If AI is leveraged to simply generate revenue or cut costs in an organization, then it’s taking the wrong direction in a journey that’s meant to offer more…
True to the hype, AI is a powerful tool that can drive tangible returns when businesses approach it with a customer-first mindset. Twilio’s State of Customer Engagement Report 2025 found that implementing AI-powered agents is a top priority (57%) among APAC businesses this year.
Yet, while the current buzz around AI may lead organizational leaders to view it solely as a direct revenue generator, a more strategic approach would be to identify where AI can make the strongest impact and then to use it as a means to an end (in this case, revenue generation).
In this interview, Inbal Shani, CPO & Head of R&D, Twilio, offers deep insights into the role AI plays in an organization’s reimagination of customer engagement, software development, and enterprise productivity.
With the proliferation of AI hype and hope, should organizations view/adopt AI as a revenue generator? Why or why not?
Inbal: This is an important conversation I have with customers and other technology leaders all the time. True, long-term success using AI stems from enhancing the customer journey and success, a metric that extends far beyond immediate revenue generation.
AI’s true value lies in strategically addressing business challenges and unlocking opportunities for higher customer success. My advice to customers: don’t chase creating customer-facing AI agents if building AI to power your true differentiation will be more effective in helping customers.
Rather than asking “How do I monetize AI?”, leaders should focus on the added value that AI brings to strengthen core business functions – such as automation, enhancing productivity, removing friction, or creating a better teammate or customer experience – all for the purpose of customer success.
For developers and other builders, AI can be a force multiplier that helps them do more with greater impact and efficiency. For customer support teams, AI-powered conversational chatbots can efficiently resolve many issues for the customer, seamlessly escalating complex or sensitive cases to human agents who are equipped with data-driven insights to provide personalized and effective assistance.
Ultimately, AI can be a transformative tool for innovation and productivity, but implementing AI requires a focus on understanding customer needs, responsible use, and alignment with overarching business goals to deliver meaningful value and ROI. By focusing on building value through intelligent systems and infrastructure around what truly differentiates their business, organizations establish a stronger foundation for sustainable growth.
What are some significant shifts in how AI is being fundamentally integrated into software and communication platforms today, especially in Asia Pacific?
Inbal: The integration of AI into software and communication platforms reflects both global trends and distinct regional nuances.
Notably, a Microsoft report indicates that the Asia Pacific region is ahead of the curve in embracing AI collaboration, with 52% of employees already viewing AI as a thought partner, compared to just 47% who see it as a command-based tool. This accelerated adoption is likely due to the region’s high digital maturity, mobile-first economies, and strong appetite for innovation-driven productivity.
Hyper-personalized customer experiences: One significant shift we’re witnessing is the move from AI as a backend optimization tool to being a more embedded, user-facing capability, directly transforming customer engagements into ongoing conversations and lasting relationships.
Of those brands building their businesses on top of the Twilio platform, we’re seeing the integration of generative AI in customer service platforms like chatbots and virtual agents especially across the sectors of banking, e-commerce, retail, and telco. The use cases vary based on outbound or inbound engagement needs. In e-commerce, for example, this could involve AI chatbots assisting with tracking orders or personalizing product recommendations.
In Asia Pacific (APAC), generative AI’s strength in customer engagement lies in its ability to meet diverse localization needs, enabling hyperlocal, multilingual, and around-the-clock services across the region’s multilingual and culturally diverse landscape. Underscoring this adoption, APAC leads globally with more than half (53%) of its leaders already leveraging AI agents to fully automate business workstreams like marketing and finance, 7% above the global average.
AI copilots in enterprise productivity: Another major trend is the rise of AI copilots within enterprise software and productivity tools. According to BCG’s 2025 report, APAC’s GenAI adoption is now second only to North America, with over 80% adoption of cloud-integrated copilots in the tech sector. These intelligent assistants empower businesses for tasks like drafting documents and analyzing data, significantly boosting efficiency.
This shouldn’t just be about drafting documents faster; the true value comes from enabling more data-driven decision-making across all levels of an organization.
AI also gives builders the time and space to focus on higher-value problems. In today’s age, this means the space to learn and practice systems thinking, in particular. In this engineering approach, product leaders and engineers take a step back to see the bigger picture of the business and system of technology, therefore understanding how tools, data and interactions fit together to drive better outcomes for customers. AI gives builders the chance to be more strategic teammates and, eventually, leaders.
Today’s broad interest in AI collaboration signals a transformative shift: businesses are moving from simple automation and are integrating AI to enhance business productivity and hyper-personalized customer experiences. The next shift I’d like to see is around R&D teammates adapting their skills to become more strategic thinkers.
How should AI be incorporated into DevOps and AppDev processes to ensure that it is genuinely addressing customer pain points?
Inbal: Incorporating AI into DevOps and AppDev processes should begin with a clear focus: enabling, not replacing, a company’s broader business strategy and differentiation.
AI is most impactful when it supports builders with intelligent assistance, streamlines deployment and maintenance, and frees teams to focus on solving more impactful customer problems. Accordingly, success hinges on access to high-quality, usable data that the AI uses to make suggestions.
Without contextual data, AI solutions risk missing the mark when it comes to addressing real customer pain points. Rather than chasing more data, organizations should invest in making their existing data actionable and trustworthy.
Integrating AI early in the development lifecycle can provide predictive insights, automate routine tasks, and surface customer-centric opportunities faster. As capabilities evolve, organizations that thoughtfully embed AI in their DevOps workflows will be better positioned to innovate quickly, reduce friction, and ultimately deliver experiences that resonate with customers’ true needs.
Today’s greatest innovators are not just applying AI – they are aligning it closely with both human expertise and customer insight. A key differentiator is the ability to “remember” – leveraging contextual data to maintain continuity across interactions, whether handled by agentic AI or a human agent. Seamlessly passing this memory back and forth ensures every engagement with the customer feels connected, relevant, and personalized.
Trade Me, New Zealand’s largest online auction website, uses AI predictions within its customer data platform to anticipate buyer and seller behaviors. By empowering marketers to run targeted campaigns without needing dedicated data science support, the online marketplace saw a 10% uplift in click-through rates and a 20% increase in open rates.
This demonstrates how AI-embedded workflows, supported by contextual data, can directly translate into better customer value and experiences.
Basic personalization has some flaws that translate into bad customer experience. How should an organization incorporate hyper-personalization to create truly context-aware and relevant customer experiences?
Inbal: Hyper-personalization should begin with a deep understanding of customers as individuals on a journey with any given brand. To achieve this, the business must understand the relationships between the customer’s engagement data, rather than observing them as isolated points. Brands also must keep an eye on building and maintaining trust with their customers at all times. I’ll explain further.
Basic personalization, like using a customer’s name or past purchase, often falls short of real value because it lacks context and the ability to acknowledge the customer’s entire journey across channels.
To move beyond this, organizations should focus on creating a unified, real-time view of their customers by harnessing actionable, contextual data. Predictive analytics can help businesses identify customers’ future behaviors such as purchase intent or churn risk, allowing companies to tailor experiences proactively rather than reactively. AI-powered segmentation and dynamic audience creation can help brands offer more timely, relevant, and genuine engagements.
However, considering your systems alone is not enough. Maintaining transparency about data usage and adhering to responsible AI practices are also critical to building lasting customer trust. Such trust is foundational for impactful hyper-personalization, which enables brands to thoughtfully anticipate needs and deliver value at the right moment, rather than bombarding customers with irrelevant communications.
Companies that weave this level of intelligence, care, and trust into their engagement strategies will set a new standard for customer experience in a world that demands relevance and authenticity.
What should organizations take into consideration to ensure AI-powered personalization and communication efforts are responsible and ethical, especially when it comes to dealing with customer data?
dealing with customer data?
Inbal: While leveraging AI for personalization, organizations must prioritize trust, transparency, and customer value at every turn. As digital trust continues to be eroded by fraud, spam, and the misuse of data, businesses must take proactive steps to build and maintain trust with their customers. If and when brands break that trust, their engagement efforts will be useless.
At Twilio, we believe that trust begins with transparency. Our latest research reveals that 55% of APAC consumers identify transparency in communication as the second most effective way for companies to earn their trust, highlighting its pivotal role in building consumer confidence.
Organizations should clearly articulate their data practices, including how consumer data is collected, processed, and protected. They must also provide privacy controls, not just for compliance, but to empower customers with autonomy over their data, including the ability to opt out or adjust privacy settings.
Handling data responsibly is just as vital. Companies should prioritize first-party data gathered with clear consent and ensure it is used to deliver meaningful value to the customer. As part of this effort, implementing role-based access controls (RBAC) can help ensure only authorized personnel have access to sensitive data, reinforcing both data security and consumer trust.
Also, organizations should embed privacy and security by design, with sound data governance policies in place for the collection, storage, and usage of both customers’ and their customers’ data. This involves regular audits and risk assessments to identify and address potential privacy risks across the entire customer journey.
AI also presents the opportunity to more proactively combat fraud, spam and identity theft. By investing in AI-powered fraud detection and cybersecurity, companies can protect both their customers and their reputations.
Just as importantly, identity verification plays a critical role in ensuring that customer engagements reach the right person and come from the right source. In an era of rising impersonation scams and phishing attacks, verifying both sender and recipient identities is essential to maintaining trust and delivering meaningful, secure interactions.
Customer communications that matter are trusted, relevant, timely, and genuinely valuable; otherwise, they are just digital noise. With transparency, responsible data use, and security, organizations can navigate the complexities of AI in customer communication with confidence and success.
