The problem with technology decisions I watched someone spend £14,000 on a CRM system last year. Six months later, their team was still using spreadsheets. The software sat there, fully paid, barely touched. This happens constantly. People see a tool, get excited about what it promises, buy it, and then wonder why nothing changed. The technology was not the problem. The decision process was. With everything happening right now, from AI music flooding streaming platforms to electric vehicles reshaping entire industries, the pressure to adopt new technology feels constant. China is positioning itself to benefit from rapid shifts into electric vehicles while the rest of the world scrambles. Spotify refuses to let users filter out AI-generated tracks. The landscape moves fast. But speed creates bad decisions. And bad technology decisions cost more than money. They cost time, team morale, and sometimes the business itself. Start with the actual problem Before looking at any tool, I ask clients one question: what specific problem are you trying to solve? Not "we need to be more efficient." Not "we should probably automate something." The specific thing that is not working. A client came to me wanting to adopt a project management platform. Their team was "disorganised," they said. We spent an hour mapping their actual workflow. The problem was not organisation. It was that two people were duplicating work because nobody had defined who owned what. They did not need software. They needed a 20-minute conversation and a shared document. This is where the 5 Whys tool on alira.london becomes useful. You state the surface problem, then ask why five times. By the third or fourth why, you usually find something different from what you started with. The questions that actually matter Once you have a genuine problem, here is what I walk through with people: What does success look like in 90 days? Not in theory. In measurable terms. If you cannot describe what changes, you cannot evaluate whether the technology worked. Who will actually use this daily? Not who should use it. Who will. If the answer is "everyone, eventually," that usually means no one. What happens if it fails? Some technology decisions are reversible. Others lock you into contracts, data formats, or workflows that become expensive to escape. Know which type you are making. What is the true cost? The subscription fee is the smallest part. Add training time, integration work, the productivity dip during transition, and the ongoing maintenance. A £50 per month tool can easily cost £3,000 in the first year when you count everything. The 72-hour rule I tell clients to wait 72 hours between discovering a tool and committing to it. Not because the tool might be bad, but because excitement distorts judgement. In those 72 hours, write down the problem you are solving and why this specific tool addresses it. If you cannot articulate it clearly after three days, you were buying a feeling, not a solution. This sounds obvious. It is not. I have seen sharp, experienced people skip this step repeatedly. The marketing is good. The demos are polished. The testimonials sound convincing. None of that tells you whether it fits your situation. When to say yes Some technology decisions are genuinely worth making quickly. Here is what I look for: The problem is costing you real money or time right now. Not hypothetically. Actually. If you can put a number on what the current situation costs you monthly, you have something to compare against. The tool has been tested by someone in a similar context. Not a case study from a company with 500 employees when you have five. Someone whose situation resembles yours. The implementation path is clear. You know who will set it up, how long it will take, and what training is required. If the vendor cannot give you straight answers on this, that tells you something. You can exit if it does not work. Avoid long contracts for unproven tools. If they insist on 12-month commitments before you have tested it, ask why. When to say no Say no when the problem is vague. Say no when adoption depends on changing behaviour that nobody has agreed to change. Say no when the main argument is that competitors are using it. Say no when you are buying hope rather than solving a problem. I worked with someone last month who wanted to implement an AI writing tool for their marketing. Their actual issue was that they had no marketing strategy. The AI would have produced more content faster, but more of what? Towards what goal? We spent two sessions building a basic marketing plan instead. Now they know what content they need. Now a writing tool might make sense. The decision matrix approach For bigger decisions, I use a weighted decision matrix. List your options across the top, your criteria down the side, weight the criteria by importance, and score each option. This forces you to be explicit about what matters. It also reveals when you are trying to justify a decision you have already made emotionally. If you find yourself adjusting the weights to get the answer you want, that is useful information. The Decision Matrix tool on alira.london walks you through this process. It takes about 15 minutes and often changes the outcome. What to do this week Audit your current tools. List every piece of software your business pays for. Next to each one, write who uses it and how often. Anything unused or underused is either a cancellation or a training problem. One or the other. Define one problem clearly. Pick the operational issue that frustrates you most. Write it in one sentence. Then use the 5 Whys to get to the root cause. Do not look at solutions until you have done this. Set your decision criteria before shopping. If you are considering new technology, write down what success looks like and what you will measure before you start comparing options. This prevents the demo from doing your thinking for you.