- Digital twinning refers to the digital twins of people, including customers, stakeholders and users.
- These human digital twins perform a very different function – capturing the logic and behavioural patterns of individuals rather than mechanical systems.
- Digital twins are built on raw existing customer insights – interview transcripts, survey results, and behavioural data. Rather than just summarising the data, they create a representation of how a particular individual tends to think. Their role isn’t to imitate someone’s exact words, but to reflect underlying logic, preferences, motivations and blind spots.
- Human digital twins are still an experimental use case, but they are already proving useful in the moments where teams would otherwise rely on instinct alone.
Leaders have invested heavily in understanding their customers, yet most of that intelligence sits in folders that are rarely mined. The truth is that valuable customer data is gathering dust as reports are saved to hard drives, transcripts are stored away for compliance, and the lived expertise of customers ends up scattered across folders and team silos.
The irony is that while companies have invested in knowing more about their customers than ever before, decisions are still being made on guesswork or intuition, and vague insights from past research. The disconnect between what organisations know and what they use to drive growth is becoming increasingly significant, especially at a time when speed and clarity matter more than ever.
So, how to make the most of the data stash that’s currently latent within businesses? One of the most effective solutions is to embed digital twins into the insight machine.
The term ‘digital twin’ originally described the deterministic modelling of physical systems in industries like aerospace and manufacturing, where engineers needed to simulate highly controlled processes before making real-world changes.
With the arrival of generative AI, the term has taken on an additional meaning: digital twins of people, including customers, stakeholders and users. These human digital twins perform a very different function – capturing the logic and behavioural patterns of individuals rather than mechanical systems.
And early studies in this field have already produced encouraging results.
More than this year’s buzzword
It’s no accident that buzzwords like ‘synthetic users’, ‘AI personas’ and ‘digital twins’ are starting to litter pitches, pilots and product demos. For businesses seeking instant, low-friction ways to test ideas, revisit past research, explore edge cases and reduce the burden of repeated recruitment, these are attractive propositions even when they’re not entirely sure about what these models actually do or how reliable they are.
Digital twins have begun to stand out because they’re not generic AI stand-ins; at their best they’re structured behavioural models grounded in real customer data. They offer a dependable way to keep insights active, consistent and available on demand. That is where their true strategic value lies.
More granularly, the best performing digital twins are built on raw existing customer insights – interview transcripts, survey results, and behavioural data. But rather than just summarising the data, they create a representation of how a particular individual tends to think. Their role isn’t to imitate someone’s exact words, but to reflect underlying logic, preferences, motivations and blind spots.
The result is a model where teams can revisit previous research in a constructive way. Teams can ask a twin follow-up questions months after the original interviews were conducted, exploring how someone might respond to a new pricing structure, a revised feature or a hypothetical scenario. Crucially, the twin can do this in a way that remains faithful to the boundaries of what the original participant revealed.
This transforms research from a static output into an ongoing, active conversation, so teams can keep interacting with the customers on whom they already have data.
Mining insight across silos
Research tends to be episodic. Teams will gather perspectives, extract themes, write reports and then move on. And then a new project begins, and the cycle starts again.
Digital twins break the chain, allowing organisations to retain the depth of past research and continuously draw from it. Product teams, strategists, researchers and innovators across the business can engage with the same customer models, creating a shared reference point across functions.
These shared learnings become invaluable when navigating uncertainty. Rather than asking what customers might think, teams can explore how a specific customer model thinks, right now, using real data.
The power of digital twins really shows up in organisations where good ideas often get stuck. You know the pattern: someone proposes something genuinely promising, and then, usually late in the process, questions about feasibility, compliance, quality or customer experience derail the momentum.
Digital twins change the order of things. They let teams explore, test and stress-check ideas before anyone commits time or budget. You can see how customers might react, where the friction points are, and what needs fixing long before those issues hit the real world.
Why 2026 is the moment to act
There’s no denying the fact that organisations have had a year of big promises and disappointing AI pilots, with the result that businesses are far more selective about what genuinely moves the needle.
For years, digital twinning has been used to model complex systems in engineering, aerospace and manufacturing, where failure is expensive and iteration must happen before anything becomes real. With the rise of generative AI, the idea of a digital twin has expanded.
After a year of rushed AI pilots and disappointing ROI, leaders are looking for approaches that actually fit how businesses work. Digital twinning does exactly that: it builds on familiar research practices, works inside existing workflows, and lets teams explore ideas safely before committing to them. Instead of slowing momentum, it helps organisations make better-informed decisions at a time when the cost of getting it wrong keeps rising.
Human digital twins are still an experimental use case, partly because they involve both human unpredictability and the emerging behaviour of large language models. Nonetheless, they are already proving useful in the moments where teams would otherwise rely on instinct alone.
For organisations heading into 2026, this blend of emerging capability and practical usability makes digital twins an attractive proposition.
Danielle Jaffit is co-founder of Quartz Labs.
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