Introduction
Are you an author, coach, or thought leader looking to expand your digital presence and enhance audience engagement with cutting-edge technology? This article is tailored for you.
The term "digital twin" initially described digital replicas of physical systems in industries like manufacturing and aerospace, used primarily for simulation and optimisation. However, advancements in AI technologies such as large language models (LLMs) and machine learning have transformed digital twins from static replicas to dynamic, interactive entities.
Today, they're being adapted into personal AI personas that not only simulate but actively engage in education, customer service, and personal branding.
For professionals like you, autonomous AI agents, acting as digital twins, now serve as invaluable tools for continuous learning, coaching, and post-implementation support. They offer an exciting opportunity to extend your digital reach, further enhance your audience engagement, and maintain the personal touch that defines your unique brand, supporting your ongoing development and interaction needs.
AI Digital Twins Overview
What is a Digital Twin?
Traditionally, the term "digital twin" has been associated with the technology sector, particularly in fields such as manufacturing and aerospace, where it's used to create detailed digital replicas of physical systems for simulation and optimisation purposes.
Evolution of Digital Twins with Advancements in AI Technologies
The evolution of digital twins has been dramatically impacted by the advancements in large language models (LLMs), natural language processing and other forms of machine learning. These technologies have broadened the scope of digital twins from physical replicas to include personal AI representatives that mimic human communication and behaviour patterns. This transformation opens up exciting possibilities for personalised interaction in various fields, including education, customer service, and personal branding.
Practical Application
To illustrate how this works in practice, AI digital twins employ Large Language Models (LLMs), natural language processing (NLP), and retrieval-augmented generation (RAG) to analyse and learn from an individual's content, which could be blog posts, videos, books, and social media interactions. This analysis allows the AI to grasp nuances in the individual’s tone, writing style, and areas of expertise. As a result, it can generate responses that not only sound natural but also closely resemble how the person would communicate in real-life situations.
This capability significantly enhances user engagement by ensuring interactions feel authentic and scalable, thereby extending the digital reach of professionals and improving the quality of customer service without compromising the personal touch that defines successful brands.
Factors Driving Growth in AI Digital Twins
The rapid adoption and development of AI digital twins can be attributed to several interconnected factors, each contributing to a landscape ripe for innovation and growth. Let's explore these key drivers that are shaping the future of personalised digital interaction.
Digital Native Generation and Evolving Expectations
Today's digital landscape is increasingly shaped by a generation that has grown up immersed in technology. According to recent data, the typical internet user now spends about 6 hours and 40 minutes online each day—a statistic that underscores the significant digital engagement of current generations. Online users not only accept but actively seek out dynamic, engaging, and interactive experiences in their digital interactions. Their comfort with technology and openness to new innovations have accelerated both the development and adoption of AI digital twins.
For authors, coaches, and thought leaders, this means an audience that is primed for more sophisticated digital engagement. Your AI digital twin can meet these expectations by providing interactive, personalised experiences that resonate with this tech-savvy generation.
24/7 Real-Time Relevance: Addressing the Demand for Instant, Tailored Content
In our fast-paced digital world, the expectation for instant, personalised interaction has become the norm. With information and options abundant, capturing and retaining audience attention requires quick, effective, and tailored responses.
AI digital twins excel in meeting this demand, offering round-the-clock availability and personalised interactions that mirror your unique style and expertise. This capability allows you to extend your reach beyond traditional hours, meeting your audience's needs whenever they arise.
Remote Culture and Digital Work Revolution
The COVID-19 pandemic dramatically accelerated the global shift towards remote work, fundamentally changing professional interactions and necessitating rapid digitalisation. As companies and individuals adapted, they digitised vast amounts of information, processes, and interactions, creating an unprecedented wealth of structured digital data. Since the COVID-19 pandemic began in 2020, there has been a sharp increase in the percentage of people who can work from home/telecommute all or most of the time, reaching 28% in 2023.
This explosion of digital content has inadvertently laid the perfect foundation for training AI. The abundance of readily available, digitised information—from video conference recordings and chat logs to online documents and training materials—makes it significantly easier and more cost-effective to create and train AI digital twins.
For professionals like you, this trend offers a unique opportunity. Your website, blog posts, videos, and online interactions now form a rich dataset for developing an AI representation of your professional persona. This AI version of you can maintain engagement with your audience regardless of physical location or time zone, drawing upon your digitised knowledge and communication style.
Accessible AI: Democratising Digital Twin Technology
The remarkable progress in AI, particularly in natural language processing, and especially through the development of Large Language Models (LLMs) like the GPT (Generative Pre-trained Transformer) series, has significantly enhanced the ability of AI systems to process and produce text that feels natural and contextually appropriate. This advancement has made AI digital twins a reality. Today's AI agents can mimic human conversations with unprecedented accuracy, making them viable stand-ins for personal interactions.
Crucially, the availability of user-friendly AI platforms has democratised this technology. Platforms like KorticalChat make it surprisingly easy and affordable to fine-tune and customise AI models, allowing professionals like you to create personalised digital twins without extensive technical expertise.
Creating Your Digital Twin - Choosing the Right AI Platform
For those looking to create a digital twin, it's essential to first choose a platform that addresses the main concerns in the AI chatbot space, such as accuracy issues (hallucinations), task coherence (when it doesn’t follow instructions), and prohibitive costs (too costly to run). Without covering these basics, your AI digital twin might have the opposite effect and damage your brand.
Secondly, with the success of ChatGPT, a myriad of AI platforms and AI builders have emerged, each offering unique capabilities. Understanding what these different platforms can build will help you decide which one best meets the needs of your AI digital twin based on your goals.
AI-First Expertise, Cover the Basics
Choose an AI-first company that delivers AI solutions, not just regular software. Unlike software, AI development is highly iterative and evolves rapidly, with models frequently being updated and becoming deprecated. An AI-first company ensures proactive model management, effective implementation of advanced technologies like Retrieval-Augmented Generation (RAG) and vector space embeddings, along with meeting other technical requirements to rapidly integrate the latest AI research and tools into their platforms. This ensures that your solutions are built on the cutting-edge of technology and are adaptable to future advancements.
With an AI-first company, you can expect them to be continually addressing the key challenges in conversational AI, such as hallucination and task coherence, so you can rely on your digital twins' performance.
Definitions:
- Hallucination: In AI, hallucination refers to the generation of incorrect or fabricated information by a model, not supported by the input data.
- Task Coherence: Task coherence refers to the ability of an AI to stay focused and provide accurate responses when faced with complex tasks. When an AI is given too many instructions or variables, it can lose track of the task, even if the prompt is within the token limit.
AI Platform with RAG Technology to Address Hallucinations
Opt for a platform that utilises Retrieval-Augmented Generation (RAG) technology. RAG allows the AI to be trained with your specific content, ensuring it accurately represents your knowledge and style. This method enhances the accuracy of responses by ensuring that they are informed by the most up-to-date and specific data available.
Without RAG, you risk having an LLM, potentially hallucinating or making up information.
AI Platform to Build AI Agents Addressing The Issue on Task Coherence
When selecting an AI platform, it's essential to understand the spectrum of options available. Typically, platforms would be to build: AI chatbots, next-gen AI chatbots or AI agents.
For an AI Digital Twin that can handle different types of queries and can adapt behaviour based on the context of the conversation, AI agents are the most suitable option.
AI Chatbots
AI chatbots, commonly seen in websites for customer support, typically run on rule-based systems or basic machine learning models. These chatbots are designed to respond by selecting answers from a predefined set of options, which restricts the freedom to ask open-ended questions and get nuanced responses.
Think of the early versions of Intercom or Tidio; they fall short of delivering a true conversational experience suitable for an AI digital twin.
Next-Gen AI Chatbots
These are what they call ChatGPT wrappers, custom GPTs or platforms that use Large Language Models (LLMs) hence providing a more natural conversational ability. Some platforms are enhanced with Retrieval-Augmented Generation (RAG) to train specifically on your data, improving their applicability.
However, they are generally confined to handling single tasks at a time due to limitations in prompt length. This means they can effectively manage specific types of tasks, such as supporting queries to locate and explain content but when you start asking about what could be relevant contents, it would fail to answer this effectively. This is because a task (or a type of query) follows a specific process and there's not enough context window to include instructions for every type of query in one prompt, hence the chatbot can only handle one task at a time.
When your audience's questions become more varied, a single bot might struggle to meet all these demands effectively and a next-gen AI chatbot may not be suitable in the long run if you want it to answer varieties of questions.
AI Agents
AI agents are functioning more like real people, attuned to the context of the conversation. Unlike next-gen AI chatbots, AI agents operate within a sophisticated framework that not only identifies the nature of the inquiry but also the specific task needed to address it. This includes using the relevant knowledge base, tools, and processes to effectively fulfill the task.
For example, if a user asks about a specific topic like "What is OKR?", the AI agent can provide detailed explanations along with further resources (Q&A role). If the conversation shifts to expressing interest in your book, the AI agent seamlessly transitions into a sales role, promoting your book and guiding the user through the purchase process. Similarly, if the user expresses interest in another book, the AI agent can engage in an exploration to better recommend other books or courses (product recommender role), all without the user noticing any transition between these tasks.
Over time, you can enhance the AI agent's capabilities to handle more tasks or queries because it is engineered to do so, making it the most suitable option for AI digital twins.
Cost-Effective Model Optimisation
An AI won't be implemented if it is too costly to run. Select a platform proficient in model optimisation for cost-effective operation. For instance, KorticalChat addresses cost challenges by strategically selecting the right model for each task. By using more cost-effective models for simpler tasks and reserving high-performance LLMs for complex scenarios, the framework optimises both cost and performance.
KorticalChat AI Agent Platform
Kortical is an award-winning, AI-first company that has been delivering bespoke AI solutions to enterprises like Deloitte, Santander, and Charlotte Tilbury since 2016.
Our KorticalChat AI agent platform, equipped with RAG technology and a robust AI agent framework, enables the creation of AI digital twins. These AI agents can be trained on your specific knowledge to accurately reflect your expertise, tone and brand style, and can be customised further to handle multiple tasks and remain contextually aware throughout interactions.
Jeff-BOT helf: An AI Digital Twin Use Case
Introduction
World-renowned product thought leader and keynote speaker, Jeff Gothelf, has leveraged KorticalChat AI agent platform to create an AI Digital Twin, Jeff-BOT Helf. It is trained on his website data—including blogs, articles, and published books—to accurately capture his tone and expertise. Hosted on his website, this AI digital twin is designed to interact with his audience by providing expert advice on Product Management and Design.
Functionality
Jeff-BOT Helf allows users to engage directly with Jeff’s extensive knowledge base through a simple chatbot interface. Whether these are questions about specific product management techniques or need guidance on design best practices, Jeff-BOT Helf offers instant, contextualised responses. This speeds up decision-making and informs users about the latest best practices in the field.
For a general overview of how Jeff-BOT Helf assists, users can ask:
- 🤖 "What topics can you help me with?"
- 🤖 "I'm conducting some user reviews, what questions should I be asking?"
- 🤖 "How do I structure the lean UX canvas?"
- 🤖 "What are the key principles of Agile Development?"
This AI digital twin is regularly updated with new content, ensuring it remains a dynamic and relevant resource. They also continuously update it based on feedback refining and enhancing its capabilities.
Impact of Having an AI Digital Twin on his Website
Integrating Jeff-BOT Helf into his website has significantly amplified Jeff Gothelf's engagement with his audience:
- Real-Time Assistance for Online Content: The AI Digital Twin provides immediate assistance to readers engaging with his content. While browsing, visitors can instantly ask the chatbot about concepts they don't understand or how to apply the ideas, enhancing comprehension and engagement with his work.
For example, some questions have included how to apply OKRs in specific contexts or seeking clarification about the lean UX canvas or what key topics to learn in product management.
- Continuous Learning and Post-Implementation Coaching Tool: For those already familiar with his work, the AI Digital Twin serves as a dynamic tool for ongoing education and support. It allows users to effortlessly revisit and reinforce previously covered concepts, making it invaluable for clients in need of periodic refreshers on key ideas. This feature ensures that they can maintain and enhance their understanding over time without manually digging through old content. By providing continuous, on-demand coaching, the AI Digital Twin helps users apply learned concepts in new contexts, keeping engagement levels high and ensuring sustained personal and professional growth.
- Interactive Feedback Channel: Jeff's AI Digital Twin creates a more accessible channel between him and his readers. His audience can easily share feedback, express appreciation for his work, or inquire about upcoming projects, fostering a sense of ongoing dialogue and connection.
For instance, one woman reached out to express appreciation for how Jeff’s book has helped her, mentioning her anticipation for the physical copy which she prefers over digital formats.
- Effective Sales and Marketing Assistant: Jeff’s AI Digital Twin becomes a powerful tool for highlighting his latest projects.
For instance, he observed 20% of interactions specifically inquiring about his new book promoted by the AI, demonstrating how the Digital Twin can generate interest in new offerings that might otherwise be overlooked on a static website.
- Valuable Audience Insights: By analysing interactions, Jeff gains crucial insights into his audience's interests and needs, which informs content development and ensures his offerings remain relevant.
For example, analysis shows that 13% of his audience inquire about OKR implementation, 20% ask specifically about his new book, and others seek deeper knowledge in key topics of product management.
Conclusion
Embracing an autonomous AI agent as an AI digital twin offers more than just technological convenience—it transforms the way you connect, interact, and grow with your audience. With capabilities ranging from providing real-time content support to generating valuable insights about your audience, digital twins represent a paradigm shift in digital engagement. They enable you to not only extend your reach but also deepen the relationships with your followers, ensuring your digital presence is as dynamic and influential as your in-person interactions.
Ready to explore the potential of AI digital twins for your brand? Schedule a consultation with our AI experts today via the contact form below and discover how you can create a personalised digital twin that can be a virtual extension of you.
If you are interested in reading more about AI agents or case studies, you can explore the following links.
