Which technology is so disruptive, its economic impact is expected to equate to $15.7 trillion by 2030?
You guessed it — AI. This statistic, shared in a global AI study from PwC, reflects the technology’s potential to shape future business growth.
And as AI’s economic weight increases, its prevalence across the industry is growing, too. Many companies have ramped up their use of AI and machine learning specifically to optimize the product development process.
Upside is one such company. The organization’s AI team uses the technology to enhance developer productivity and velocity, which has ultimately bolstered its revenue and profit.
Meanwhile, at Yieldmo, the company’s engineers leverage AI and ML to offer a platform that delivers the best possible experience to customers. AI makes it easier for Yieldmo engineers to understand errors and debug their code.
For both companies, the use of AI and ML has played a critical role in driving business success. Read on to see how Upside and Yieldmo are leveraging AI and machine learning to enhance the product development process and pave the way for future progress.
Upside’s mobile app enables consumers to earn cash back on purchases at restaurants, grocery stores and gas stations.
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
Upside is integrating AI, specifically GitHub Copilot, into its product development process, focusing on enhancing developer productivity and velocity. Data has shown that our developers using Copilot are 56 percent more productive and complete tasks 36 percent faster than those not using the AI tool. For example, a project that would typically take 100 days can now be completed in 64 days with Copilot. These improvements have directly contributed to faster product delivery, which in turn positively impacts our revenue and profit.
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
To keep up with rapid AI and ML advancements, we allocate resources to internal research and development teams to explore and prototype new applications, ensuring we stay ahead of the competition and integrate the latest AI innovations for better user experiences and operational efficiencies. Additionally, we prioritize upskilling our teams through training sessions and demos and provide access to cutting-edge resources, keeping our teams up to date with the latest technologies and fostering a culture of continuous learning and innovation.
“We prioritize upskilling our teams through training sessions and demos and provide access to cutting-edge resources, keeping our teams up to date with the latest technologies and fostering a culture of continuous learning and innovation.”
Can you share some examples of how AI and ML have directly contributed to enhancing your product line or accelerating time-to-market?
One example of how AI is poised to enhance Upside’s product line is our ongoing work on AI-powered semantic search for our app. We’re focused on significantly improving the user experience by shifting from simple keyword matching to understanding the intent behind a user’s search. This AI-powered search will allow users to enter natural language queries, such as, “I want a salad, but my kids want pizza,” and receive results that match the meaning and context of their requests. It will support multiple languages, handle spelling errors and interpret user needs with high accuracy.
We anticipate this improvement will directly impact user engagement by helping people find what they need more effectively, increasing offer claim rates and reducing frustration and churn. Once launched, this will be Upside’s first AI-powered feature that directly benefits the end user and will help accelerate time to market by leveraging vector databases and AI embedding models for faster, more relevant search results.
Yieldmo’s programmatic advertising platform is designed to help brands improve the digital ad experience, offering bespoke ad formats, privacy-safe inventory curation and more.
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
Our teams lean on AI throughout the development process whenever it’s possible. Some examples of how team members do this include the use of automated unit tests generation and code autocomplete in integrated development environments. Engineers also leverage AI to understand errors and debug their code.
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
When we interface with an outside service to support our AI and ML needs, we ensure that it goes through an abstraction layer, which supports the ability to configure one to multiple providers at any given time. This affords us the flexibility to easily explore and evaluate new offerings without having to overhaul core portions of the codebase.
“When we interface with an outside service to support our AI/ML needs, we ensure that it goes through an abstraction layer, which supports the ability to configure one to multiple providers at any given time.”
Can you share some examples of how AI and ML have directly contributed to enhancing your product line or accelerating time-to-market?
Science underpins everything we do at Yieldmo. We use AI and ML to curate the optimal supply to meet advertisers’ objectives and tailor ads to ensure end users get an ad experience that’s motivating and respectful. This creates a win-win situation on behalf of both the advertisers and end users. Our head of data science just appeared onstage at a Snowflake World Tour event to share a case study of how we deployed 10 times faster at half the cost, so our systems are always improving.
