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How to Establish Trust and Unlock Customer Data to Turn the AI Promise into Profit

Kathryn Murphy
Twilio

Businesses are dazzled by the promise of generative AI, as it touts the capability to increase productivity and efficiency, cut costs, and provide competitive advantages. With more and more generative AI options available today, businesses are now investigating how to convert the AI promise into profit.

One way businesses are looking to do this is by using AI to improve personalized customer engagement. In fact, seven out of 10 companies say they are already using AI to personalize content and marketing.

AI makes it possible to deliver truly unique and individualized experiences for every single customer, building loyalty and increasing efficiency along the way. Using AI, businesses can move away from the one-to-many marketing strategy and provide truly individualized, one-to-one experiences.

With any emerging technology, there are also major risks — including lack of access to the right data, transparency, and trust. In order to gain a return on AI customer experience investments, businesses must maximize their data and prioritize trust.


The Customer Data Disconnect

While businesses have put a greater focus on collecting data, many are struggling to put this data to work, especially when it comes to AI.

According to Twilio's 2024 State of Customer Engagement Report, which surveyed over 4,700 executives and 6,000 consumers, only 16% of businesses strongly agree that they have the data they need to understand their customers. This lack of data is a huge and potentially costly setback for businesses. Without the right customer data, businesses risk building experiences and communications that are at best generic or at worst wrong. With more and more businesses actively considering how to incorporate AI into their experiences, having the right customer data is essential to success.

In order to best understand where customer experience data gaps exist, businesses should start with these tactics:

Conduct a customer journey audit to find out what's missing and identify the pain points preventing conversion. This audit is the foundation for a holistic customer journey map that will help businesses activate their data to provide better customer experiences.

Unlock warehouse data. While most businesses have a wealth of information in their data warehouses, few can actually activate it. To solve this problem, businesses can connect their customer data platform (CDP) data with the data in their warehouses, promoting a deeper understanding of each customer and their interactions with the brand.

Integrate AI directly into the contact center to collect and share data in real time, including events across platforms leading up to the moments prior to a customer calling a contact center. This helps customer service agents provide personalized experiences without getting overwhelmed by the deluge of information, and ultimately resolve customer issues faster and more effectively.

AI opens the opportunity to analyze the large volumes of collected customer data to gain actionable insights and deliver personalized experiences to customers. Taking stock of what data and potential barriers exists will help businesses capitalize on the AI opportunity in a way that meets customer expectations.

Navigating Today's AI Trust Landscape

However, with emerging malicious use cases like AI-generated images diminishing consumer trust in AI, it's essential that companies improve data transparency and security when delivering customer experiences powered by AI. Failure to do so puts customer trust and loyalty at risk.

Consumers want to know exactly how businesses leverage their data. Almost half (49%) of consumers say they would trust brands more if they openly disclose the use of customer data in AI-powered interactions. The problem, however, is that businesses believe they are already doing this well. According to this same report, while 91% of brands say they're transparent with customers around how AI uses their data, only 48% of customers agree. The bottom line for businesses is consumers need to experience personalization in a trusted way. It has to be clear to the consumer that the brand knows certain information and is leveraging it to personalize for the end users benefit. There is a thin line between too much and awesome — brands need to be wary to not cross it.

AI's value is unlocked when businesses can securely harness customer data to develop a deep understanding of each customer and improve each interaction over time. But to build customer trust, businesses need to be open and clear with their customers on how they are using customer data in AI-powered experiences. Twilio introduced a clear and concise way for businesses to provide greater transparency in how they use this data, called AI nutrition labels. These AI nutrition labels operate like the labels on everyday grocery items and give an inside view into exactly what data went into creating each AI-powered experience, how exactly it is being used and more. This transparency helps to instill trust and shows a commitment to the privacy of customers.

Unlocking the AI ROI Promise

When businesses are able to build trust through transparency and activate their data, using AI in customer engagement stands to impact the bottom line. According to the report, consumers will spend 54% more with a brand that personalizes to them. AI can help power the personalization that secures this loyalty — 48% of consumers say they've made a repeat purchase from a company based on the level of personalization they received.

Businesses who use AI to power personalized customer experiences are reaping the rewards. Seven in 10 companies already leverage AI to personalize content and marketing and are realizing a number of benefits, including higher customer satisfaction scores (45%), better data-driven decision-making (41%) and improved market segmentation and targeting (41%).

AI and generative AI have the potential to transform how businesses connect with and retain their customers. The businesses that invest in trust and in building a strong data foundation stand to not only maximize their AI investments, but also maximize their potential profits.

Kathryn Murphy is SVP of Product and Design at Twilio

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How to Establish Trust and Unlock Customer Data to Turn the AI Promise into Profit

Kathryn Murphy
Twilio

Businesses are dazzled by the promise of generative AI, as it touts the capability to increase productivity and efficiency, cut costs, and provide competitive advantages. With more and more generative AI options available today, businesses are now investigating how to convert the AI promise into profit.

One way businesses are looking to do this is by using AI to improve personalized customer engagement. In fact, seven out of 10 companies say they are already using AI to personalize content and marketing.

AI makes it possible to deliver truly unique and individualized experiences for every single customer, building loyalty and increasing efficiency along the way. Using AI, businesses can move away from the one-to-many marketing strategy and provide truly individualized, one-to-one experiences.

With any emerging technology, there are also major risks — including lack of access to the right data, transparency, and trust. In order to gain a return on AI customer experience investments, businesses must maximize their data and prioritize trust.


The Customer Data Disconnect

While businesses have put a greater focus on collecting data, many are struggling to put this data to work, especially when it comes to AI.

According to Twilio's 2024 State of Customer Engagement Report, which surveyed over 4,700 executives and 6,000 consumers, only 16% of businesses strongly agree that they have the data they need to understand their customers. This lack of data is a huge and potentially costly setback for businesses. Without the right customer data, businesses risk building experiences and communications that are at best generic or at worst wrong. With more and more businesses actively considering how to incorporate AI into their experiences, having the right customer data is essential to success.

In order to best understand where customer experience data gaps exist, businesses should start with these tactics:

Conduct a customer journey audit to find out what's missing and identify the pain points preventing conversion. This audit is the foundation for a holistic customer journey map that will help businesses activate their data to provide better customer experiences.

Unlock warehouse data. While most businesses have a wealth of information in their data warehouses, few can actually activate it. To solve this problem, businesses can connect their customer data platform (CDP) data with the data in their warehouses, promoting a deeper understanding of each customer and their interactions with the brand.

Integrate AI directly into the contact center to collect and share data in real time, including events across platforms leading up to the moments prior to a customer calling a contact center. This helps customer service agents provide personalized experiences without getting overwhelmed by the deluge of information, and ultimately resolve customer issues faster and more effectively.

AI opens the opportunity to analyze the large volumes of collected customer data to gain actionable insights and deliver personalized experiences to customers. Taking stock of what data and potential barriers exists will help businesses capitalize on the AI opportunity in a way that meets customer expectations.

Navigating Today's AI Trust Landscape

However, with emerging malicious use cases like AI-generated images diminishing consumer trust in AI, it's essential that companies improve data transparency and security when delivering customer experiences powered by AI. Failure to do so puts customer trust and loyalty at risk.

Consumers want to know exactly how businesses leverage their data. Almost half (49%) of consumers say they would trust brands more if they openly disclose the use of customer data in AI-powered interactions. The problem, however, is that businesses believe they are already doing this well. According to this same report, while 91% of brands say they're transparent with customers around how AI uses their data, only 48% of customers agree. The bottom line for businesses is consumers need to experience personalization in a trusted way. It has to be clear to the consumer that the brand knows certain information and is leveraging it to personalize for the end users benefit. There is a thin line between too much and awesome — brands need to be wary to not cross it.

AI's value is unlocked when businesses can securely harness customer data to develop a deep understanding of each customer and improve each interaction over time. But to build customer trust, businesses need to be open and clear with their customers on how they are using customer data in AI-powered experiences. Twilio introduced a clear and concise way for businesses to provide greater transparency in how they use this data, called AI nutrition labels. These AI nutrition labels operate like the labels on everyday grocery items and give an inside view into exactly what data went into creating each AI-powered experience, how exactly it is being used and more. This transparency helps to instill trust and shows a commitment to the privacy of customers.

Unlocking the AI ROI Promise

When businesses are able to build trust through transparency and activate their data, using AI in customer engagement stands to impact the bottom line. According to the report, consumers will spend 54% more with a brand that personalizes to them. AI can help power the personalization that secures this loyalty — 48% of consumers say they've made a repeat purchase from a company based on the level of personalization they received.

Businesses who use AI to power personalized customer experiences are reaping the rewards. Seven in 10 companies already leverage AI to personalize content and marketing and are realizing a number of benefits, including higher customer satisfaction scores (45%), better data-driven decision-making (41%) and improved market segmentation and targeting (41%).

AI and generative AI have the potential to transform how businesses connect with and retain their customers. The businesses that invest in trust and in building a strong data foundation stand to not only maximize their AI investments, but also maximize their potential profits.

Kathryn Murphy is SVP of Product and Design at Twilio

Hot Topics

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...