How to create seamless customer journeys with generative AI

Before the proliferation of generative artificial intelligence (GAI) tools like ChatGPT, customers had (and still have) high expectations for brands. A recent study found that 71% of consumers expect companies¹ to deliver personalized interactions and 76% get frustrated when companies fail to deliver. Another report found that 75% of consumers find it important for brands to offer an entirely self-service customer care option² to answer questions. It’s clear consumers want personalized, on-demand experiences.

Now, it’s up to IT leaders to invest in technology that delivers³ these experiences. Enter GAI.

GAI’s impact on the customer experience

Consumers have become accustomed to finding on-demand, conversational information using AI-powered apps. Delivering highly relevant information in natural language formats when and where people need it is now becoming fundamental for all organizations — an area where GAI tools excel.

With GAI, the possibilities are endless.

Travel companies can utilize GAI chatbots in their digital experiences to help customers curate the perfect vacation itinerary and book it right through the tool. Clothing retailers can tap into GAI to help their customers find the ideal outfit for any occasion in the right size, color, fit, and style in just seconds.

Similarly, IT teams can use GAI while monitoring their infrastructure and applications to help identify and resolve issues to keep online applications up and running. This helps ensure reliable digital experiences, all while building brand trust.

Any company — B2C or B2B — can harness the power of GAI to meet customers’ demands and improve their experiences. Your job as an IT leader comes down to ensuring your organization has the infrastructure to help them find what they’re looking for right away, every time. And that starts with your data.

Data is the key to GAI success

You collect mountains of data to help ensure customers have excellent brand interactions and speed up their time to conversion. Now, you can use GAI to surface relevant results from that data (and your untapped data) to make an even more significant impact. However, integrating off-the-shelf GAI tools into your customer experience workflows can be risky and costly.

While the possibilities of GAI are endless, so are the data stores the tools leverage to provide information. GAI chatbot tools use large language models (LLM) to generate their responses, pulling from data and information from across the entire internet since its inception. That means they’re prone to hallucinations — presenting incorrect information as if it were accurate. And you can’t afford to provide the wrong information to your customers.

So, if your organization is that previously mentioned travel company that is using GAI for their booking chatbot, customers could end up getting itinerary ideas from every corner of the internet, not just the ones you offer. They might end up booking with a competitor, with help from your chatbot!

However, if you provide the GAI tool with your data to pull from instead, they’ll be able to find the options you offer. You can provide a tailored, relevant, secure customer experience and propel them toward conversion — in this case, a travel booking.

Similarly, if your IT team leverages GAI for observability⁴, it needs the most relevant, accurate responses possible to eliminate downtime and provide a reliable customer experience, not just best practices for issue resolution.

Securely passing your organization’s telemetry data, like metrics, logs, and traces, to a GAI tool can alert your team to any issues that would prevent your users from digitally interacting with your brand. It will help you resolve problems quickly, meaning less dropoff and customer frustration and more brand satisfaction.

How to turn GAI’s promises into tangible results

To harness the power of GAI to augment your customer experience, you need a unified data platform that transforms all your data into outcomes and questions into answers in real-time.

Technology like the open and flexible Elastic Search Relevance Engine (ESRE) and Elastic AI Assistant enables your team to create custom, highly relevant AI search applications, making it possible to surface relevant, organization-specific data in real-time, at scale, and securely pass it along with prompts to a GAI tool.

Your IT team can get relevant observability data and alerts to keep your applications running, your security team can surface breaches quickly and mitigate risks, and your customers can find the brand-specific information they’re searching for lightning-fast every time. The result? A seamless GAI-driven customer experience.

Is your data platform flexible and open enough to take advantage of all GAI offers? Find out more about harnessing GAI to decrease downtime, mitigate risk, and improve customer experiences.

Elastic, Elasticsearch Relevance Engine, ESRE and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries, used here with permission.

“What is personalization?” McKinsey and Company, 2023.
“Top 40+ customer experience statistics you need to know in 2023,” Emplify, 2023.
“CIOs heed the call for customer-centric IT,” CIO.com, 2023.
“From vision to reality: Your guide to using generative AI to improve operational resilience,” CIO.com, 2023.

About Author

AndyC

Andy Curtis is an award-winning security consultant, researcher and public speaker. He has been working in the computer security industry since the early 1990s, having been employed by state and federal government, leading healthcare and banking providers across three continents. He has given talks about computer security for some of the world’s largest companies, worked with law enforcement agencies on investigations into hacking groups, and is a regular voice on TV and radio explaining IT security threats.

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