I’m learning more about building products infused with AI. What does an AI product mean and how building it is different from building a regular product? You can differentiate an AI product vs a traditional product through the following criteria:
AI product starts with existing technology
Traditional product development cycle starts with understanding a specific customer or business problem. Once the problem is identified then Product, Design and Eng work together to build a solution that satisfies that problem. However contrary to traditional product an AI product starts with available technology and it’s Product Manager job to find use cases solving or improving customer or a business problem using that technology. I heard from a PM who worked at Google that for a long time they had voice-to-text technology but didn’t know what to do with it. They iterated quite a bit from trying search by voice in browser to other features until they developed Google Home, a smart speaker designed to respond to voice commands. Though voice-to-text is not much used in browser because of easy access to keyboard, voice control is now an integral tool in our everyday tasks to search on TV or interact with smart devices.
AI is here to make the solution smarter
While a traditional product provides solution to address pain points, AI use cases are usually applied on top of it to make that solution “smarter”. This mean looking at patterns and trends within the existing data to to either optimize the solution, provide recommendation and insights or make the solution customized at scale. I use advertising industry where I work to provide an example of each use case.
I work at a digital advertising platform that helps brands and agencies run advertising campaigns across internet. In advertising ecosystem content providers (such as New York Times, Netfelix, Walmart) initiate a bid request for ad placeholders on their website, videos, podcasts etc while companies like mine responds to those bid requests to place a variety of ads in those inventories:
- Optimization: Advertisers have a budget to spend for campaigns and along with it a set of objectives and key performance indicators, such as maximizing conversions, clicks, or visibility. They want to ensure they maximize return on marketing dollars while achieving these goals. It’s the system responsibility to determine the most effective bid amount to maximize the chance of wining without overpaying. The system analyzes data such as historical performance data and user behaviour among others data types and uses machine learning algorithms to predict bid outcomes and make real-time adjustments based on this data and campaign goals.
- Recommendation: One of the main goal of advertising is to reach the right people looking for the solutions/services advertisers offer. Advertisers have specific criteria on who to target based on geographic location, demographics, user’s action etc. To expand their customer reach, advertisers look for recommendations on other groups of people to target. The system use the characteristics, behaviours, and interests with an existing audience and use them to identify and recommend new users who closely resemble the seed audience based on these characteristics.
- Personalization: Personalization has become increasingly important in advertising because personalized ads increases consumer engagement, brand loyalty, and overall marketing effectiveness. Machine learning solutions can be used to create various versions of a creative and messages based on user’s data and past behaviours in mass scales.
AI solutions have existed for a long time without being specifically called AI, while generative AI solutions exploded more recently with introduction of chat GPT and other large language models.
Many AI solutions have minimal User Interface
While traditional products have strong element of UI where the user gets to interact with it to derive the value, AI solutions usually don’t offer much of a UI. While massive amount of data and models goes into building and generating optimization, recommendation or personalization services they’re usually behind the scenes and is transparent to the user. AI recommendation Generative AI interaction is conversational text based and is mostly done through the prompts, voice though the field is quickly evolving and we may witness more interaction.
Having above categories have helped me to quickly identify if I’m dealing with a traditional product or if the product is AI based. I hope it helps you too.
