The question everyone's asking I've been getting asked about digital twins a lot lately. People want to know if they should be using them. The honest answer is: it depends entirely on what you're trying to fix. Digital twins are getting real attention right now, and for good reason. The idea is straightforward. You create a virtual model of a process, a system, or even a person's workflow. Then you run scenarios through it, test things, optimise without touching the real thing. No downtime. No risk. Just data. But here's what I see happen. Someone reads about digital twins making staff more productive, and they think it's a silver bullet. It's not. It's a tool. And like any tool, it only works if you're trying to solve something that actually needs solving. Where digital twins actually work I've watched digital twins deliver real value in specific situations. Manufacturing is the obvious one. You've got complex processes, expensive equipment, and high stakes if something breaks. Running a digital version first saves time and money. That's measurable. But I'm also seeing people in operations and logistics build them successfully. One client I worked with modelled their entire supply chain in a digital twin. They could test what would happen if a supplier went down, or if demand spiked by 30%. They ran 200 scenarios in a week. Would have taken six months to gather that data any other way. The pattern here matters. Digital twins work best when you've got repeatable processes, lots of variables, and a real need to test before you commit resources. Where I've seen them fail I've also seen people build them for the wrong reasons. They've got a team of five people doing something straightforward. No major complexity. No pressing operational bottleneck. But they build a digital twin anyway because it sounds modern. They spend three months building it. It costs money. It requires ongoing maintenance. And at the end, they find out they could have just asked their team what was actually slowing them down. There's another problem I'm thinking about more these days. If you're building a digital twin of how your team works, you're creating a detailed model of their behaviour, their workflows, their patterns. That data is valuable. That data is also sensitive. You need to think hard about security and privacy before you go down this road. I'm not being paranoid here. We've seen enough evidence recently about what happens when systems that shouldn't be exploited are left vulnerable. You need to know who has access to that model, how it's stored, and what could happen if someone got hold of it. Then there's the legal side. If you're using a digital twin to optimise how your team works, you need to be clear about that. You need consent. You need transparency. The idea of turning someone into a "superworker" by optimising their digital twin sounds efficient until you're the person being modelled without knowing it. What you actually need to ask first Before you even think about building one, answer this: What specific problem are you trying to solve? Not "be more efficient". That's too vague. Are you losing time because you don't know what happens when something fails? Are you making expensive decisions based on guesses? Are you running the same test repeatedly and it's costing you? Those are problems digital twins can solve. Second: Is this a repeatable process? Digital twins work on patterns and data. If what you're doing changes dramatically every week, a digital twin won't help you much. Third: Do you have the data? You can't build a useful digital twin without good data flowing in. If you're not currently measuring what you're trying to model, you'll spend months just gathering baseline information. Fourth: Who needs access to this, and why? Be specific. Think about security from the start, not after. I'd suggest running through a Decision Matrix if you're seriously considering this. Map out the cost, the time to build it, the actual problem it solves, and what happens if you don't build it. Sometimes the answer will be clear. Sometimes you'll realise there's a cheaper way to solve the same problem. The real cost Digital twins aren't cheap. You need either the expertise in-house or you're paying someone to build it. You need ongoing maintenance. You need to keep the data fresh or it becomes useless. I've seen projects that looked like they'd cost £20,000 end up at £80,000 because the scope kept expanding. That's not necessarily a reason to avoid them. But it's a reason to be honest about whether the problem justifies the spend. What to do this week If you think a digital twin might be relevant to your business, do three things. First, write down the specific problem you want to solve. Not "improve efficiency". Specific. "We lose three days every time we need to test how our process handles a supplier failure" or "We make pricing decisions on estimates instead of real data". If you can't write it down clearly, the digital twin isn't the answer. Second, talk to your team. Ask them what takes the most time, what frustrates them, where they think things could be faster. They'll often spot problems you've missed. Third, if you're still convinced it's worth exploring, look at your security and data governance. You need that sorted before you build anything. Don't leave it as an afterthought.