Empowering Developers Is Critical to Drive AI Innovation

Artificial intelligence and emerging technologies are moving at a blistering pace and are transforming work, life, and how people interact with the world. With the global market for AI amounting to nearly $200 billion in 2023 and projected to increase at an annual growth rate of nearly 40% from 2023 to 2030, businesses are keen to leverage AI quickly and see a return on their AI investments. Organizations face fierce competition to innovate, and developers are the tip of the spear, building the models and applications that will impact every industry. Improving customer relationships is fertile ground for this.

According to Twilio’s latest State of Customer Engagement Report, customer experience is the first place we’ll see AI returns — brands using AI to deliver personalized customer communications are seeing higher satisfaction scores (45%) and improved market segmentation and targeting (41%). The upside is enormous, and developers have never been better equipped to build smart solutions. So, where to start?

Understand the Problem Before Deploying AI

As customers’ expectations and preferences evolve, developers can continue shaping the building blocks that help organizations maximize the impact of AI.

Before getting to the technical part of problem-solving, developers must understand the more significant problems they are solving, why they matter to the customer, and the range of possible solutions. This starts with ensuring developers regularly use the product to ensure they’re comfortable and familiar with the customer’s pain points. It’s also essential to ensure developers are part of the conversations with customers so they have direct insight into their problems and concerns. This allows developers to be creative, as they can “wear the customer’s shoes,” and solve problems first-hand. When developers understand the problems they are trying to solve and the tools they are given, they can work to build creative solutions.

We can get to the building once the problem is deeply understood. Thanks to LLMs and generative AI, building a minimum viable product (MVP) of an AI-powered feature is easier than ever. Gone are the days of collecting massive amounts of data first, assembling a team of ML engineers, and having long training cycles to nail your model before getting in front of your first customers. Today, you can leverage prompt engineering, some basic fine-tuning, and your full-stack developer team to get an MVP out in the world to validate how well it solves a problem for your customer before you invest more heavily. Bringing developers into this cycle means you can use customer challenges as a north star for innovation, brainstorm creative solutions, and then rapidly test and iterate to roll out the most effective fix.

One example that comes to mind is Learnfully, where leaders empower their developers by giving them a sense of autonomy and ownership of their work. As a result, Learnfully developers can develop empathy for customers and better understand how to solve their problems.

Transparency and Flexibility Are Crucial to Sustaining Developer Innovation

AI is evolving at a pace like no other technology. Every day, new advances, models, and papers are published, all of which broaden the definition of what is possible. At this pace, a traditional software development cycle might result in missed opportunities. Organizing your team to prioritize transparency, flexibility, and curiosity will set them up to build and innovate successfully.

There are a few simple ways to do this:

1. Practice flexibility. Experiment and don’t be rigid, especially when it comes to models. Don’t be tied to them, as they can be outdated. Flexibility in the ideation and development stages is especially important, as customers’ expectations can quickly invalidate early prototypes due to the pace of innovation. Transparency is also crucial in the early stages of building a product, especially for teams focused on building concrete prototypes for customers to engage with. As builders, developers should be able to provide feedback on prototypes early and often to ensure teams across the organization are clear on the expectations and capabilities of AI models during the product development process.
2. Find naturally curious developers and foster their curiosity. At Twilio, we interview engineering candidates with curiosity, focusing on how they like solving problems and what they’ve learned recently. Once in the role, we give our developers room to explore new technologies to help identify what people are passionate about and how we can naturally lift them in the workplace.
3. Give developers room in your sprints to explore, share, and learn. Establish channels where they can regularly share things they are learning, such as recent AI news and upcoming developer meetups. An interesting example of creating opportunities to learn from developers inside and outside your company: Twilio launched the Twilio Alpha program to give customers an early look into future innovation while separating exploration from the standard product portfolio. This allows developers to explore, share, and learn about emerging technologies and trends.

Supercharging Developer Innovation

Building a culture of sharing and transparency boosts innovation for developers, and empowers them to own and creatively solve customer problems rather than just producing solutions. To fully take advantage of AI and other emerging technologies, developers should work backward from the customer to build innovative solutions that help brands solve common customer engagement problems and connect more effectively with their customers.