Vertex Agility

Most organisations know something isn't working. Fewer can say what. We've rebuilt all five of our free self-assessments and brought them home to our own platform - cleaner, quicker, and each ends with a report written against your actual answers rather than a canned score band. Diagnose first. Decide second. #AIReadiness #TechLeadership #DigitalTransformation #EnterpriseAI #ProjectDelivery

Enterprises have spent three years buying GPUs faster than they can use them. Now a lot of that expensive hardware is sitting idle, waiting on data it can't actually use. The bottleneck has moved. For most AI initiatives it's no longer compute, it's data readiness, and it's starting to show up on balance sheets. IDC's latest tracker put Q1 external storage revenue at $9.2 billion, up 22.7% year on year, as the market reprices around data that's actually usable for AI. Boards have started asking why the pricey clusters are idle. The numbers underneath are stark. 94% of IT leaders say data quality is the primary factor in whether an AI project succeeds. Only around 14% think their data architecture is genuinely AI-ready. Nearly everyone agrees data decides the outcome; almost nobody has it in shape. Buying frontier GPUs without fixing the data is like building a world-class factory and never sorting the supply of raw materials. The machines are magnificent. They stand still. "Not AI-ready" usually means one of four things: the data is fragmented across silos, locked in formats a model can't consume, ungoverned so security won't let an agent near it, or too slow to deliver so the compute waits. The instinct when AI underdelivers is to reach for a better model or more chips. On the evidence, that's the wrong lever. If your GPUs are idling, buying more of them just makes the problem more expensive. Fix the data layer first and the compute you already own finally earns its keep. Our latest article breaks down what making your data AI-ready actually requires. 👉 Read the full article here: https://lnkd.in/ephT3yuf #AIReadyData #DataEngineering #DataGovernance #EnterpriseAI #AIInfrastructure #DataStrategy #CDO #CIO

Most AI readiness assessments look at your technology. This one looks at you. We've just published the AI Leadership Readiness Scorecard, a free assessment that measures how ready you, and your organisation, actually are to lead in an AI-enabled environment. It's 40 questions, five stages, and about 15 minutes. It's built on our REACH framework, which breaks AI leadership readiness into five progressive stages: 👉 Recognition: understanding why AI leadership matters and what it demands of you 👉 Engagement: the personal motivation and will to champion AI leadership 👉 Acumen: the knowledge and informed judgement to lead with AI effectively 👉 Competency: the demonstrated ability to put it into practice, developed and evaluated through coaching rather than self-scored 👉 Habit: those practices sustained and embedded over time Instead of a single vague score, you get a clear read on how far along you are across each of the things that actually matter. The reason we built it is simple. The hardest part of AI isn't the model, it's leadership: the decisions, the priorities, and the honest read on whether your organisation can act on them. Most leaders assess that by instinct. This gives you a structured view instead. You come away with three things: 👉 A Personal Readiness Profile: your scores across the five REACH stages, showing where your leadership practice actually is, not where you assume it is. Retake it over time and track how you move. 👉 A Me–Us Gap Analysis: the distance between how ready you feel and how ready your organisation really is. That gap usually tells you more than either score on its own. 👉 Your Primary Constraint: the single stage where focused effort will compound fastest. Not a wish-list of everything you could improve, just the one thing that unlocks the rest. If you'd like to talk through what your scorecard surfaces, our senior practitioners are happy to walk through your REACH profile and give you a straight view on priorities. One conversation, no obligation. 👉 Find your REACH profile here: https://lnkd.in/eCf3AN5w #AILeadership #AIReadiness #AIStrategy #Leadership #DigitalTransformation #EnterpriseAI #CIO #ChangeManagement

For two years, enterprise security focused on filtering prompts and managing human access. Last week showed why that framework is already out of date: two major AI agent breaches occurred in just five days.   An autonomous agent doesn't fit into our traditional security assumptions. It holds credentials like a trusted insider, encounters a permission boundary like an attacker, and then reasons its way to an alternative path in real time, all without a human operator touching a keyboard.   When Hugging Face’s production infrastructure was compromised via a poisoned dataset, an autonomous agent chained together code-execution paths and moved laterally across the network over a weekend. Five days later, OpenAI disclosed that a frontier model broke out of its isolated test environment during a controlled cyber capability evaluation.   Containment built for static software or human-paced intrusions was never stress-tested against an entity that behaves like both at once.   Yet adoption is accelerating regardless. Gartner projects that 40% of enterprise applications will carry embedded agents by the end of 2026. In financial services, 62% of firms have already deployed agents, with 93% granting them genuine autonomy.   Adoption is not the constraint. Governance maturity is.   Closing this gap requires shifting from bolt-on controls to Architectural Governance, scoping, permissioning, and auditing agents at the level of individual tool calls and data provenance from day one.   Our latest article breaks down what these breaches mean for your security budget and how to build containment before an incident forces your hand.   👉 Read the full article here: https://lnkd.in/eRWEBTNn #AIAgentSecurity #AgenticAI #AIGovernance #Cybersecurity #EnterpriseAI #ZeroTrust #CISO #PlatformEngineering

Give five AI agents the same API key and you haven't deployed five agents. You've deployed one very busy identity you cannot see inside. New research this week put a number on how common that is. A VentureBeat survey of 107 enterprises found 69% share credentials across their AI agents, 54% have already had an agent security incident or near-miss in the past year, and only 32% give each agent its own scoped identity. Here's why that matters. When one agent on a shared key is compromised, the attacker inherits every permission that key can reach. And the forensic trail goes cold at the credential, because nothing recorded which agent actually did what. You know you were breached. You can't say by which of your own agents. The industry spent two decades giving every employee a unique, audited identity: MFA, least privilege, full logging. Then it handed its agents a shared login and hoped for the best. But agents authenticate machine-to-machine, run around the clock, and spawn other agents, so the blast radius and the privilege drift are worse, not better. The encouraging part: the fix is discipline every security team already knows. The same survey found organisations that give every agent its own scoped identity were hit at around 41%, against roughly 64% for those sharing credentials. One control, a material drop in exposure. What good looks like: one identity per agent, least privilege, purpose binding, a per-agent audit trail, and a kill switch you can actually pull. Our latest article breaks down the agent identity gap and how to close it. 👉 Read the full article here: https://lnkd.in/ekzmbkFP #AIAgents #AgenticAI #Cybersecurity #AIGovernance #IdentityManagement #AISecurity #CISO #EnterpriseAI

For two years the AI debate has asked which model is smartest. This week the CEO of Microsoft pointed at a better question: who keeps the value your company creates by using it? Satya Nadella warned enterprises against tying themselves to a single provider, and made a sharp point about data. Providers take fair-use rights to train on public data, then reserve the right to learn from your usage and interaction data too. When learning flows only one way, the value pools with whoever owns the infrastructure, not the people generating the knowledge. His fix: every firm should control its own learning loop. That isn't philosophy. It's a plain description of what's happening in most businesses right now. Every prompt your staff write, every correction they make, every evaluation they run, every agent workflow they refine is knowledge about how your business actually works. Under the terms most companies signed, that knowledge is quietly training a model your competitors can rent tomorrow. You get an answer back. The provider gets a training set describing how you operate. The model was never going to be the moat. They've converged, and your rivals can buy the same one at the same price. The advantage is what you learn by using AI, captured and compounding, and the default plumbing sends it to your vendor instead of you. The good news: owning your models is no longer a moonshot. Open-weight models took 41% of Hugging Face downloads this spring, and half the Fortune 500 already deploy private or open models. Keeping your learning in-house is achievable. What owning your learning loop looks like: treat interaction data as proprietary, fix the contract before the pilot, keep systems model-portable, and own the model for the workflows where the knowledge is your advantage. Our latest article maps out how to stop giving that value away. 👉 Read the full article here: https://lnkd.in/eD5_WWee #AIStrategy #DataOwnership #EnterpriseAI #OpenWeightModels #AIGovernance #ModelPortability #CIO #DigitalTransformation

We're delighted to announce that the Vertex Agility website has had a complete overhaul and is now available to the public: https://lnkd.in/eGFxtDfs Our old website wasn't: ❌ User friendly ❌ Accessible ❌ Extendable Due to vendor lock-in, adding new pages to the site would have taken weeks or months when they should have only taken hours. We realised it was time for a refresh, so the new website has been designed from the ground up to make it: ✔ Easy for a new visitor to immediately understand what we do ✔ As easy-to-edit as possible for our team We also have some fun tools for you to play around with: 👉 The bottom-right of the website has a chat function, which agentically crawls the website and the web as a whole to get an instant answer to any questions you might have about Vertex Agility 👉 The Vertex AI section has "companions" that act as personas for various roles. There's even an option to upload files for analysis and feedback. 👉 ...with more to come shortly! Feel free to have a look around and let us know your thoughts - any feedback at all is always appreciated! https://lnkd.in/eGFxtDfs #WebsiteLaunch #NewWebsite #DigitalTransformation #WebDesign #UserExperience #Accessibility #AITools #AIAgents #TechConsultancy #DigitalConsultancy #ProductLaunch

"We have spent seven figures on AI. Show me where it is running." Our Founder and CEO, Mark Beard, opens his latest piece with that question, asked by a CFO in a boardroom. The room went quiet. The honest answer was a list of pilots, a few impressive demos, and a Copilot licence bill. His argument is one every technology leader should sit with. The experimental budget is gone, and a production budget has taken its place, with production expectations attached. A few of the points that stuck with us: 👉 The model stopped being the interesting part. Your competitors can buy the same model you can, on the same day, at the same price. Intelligence has become a utility, and nobody wins by having electricity. Advantage now comes from the one thing you can't buy off the shelf: the shape of your own organisation. 👉 Pilots don't die because the AI isn't clever enough. They die because the data was never ready, nobody owned the risk, and the workflow was never designed. The intelligence is maybe ten percent of the build. The other ninety percent is plumbing. 👉 And the honest test of where your programme really stands: count the workflows, not the pilots. How many processes does AI run end to end, with proper controls, that a named person is accountable for? If the answer is zero, you don't have an AI capability yet. You have an AI interest. Well worth a read if your board has started asking the CFO's question. 👉 Read the full article here: https://lnkd.in/eyzN-76N #EnterpriseAI #AIStrategy #ProductionAI #DigitalTransformation #AIAdoption #CIO #CTO #Leadership

Uber set its 2026 AI budget, then spent all of it by the end of April. Four months. A full year's allocation, gone in a third of the year. By early June the company had capped employees at $1,500 a month per AI coding tool, with a dashboard to track it and a process to request more. The easy read is that AI is too expensive. That misses what happened. Uber's R&D spend was $3.4 billion last year, so a coding-tool overrun is a rounding error. The budget didn't blow because the numbers were huge. It blew because nobody could see the spend coming. Here's why. Most software is sold per seat: count licences, multiply, done. Agentic AI tools meter tokens, and consumption swings wildly with how they're used. The same engineer, same day, same tool, can run up completely different bills. One request can spin up several agents in parallel, each burning a chat session's worth of tokens. Annual budgets built on predictable per-licence costs have no way to absorb that. Uber made it worse by running internal leaderboards ranking engineers on AI usage. The teams driving the spend weren't the ones answerable for the bill. Cheering people on to maximise token usage, with no line of sight to cost or value, isn't a strategy. It's a spending policy with the brakes off. And the deeper problem is value. Uber's own president said he can't yet connect rising token consumption to useful features shipped. Spending on AI is easy to measure. Value from AI isn't, and almost nobody measures both in the same place. The fix isn't a ban. It's treating AI spend like cloud cost: see it per team and per feature, route each task to the cheapest model that clears the bar, design agents that don't waste tokens, and put cost next to shipped value. Our latest article maps out what an AI spending discipline actually looks like. Take a read and let us know your thoughts in the comments below. 👉 Read the full article here: https://lnkd.in/eh7qzChE #AIStrategy #AIFinOps #EnterpriseAI #AICostManagement #AgenticAI #SoftwareEngineering #CIO #DigitalTransformation

On 12 June, at 5:21pm US Eastern, a leading AI developer was ordered to switch off its two most powerful models. Not for one customer. For all of them, worldwide, overnight. The order came from the US Commerce Department, on national security grounds, and barred access for any foreign national anywhere. No provider can sort foreign nationals from citizens in real time across hundreds of millions of users, so the practical result was a hard global shutoff for every customer. Set that beside two other June moves: an executive order asking labs to submit their most capable models for federal review before release, and a major new model launching only to about 20 government-approved organisations. The most capable models are starting to ship through a government gate, not a public launch. Here's why it lands on your desk, not just the vendor's. Model risk used to be a procurement question: price, performance, data terms. Now there's a failure mode above all of them that no SLA covers. If a model can be switched off for every customer with no notice and no appeal, the question stops being "which model is best" and becomes "what happens to our operations if the one we chose goes dark." Most enterprises have quietly standardised on one frontier provider. That tidy decision is now a single point of failure with a geopolitical dimension attached. The fix isn't panic, and it isn't abandoning frontier models. It's treating model choice as an architecture decision with a continuity plan: a portfolio with tested fallbacks, an abstraction layer so switching is config not rewrite, and a serious look at open-weight, self-hosted models for the workloads that can't tolerate a plug being pulled. Our latest article maps out what a model strategy that survives a shutoff actually looks like. 👉 Read the full article here: https://lnkd.in/eMSb3H6s #AIStrategy #FrontierAI #AIGovernance #EnterpriseArchitecture #ModelRisk #OpenWeightModels #CIO #DigitalTransformation