Advances in artificial intelligence (AI) will have a massive impact on digital experience (DX) solutions. AI will transform how brands produce, manage, and optimize digital experiences that define their customer engagement solutions. It will also enable the emergence of new, more immersive types of digital experiences made possible through generative AI.
As we continue to witness the accelerated incorporation of AI capabilities into DX solutions, we can expect AI’s impact on customer experience to be even more profound than when the internet became a mainstream business channel. Brands must take seriously how they can effectively utilize AI. This means identifying opportunities to use AI across the entire process of delivering and managing digital experiences. Throughout this process, brands should focus on how AI can be employed to better understand customers and their needs, deliver more seamless and efficient customer experiences, and automate tasks to drive operational efficiencies. Those brands that can effectively adopt AI into their DX solutions will have a substantial competitive advantage.
How AI is currently being used
For several years now, most leading Digital Experience (DX) vendors have incorporated some level of AI capabilities into their solutions. As a result, many brands have been utilizing AI in their DX solutions, whether they realize it or not. Let’s explore some of the more common ways AI is employed to support digital experience solutions:
- AI-powered personalization: AI is being used to personalize websites and marketing campaigns more sophisticatedly and effectively than ever before. For example, AI can be used to analyze customer behavior data such as past purchases, browsing history, and other factors to identify patterns and trends, which can then be used to personalize content and product recommendations.
For example, Dynamic Yields’ AI Personalization Platform
- AI-powered product recommendations: AI can be used to make product recommendations to customers by analyzing customer behavior, browsing history, purchasing patterns, and other factors to suggest products that customers may be interested in to enhance the shopping experience and increase sales.
For example, Coveo’s Recommendation Engine
- Website Search – AI (Natural language processing) is used to learn from website user behavior, search history, demographics, and preferences to more accurately understand a user’s query and intent to provide more relevant, personalized, and targeted search results.
For example, Alogia’s Neural Search
- Customer Service and Support: AI-powered chatbots can provide 24/7 customer services and support, handling common customer inquiries and freeing up human representatives to address more complex issues. This can help brands improve customer satisfaction and loyalty. It also improves efficiency and reduces response times.
For example, Cognigy Conversational AI
- Virtual try-on: Customers can virtually try on clothes, accessories, and cosmetics before buying them. This will allow them to see how the products look on them and ensure they are the right size and color. These virtual mirror solutions use augmented reality (AR), and AI can be offered both online and in-store, such as Walmart’s virtual try-on app.
For example, Zeekit
- Data enriching visual media – Digital Asset Management platforms employ AI image recognition capabilities to identify objects and scenes and other descriptive data in images that are then used to data enrich images with metadata. This metadata is used to improve SEO, user accessibility, enable AI automation, and support personalization. For example, Pimcore DAM
- Content creation: Generative AI is being used by brands to create text and image content for websites, blog posts, articles, and marketing materials. This can help brands save time and money and produce high-quality content that resonates with their target audience.
For example, Contentstack AI Assistant
- AI insights and decision making: AI can analyze large amounts of data to identify trends and patterns that would be impossible for humans to see. For example, AI can be used to track customer engagement, identify trends, and predict future behavior. This information can be used to improve and optimize a Brand’s digital experience strategies. For example, Adobe AI-driven insights
- SEO optimization – AI is rapidly changing how search engine optimization (SEO) is carried out. From keyword research to content optimization, AI improves rankings and automates many SEO-related tasks. Using AI-powered tools and services, brands can gain a competitive edge in search engine results.
- AI-powered marketing: AI capabilities in marketing automation tools enable marketers to be more efficient, effective, and strategic in targeting marketing activities to specific customers based on their interests, demographics, and other factors that will drive desired business outcomes. For example, Salesforce’s Einstein GPT for Marketing.
- AI-powered pricing: AI is used to analyze supply, demand, customer data, and other factors to determine the optimal price for products in real-time. AI can also be used to deliver personalized pricing based on a customer’s past purchases, browsing history, and interests. This can help to increase profits and improve customer satisfaction. For example, Wiser’s AI Pricing Management
- Inventory management: AI is used to optimize inventory levels by predicting demand for specific products based on historical data and current trends. This can help brand’s to avoid understocking and overstocking, which can save them money and improve customer satisfaction.
For example, Remi AI Inventory Optimization
- Fraud detection: AI-powered fraud detection systems are already playing a crucial role in swiftly and accurately identifying fraudulent transactions, thereby minimizing the need for labor-intensive manual fraud detection processes. These systems have the ability to learn and adapt over time, enabling them to enhance their fraud detection capabilities with increased exposure to data. As a result, AI-driven fraud detection systems continuously improve their effectiveness in combating fraud. For example, Signifyd’s Commerce Protection Platform
The use of AI in digital experience solutions goes far beyond what has been mentioned. The list of AI applications for DX is extensive and will expand further as AI technology advances and gains wider adoption.
The emergence of publicly available Generative AI represents a significant advancement over earlier forms of AI. While earlier forms of AI primarily focused on solving specific tasks based on pre-defined rules and patterns, generative AI goes beyond this, leveraging deep learning techniques that enable it to simulate human-like creativity in generating new original, and meaningful content.
Generative AI solves four of the biggest challenges of delivering highly effective personalized digital experiences at scale.
- Content at Scale: Producing personalized content at scale is one of the most significant barriers to implementing personalization. Creating a large volume of diverse content requires a lot of resources, time, and money. Generative AI can help solve this challenge by automatically generating content and content variations. Generative AI models can adapt and generate content variations based on user preferences, generate content in different styles or genres, and even generate content conditioned on specific attributes or features. In the future, we can expect Generative AI to be employed to deliver personalized content optimized to the individual user’s interests and needs, all in real-time.
- Manage Complex personalization at scale: Evaluating and optimizing complex multi-layered personalizations and accurately attributing the impact of multi-layered web personalizations on desired outcomes can be extremely challenging. Generative AI can analyze and leverage vast amounts of customer data and interactions to continually refine and personalize digital experiences to be more persuasive and drive desired business outcomes.
- Content data enrichment at scale: Normally a resource-intensive exercise, Generative AI can streamline content data enrichment at scale by automatically adding metadata to both AI-generated and existing content. This includes descriptive, contextual, geolocation, and sentiment metadata for text, images, and videos. By enriching content with this information, Generative AI will enable the efficient deployment of automated experience optimization at scale.
- Managing personalization rules and experience automation: Managing personalization rules can be complex, challenging to scale with demand, and resource-intensive. However, the future holds the promise of using Generative AI to deliver highly personalized and compelling one-to-one customer engagement at scale. By leveraging comprehensive customer data insights, user profiles, and behavioral data, Generative AI can be trained to provide personalized experiences that captivate and resonate with individual customers. This approach will eliminate the need to manually define and manage specific personalization rules, paving the way for more efficient and effective customer engagement.
On its own, Generative AI already possesses formidable content creation capabilities that can significantly impact DX solutions. However, when harnessed in conjunction with complementary DX technologies, it can open the door to a new realm of possibilities, enabling the delivery of truly captivating and personalized experiences that are highly immersive.
Generative AI is rapidly advancing and will soon become a major component of digital experience solutions. Its impacts on digital experience solutions will be vast and lead us into the era of “Generative DX.” This new generation of digital experience solutions will be more personalized, engaging, and informative than ever before.
Generative DX (GDX) represents the next step in the evolution of digital experience solutions and will bring us one step closer to the promise-land of Fully Autonomous Digital Experience (FADX) solutions.
The DX industry’s current emphasis on Composable DX forms the foundation for Generative DX, providing brands with choice and flexibility in constructing their DX stack. This approach simplifies the integration of emerging DX technologies that support Generative DX, enabling brands to adapt swiftly. By decoupling the front-end from DX technologies, DX solutions become future-proofed, allowing for the utilization or development of front-ends tailored to enable generative digital experiences. The interoperability of Composable DX further facilitates the seamless integration of novel DX technologies that emerge to support Generative DX. The table below summarizes the evolution of digital experience solutions.
The Evolution of Digital Experience delivery
Generative DX (GDX) will be a game-changer for digital experience delivery. Its core defining capability is an AI model trained to produce digital experiences for a specific customer segment, persona, or even individual user. The content can be optimized or even entirely generated by AI to maximize desired business outcomes.
Here are some of the key benefits of Generative DX:
- Prompt-based personalization: Instead of rule-based personalization, we have prompt-based personalization, where prompts are formed from user interactions and existing profile data.
- Continuous CX Optimization: AI will learn from every customer interaction and use those learnings to shape and optimize customer experiences in real-time.
- Generative Content Experiences: AI can optimize, alter existing content, and generate new content in order to create more engaging and persuasive digital experiences.
- Customer Journey Optimization: AI will be able to assess, optimize and improve customer journeys in real-time, creating seamless experiences that feel more intuitive and responsive to the customer.
- Generative front-end experiences (GXEs): Generative front-end experiences are created through verbal or text prompts. They can range from campaign landing pages and microsites to entirely new forms of front-end experiences. For example, by leveraging the combined power of generative AI with other DX technologies, such as AI-powered customer data platforms (CDPs) and augmented reality (AR) solutions like Apple Vision Pro, brands will be enabled to deliver new forms of highly immersive experiences fueled with rich, personalized content generated by AI in real-time.
It’s not just about how brands can use AI but also how to support customers enabled by AI. Emad Mostaque, the founder of Stability AI, predicts that personalized stand-alone mobile versions of ChatGPT will be available by the end of 2024. Such an advance in personalized AI means that personalized AI shopping assistants are not far from becoming a reality. These assistants will be able to interact with businesses and brands in much the same way that human customers do. These AI shopping assistants will be able to learn and cater to the unique preferences and biases of the person they serve. Then taking these preferences and biases into consideration, these AI shopping assistants will be able to search for products across multiple vendors that best meet the shopper’s personal preferences, find the best prices for an item, carry out product research, and evaluate reviews, and then either make product recommendations or complete the purchase. Brands will need to plan for “AI customers,” as their customers utilize AI to be more efficient at getting the best product at the best price.
As artificial intelligence continues to advance at an accelerated pace, we are steadily approaching a future where fully autonomous digital experience solutions become a reality. These remarkable advances not only bring about innovative changes but also disrupts the landscape of DX. AI will empower new forms of digital experiences and provide huge efficiencies in implementing, managing, and optimizing DX strategies. To thrive in the years to come, it is crucial for brands to proactively prepare for the transformative impact of AI, ensuring their long-term success. Brands that fully embrace and effectively leverage AI capabilities within their DX solutions will gain tremendous competitive advantage, while those who fail to adapt to the rapid changes brought by AI will inevitably be left behind.