The Curve

Most AI partners will help you build a model. Very few will tell you whether you should. Your data may not be in a state to support it. Your teams may lack the capability to adopt it. The use case you are excited about may have a compliance exposure nobody has mapped. The cultural resistance that killed the last initiative may still be sitting underneath the surface. These are not secondary questions. They are the questions that determine whether AI delivers or stalls. The gap most organisations fall into is not a technology gap. It is the space between an AI idea and the conditions required for that idea to work in practice. AI Discovery is how we close that gap. Not a workshop that produces a slide deck. A structured process that gives you a clear, evidence-led view of where AI can genuinely create value, what needs to be true before you commit, and what a realistic roadmap looks like given your actual data, infrastructure and capability. The model is the easy part. Everything around it is where the work is. Find out more about our AI Discoveries here: https://lnkd.in/eRbH2BKj

Most organisations acknowledge that their data is not perfect. The conclusion many draw from that is to wait. Get the data right first. Then explore AI. It sounds logical. In practice it tends to produce the opposite of what is intended. Data that sits dormant retains hidden inconsistencies, ambiguities and structural weaknesses that nobody notices because nobody is using it for anything demanding. Those problems only become visible once the data is put to work. Which means waiting for perfect data before starting is not just unnecessary. It may prevent the very process that makes the data better. The real argument is for sequencing, not perfection. Understanding what your data actually looks like before committing to an AI initiative, so the initiative can be scoped around what genuinely exists rather than what is assumed to exist. Ask most organisations where their data lives and you will get a confident answer. Ask them to prove it and the picture changes quickly. AI does not create data problems. It reveals them. At speed. James Ridgway has written a breakdown of how to approach this honestly, before it becomes a problem mid-initiative. Read the article here: https://lnkd.in/evCU9mF7

Something built on a Tuesday afternoon to solve a specific problem can become critical operational infrastructure within weeks. No owner. No documentation. No security assessment. No compliance review. This is the governance gap at the heart of vibe coding. And most organisations have no idea how exposed they already are. The conversation about vibe coding has focused on code quality and development speed. The more important question is simpler and harder to answer: when a tool processes customer data, automates a decision or sits inside a workflow teams depend on, who is actually accountable for it? Asking an AI to make something secure is not the same as it being secure. The person who built it through prompting does not know what security decisions were made on their behalf. By the time governance becomes a priority, the tool is already embedded. Retrofitting accountability is substantially harder than building it in from the start. James Ridgway has written a detailed breakdown of what to do about it. Link to article: https://lnkd.in/ehse-FWJ

A failed technology implementation does not have to mean starting over with the same approach. EPC Global Solutions UK had spent 18 months trying to replace their legacy ERP system with a previous supplier. Despite significant investment, they ended with nothing usable. The stakes were real. EPC sits at the centre of a tightly connected operational model, managing the end-of-life lifecycle of leased assets for a £70m turnover business. Sales, invoicing, inventory and financial reporting all depended on a platform that was approaching end of life and had no working replacement. The decision was to reset entirely rather than continue from a partially delivered system. Starting from first principles, we delivered a full Microsoft Dynamics 365 Business Central implementation in 5 to 6 months. One third of the time the previous programme had consumed. With a working, integrated system at the end of it. EPC closed one financial year on the legacy platform and opened the next entirely on Business Central, without disruption. Find the full case study here: https://lnkd.in/e2vF5cpk

The barrier to automating your operations is probably not what you think. Most SME leaders assume advanced automation is not for them yet. Too complex. Too expensive. Too much technical infrastructure required. In practice, that stopped being true some time ago. Modern platforms, cloud-based tools and AI services already embedded in the software most businesses use have largely removed the technical barrier. The cost of entry is considerably lower than it was even a few years ago. What holds most organisations back is not capability. It is clarity. Knowing which processes have a consistent enough logic to hand over to a system. Getting the underlying data clean enough to trust. Documenting rules that currently live in people's heads rather than anywhere a system could act on them. We worked with one organisation that wanted to automate their invoicing workflow. When we mapped the process, invoices were arriving through three different channels, in different formats, with different approval rules per department. None of it was written down anywhere. Automating that would have created a faster version of the same inconsistency. The right starting point was clarity first, automation second. Paul Ridgway has written a practical breakdown of where that approach leads. Find the article here: https://lnkd.in/eSbHbdYm

According to IBM, 76% of large companies now have a Chief AI Officer. Most mid-sized organisations don't have that luxury. They've got a founder wearing six hats, an ops manager who's curious but time-poor, and a team that's heard the AI pitch a hundred times but hasn't seen it land in their workflow yet. That's not a weakness. It's just a different starting point. The businesses that are pulling ahead aren't the ones with a CAIO. They're the ones that have decided to stop waiting for the right moment and started making practical moves. If you're trying to figure out what those moves look like for your business, that's exactly what we work on. Full IBM report here: https://lnkd.in/eGAArAi6

Big tech talks about AI at scale. Analysts talk about AI in theory. This webinar talks about AI in your business. James Ridgway, CTO at The Curve, will walk you through how to find the right starting point, build on solid foundations and implement something that actually delivers value. No matter what stage of your AI Journey, you'll find value in this session. Free. 30 minutes. Register here: https://lnkd.in/eEP5gGSJ

Are you navigating AI investment decisions? The question is not whether to adopt AI, It's knowing where to start in a way that works for your organisation. Is AI the Answer? AI in Practice: What Actually Delivers Value Inside Organisations, explores what good AI delivery looks like for mid-sized organisations, from data readiness to governance to practical implementation, one step at a time. Download our expert guide here: https://lnkd.in/eDxieef7

Most businesses are using AI as a tool. The ones pulling ahead are treating it as infrastructure. There is a meaningful difference between AI that sits alongside how work gets done and AI that is embedded inside it. One improves individual productivity. The other changes how the business operates. When AI is integrated into core workflows, three things happen consistently. Processes that took hours complete in minutes. Data moves between systems without manual re-entry. And output increases without headcount growing at the same rate. That last point matters most right now. With labour costs rising and margins under pressure, absorbing more volume through better systems rather than more people is increasingly what separates businesses that scale from those that plateau. The shift from experimentation to integration is not a technology decision. It is an operational one. That is where value begins to compound. Written by our CEO, Paul Ridgway, you can read the full article here: https://lnkd.in/e2f2ZxJt

We are delighted to welcome Will Thompson to The AI Reality Check roundtable in Leeds on 10th June. Will is Managing Director UK at EPiC Agile, a transformation consultancy that works with leaders to fix, build and run the systems that make organisations actually deliver. With a decade of hands-on experience leading major transformation programmes across Australia and the Asia Pacific, he has redesigned operating models for organisations across retail, energy and higher education. His approach is exactly what this roundtable is designed around. Getting into the detail with leadership teams on how work flows and how decisions get made. No theory. No vendor agenda. Just honest, practical experience from someone who has done it at scale. Places are limited. 10th June, Platform, Leeds. 📅 10th June | 🕐 8:30am | Platform, New Station Street, Leeds Link to register: https://lnkd.in/ebECin_8