Writer and tech enthusiast Shauli Zacks interviewed Gravitate's Managing Director for the SafetyDetectives.com audience. We've recapped a few of Qiuyan's key points here, or you can visit their site to read the entire interview.
✅ How companies can measure the ROI on AI investments
✅ AI expertise to keep in mind for startups just getting started
✅ How Gravitate manages design, development and putting AI-powered products and processes into production for our customers.
How AI is transforming specific industries like FinTech, RetailTech, and healthcare
AI can be a discovery tool so professionals can more quickly explore new concepts and find results. Any industry will have more efficient services and products as AI becomes a necessity.
In my opinion, there’s different levels of transformation. On one level there are a lot of key breakthroughs enabling something innovative that was not possible before.
A different level of transformation is similar to when the internet or smartphone applications started, where there were select, smaller groups who were able to use them but eventually it became a necessary utility completely integrated into everyone’s daily lives. Similarly, I think AI and ML will become a common necessity across all industries where it transitions from “it’s good to have” to “you need to have” for more efficient services and products.
How companies can measure the ROI on AI investments
There are several questions still open about how to best test and measure for AI impact, which makes measuring the ROI of AI a little tricky.
Product-oriented AI components require research and analysis to measure product performance, which is normally already built into the product itself. Determining a long-term ROI for the AI as a stand-alone value is more challenging.
My recommended approach is to make sure you have a long-term vision with a clearly defined, ultimate goal based on a very milestone oriented, tangible roadmap for AI development with smaller goals to achieve. Instead of spending a multi-million dollar investment on what you think is going to happen five years later, it’s about breaking down that investment using the agile product approach, applying AI, and being able to make it more tangible and measurable.
AI expertise to keep in mind for startups just getting started
Step 1: Know the user problem the AI will be working to solve.
Streamline internal processes to be more efficient
Create more user friendly applications
You can spend tons of money to do very complicated AI applications or sometimes you can just call Open AI and then wrap stuff around. But that all depends on the stage of the startup, how to prioritize development along with other business and product needs, and how to optimize a budget allocation while making sure the roadmap not only hits short-term goals, but also keeps the long term in mind so they don’t waste time and have to completely rebuild something later.
Step 2: Analyze the vision for the AI use and whether it makes sense to build it up as a key differentiator or whether using out-of-the-box is a better option.
From a strategic perspective, startup founders should think about if that AI component is going to be part of their core IP. There are situations when it’s not necessarily the case. They might have a product built for an industry that solely relies on other features where AI only plays a supporting role to make things a little better.
To learn more about the build verses buy decision startup founders face, request the new Generative AI Quick Reference Guide.
How Gravitate AI manages design & development for AI-powered products
Startups can be in different stages of AI development, for some businesses, it's important to start with microservices so their software platform can quickly return results form AI algorithms. In other cases, the company has not yet organized the data collection process and a Gravitate team is tasked with developing a data pipeline.
We specialize in solution architectures, detail-oriented model building, ML operation, and the modern AI technical stack, including LLM, NLP, image analysis using deep learning models, and their deployment in scalable systems.
Full service AI shop – from gathering initial requirements to maintaining a customer’s AI applications, most of which are a part of their software. So we create a microservice for their software platforms (whether web or mobile applications) that can directly call these AI services and return the results through the AI algorithms.
Data engineering - Part of this is also building the data pipeline and accumulating data assets because the majority of AI algorithms must be customized and trained on industry or business specific data and that’s a big component. We also ensure the data components (the pipelines, the processes) are designed correctly and are scalable and robust, which is crucial.
Gain AI momentum with the right product opportunity
Learn about the different options startups have for building AI-powered products with Gravitate AI as an experienced partner. You can also get ideas for different types of AI applications on our Solutions page.