Revolutionizing CX: Generative AI’s Emerging Impact
- AI’s impact. Generative AI in customer experience is reshaping the game.
- Current presence. LLMs advance AI chatbots for better customer engagement.
- Creative boost. AI expedites asset creation and visualization.
Generative AI is already starting to have an impact on the way some companies approach CX, and it’s almost certain that more customer experience organizations will start working with generative AI soon. That’s because generative AI can drive efficiency as well as creativity and productivity, providing the potential to create campaigns faster and personalize engagements better. Because of the short time to ROI it offers across major industries, generative AI topped Forrester’s Top 10 Emerging Technologies of 2023.
Let’s take a look at the benefits of generative AI in customer experience.
It’s worth noting that generative AI in customer experience has been present in applications for years, primarily since large language models (LLMs) helped AI chatbots evolve into tools for better customer engagement. However, with generative AI and new enterprise tools to work with it, we’ve reached an interesting tipping point. We’re now seeing generative AI raising customer expectations about how promptly brands respond to their requests and how personalized their engagements with brands can and should be.
Generative AI in Customer Experience: Powerful New Tools for Conceptualization and Personalization
One of the most exciting potential CX applications for generative AI is accelerating the creative process for new assets. The traditional process is to develop and articulate concepts, storyboard them and then iterate until there’s a final version of the asset. With generative AI visual art tools, it’s easier and faster to create different visuals for concepts, so you can move more quickly to presenting the ideas to stakeholders, coming to a consensus, moving into production and finalizing the assets.
Generative AI in customer experience also has the potential to speed up the process of mapping the customer journey to identify personalization opportunities. Given the right data, generative AI can plot those experiences almost automatically. That capability creates opportunities to optimize the funnel through dynamic copy, imagery and product recommendations based on data — all in a more immediate and responsive way that isn’t possible without AI.
An example from the hospitality industry is a luxury resort chain that’s starting to use AI tools in its enterprise platform to create more deeply personalized experiences that ultimately convert at a higher rate and provide a higher level of guest satisfaction. Rather than manually tagging and content mapping the journey for different personas, like parents traveling with young children, the platform’s AI allows for real-time, seamless personalization and frees up team resources for other initiatives.
Related Article: Mastering Personalization in Digital Marketing Strategy
Driving CX Efficiencies for Higher Online Conversion Rates
Some enterprise solutions that CX teams use are already embedding generative AI capabilities within their tools. So, we’re already seeing some marketing organizations and internal experience teams leveraging some of those tools to make better decisions in real time.
For example, generative AI can create automated split tests to measure the real-world performance of different assets. The AI can then report the results and even optimize the experience in real time, based on the outcome of the tests. The ability to automate those tests and act on the results without any need for human intervention creates greater efficiency. It also ensures that the best experiences are deployed at the right time to the right people, driving better conversion rates.
Related Article: 3 Ways AI-Powered Predictive Analytics Are Transforming Ecommerce
Setting up Generative AI to Succeed at Staying on Brand
As impressive as generative AI is, it still makes mistakes. Recent headlines about generative AI chatbots going off topic may make some marketers nervous about using this technology for asset creation and engagement personalization. How can they ensure that generative AI will stay on brand and on message? As with most things AI-related, the solution is data — and the more the better.
Brands that have large repositories of assets are in the best position to train their generative AI for brand- and message-related tasks because larger datasets help AI learn to create brand-appropriate assets better and faster. For example, established global brands that have spent a lot of time and energy on codifying their brand across a variety of regions and touchpoints have a head start on leveraging AI for marketing asset generation. But brands of all sizes should feed their AI-driven marketing efforts with consistent branded content, even if that means they have to start by investing in creating that consistency across their assets.
Starting out Right With Generative AI for CX
We’re seeing many companies starting to explore and experiment on the asset-creation side. In addition to training their AI on a consistent set of brand assets, companies need to consider some other AI-specific issues before they take their first use cases live. For example, the AI will need safeguards to ensure that any proprietary data it ingests during training won’t end up in the public domain.
Brands will also need to put access controls in place to ensure that only designated employees can leverage generative AI for asset creation, AB testing and other CX processes, to ensure consistent, quality results that align with brand goals and best practices. It’s also critical to have resources that ensure that any assets generated by the AI comply with copyright rules and any applicable licensing agreements. Working with an enterprise AI solution that’s designed for creative production can help to address these concerns.
Generative AI in customer experience is still in its infancy compared to what’s possible, but it’s advancing at an alarming rate, so now is the time for CX teams to start trying it out and finding use cases that help them work smarter and generate better results. With the right use cases, careful controls, and a good data set to train the AI, it’s possible to start realizing efficiency and conversion benefits quickly with generative AI in your CX toolkit.
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