How to Build Scalable AI Products That Deliver Value
The rapid evolution of technology, marked by key moments like the rise of the internet, cloud computing, and mobile platforms, has now brought us to the age of generative AI (GenAI). This transformation isn’t just a buzzword – it’s a profound shift that has the potential to completely redefine how businesses operate. But amidst all the hype, the question remains: How do you build AI products that not only showcase innovation but also deliver measurable business value?
In a recent webinar, Karen Chow, Chief Product Officer at Flowspace, shared her journey of building Flowspace AI, an AI-driven platform designed for operational excellence in the logistics and fulfilment industry. With over 20 years of experience across Apple, startups, and high-growth organizations, Karen’s story is a masterclass in product leadership during technological change. For leaders in Canada’s business ecosystem – whether in energy, public sector, logistics, or healthcare – her lessons are invaluable.
This article will unpack Karen’s insights, offering a blueprint for creating scalable AI solutions that work in real-world business contexts while delivering lasting value.
The Challenge: Driving Value in an AI-Powered World
Generative AI is a game-changer, but its potential can be hard to translate into practical applications. As Karen highlighted, technology alone isn’t a differentiator – it’s leadership that drives meaningful change. This requires a customer-centric mindset, a focus on real business problems, and the ability to navigate the complexities of new technology.
For Flowspace, the challenge was clear: How could AI be integrated into their fulfilment platform to offer advanced capabilities, such as real-time answers, customized workflows, and proactive decision-making? And crucially, how could this be done while ensuring that the solution added tangible value for their customers?
Key Lessons for Building Scalable AI Products
1. Start with Customer Empathy
Understanding your customers is the foundation of any successful AI initiative. Karen emphasized the need to go beyond what customers say they want and focus on their true pain points. For example:
- Different businesses (e.g., beauty brands, beverage companies) have unique operational challenges, from managing promotional surges to tracking inventory expiry dates.
- Customers often don’t need more data; they need faster, tailored decision-making capabilities.
By deeply empathizing with their customers, Flowspace identified that the true value of AI lay in its ability to deliver personalized, actionable insights – not just raw information.
2. Understand Your Technology
The best way to understand AI’s potential is to experiment. Early prototypes allowed Flowspace to explore how generative AI could transform their operations. For instance:
- An initial prototype used a large language model to query their database, but the results were inconsistent and unreliable.
- This revealed the need for tighter guardrails, better architecture, and robust prompt engineering to ensure the AI produced trustworthy outputs.
This iterative learning process is critical for any business adopting AI: experiment, identify limitations, and refine.
3. Prioritize Real-World Use Cases
One of the most challenging aspects of AI adoption is identifying the right problems to solve. Flowspace followed a structured approach:
- Criteria for Use Cases: They evaluated ideas based on feasibility, value, and effort, ensuring every initiative aligned with their business goals.
- Cross-functional Collaboration: By involving teams across the organization, they ensured alignment and surfaced pain points that AI could address.
For example, the AI-powered search and tagging capabilities in Flowspace AI were developed to solve operational problems like inventory management and order prioritization.
4. Ship Fast, Iterate Faster
In the world of AI, speed matters. Karen stressed the importance of launching minimum viable products (MVPs) to gather real-world feedback:
- Alpha Version: Focused on a narrow dataset and a single action (searching orders by date).
- Beta Version: Introduced expanded functionality, such as adding tags and managing inventory, based on user feedback.
These early versions not only validated the platform’s direction but also helped secure organizational buy-in by demonstrating tangible customer adoption and ROI.
5. Build Trust in AI
Trust is a cornerstone of successful AI adoption. Flowspace tackled this by:
- Improving Transparency: Their platform displayed how the AI interpreted queries, helping users understand the logic behind its answers.
- Educating Users: They introduced prompt libraries and user guides to reduce confusion and improve adoption.
This attention to trust and usability ensured that customers felt confident using the platform – a critical factor for scaling AI solutions.
6. Measure and Iterate
Post-launch monitoring is essential for improving AI products:
- Customer Feedback: Flowspace analyzed every query submitted to the AI, categorizing them to identify gaps and opportunities.
- Internal Adoption: Surprisingly, their own teams began using the platform to improve efficiency and reduce costs, showcasing AI’s versatility.
By embedding this feedback loop into their development process, Flowspace continuously refined their product, delivering more advanced capabilities over time.
Examples of AI-Driven Transformation at Flowspace
1. Context-Specific Workflows
Flowspace AI enabled customers to design workflows tailored to their unique challenges:
- Perishable Goods: A customer shipping temperature-sensitive products used the AI to quickly identify and prioritize shipments to regions experiencing heat waves, saving costs and improving customer satisfaction.
- Inventory Allocation: Another customer with limited inventory used the AI to prioritize existing subscribers over new ones, improving retention and maximizing ROI.
2. Internal Efficiency
Flowspace’s internal teams also adopted the AI, using it to reduce ticket volumes and improve customer service efficiency. This dual benefit – external and internal value – demonstrates the scalability of well-designed AI solutions.
Future Opportunities in AI: The Shift to Agentic Systems
As AI evolves, the focus is shifting from simple question-and-answer tools to systems that can take autonomous actions. Karen shared her excitement about agentic AI, which can proactively monitor workflows, flag urgent issues, and even interact with other systems to drive outcomes across fragmented ecosystems like logistics and supply chain management.
For Canadian organizations, this represents an opportunity to harness AI not just for operational efficiency but also for strategic differentiation.
Key Takeaways
- Start Small with Big Vision: Focus on foundational use cases that align with customer pain points and business objectives.
- Prototype and Learn: Early experimentation is critical to understanding AI’s capabilities and limitations.
- Engage Cross-Functional Teams: Collaboration ensures alignment and surfaces valuable insights.
- Build Trust Through Transparency: Show users how AI works to increase adoption and confidence.
- Measure What Matters: Use customer feedback and analytics to iterate quickly and refine your product.
- AI Isn’t Just External: Look for opportunities to use AI within your organization to improve efficiency and reduce costs.
- Prepare for Agentic AI: The next wave of AI will bring autonomous, multi-step systems that can transform complex industries like logistics and healthcare.
Conclusion
Building scalable AI products isn’t just about leveraging the latest technology – it’s about solving real problems, driving business outcomes, and creating lasting value. Karen Chow’s experience with Flowspace AI is a powerful reminder that customer empathy, iterative development, and thoughtful leadership are the cornerstones of success.
As Canadian businesses navigate their own AI journeys, these lessons provide a roadmap for harnessing AI’s potential to modernize operations, optimize processes, and maintain a competitive edge in an increasingly digital world. The future of AI is here – are you ready to embrace it?
Source: "AI Product Strategy Unveiled: Building Scalable Solutions That Actually Work" – How to Product, YouTube, Aug 29, 2025 – https://www.youtube.com/watch?v=z-_ItATpoYo
Use: Embedded for reference. Brief quotes used for commentary/review.
