Inbal Shani, CPO of Twilio, chats about the impact of LLMs and AI models on product and software development, future trends in AI and customer engagement and more about technology innovation at Twilio in the following Q&A: 

———-

Hi Inbal, please talk about key milestones in your journey as a tech leader that shaped your approach to leading large-scale, complex technology initiatives.

Throughout my career, I’ve held leadership roles at GitHub, AWS, Microsoft, and TomTom, gaining deep expertise in designing and delivering cutting-edge technology products. I began as an applied scientist in the aerospace industry and earned a master’s degree in mechanical engineering, where I specialized in using genetic algorithms to tune MIMO control systems, an early form of machine learning. My work has spanned self-driving cars, robotics, and Amazon devices, including Alexa, as well as leading the development of conversational AI and chatbots for support and sales. At AWS, I led container services for AWS native solutions, and as Chief Product Officer at GitHub, I drove platform expansion, advanced security, and the launch of Copilot. Now at Twilio, I’m leveraging these experiences to create innovative solutions for developers and customers alike.

Also Listen: AI Inspired Series by AiThority.com: Featuring Bradley Jenkins, Intel’s EMEA lead for AI PC & ISV strategies

How have you seen LLMs and AI models evolve, and what impact do these advancements have on your approach to product and software development?

LLMs and AI models have evolved rapidly, and one of the most meaningful impacts of these advancements is the lowering of barriers to entry, making AI accessible to a broader audience for productivity and automation. We see this reflected in everything from conversational agents to developer productivity tools, fundamentally transforming how people interact with technology.

However, as powerful as LLMs are, it’s important to recognize that they are tools, not cure-alls. While they can greatly enhance productivity, they are most effective when used with intention and clarity of purpose. For developers, these tools help by reducing friction in repetitive tasks like code completion, test automation, and documentation generation. AI-powered security features also enhance development, allowing teams to focus on system thinking, system design, and creating higher-quality deliverables.

Ultimately, the true impact of these AI advancements is that they allow developers to focus more on solving customer problems holistically, improving not just their efficiency but also the quality and innovation in the products they deliver.

You’re passionate about enhancing the developer experience (DevEx). What are the top strategies or tools you’ve implemented at Twilio to optimize DevEx and boost productivity?

At Twilio, enhancing the developer experience (DevEx) is one of our top priorities. We’re on the verge of a significant shift as developers—whom I often refer to as builders—and software engineers adopt systems thinking at an unprecedented scale. This mindset is crucial when designing, building, and managing large, complex systems, where the interactions between components and subsystems are key to the overall success of the product.

What differentiates leaders from other builders is an interdisciplinary approach, one that views problems holistically and connects insights across various domains to create innovative solutions. At Twilio, one of our core strategies is staying deeply connected to our customers, a practice we call ‘wearing the customer’s shoes.’ By engaging directly with our users, we ensure that the solutions we build aren’t just technically sound but also address the real-world challenges our customers face.

Our focus on DevEx begins with understanding the customer, both externally and internally. For external customers, we prioritize reducing friction in product adoption, shortening time-to-value, and maximizing ROI from our products. Internally, we aim to optimize the tools and processes for developers at Twilio, enhancing their productivity and creativity. This not only shortens the time to innovation but also unlocks safe innovation at scale.

Here are some of the key tools and strategies we’ve implemented to optimize DevEx:

1. Automation for productivity: We’ve introduced tools like code completion, test automation, and AI-driven documentation generation, which allow developers to focus on high-level system design and innovation.
2. DevEx feedback loops: Regularly gathering feedback from our developers helps us continuously improve our platform, from enhancing APIs to refining onboarding processes and developer tools.

By focusing on systems thinking and customer-centric development, we’re able to deliver unique, creative solutions while boosting developer productivity and driving innovation at Twilio.

The recent implementation of Twilio Flex for Polaris showcases its impact in critical areas. Highlight lessons learned from this project that could be applied to other industries or sectors.

 The future of customer engagement relies on leveraging contextual data to deliver the right information through the right channels, with AI acting as an accelerant. When organizations build a platform that simplifies access to this information, it results in a seamless experience and happier customers. For organizations like Polaris, the stakes are even higher—ensuring volunteers have immediate access to the right information is critical, as seconds and minutes can make a difference in the safety of a victim.

Twilio Flex enabled Polaris to significantly reduce wait times by unifying communication channels into a single platform with an intuitive interface that quickly provides the necessary data. This level of efficiency and integration empowers agents to act swiftly and decisively, improving outcomes in life-or-death situations.

A unified platform, like the one Polaris developed, can be applied across industries to improve customer experiences. With easy navigation and relevant customer information displayed at the right time, agents can gather the insights they need to deliver more personalized, effective interactions.

The recent addition of Personalized Flex using a unified profile demonstrates how customer engagement is becoming increasingly personalized and relevant, significantly improving both the customer and user experience. By leveraging a unified profile, businesses can provide tailored interactions across all channels, ensuring that each communication is contextually aware and aligned with the customer’s unique needs. This level of personalization not only enhances satisfaction but also drives deeper connections between the brand and its customers, ultimately leading to more meaningful and effective engagements.

Also Read: AiThority Interview with Brian Gumbel, President and Chief Operations Officer at Dataminr

In today’s communication landscape, where the quality of messaging channels is declining, the solution isn’t simply more communication, but more personalized, contextual communication that resonates with customers.

Share with us about the top five emerging trends in AI and customer engagement that you believe will shape the future of the industry.

• Customer engagement will be one of the first use cases where AI investments will drive tangible returns. As businesses in every industry are rushing to capitalize on endless opportunities in AI, they are struggling to realize and measure true ROI. AI can be incredibly powerful, but it has to be fed with the right data and context for this to drive good outcomes. Digital engagement is the one area already experiencing noticeable ROI. According to our State of Customer Engagement Report, brands that are using AI to deliver personalized customer communications are seeing higher satisfaction scores (45%) as well as improved market segmentation and targeting (41%).

• The software developers will be transformed from SDEs to builders as a result of AI. This means we’ll witness the rise of more systems thinking as the best builders adapt to navigate ambiguity and influence strategic change. AI will become a new coding language for developers, like the introduction of new coding languages throughout the years, making it a force multiplier and not a replacement.

• Trust and transparency will be table stakes for organizations using AI. Both will be a requirement in order to build and maintain customer relationships. In fact, 49% of consumers say they would trust a brand more if it disclosed how their data was used in AI-powered interactions and 6 in 10 say protecting their data is the top way for brands to earn their trust, according to a recent Twilio report.

• Developers will prefer a more integrated approach to their development environments. Developers now expect other teams, such as product and marketing, to make the shift and adapt to these integrated environments. However as we continue to embrace new technologies, organizations must address the potential skills blind spots that arise as new tools and methodologies are adopted. Continuous learning and upskilling will be necessary to ensure that developers and teams can effectively leverage these integrated environments and automation tools.

• AI will be used as a tool to fight fraud. Consumers are on the receiving end of an increasingly annoying barrage of spam and fraudulent communications. While AI is introducing additional complexities, it is also a critical tool in fighting fraud. We have to rebuild trust in the ecosystem, a resource that is going to become increasingly precious as AI becomes more ubiquitous.

What are some of the biggest challenges you’ve faced in driving technology innovation at Twilio, and how have you addressed them?

Twilio has always excelled at abstracting away complexity for our customers. Digital engagement is an increasingly complex ecosystem, with businesses struggling to navigate the proliferation of channels, evolving regulations, and rising consumer expectations. My focus is to build on Twilio’s legacy of simplification, ensuring that we keep a seamless customer experience at the core of everything we do. By continuing to simplify these complexities, we empower businesses to focus on what truly matters—delivering exceptional, personalized engagement to their customers without the friction of navigating a complicated digital landscape.

Before we wrap up, share a few thoughts on the future of AI in the SaaS and telecommunications industries.

AI is an incredibly powerful tool that will accelerate innovation across these industries, but it’s not a replacement for human ingenuity. Leaders need to focus on identifying where AI can make the strongest impact, rather than adopting it simply for the sake of using AI. Those who take a thoughtful approach during these early stages—carefully considering where AI can be effectively implemented—will position themselves ahead of the curve as AI continues to evolve.

Secondly, don’t fear change—embrace it responsibly. Encourage your developers to leverage the benefits of AI, but don’t just ask them what your AI strategy is. Instead, ask, how can we innovate faster? How can we improve the customer experience? What doors can AI unlock that will allow us to create more value for our customers? Shifting the focus to these questions will help guide your team toward meaningful, AI-driven advancements while keeping customer needs at the center.

Thank you, Inbal, for sharing your insights with us.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]