The AI Revolution: Beyond Hype To Practical Solutions In The New Digital Era

Introduction

The presence of Artificial Intelligence (AI) is now an undeniable reality that can no longer be overlooked. It has captured the interest of clients across all budgets and industries.

Many organizations are still confused about what they want to achieve with AI. This confusion also extends to Digital Experience Platforms (DXPs), where companies are often unsure how they can utilize AI to enhance customer experiences, automate workflows, or ensure holistic automation.

The Launch Of ChatGPT

It’s not that AI didn't exist before ChatGPT, it did. But the advent of ChatGPT in November 2022 transformed AI's perception. For the first time, everyone could access this tech in its simplest form, a chatbot.

Tools like Dall-E and Midjourney emerged, generating graphics and images indistinguishable from real photos, blurring the lines of reality. 

Similarly, DXPs began using these tools to create engaging content, deliver personalized digital experience, help improve customer service, and enhance how brands connect with their audiences.

ChatGPT did to AI, what Tesla did to electric cars, Netflix did to streaming and what iPhone did to Smartphones.

Swarad Mokal | Technical Program Manager, Axelerant

Google Search trends show a clear correlation between the terms AI and ChatGPT.

To understand where AI can be used, it is important to understand what AI is. The simplest definition is a machine exhibiting human-like intelligence, having the ability to think and respond to tasks like a human would. 

Understanding The Terminology

There are different terms related to learning and working with AI.

Machine Learning And Deep Learning

Machine learning (ML) and Deep Learning (Dl) are two of the most important terms related to AI and can be considered as subsets of AI.

Machine Learning is a branch of AI that learns patterns to make predictions, recommendations, or decisions based on the data fed to the system. Once it has learned from the data, it can identify patterns and perform tasks autonomously.

DXPs often use Machine Learning to analyze user behavior, predict customer needs, and suggest personalized content, enhancing user engagement and retention.

Deep Learning is a subset of ML that uses many-layered neural networks to model complex data and its relationships, hence the term “deep.” DL setups are particularly useful when dealing with multi-dimensional data like images, language, or speech. For example, Natural Language Processing (NLP), an application of Deep Learning, enables DXPs to offer voice search, multilingual support, and more personalized customer interactions.

Generative AI

Generative AI is a subset of Deep Learning. It is an artificial intelligence system that can generate new content, such as text, images, audio, video, and even programming code. Unlike other AI models which focus on recognizing patterns or making predictions, generative AI models create new data based on already learned patterns.

In a digital experience platform, generative AI can be used to dynamically generate personalized content such as custom product descriptions, individualized email campaigns, or unique landing pages tailored to user preferences.

Large Language Model

A Large Language Model (LLM), is an AI system designed to understand and generate human language. These models are trained on vast amounts of text data, enabling them to perform tasks like answering questions, summarizing information, and generating content that mimics human writing. Examples of LLMs include GPT-4, Gemma, Llama, Mistral, and Claude.

LLMs can be open or closed, with key differences in accessibility and transparency. An open LLM is available for public use and is usually open-source licensed. The model’s code, architecture, and data used for training the LLM are disclosed and available openly.

Closed LLMs are typically proprietary, which means access is restricted and might be available through a paid subscription or license. The internal workings, code, architecture and training data are not disclosed.

For a DXP, the choice between open and closed LLMs depends on customization needs, cost considerations, and the importance of transparency.

Vector Database

Humans interact with data like a relational database system. Relational databases are essentially two-dimensional, which means users store data in rows and columns. To fetch a cell in this 2D grid,  the user will require a query.

Instead of using this system, AI uses Vector Databases to store and interact with data. Vector Databases store higher dimensional data. Higher dimensional data is more than just rows and columns. It is not just storing data but also memorizing how data is related to each other.

For example, in a box of LEGO bricks, a traditional database will just store information like shapes, sizes, and colors. But a vector database will also store data about how they connect with each other so that you can build a cool racing car with it. 

RAG

RAG (Retrieval-Augmented Generation) is an AI approach that blends information retrieval with the power of generative language models (LLMs). This combination allows the AI to produce more accurate, current, and relevant text by using targeted knowledge along with its language abilities.

Prateek Jain, Director of DXP Services at Axelerant, and Swarad Mokal, Technical Program Manager at Axelerant, discuss the basics of the RAG AI framework and its possible use cases in a recent webinar.

Privacy And Security

AI models are trained on data, requiring large amounts of data to be trained properly. This raises concerns about personal information being gathered without consent.

As AI becomes embedded in DXPs, it becomes critical to responsibly handle large volumes of user data. DXPs also need to ensure compliance with data protection regulations like GDPR and CCPA while providing seamless and personalized experiences. 

AI models often act as "black boxes," where the decision-making process isn't transparent. Ensuring that AI-driven DXPs maintain high levels of trust and transparency is essential for building and maintaining user confidence.

It is also critical to safeguard against potential AI misuse. For example, if a ​​malicious individual knows that the organization is allowing an AI model to read all incoming emails, they can send an email to talk to your AI model. This email will subtly manipulate the AI model or even send misleading messages and information.

That is why it is important to first come up with a set of AI policies to decide how to integrate AI, for example: a general good practice is to always have a Human in the loop.

How Can Axelerant Help You Transform Your Customer’s Digital Experiences With AI

The experts at Axelerant have been constantly exploring many possibilities for integrating AI into Digital Experience Platforms. 

How Axelerant Successfully Implemented AI In DXP Workflows

Axelerant’s experts have implemented multiple Proof of Concepts (POCs) that showcase how AI can be seamlessly integrated into DXP workflows. 

Axlebot

Axlebot is a cloud-based AI model trained on localized knowledge bases like Confluence, Handbooks, and GitHub repositories. It can answer queries with citations through Slack integration.

Contextual Learning Search With Drupal

This is a RAG AI setup integrated with Drupal for sites with large content stores. It offers direct answers to queries rather than simple search links, making it ideal for universities, government sites, and NGOs.

AI In Content Search

This POC demonstrates how organizations can integrate the Drupal AI module with OpenAI and Anthropic LLM models to improve content search. The search results were further improved by integrating Pinecone’s vector database for indexing. 

AI-Generated Alt Tags And Meta Tags

By integrating OpenAI with Drupal 10, AI can generate meta tags such as title, description, abstract, and keywords. This data is based on content fields like the body and title. If the body field is empty, AI will generate tags from the title alone. To fit specific content needs, customization can be implemented by modifying the module code.

Interested In A Demo?

Reach out to the experts at Axelerant for a demonstration of how to make your customer’s digital experiences more intelligent, responsive, and capable.