- Video Insight
Digital Twins in the Workplace: Game-Changer or Privacy Nightmare? | AI Webinar Insights
- 2 mins
FULL TRANSCRIPT
Toby George Jones (Knowello), Kerri Buttery (VETNexus), Daniel Hulme (WPP)
One of the things I’ve been excited about for many years, actually before AI happened three years ago, is a concept that people call digital twins. So you can build a digital twin of an employee. I can take a large language model and train that model on your digital footprint: your email, your calendar, your feedback, and your communications.
Historically, what you had to do was use machine learning to understand your skills or your career aspirations. You would have had to build 50 different AI models to try to understand these different characteristics of you. What we can do with digital twins is essentially bring together all of that data. What we’re finding is that these digital twins represent people incredibly well.
So I can ask the digital you, if I put you on this project, will you thrive? If I put you on this team, will you be developing the skills you need to develop? That might sound really creepy, but the reality is that a digital twin represents you better than five numbers in an HR database.
However, it does also raise interesting privacy issues. By bringing different data sets together, you can create what are called toxic insights. So I could, for example, determine secret relationships in my company based on communications. I could identify if people are going to leave the company before they even know it themselves, again based on their behaviours.
So the promise of digital twins is to be able to understand people much better than we currently do, so that we can allocate them to work or training opportunities in a much more adaptive way. But on the flip side, you have to do that in a very safe and responsible way.
Key Points
- What are digital twins? An AI model of an individual employee trained on their digital footprint — emails, calendar entries, feedback, and communications — that represents them more richly than a traditional HR record.
- Consolidating AI models: In the past, understanding an employee's skills and career aspirations required building up to 50 separate machine learning models. Digital twins bring all that data together into a single, unified model.
- Predictive workforce planning: Digital twins can answer questions like "Will this person thrive on this project?" or "Will they develop the skills they need on this team?" — enabling smarter talent allocation.
- Personalised learning and development: Because the twin understands the individual holistically, training and growth opportunities can be matched adaptively to each employee's profile.
- Ethics and responsible use: The promise of digital twins — better understanding people to allocate them to work and learning more effectively — must be balanced against the need for safe, transparent, and responsible data governance.