. Whether it's legal contract writing or screenwriting, generative applications can offer substantial value and efficiency.

The potential of generative AI extends beyond text generation. GitHub Copilot, for example, is now responsible for generating nearly 40% of code in projects where it is installed. This opens up the possibility of democratizing coding for consumers, enabling individuals without extensive coding knowledge to create their own applications. Learning to prompt may become the ultimate high-level programming language, bridging the gap between users and machine-generated code.

Social media and digital communities also stand to benefit from generative AI. New ways of expressing ourselves using generative tools are emerging, leading to innovative social experiences. Platforms like Midjourney are already creating opportunities for consumers to create in public, fostering engagement and collaboration. By leveraging generative AI, businesses can develop plugins as an effective wedge to bootstrap their own applications. This approach helps overcome the chicken-and-egg problem of user data and model quality, ultimately driving user growth and engagement.

In conclusion, the convergence of product strategy and generative AI presents an exciting landscape for businesses to explore. By incorporating personalized experiences, social engagement, unique pricing models, and entertainment-focused offerings, companies can leverage generative AI to enhance their product strategies and deliver greater value to customers. As businesses embark on this journey, here are three actionable pieces of advice:

1. Embrace the power of personalization: Continuously strive to make the user experience simpler and more tailored to individual preferences. Use generative AI to personalize content, previews, and recommendations to enhance delight and margin.

2. Explore vertical-specific generative applications: Identify opportunities within your industry or niche to develop generative AI tools that cater to specific end markets. This can contribute to increased efficiency and creativity in tasks such as writing, coding, and content creation.

3. Foster user engagement and model performance: Establish a flywheel effect by driving exceptional user engagement, leveraging user data to improve model performance, and using improved models to attract more users. Continuously iterate and refine the generative AI models to create a virtuous cycle of growth.

By leveraging the potential of generative AI within their product strategies, businesses can unlock a creative new world of possibilities and drive significant value in the digital landscape.