No Such Thing As An AI Product Manager
Unless you are building a language model or researching improving AI.
These days, in my circles — AI is the buzzword dancing on everyone’s lips. It doesn’t matter what their specialty is, once the talk turns to AI, it’s like everyone’s just had a triple shot of espresso.
We’re living through a revolution here, where AI is not just a hot topic but a mighty force reshaping industries and roles.
In these exhilarating times, I’ve been mulling over three radical ideas that have been emerging like Phoenixes from the ashes of our discussions:
- Scrap the tweaks, welcome to the age of bold, out-of-the-box innovation.
- ‘AI Product Manager’? Nonsense. All product managers now have to swim in the AI pool. I’ll share a step-by-step self-reflection for product managers.
- The AI landscape is a new wild west, buzzing with excitement and peril. Navigate it carefully, and you might just hit the jackpot.
The Renaissance of Classic Product Management with AI
Do you (if you are in your late 30s +) recall those tantalizing times when fresh tech would burst onto the scene, bringing a tidal wave of opportunities? When did product managers really stand out? When could they take a technology and wield it to solve user problems?
Think Google and Wikipedia, platforms that took technology and used it to offer unparalleled user experiences. Then came the mobile internet, giving birth to short videos and forever changing our digital lives.
For quite a while, such game-changing tech became a rarity. Us product managers started focusing on the sculpting work.
We became button placers, conversion makers, document writers, you name it. These became the norm, and the business and growth product manager roles involved squeezing dividends and conversions out of every nook and cranny.
Enter AI, the disruptor. Unlike past so-called tech innovations, AI is heralding changes with fierce certainty. Whether it’s in natural language interaction, knowledge acquisition, and information processing, the “effectiveness” is surging forward, opening up a universe of possibilities. We’re at the stage to look beyond the growth optimization, and should be seeking groundbreaking changes instead.
Just a couple of months ago, I was getting the hang of texting with ChatGPT, and now, we’ve got multimodal papers. This tech is allowing product managers to rethink the impossible: New interactions based on natural language, major product changes, new tools based on AIGC, reshaping documents, spreadsheets, drawing, audio and video editing functions, creating new scenarios based on AGI. Think AI-generated games, game NPCs turning smart, and even AI-powered therapy.
While the exact challenges these pathways will pose are still unclear, their feasibility is already apparent. We’re on the brink of a new era set to inject fresh life into the ‘classical’ product role. As the AI sector burgeons, product management, a profession that has come into its own only in the last 10–15 years, will experience a renaissance.
‘AI product manager’ is a pseudo-concept: Let’s Get Real
The AI wave has sparked a somewhat misguided idea — the need to become an ‘AI product manager’. Often, this title is linked to product managers dealing with AI-driven products like smart speakers or strategic algorithms.
However, the real focus shouldn’t be AI, but how it’s applied.
Rather than trying to morph yourself into an ‘AI product manager,’ think about how your current expertise could mesh with AI. You’re a product manager navigating a business landscape increasingly influenced by AI, not an AI expert.
The term ‘AI product manager’ is a pseudo-concept.
It’s like saying you’re a ‘Python product manager’ or an ‘Android product manager’. Your focus shouldn’t solely be the technical entity.
Product managers are all about prioritizing users and application scenarios. If you’re working on smart speakers, you’re a speaker product manager. If you’re developing an autonomous driving system, you’re an autonomous driving product manager.
The fuel behind successful product management doesn’t come from a specific technology, be it AI or blockchain, but a user-centric approach.
In the future, we’ll undoubtedly see product managers involved in AI-related product development, but we won’t be labeled ‘AI product managers’. Instead, our roles will likely be defined by the specific scenarios and businesses we cater to.
Here’s a reflection session that might help you understand your value as a product manager over the next half-decade:
- Forget Becoming an AI Product Manager: How can your experience and AI blend together to add value to your role?
- Identify the Problems: Know your target users and the problems you aim to solve, as we always do.
- Understand AI’s Potential in Your Field: If you’ve been working on a content or trading platform, how can AI improve user experience or streamline the process? This understanding will lead to better product management strategies.
- Ask, Why You?: The key question is, why should you integrate AI into your current role? What unique value can you bring?
- Tap into Your Unique Knowledge and Experience: Think about how your unique knowledge and experience can stimulate growth and innovation in your field with AI.
In a nutshell, being a successful product manager in an AI-driven world isn’t about becoming an ‘AI product manager’. It’s about understanding how AI can be used to enhance your current strategies, while staying true to the core principle of product management: identifying and addressing user ‘jobs to be done’.
You’re defined not by the tech but by how you use it to better fulfill these jobs, using your unique expertise to craft solutions that create real value.
The AI Lottery: Strike Gold or Strike Out?
There’s no denying AI’s magnetic appeal. But keep in mind that the rush to capitalize on AI’s potential can be like trying to board a rocket that’s still being built.
The current AI scene reminds me of the early days of iOS development — oozing with promise but still a work in progress. While early exploitation of AI’s potential may be profitable, it’s not a sustainable business strategy. Many (myself included) are feeling the heat, thinking they need a head start to reap the rewards.
However, hoping to jump on early and ride the right wave is a bit of a lottery. For instance, even if you’d joined the internet bandwagon a decade ago, it doesn’t guarantee a win.
The current AI landscape is still shaky. Right now, it feels like people are trying to create their own iOS, which can be a platform for others to build on. But that’s a costly endeavor that only a handful can pull off commercially. A few winners will develop an ecosystem of utilities built around them. Opportunities abound but are often snapped up by major corporations with deep pockets.
Stanford’s Alpaca chatbot story offers an exciting twist. This chatbot was developed by Stanford researchers, proving that even smaller groups can make substantial contributions to the AI field. Despite being a more minor team, they designed a chatbot that performed on par with OpenAI’s GPT-3.5 model.
However, they eventually pulled the plug on the Alpaca chatbot demo due to “hosting costs and the inadequacies of our content filters.” This decision underscores the hurdles smaller groups face when attempting to maintain and enhance AI technologies over time, especially when compared to larger companies with more resources.
This doesn’t mean that smaller teams or individuals can’t make a dent in the AI field. While you might lack the resources to create big platforms, you can still make meaningful contributions. Maybe you could focus on developing applications or services that leverage AI or specialize in a specific problem area where you can bring your unique skills and knowledge to the table.
Remember, there’s no need to rush — there’s always a next opportunity. There’s plenty of room above the operating system for everyone. But remember, the ecosystem is still a work in progress. Unless you’re eyeing a quick cash grab, like creating a simple flashlight app, understand that AI is still in its flashlight app stage. It can definitely make you a quick buck, but it’s not a long-term business strategy.
Braving AI’s New World: Dream the Undreamt
Amid layoffs triggered by the pandemic-induced economic downturn, most of us in the IT sector are feeling the heat. Those not working for significant corporations either lower their expectations, join small to medium-sized enterprises, or switch lanes to start businesses. Those still in the big corporations, as long as your positions are not particularly uncomfortable, most of us aren’t likely to consider starting a business.
The opportunities are vast, especially in spaces above the operating system. Here are the main application scenarios of AI, broken down into three categories:
- Content: From creating tools for creators to becoming creators ourselves.
- White-collar work: From serving as an assistant to becoming a worker.
- Human-machine interaction: From acting as a search engine to being an interface.
Let’s approach AI with a sense of realism and an appreciation for its current and future potential. Like in any industry, riding the AI wave isn’t about being the first to jump on. It’s about understanding the landscape and making strategic, well-informed moves.
