Blog Post

As we enter a tumultuous World in 2026 I thought it worth reflecting on the impact AI made in 2025 and what, if any lessons we learnt along the way, By the end of 2025, the narrative around artificial intelligence shifted. Early excitement, hype and experimentation gave way to hard lessons in operational value, governance, and integration. Organisations discovered that deploying AI at scale is fundamentally a business transformation challenge and not just a technology change. Companies need to want to change if they are to leverage AI as an advantage. They have stopped ‘just going along with it’ to now looking for the economic benefits of using it. There is also, in my view a slight undercurrent of caution and when AI is discussed in context of, for example a legal firm, there seems to be a reluctance to embrace the many benefits quite possibly because of the potential cultural/personal impact of staff/clients.  Below are five key lessons from 2025 that could shape how business leaders approach AI in 2026. 1. Proofs of Concept Abound—but Many Failed to Deliver In 2025, the transition from pilots to scalable systems was a persistent challenge for organisations. Many AI initiatives that looked promising in controlled environments stalled when put into production. Context and SourceMcKinsey’s 2025 state of AI survey highlighted that while AI adoption increased, the transition from pilots to scaled impact remained a work in progress in most organisations. (McKinsey & Company) Illustrative ExampleNumerous studies throughout 2025 confirmed the pattern: companies build numerous PoCs, but only a small percentage reach sustained business outcomes because they’re not designed with integration, data quality, or ownership frameworks in mind. Reports indicate roughly only 54% of AI projects make it to production, with organisational readiness cited as a major cause of failure. (IBM). This certainly rings true in my experience, where a company looks to deploy AI without understanding the underlying requirements around data, culture and processes Why this mattersThis pattern forced a reset: in 2026, successful AI initiatives should start with operational metrics and production pathways, not just exploratory pilots. 2. Fitness for Purpose Outperformed “Bigger Models” In 2025, business leaders learned that larger models do not automatically deliver better value—especially in real operational environments where accuracy, trust, cost, and control matter more than raw capability. Context and SourceAcross industries, organisations focused on aligning AI with specific business needs and data domains rather than chasing the latest large models, as documented in year-end use-case surveys showing a shift toward targeted workflows such as fraud detection, customer service, and predictive analytics. (Databricks) Illustrative ExampleEnterprise AI blueprint collections in 2025 emphasised workflow-specific AI applications that reliably integrate with core systems such as CRM, ERP, and customer service platforms—rather than generic models running in isolation. (Google Cloud) Why this mattersIn 2026, organisations will increasingly prioritise domain-tuned models and embedding AI into existing systems rather than deploying large, standalone models that don’t provide stable ROI. 3. Redesigning Workflows Adds Real Value Organisations that rethought their processes to leverage AI saw greater benefits than those that simply grafted AI onto existing workflows. Context and SourceIndustry surveys of AI use cases show that companies are increasingly embedding generative AI into operational workflows such as document processing, customer service automation, and decision support—transforming work rather than automating single steps. (econocom.co.uk) Illustrative ExampleDatabricks’ review of top AI use cases in 2025 pointed to real-world deployments across analytics, automation, and operational improvement functions that demonstrate the value of aligning workflow redesign with AI capabilities. (Databricks) Why this mattersIn 2026, workflow integration—not task automation—will be the catalyst for measurable productivity gains. 4. Governance and Board Oversight Became Core Business Issues As AI moved deeper into business processes, governance, compliance, and risk oversight moved into the Boardroom. Context and SourceAI risk and governance began showing up in annual disclosures and board discussions, with nearly half of companies in some surveys reporting board-level oversight of AI risk and strategy. (Harvard Law Forum on Governance) Further, corporate governance thought leadership emphasised that Boards must actively shape AI governance posture and review it regularly to keep pace with evolving technology risks. (WTW) Illustrative ExampleIncreased AI oversight disclosures in 2025 filings demonstrate that companies are starting to embed AI risk into enterprise-wide governance and risk frameworks, covering accuracy, data use, and cybersecurity. (EY) Why this mattersFor 2026, organisations can no longer treat AI governance as a bottom-up IT project; it must be a strategic, Board-level competency. 5. Adoption Spread Rapidly—but Shadow AI and Risk Emerged Usage of generative and agentic AI tools expanded dramatically in 2025—but this created shadow AI use and data governance challenges. Context and SourceReports from late 2025 indicate widespread adoption, with usage rising significantly year over year. (Netguru) At the same time, security reports observed sharp increases in policy violations related to unsanctioned AI use—highlighting risks when employees adopt tools outside controlled enterprise environments. (TechRadar). We also saw this with the rise in Cloud file sharing sites like Dropbox and group messaging systems such as WhatsApp Illustrative ExampleSecurity reports highlight that as more users adopted generative AI tools, organisations experienced rising incidents where regulated or sensitive data was entered into public AI platforms—creating compliance, IP, and cyber risk challenges. (TechRadar) Why this mattersIn 2026, organisations must balance speed of adoption with governance and risk control to scale AI responsibly. Overall: 2025 Was the Year of Operational Reality Across industries, one message was unmistakable: AI has stopped being a novelty and become a line-of-business issue—driven by workflow integration, risk frameworks, and operational governance. As AI seems to be gaining traction in businesses, we might expect to see more caution or reluctance as people wrestle with the notion of hiring freezes (as AI will do those jobs now), the cultural impact of not hiring the next intake of young/future professionals and maybe the realisation that our own offices jobs may be impacted. Let’s see what 2026 has in store… Simon

Better Marketing with AI Marketing is one of those departments that rarely gets the credit it deserves.It keeps the funnel full so Sales can do their thing, it keeps existing clients engaged so Operations have someone to serve, and it shapes how the world sees your brand through your website, tone of voice, campaigns and content. And yet… Marketing is often the one fighting hardest for budget, time and Executive attention. Why?Because it’s hard to prove its exact value.You can measure open rates, clicks and traffic—but how do you prove that the £10,000 order last week wouldn’t have happened anyway?Was it that LinkedIn post, the SEO tweak, or the sales call that finally tipped them over the edge?Marketing sits in that grey area between cause and effect, and in most boardrooms, that makes it a tough sell. Add to that the fact that your audience is being bombarded from every angle—emails, reels, ads, TikToks—and it’s no wonder cutting through the noise feels harder than ever. (I’ve personally bought more from Instagram ads than I care to admit… some items are still in their wrappers.) So in a world of limited budgets, noisy channels and impatient execs, how can AI help your Marketing team stand out and actually prove its worth? Let’s look at a few powerful examples. 🎯 AI-Driven Prospect Targeting If you’ve got a CRM—and let’s face it, who doesn’t—it’s a goldmine that’s probably underused. AI can dig through that mountain of data to spot “high-intent prospects”—the people most likely to buy from you next.Think of it as pattern-spotting on steroids. The key is asking the right questions of your data. AI will do the rest—turning your CRM from a digital filing cabinet into a profit engine. 🔥 Inbound Lead Scoring (The Smart Way) Inbound leads are marketing gold.And yet, how often do they end up lost in someone’s inbox or buried under automated replies? AI can fix that by instantly scoring every lead based on signals like: That means your hottest, highest-value leads go straight to your best salespeople—no delay, no missed opportunity. It’s like having a 24/7 lead triage nurse making sure nothing valuable slips through the cracks. ✍️ AI Content Creation (The Right Way) Most Marketing teams are already dabbling with AI for blogs, posts, or emails.But let’s be honest—some AI-written content still reads like it was… well, written by a robot. That’s not necessarily bad—it depends how you use it. If AI helps your team produce more content, faster, and that content drives traffic or enquiries, great.But if it floods your channels with bland filler that adds nothing to anyone’s day, it’s doing more harm than good. Your job is to guide AI, not let it run wild.Use it to draft, refine and brainstorm—but always inject your brand’s voice and insight before hitting “publish.”And make sure your team is using it securely, not through some random free site your intern found last week. 💬 Chatbots & Conversational AI We’ve all been there—stuck in chatbot limbo, typing “human please” for the fifth time. The problem isn’t chatbots—it’s bad chatbots.AI is changing that with conversational models that feel far more natural and useful. With the right setup, an AI assistant can answer complex product or service questions using your own data—datasheets, PDFs, FAQs, anything. Imagine a prospect typing: “I’m a 45-year-old golfer with a 15 handicap and a 90mph swing—what driver should I buy?” AI can not only give a relevant answer but explain why, referencing your actual stock and brand details. It’s not quite human, but it’s miles ahead of the “click 1 for pricing” experience we all hate.And it’s just as powerful internally—think AI HR agents handling holiday queries or payroll questions before they ever hit a human inbox. 🚀 So What’s the Big Picture? AI won’t replace your Marketing team—it’ll amplify it.It helps identify better prospects, produce smarter content, respond faster, and personalise at scale. The question isn’t “Should Marketing use AI?”It’s “Are you using it properly?” Because with the right implementation—good data, smart prompts, secure systems—AI can turn Marketing from the department that’s always “justifying its budget” into the one driving measurable, repeatable growth. Marketing has always done more with less.Now, with AI, it can finally do it smarter, faster and louder than ever before. Simon – AI Guy

As this is my first post, I was tempted to launch into a long ramble about my background, hobbies, and what makes me tick. But let’s be honest — you didn’t come here to read my autobiography. So instead, here’s the abridged version: I’ve worked in and around IT since 1997, but even before that I was fascinated by how technology shapes our lives. As a teenager, I remember typing hundreds of lines of code into a Spectrum 48k with a mate just to make it whistle out the Hamlet cigar tune. Later, I lost an entire Sunday afternoon racing Gran Turismo in “real time” with my brother-in-law-to-be… only to crash spectacularly near the finish. And don’t even get me started on late-night Halo multiplayer sessions. Now, I’m not a developer. I can’t code. But I am — proudly — “quite nerdy.” I like to understand how things work. I’ve spent countless hours watching YouTube tutorials to figure out everything from fixing my TV to putting up a shed to (yes) attaching a domain name to this very blog. That same curiosity has carried over into my professional life, where I spend a lot of time with business owners diagnosing why their numbers aren’t stacking up — and finding ways to fix it through new tools, processes, or tech. Which brings us neatly onto the big one: Artificial Intelligence. Why AI Feels Different It’s easy to dismiss AI as “just another trend.” After all, we’ve seen big shifts before: All of these were game-changers — but mostly for people already working in tech. AI is different. AI is spilling out of the IT department and into every department. With the possible exception of the internet itself (and maybe social media), we’ve never seen a technology so quickly reshape both work and culture. If you’re in Sales, Marketing, Legal, Finance, or Healthcare — AI is already rewriting parts of your job. If you’re in IT, HR, Customer Services, or pretty much any role where you look at information, make decisions, and communicate  — the AI ripple is heading your way. The C-Suite Gets Curious And here’s where it gets really interesting. Traditionally, boards only really cared about IT when: But AI bucks this trend. Suddenly, CEOs and CFOs are leaning forward asking: And in many organisations, those questions are met with awkward silences. For once, change isn’t bubbling up from the bottom — it’s rolling downhill from the top. Where Next? I’m not here to declare whether AI is good or bad, or to advise you to steer your kids towards plumbing instead of programming. But I do think AI represents one of those rare technological moments where everyone — from interns to CEOs — has to stop and pay attention. And that’s what this blog will be about: unpacking what’s happening, sharing ideas, and hopefully sparking conversations about how we navigate it all. So, welcome. Let’s see where this goes. Simon

Everyone is talking about AI—on LinkedIn, in podcasts like The Rest is Politics, even in the pub at the weekend. It’s clearly entered the zeitgeist… but you might be thinking: “Should I be using it? And if so, where do I start?” Sound familiar? You’re not alone. With Microsoft pushing Copilot, everyone seemingly subscribing to ChatGPT Plus, Google talking Gemini, and Grok being a thing, it’s easy to feel overwhelmed. The first step is to pause and think about your business—what it does and how it operates. Are you: Clearly, each business operates differently, and their AI use cases will differ—but the approach to implementing AI is the same. Ask the Right Questions Could what you do—and how you do it—be made simpler or more efficient with AI?Are there repetitive tasks—like invoicing, drafting, comparing documents, analysing spreadsheets, or even frying chips—that could be automated?Could AI help you: If the answer is “yes” (or, more likely, probably), your next step is mapping your current processes. Identify gaps, inefficiencies, and duplication of effort. Map, Rank, and Value Your Processes Start by mapping processes at a high level. Then: This exercise also highlights: Consider a Pilot or Proof of Concept Once you’ve identified promising processes, try a small-scale AI pilot. For example: an AI agent could read incoming customer emails, classify them (order, complaint, enquiry, spam), and even start processing them: The result? Significant time savings and faster, more consistent customer service. Don’t Get Distracted by the Hype AI can seem technical and intimidating—especially with new versions, platforms, and buzzwords emerging constantly. But here’s the truth: the tool doesn’t matter until you understand where AI can actually help your business. Without that understanding, any implementation risks being a misfit: users bounce off it, processes fail, and your shiny AI project ends up in the “great bin of failure in the sky.” Assess the value, map your processes, run small pilots, and plan strategically. Then, when the next wave of weather-battered tourists comes in—or your business faces any recurring challenge—you’ll be ready.