5 tips to create AI products your users NEED

I attended the AI meet-up hosted by Maidstone yesterday. Lizzie Metcalfe's presentation on the intricate relationship between humans and companion AI bots was fascinating.

She dove deep into the motivations behind our growing relationship with AI, uncovering layers that go well beyond the surface. It turns out, our reasons originate from fundamental human instincts and needs.

As a product manager, it's easy to get caught up in the temptation of new features and suffer from the shiny object syndrome. Despite Henry Ford's famous quote: "If I had asked my customers what they wanted, they would have said a faster horse." We often overlook what our users genuinely need from us.

This highlights a crucial aspect of MVP and product discovery. It's not just about validating our ideas or building something we think users want. Instead, it's an opportunity to understand our users' core needs deeply.

We easily assume we have all the answers, especially since the internet (predominantly Web 2.0) has been part of our lives for over two decades. This familiarity breeds a certain level of comfort in suggesting how users can better navigate it. Yet, AI introduces a new paradigm that many of us are still trying to grasp. We're at risk of merely sprinkling AI on top of existing frameworks and processes, much like the early days of online newspapers, which were essentially PDFs on a webpage. It took traditional media (a handful only) 2 decades to work out how to be profitable online.

"As a product manager, it's easy to get caught up in the temptation of new features and suffer from the shiny object syndrome"

Andrew Cheung, Senior Product Manager & Mentor

For businesses venturing into AI-led products, here's a strategic approach to discovery and creation:

1. Reassessing Your User Journey: A Guide to Integrating AI
   
   In the fast-paced world of product management, most businesses build their processes and models atop existing technology. Yet, adaptation is only half of the equation in staying ahead. The other half is re-architecting your business to leverage AI effectively. My analysis for a leading company revealed a striking potential: strategically placing AI features at different stages of the user journey improved the time to basket by an impressive 25% to 65%.
   
   

2. Role of AI is beyond what you see
   
   

   • The adoption of AI should not be limited to isolated experiments or the pursuit of quick wins. A strategic approach, focusing on scalable and sustainable integration into product lifecycles, is essential for long-term success. This involves identifying high-impact use cases that align with business goals and customer needs, ensuring AI solutions enhance product value and user experience.
     
     

   • Beyond Efficiency
     
     While AI can significantly enhance operational efficiency, its potential extends further. Innovative applications of AI in product management include enhancing user interfaces, personalising customer experiences, and leveraging predictive analytics for informed decision-making. This diversification can unlock new avenues for product innovation and competitive differentiation.
     
     

   • A Synergistic Relationship
     
     The synergy between AI and human creativity is a cornerstone of successful product management. AI's data-processing capabilities, combined with human insight and domain expertise, can lead to developing more intuitive, user-centric products much faster than the traditional development cycle.
     
     

   • Scalability and Future Readiness
     
     For AI initiatives to be truly impactful, they must be designed with scalability in mind. This entails preparing the technological and organisational infrastructure to support the seamless integration of AI into existing and future product lines. A scalable AI strategy ensures that your products remain at the cutting edge, adapting to evolving market demands and technological advancements.
     
     

3. Spot Opportunities: Stay open to possibilities without jumping to solutions.
   
   Resisting the urge to jump to conclusions ranks among the most challenging tasks, especially when faced with OKRs, mounting pressures, and the relentless pursuit to outpace competitors. Take time to understand what AI means for your business thoroughly. As a relatively new addition to the technology landscape, AI presents uncertainties, unexplored solutions, and innovative ways of working or creating user value. Before innovations like Google and Facebook, there were no roles in SEO or social media operations and advertising. While the future of AI over the next decade remains uncertain, it's crucial to be aware of two main pitfalls:
   
   

   • Launching an AI product without comprehending the operational support required post-launch. It's essential to understand that MLOps and DevOps are distinctly different disciplines.

   • Sprinkling AI on top of an existing product offering or business model is counterintuitive. It creates confusion and makes it hard for users to complete their tasks.

4. Formulating Hypotheses: Setting Clear Expectations for AI
   
   Asking the right questions is crucial. What unique benefits does AI offer your scenario that other solutions cannot? Different AI solutions serve different expectations, from enhancing efficiency to navigating the challenges of false positives or negatives in AI outcomes. Setting clear, AI-driven metrics is a key success factor.
   
   

5. Prioritising Strategies: AARRR, HEART, and Kano Models
   
   Why these 3? They covers each others blindspots.
   
   

   • The AARRR framework focuses on metrics directly impacting business health, encompassing Acquisition, Activation, Retention, Referral, and Revenue. It is ideal for aligning AI initiatives with product objectives. For example, an AI solution tailored for an Acquisition strategy will differ significantly from one designed for Referral.

   • The HEART framework, developed by Google, aims to enhance UX and stands for Happiness, Engagement, Adoption, Retention, and Task success. It's crucial to identify how AI can enhance your underserve UX in your products. AI's role in UX is often overlooked, but this framework offers guidance on integrating AI within UX strategies.

   • The Kano Model organises feature ideas into a development plan focused on enhancing performance and customer satisfaction. Although you may believe you have the best idea ever, the Kano Model offers a reality check by categorising potential customer reactions into five types, from Dissatisfaction to Indifference. This includes 'Indifferent' features, which customers may ignore, and 'Dissatisfaction' features, which could upset them. It's an excellent approach for those with limited resources seeking impactful, performance-driven features.

Bonus Tip: Embrace an Experimental Mindset

The journey with AI is as much about exploration and adaptation as it is about implementation. Adopting an experimental mindset opens up new avenues for using AI to better serve user