4 New Realities for AI Product Management
With the advent of AI, most product roles have taken on a whole new dimension. While even the most evolved non-AI product management focuses on client needs, user interfaces and functional requirements, AI management demands a broader perspective.
1. Focus on User Conversance (UC) vs. User Experience/Interface (UE/UI)
Many products are increasingly defined by AI capabilities needing a focus shift from UI elements—buttons, icons, and layouts, and user experience in product Journey, into User Conversance (UC) i.e. knowing the users. Both hardware and software can now track user interactions in real time, generating valuable data insights. This ability does not just enhance design; it necessitates a user-focused approach to product development.
The shift breaks users free from the tyranny of visual appeal.
For AI-driven products, understanding users' thoughts and emotions is key. There are available technics for establishing such understandings. Visual recognition, predictive analytics, and personalized content allows for the creation of impactful experiences that resonate at a much deeper level with users than ever possible. Product should now anticipate user needs instead of just reacting to them, leading to tailored experiences that make users feel acknowledged and valued.
Here is an example of a UC implementation: To understand how satisfied users are with a fresh feature, we can't just rely on a simple thumbs up or thumbs down approach or through intrusive, obstructive ‘would you like to rate’ pop ups. We now can evolve: very soon we will be able toconsider using visual cues and sentiment analysis. For example, we could evaluate customer engagement with an AI response by assessing facial expressions with enough certainty. No need for user input. Expect capabilities around this from companies like Realeyes - Vision AI.
2. Expect Complex MVPs
The process of developing AI MVPs is inherently more complex than in non-AI scenarios. AI MVPs demand a solid grasp of the problem at hand, necessitating the incorporation of demo data to inform the product's functionality. Unlike traditional products, which may allow for iterative features, AI products must be fast and scalable right from the start. So this requires Products to incorporate non-functional needs, faster response times, and even edge cases upfront. In particular, Product needs to consider that AI MVPs:
Require substantial data collection and preparation for training AI models: high-quality and relevant to the problem space.
Need integration of AI components. There will be intricate algorithms and machine learning models involved specific to your prevalent use case.
Need to be designed with scalability in mind from the outset, as they often deal with large datasets and real-time processing.
Require continuous iteration and learning as AI models improve with more data and feedback. This involves refining algorithms, retraining models, and adapting to changing user behavior. Expect very short iterations for this: sub-week.
3. The Lab Mindset - a Mark of an Elite AI PM
AI product managers often find themselves engaging in explorative activities that, while crucial for innovation, may not yield any quantifiable effects on business metrics. This "lab mindset" is essential for nurturing the kinds of innovative solutions that AI can uniquely provide. A lab orientation propels the team to engage in prototyping, hands-on tool testing, and team hackathons fostering an environment that encourages creativity and experimentation.
Without the lab mindset, product will most definitely fall somewhere between subpar to failure.
You can expect a top 5% AI PM to incubate a group for groundbreaking ideas that can redefine market standards. Top AI PMs master the balance between agility and strategy, creating room for exploration while still keeping an eye on overarching goals.
4. Strategy outshines Execution
As AI continues to transform the business landscape, it’s becoming clear that strategy should take precedence over execution. AI tools can streamline traditional tasks such as writing product requirement documents (PRDs), road mapping, and meeting management: liberating valuable time for teams to focus on higher-level ideation and strategic thinking.
But i'd like to assert that this shift is for far more important yet less talked about reason: the need for AI product function to influence rather than adapt. Strategic product managers develop a mix of market awareness, foresight, and creativity. They don't just respond to changes; they don't just foresee them: They influence them.
Product strategy is now an imperative: top 10% AI PMs make smart decisions that align with today's trends and future expectations. Top 1% define those trends and expectations.
This top 1% have a lot more under their radar. Their influence starts with the realization that:
Clients need guidance navigating AI capabilities
There are more nuanced explanation of technological potential
They need to address AI's "black box" perception (via regular model performance reports, transparent disclosures of the error correction processes, and clear communication about model capabilities)
This dynamic two-way dialogue with end users empowers these elite PMs to shape the product journey even before users can envision it. The influence of these top PMs goes well beyond soon-to-be-outdated data analysis and KPI metrics defining, owning, chasing; it introduces a new way of thinking that sparks creativity. It's an exciting time for other product managers to take notice and adapt to this change!
A tip for aspiring AI product manager -> If you come across a Senior or Director of PM job description that's filled with qualifications around user story writing, agile ceremony expertise, monitoring KPIs, or similar, feel free to click out!
