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The framework for building great AI products

The framework for building great AI products
Liran Hason Liran Hason 7 min read Sep 21, 2023

In the first installation of our guide to building great AI products, we discussed the challenges of deploying models to production and shifting your mindset from ML models to AI products. In this guide, we’ll break down the framework for putting that shift in mindset into practice. 

What makes a great AI product

Building an effective AI product is not just about integrating the latest algorithms or implementing the newest tech, but rather creating a product that aligns with business goals, performs efficiently, and upholds regulatory and ethical standards. Let’s look at the components that contribute to a great AI product:


At the heart of a successful AI product lies a strong alignment with business objectives. This business-driven approach ensures that rapid deployment, agility, and cost efficiency are front and center. By reducing time to market, an AI product can align swiftly with evolving market needs, outpacing competitors, and accommodating shifts in customer demands. A relentless focus on cost-effectiveness not only drives ROI but uncovers new revenue streams, and helps save on cloud spend to ensure the product is economical. But it doesn’t stop there; bridging the gap between data science teams and business goals guarantees that the AI product is in sync with the organization’s larger mission.

  • Rapid time to market
  • Agile
  • Cost-effective
  • Data science-business alignment

High Performing

When it comes to performance, visibility is crucial, being able to track both data science and business metrics enhances understanding of needs and resources across your organization. Monitoring tools help in detecting model drift, ensuring accuracy and relevance. Preventing the risks of AI hallucinations, through rigorous validation and continuous oversight, preserves the product’s integrity, and prompt root cause analysis ensures a smooth user experience, ensuring low TTR (Time To Repair). 

  • Visibility
  • Monitoring
  • Control
  • Root cause analysis


An AI product must also be developed and deployed responsibly. This means maintaining AI integrity through consistency, transparency, accountability, and reliability. To ensure the fairness of your AI product, implement measures to monitor and minimize bias, promoting equal and ethical outcomes. Data security and regulatory compliance further safeguard your product’s integrity, transparency, and trust. Together, these elements converge to create an AI product that’s not just technically sound, but business-savvy, high-performing, and ethically responsible.

  • AI Integrity
  • Bias & Fairness
  • Security & Compliance

The framework: 7 principles for building great AI products


It’s not about YOU. AI Products are built for the benefit of customers, whether internal or external. To strive for success, organizations must understand their users, their challenges, and what the AI product will do for them. In the end, it’s not just about how the model works behind the scenes, but if it’s driving value for the end users. 


We can always be better. Data science teams consistently strive to achieve better results, but it’s not just about tweaking the model for higher accuracy. What is the goal? And what result will provide our users with the most value?

The different goals need to be defined, iterated, tracked, and improved to ensure all machine learning and business stakeholders understand their responsibility and contribution to the end product. Clear goals assist in prioritization, facilitate better collaboration, and ensure you’re on the right track to success.


It’s crucial to envision the world with your AI in it. What does it look like? Where is it on the screen? What’s the first touchpoint with the user?

Draw it out and bring your AI product to life with visualization tools to gain new perspectives of the look and feel of your product. Is it a web application for internal users? Is it a REST API endpoint that another service pulls from? If so, what does that REST API look like? List the endpoints, define the parameters, talk about risks, and visualize model interaction. 

Pro Tip: Use visualization tools like Excalidraw to mock up your vision


Different people in the organization have different definitions of success. It’s vital to get all stakeholders aligned on the goals and metrics for their AI Product. Next on the to-do list, write everything down and share it across the organization.

This will act as a single source of truth for perfecting your AI product. Remember to use common language so everyone in the organization can understand – While data scientists celebrate a high NDCG score, it won’t mean much to most product managers and business leaders. 

Ask Questions

Assume nothing. Instead, make sure to ask questions that extract objective facts and not subjective opinions. This will flush out any confusion about the goals while elevating the discussion about how to perfect the product for the end users. What do your users care about the most? What’s their current workflow? How will they work with your AI Product?

You don’t want to waste valuable time and resources on a project that people think is just “nice”. You need questions that raise real concerns to drive objective facts that lead to a product people will actually use and benefit from.

Pro Tip: “The Mom Test” is an excellent book that shows how to get past the pleasantries and subjective feelings, and drill down to the core essentials to find the real pain points.


Be data-driven. We talked about setting goals and aligning stakeholders, but how do you measure the success of our AI Product? By measuring everything. It’s time to define, track and monitor: Usage metrics, Business KPIs, Data Science metrics, etc. These metrics will tell if you’ve moved the needle, and help quickly derive the next steps for your AI product.

Once the key metrics have been measured, you should share them across your organization and make relevant stakeholders accountable for each action item that comes up. This will help create a coherent overview of the end goal and provide progress updates for business leaders.


If a tree falls in the forest, and no one is around, does it still make a sound? Similarly, you can build an awesome model, but if it’s just chilling in your S3 bucket – have you really made an impact? This is why communication is key to getting a great AI product off the ground. 

Questions and outcomes should constantly be shared between relevant stakeholders. This way there are fewer to no surprises along the way.

Pro Tip: Schedule a weekly or quarterly meeting with all relevant stakeholders to discuss the progress of the AI product – ask questions, raise concerns, and communicate facts.

Final thoughts

To ensure that your AI efforts drive real-world impact — creating a successful AI product requires a holistic approach that considers the business goals, the target audience, and the collaboration of different stakeholders, alongside some badass algorithms. By breaking down silos and working together, you can turn your machine-learning engines into valuable AI products that drive real-world results.

Whether you’re approaching a new project, or already in the middle of one, it’s definitely worthwhile to gather all relevant stakeholders, open the framework, and discuss the objectives, metrics, and measurements to make sure that everyone is aligned on the goals. It’s time to move beyond traditional data science-centric approaches and embrace a more integrated, product-driven approach.

Reach out to us with any questions about building great AI products.

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