Harnessing AI for Growth: Insights from Google's Gen AI Product Lead

The Evolution of AI in Product Development

Artificial intelligence has come a long way from its early days in video games. Marily Nika, Google's Gen AI Product Lead, shares her journey from computer game enthusiast to AI product leader. She highlights how AI has evolved from simple probability-based learning to advanced techniques like deep learning and generative AI.

Nika emphasizes that AI is not just about tools or models, but about enhancing user experiences. As she puts it, "AI isn't the product - AI tools, AI models, AI agents, these are not the product. The experience is the product."

AI as a Growth Accelerator

For growth teams, AI offers powerful capabilities to improve measurable growth. Some key ways AI can drive growth include:

• Personalized onboarding experiences
• Improved targeting and recommendations
• Predictive analytics
• Automated customer support
• Personalized experimentation

Nika advises growth teams to focus on how AI can enhance the customer experience and deliver real value, rather than just adding AI for the sake of it. The foundation of growth is still about value creation - AI is a new tool to achieve that more effectively.

Measuring Success with AI Products

When it comes to measuring the success of AI-powered products and features, Nika outlines three key buckets of metrics to consider:

1. AI proxy metrics - How accurate is the model? (e.g. false accepts/rejects)
2. Classic product metrics - Growth, retention, engagement etc.
3. System health metrics - Can the system handle scale?

She emphasizes the importance of having "AI awareness" across the organization to understand these different considerations.

The Experimental Approach

Nika advocates for an experimental culture when it comes to implementing AI:

"We need to have this experimental culture in our companies where you hypothesize a lot and validate. If you say something like 'We are going to use AI to personalize onboarding for growth', you need to say 'We expect a 10% increase in day 7 retention' or something like this. But then you need to validate that hypothesis with some small pilot or AB test."

She recommends tools like Optimizely for running these types of experiments efficiently.

AI for Early-Stage Startups

For early-stage startups with limited data, Nika suggests several approaches:

• Use pre-trained models and APIs (e.g. GPT-4, Hugging Face)
• Fine-tune models on smaller datasets
• Synthesize data using tools like Snorkel AI
• Start with narrow use cases and hypotheses

She advises startups to focus on validating whether AI will add value before worrying about data acquisition. Synthetic data and pre-trained models can help bootstrap the process.

The PM-Engineer-Scientist Trio

Nika highlights how AI is changing team dynamics, introducing a new "user persona" - the research scientist. Product managers now need to balance trade-offs between:

• The engineer integrating everything
• The scientist creating the AI model
• The PM's desire to launch quickly

She notes that scientists often want perfection, while PMs push for faster launches. Navigating these dynamics requires new skills for product leaders.

AI-First vs AI-Enhanced Products

When it comes to building new AI-first products versus enhancing existing ones with AI, Nika says it depends on company strategy and risk appetite:

"It goes back to strategy and how much risk you want to take as a company. Are you the company that wants to just keep going, or are you the company that wants to be the leader?"

She sees some companies making bold bets on entirely new AI products, while others focus on enhancing existing offerings. Both approaches can work depending on the context.

The Future of AI in Growth

Looking ahead, Nika is excited about real-time personalization powered by AI:

"I'm fascinated by real-time personalization that adapts an experience instantly based on who is there. An example is some startups using LLMs to rewrite in-app copy on the fly to match someone's interests or skill level. Every user journey being dynamically tailored to maximize engagement - that real-time personalization is just phenomenal."

She also sees roles in growth becoming more holistic, with AI enabling individuals to take on broader responsibilities.

Key Takeaways for Growth Leaders

For growth professionals looking to level up their AI skills, Nika recommends:

1. Identify a real problem in your funnel/product
2. Spend 30 minutes experimenting with no-code AI tools to brainstorm solutions
3. Spend 30 minutes studying how competitors solved similar problems with AI
4. Attend conferences and talk to peers to stay current

She emphasizes the importance of first-hand experimentation before looking at others' solutions.

The Human Element Remains Critical

Despite AI's growing capabilities, Nika stresses that human judgment and creativity are still essential:

"AI is not a magic wand. Remember, it's as good as the data you have, as the problem you have. You've got to be very user-centric whenever using it. Just adding AI is not enough - it doesn't guarantee any success. Always remember to add strategy in there and be thoughtful about it."

By keeping the focus on solving real user problems and creating value, growth leaders can harness AI's power while avoiding common pitfalls.

Conclusion

AI is transforming product development and growth strategies across industries. By understanding AI's capabilities, experimenting thoughtfully, and keeping user value at the center, growth leaders can leverage this powerful technology to drive meaningful results. As AI continues to evolve, staying adaptable and focusing on fundamentals will be key to long-term success in the AI-powered growth landscape.

Article created from: https://www.youtube.com/watch?v=KlkNwGxf9GQ