Data-driven AI as the future of product innovation

Joe Reeve, Engineering Manager at digital analytics platform Amplitude, discusses the future of data-driven AI and the impact on product innovation

There’s no doubt that AI is all anyone’s talking about right now. The technology is predicted to create over £400 billion for the UK economy by 2030, according to a Google UK Economic Impact report. 

Calling the impact of AI on how we build, iterate on, and gather feedback from apps and other digital products is “astounding”, Engineering Manager at Amplitude Joe Reeve, explained how the best companies know that ​​the key to building better digital products and experiences is user behaviour data. 

“Every company is looking to create that Netflix-level personalised experience, but many of them don’t have the resources or the time to do so,” he said. “And AI will not only improve customer experiences, it will improve the lives and productivity of the engineers and product managers who are working on bringing AI-powered digital experiences to life.”

Here, Reeve explains how.

AI and LLMs as an engineering time saver

Engineers around the world have already begun to harness Large Language Models (LLMs) like ChatGPT and Github Copilot to take on bits and pieces of the coding process. Automating parts of the coding process can improve efficiency across engineering teams, while freeing up engineers to work on more complex or business-specific code that helps power product innovation. One area where AI has helped make engineers better in the coding process is forcing people to be good at naming the code they’re writing. Naming things adequately in programming can be hard, but by using a more descriptive name it helps tools like Github Copilot improve its understanding of what needs to go into the code.

Another interesting use case is around APIs and integrations. Engineers often write what’s known as ‘glue code’. This is code that integrates multiple APIs and applications together in a relatively simple way. This code is often repetitive, simple, and pulls heavily from public documentation, making it very easy for LLMs to learn it. With LLMs, engineers can teach the models how to update code for the API integration — when X happens, do Y — which can then be automated for future integrations. 

Training models to understand and generate code isn’t a perfect science yet, but we’ve already seen the benefits of how it can save critical time, eliminate common or duplicative coding tasks, and drive real-time innovation.

Using AI to achieve next-gen personalisation

AI won’t just impact how engineers build products, it will also impact how they iterate. One crucial way in which AI can improve a product is personalisation. Salesforce research shows 66% of customers expect brands to understand their wants and needs. Today, strategic personalisation has become a driving force behind customer engagement and loyalty.

This means we’re entering a phase where your apps should learn you, not the other way around. And the success of this ability — and the success of personalisation in general — depends on one key thing: data quality. You’ve heard the phrase ‘garbage in, garbage out’. It’s the same for AI models. If companies do not have good data governance practices in place, the model will not be accurate, and the customer experience will suffer. The best way to tune AI models – down to the specific user — is through user behaviour data. This creates an essential customer feedback loop, made more powerful than ever by AI. The loop looks like this: a company will use behavioural data insights to inform its AI models, leading to more accurate AI models and subsequently improved personalisation. Ideally, this will increase customer usage of your product. With this increased usage, you can start the loop again with more data to improve your models continually. If done successfully, this iteration process can become a critical competitive advantage for your company. 

Now of course, when it comes to AI models, it’s not enough just to have high-fidelity data, you also need a lot of it. If you don’t have enough data for a model to make predictions off of, it won’t be able to make accurate recommendations. This is where engineering and product teams need to work together to align on which parts of a product are suitable for recommendations as part of the product development process. If building AI-friendly software isn’t built into the team’s process, the appropriate data might not be collected up front, or surfacing intelligence will require reworking the user experience. I’m extremely excited about making the difficult parts of this process transparent for product teams, so they can make their product intelligent without needing deep AI expertise.

What’s next?

For app builders, we’re in exciting times. Digital products transformed the way users interacted with products, with other users, and with companies themselves. Now we’re moving into the age of intelligent products, where AI won’t be solely a feature, but something that is ingrained throughout a product, making the customer experience that much more valuable. While AI will impact every part of the product-building process, we’re still in the early days. The teams that take a proactive look at how AI can improve their current processes and harness the power of their data will ultimately win over customers and win their market. 

Joe Reeve is an Engineering Manager at Amplitude. He has built and launched digital products for enterprises, startups, and charities, and enterprises for almost a decade and serves as a consultant. He is the founder of Gived, an R&D design and development agency, focused on projects that improve the world.


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