Skip to main content

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...