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“Fetchify turned what felt like a crisis into a straightforward fix - and in just a couple of days. We went from not being able to contact anyone to generating four new client applications from a single send. The data cleanse didn't just fix a problem - it opened the door again.” – Marcel Stirling, Phoenix Insolvency

There is a lot of enthusiasm right now about what AI can do for ecommerce and CRM teams. Personalisation at scale. Predictive analytics. Automated outreach that learns and adapts. The pitch is compelling, and much of it is real. But there is a foundational question that almost nobody is asking loudly enough: what happens when you run AI on bad data? The answer is not that the AI fails gracefully. The answer is that it fails at scale, confidently, and in ways that are harder to trace than a simple spreadsheet error. This is not a theoretical risk. It is already happening inside the organisations that have moved fastest to adopt AI-driven tools without first addressing the quality of the data those tools run on The assumption nobody questions Most organisations treat AI as a layer that sits on top of their existing data. Feed in the CRM, connect the customer database, and point the model at the transaction history. The assumption is that AI is smart enough to work around imperfections. It is not. AI systems are pattern recognition engines. They find what is consistent in the data and treat it as a signal. If your data consistently contains errors - outdated addresses, duplicate records, lapsed contacts still marked as active - the AI learns those patterns as the truth. It bases its predictions, segments, and recommendations on a foundation that does not reflect reality. B2B contact data decays at 30% per year. For a database of 100,000 records, that means 30,000 entries become inaccurate every 12 months. When an AI personalisation engine is drawing on that data to decide who to target, when to contact them, and what to offer, it is working with a picture of your customer base that is one-third wrong AI doesn't fix bad data. It amplifies it. What this looks like in practice The problems that emerge are not dramatic. They are quiet and cumulative, which makes them harder to catch. Automated email sequences reach the wrong people or the wrong addresses, generating hard bounces that damage your sender reputation and, in serious cases, trigger blocks from email service providers. Personalisation that references a customer's last purchase or location draws on a record that has not been updated in two years. Predictive models identify high-value customers to target for retention campaigns - but a portion of those customers moved, changed roles, or lapsed long ago. Each of these is a cost. Collectively, they represent a significant drag on the performance of tools that were supposed to be driving efficiency. The irony is that AI makes these problems less visible, not more. A human reviewing a list might notice that an address looks wrong. An AI processes it at speed and acts on it. A case study: what happens when AI meets dirty data A professional services firm recently experienced this directly, who work with our sister company FLG for lead management. The team began bulk emailing an existing database through their email marketing system - a reasonable use of automation for a business trying to re-engage contacts at scale. The data, however, was old. Hard bounces accumulated quickly, and their account was flagged and blocked from sending. Fetchify cleansed the data. Contact information was standardised, and inactive or undeliverable entries were identified and removed. When they resumed outreach, the results were immediate - higher engagement, no delivery issues, and the kind of performance the automation was always supposed to deliver. The AI-driven outreach did not fail because of the tools. It failed because the data had not been maintained. Once the data was clean, everything else worked as intended. The AI readiness question organisations should be asking As AI becomes a standard component of ecommerce and CRM operations, the conversation around data quality needs to change. It is no longer just a compliance issue or an operational nicety. It is a prerequisite for AI to function as intended. Before deploying any AI-driven personalisation, automated outreach, or predictive analytics tool, the right question is not 'which AI platform should we use?' It is 'is our data clean enough for AI to learn from?' For most organisations, the honest answer is no - not without first running a data cleanse. The good news is that this is not a complex or expensive process. It is a one-time exercise that resets the foundation, followed by ongoing validation to prevent decay from accumulating again. What clean data actually enables Organisations that address data quality before deploying AI achieve fundamentally different outcomes. Personalisation engines draw on accurate records and produce recommendations that reflect the real customer base. Automated outreach reaches real inboxes and generates real responses. Predictive models identify genuine opportunities rather than ghost records. The regulatory dimension is worth noting, too. The ICO can issue fines of up to £17.5 million or four per cent of global annual turnover under UK GDPR for data governance failures. AI that acts on inaccurate or out-of-date data does not protect organisations from that exposure - it amplifies it, at speed and scale. Clean data is not an enhancer of an AI strategy. It is the essential prerequisite that makes an AI strategy viable. The organisations seeing the best results from AI aren't necessarily the ones with the best tools. They're the ones with the cleanest data. Start with a free data health check and find out where you stand.

A fresh chapter begins After eight years of travelling to exciting places in the world of events, Sarah has finally unpacked her bags and settled into a brand‑new adventure with the ClearCourse group at Fetchify. What makes this move so exciting is that Sarah isn’t just bringing a suitcase full of experience - she’s also carving out space to learn, grow, and lend her support wherever it’s needed. Whether it’s her customers, her teammates, or her family, Sarah has a knack for showing up with warmth and dedication. We caught up with her to hear how the transition is going, and true to form, she’s embracing the change with positivity and an eagerness to learn. It’s clear she’s already making her mark, blending her event‑world expertise with fresh energy for this next chapter. Closing a chapter at Fusion I loved my job and team at Fusion - being able to travel to places both at home and abroad was truly the opportunity of a lifetime. Having started as an Account Exec, I was a Senior Account Manager before the arrival of my first child. After taking a break to spend precious time with my little one, I later returned part‑time in a Customer Success role. Fast forward a few years, and with the arrival of my second child last September, I felt the pull for something new - a fresh challenge, a different rhythm. The opportunity to join the team at Fetchify came at just the right moment, offering me the chance to blend my wealth of experience with the excitement of a new chapter. Stepping into my new role My new adventure starts as Customer Service Manager, taking charge of support queries that come through the helpdesk and lending a hand wherever I can - whether that’s to customers or my teammates. Coming from a role where I knew the ins and outs like the back of my hand, it feels a little strange to be starting fresh again. But that’s part of the excitement: everything is new, and every day brings a chance to learn. With so many different aspects to Fetchify, I’m on a huge learning curve, and while that can feel daunting, it’s also energising. I’m ready to grow into this role and make it my own. The thrill of something new I’m really excited about the chance to learn and develop new skills. This role feels like an opportunity to carve out a fresh level of dedicated support for customers - one that’s not only effective but also personable. My background gives me a unique edge in supporting the team, too, especially the account managers. Having walked in their shoes, I know what’s required and, in time, I hope to anticipate where I can step in to help. It’s a win‑win all round: customers get thoughtful, tailored support, and the team gains a colleague who understands their world inside out. And for me, it’s the excitement of growing into something new while making a real difference. Finding my place in the team I feel so fortunate to be working with amazing people again - I’m absolutely loving my new team. They’re inspirational, friendly, and did I mention knowledgeable? Whatever you need, you just have to ask, and someone is always on hand to support me at this stage. It’s also great to be in such a flexible role, where the team trusts you to work unsupervised because they know you’ll work hard and give 100%. I’m really looking forward to contributing in ways that take some of the load off them and free them up, whether that’s by stepping in or taking initiatives along the way. Life beyond the desk When I’m not working, I happily spend all my time with my family. We love getting outdoors - whether it’s exploring country parks, going for long walks, or just enjoying nature together. Spending time with close friends who also have kids is another favourite. The children play, we catch up, and it always feels easy and fun. Honestly, anything goes as long as my children and family are with me. Family comes first, always. When I became a mum, I promised myself I would be there to spend time with them, and that’s something I hold onto every single day. Looking ahead Working as an Account Manager in events was a lot like project management - overseeing every detail and making sure everything came together. That’s something I’ve always enjoyed and feel confident in, so if the chance comes up to use those skills again later, that would be great. For now, though, I’m really happy on this new path. It’s fresh, it’s challenging, and I’m enjoying everything it brings.