# Rodan > Rodan is a London-based data science, AI and agentic systems consultancy. We help mid-market and enterprise organisations deploy analytics, AI and commercial data products that create measurable outcomes. Every HTML page has a Markdown twin. Request `Accept: text/markdown` on the canonical URL, or fetch the same path with a `.md` suffix (`/` → `/index.md`). The catalog below points at those Markdown URLs so agents do not have to guess. JavaScript widgets (Tally forms, the homepage globe, interactive assessments) have HTML noscript or mailto fallbacks. Forms are also at `https://forms.rodan.io/rfp`, `https://forms.rodan.io/contact`, and `https://forms.rodan.io/newsletter`. Contact: hello@rodan.io. Sales: sales@rodan.io. ## Core - [Home](https://rodan.io/index.md): Accelerating Innovation & Efficiency. Specialists in AI, data science & analytics. Find out how we can help your organisation today. Trusted by FTSE 100 companies for insights, efficiency and competitive advantage. - [About](https://rodan.io/company/about.md): Rodan is a London-based AI and data science consultancy. Founded in 2021, we build and deploy data platforms, AI systems and analytics solutions for mid-market and enterprise clients. - [Contact](https://rodan.io/contact.md): Find out how Rodan can help your organisation with data science, AI and analytics. - [Request a proposal](https://rodan.io/request-a-proposal.md): Bespoke proposal form; also at https://forms.rodan.io/rfp - [FAQ](https://rodan.io/faq.md): Find answers to common questions about Rodan’s AI, data science, and analytics services. Learn how we can help your organisation innovate and achieve efficiency. ## Products, solutions and proof - [AI Products & Tools for Business Transformation](https://rodan.io/products.md): Explore Rodan’s suite of products designed to unlock the power of AI and data analytics, providing actionable insights and driving strategic growth. - [Quantsole](https://rodan.io/products/quantsole.md): Natural language queries, automated insight generation and real-time commercial reporting. - [Eclipse](https://rodan.io/products/eclipse.md): Deploy, orchestrate and scale autonomous AI systems across your organisation. - [VOX](https://rodan.io/products/vox.md): Deeper insight into your customers, markets and competitors. - [Tailored Data-Powered Solutions for Business Innovation](https://rodan.io/solutions.md): Discover how Rodan’s data-driven solutions empower organisations with AI, analytics, and digital transformation to solve complex business challenges. - [AI & Data Analytics Solutions Across Industries](https://rodan.io/industries.md): Rodan delivers tailored AI and data solutions across diverse industries, driving innovation and operational efficiency to meet sector-specific challenges. - [Rodan Labs](https://rodan.io/company/labs.md): Rodan Labs is where innovation meets experimentation, developing cutting-edge AI and data science solutions to push the boundaries of what's possible. - [The Atrium](https://rodan.io/labs/atrium.md): Blending creativity with technology to create digital assets for modern media in the latest formats. - [irl](https://rodan.io/labs/irl.md): Software engineering for AR, MR and VR products - systems, tooling and experiences built to ship. - [RennLab](https://rodan.io/labs/rennlab.md): Utilise AI for predictive maintenance and performance optimisation in motorsport and fleet management. - [Outcomes, not outputs](https://rodan.io/company/case-studies.md): A sample of what we've built and the commercial results it created. ## Frameworks and tools - [Rodan Data Stack](https://rodan.io/guides.md): Practitioner frameworks for governance, maturity and insights - [Data Governance Toolkit](https://rodan.io/governance.md) - [Data Maturity Pyramid](https://rodan.io/maturity.md) - [Insights Framework](https://rodan.io/frameworks/insights.md) - [Tools](https://rodan.io/tools.md) - [Resources](https://rodan.io/resources.md) - [Glossary](https://rodan.io/glossary.md) ## Legal - [Privacy and Data Protection](https://rodan.io/legal-privacy/privacy-policy.md): How Rodan collects, uses and protects personal information on our websites and services. - [Legal Notices](https://rodan.io/legal-privacy/terms-of-service.md): Terms of Service governing use of the Rodan website and related services. - [Responsible AI](https://rodan.io/legal-privacy/responsible-ai.md): Our Responsible AI strategy and governance framework for building and deploying AI systems. - [Data Protection Policy](https://rodan.io/legal-privacy/data-protection-policy.md): How Rodan collects, stores and protects personal data during client engagements and site use. - [Anti-Corruption & Business Ethics](https://rodan.io/legal-privacy/anti-corruption-business-ethics-policy.md): Our zero-tolerance approach to bribery, corruption and unethical business conduct. ## Optional Skip this section when context is limited. Full insight index: `/insights.md`. - [What does a data-driven ecommerce growth strategy actually look like?](https://rodan.io/insights/what-does-a-data-driven-ecommerce-growth-strategy-actually-look-like.md): Most ecommerce businesses have more data than they know what to do with. Sessions, conversions, basket size, return rates, cohort retention - the dashboards exist. The problem is not access to data. The problem is that very few organisations have built a system that turns that data into... - [How to choose an AI analytics tool for your ecommerce stack](https://rodan.io/insights/how-to-choose-an-ai-analytics-tool-for-your-ecommerce-stack.md): Most ecommerce teams do not have an analytics problem. They have a decision quality problem. The data exists. The dashboards exist. What does not exist is a reliable path from signal to action - and the longer that gap persists, the more it costs in missed revenue, poor campaign allocation and... - [AI for social commerce: what brands need to know in 2026](https://rodan.io/insights/ai-for-social-commerce-what-brands-need-to-know-in-2026.md): Social commerce is no longer a channel you can monitor from a distance. It is where purchasing decisions are made, often in under ten seconds, often without a brand's direct involvement. The mistake most consumer businesses make at this stage is treating it as a subset of social media strategy - ... - [The Data Layer the Humanoid Robotics Industry Was Missing - Insights](https://rodan.io/insights/the-data-layer-the-humanoid-robotics-industry-was-missing.md): At Rodan, we spend most of our time deep in data - building analytics platforms, training models and helping some of the world's largest organisations make sense of complex information. So when a friend came to us with an idea that sat squarely at the intersection of AI, data int... - [How AI is changing the role of the marketing analyst](https://rodan.io/insights/how-ai-is-changing-the-role-of-the-marketing-analyst.md): Most marketing analysts are buried in reporting. They spend the majority of their week pulling data from disconnected platforms, reformatting it into dashboards nobody reads carefully and answering the same ad hoc questions they answered last month. The analysis that would actually change a... - [What is propensity modelling and how do marketing teams use it?](https://rodan.io/insights/what-is-propensity-modelling-and-how-do-marketing-teams-use-it.md): Most marketing teams are spending money they cannot justify on audiences they cannot define. They run broad campaigns, apply rough segmentation and then attribute whatever comes back to the last click that fired. The model is familiar. It is also quietly expensive. The problem is not effort.... - [How to use AI to improve customer lifetime value in ecommerce](https://rodan.io/insights/how-to-use-ai-to-improve-customer-lifetime-value-in-ecommerce.md): Most ecommerce businesses know their acquisition cost down to the penny. They can tell you CAC by channel, by campaign, by device. Ask them what a customer is actually worth over three years and the room goes quiet. That asymmetry is expensive. When you optimise for acquisition without a clear... - [AI and first-party data: how to prepare for a cookieless future](https://rodan.io/insights/ai-and-first-party-data-how-to-prepare-for-a-cookieless-future.md): The targeting infrastructure most ecommerce and consumer technology businesses built their growth models on is being taken apart piece by piece. Third-party cookies are going. Signal loss from iOS changes has already eroded attribution accuracy. Walled gardens are tightening their grip on... - [How to measure AI ROI in a performance marketing context](https://rodan.io/insights/how-to-measure-ai-roi-in-a-performance-marketing-context.md): Most marketing teams adopting AI are measuring the wrong things. They are tracking outputs - content produced, hours saved, tasks automated - and calling it return on investment. It is not. Output metrics tell you whether the machine is running. They do not tell you whether it is running in the... - [The data foundations every ecommerce brand needs before using AI](https://rodan.io/insights/the-data-foundations-every-ecommerce-brand-needs-before-using-ai.md): Most ecommerce brands adopting AI are doing it backwards. They buy a tool, connect it to whatever data they have available and wait for insight to appear. When it does not, they blame the tool. The real problem is earlier. AI does not create good outputs from poor inputs - it amplifies whatever... - [How to use AI-generated insights in your marketing strategy](https://rodan.io/insights/how-to-use-ai-generated-insights-in-your-marketing-strategy.md): Most marketing directors are sitting on more data than they know what to do with. CRM exports, ad platform dashboards, web analytics, customer surveys, social listening feeds - the data exists. The problem is not collection. The problem is that none of it talks to each other, analysis takes too... - [AI for inventory management: what the data actually tells you](https://rodan.io/insights/ai-for-inventory-management-what-the-data-actually-tells-you.md): Most ecommerce businesses think they have an inventory problem. They are usually right, but for the wrong reasons. The problem they name is stockouts, overstock or margin erosion. The problem they have not yet named is that their decisions are running on data that is weeks old, manually... - [How ecommerce brands are using AI to compete on margins](https://rodan.io/insights/how-ecommerce-brands-are-using-ai-to-compete-on-margins.md): Margin pressure is the defining commercial problem for ecommerce right now. Acquisition costs are up. Returns are eating fulfilment budgets. Promotional cycles have trained customers to wait for discounts. And the brands that built their models on growth-at-all-costs are finding that scale... - [What is a customer data platform and do you need one?](https://rodan.io/insights/what-is-a-customer-data-platform-and-do-you-need-one.md): Most marketing teams think their data problem is a technology problem. It is not. It is a fragmentation problem - and buying another platform rarely fixes fragmentation. It usually deepens it. Customer data platforms have been sold hard over the past five years. The pitch is compelling: one... - [How to use AI to improve your email marketing performance](https://rodan.io/insights/how-to-use-ai-to-improve-your-email-marketing-performance.md): Most email programmes are underperforming quietly. Open rates look acceptable. Click rates are not catastrophic. Nobody raises it in the Monday meeting. But the revenue attributed to email has been flat for two years, the unsubscribe rate creeps up each quarter, and the team is spending three... - [AI for retention marketing: how to reduce churn with machine learning](https://rodan.io/insights/ai-for-retention-marketing-how-to-reduce-churn-with-machine-learning.md): Most retention programmes are built on the wrong assumption. They treat churn as something you react to - a cancellation, a lapsed order, a support ticket that went badly. By the time those signals appear, the customer has already decided to leave. You are just processing the paperwork. - [How AI is improving product recommendation engines](https://rodan.io/insights/how-ai-is-improving-product-recommendation-engines.md): Most recommendation engines are not doing what their owners think they are doing. They surface popular products, reward repeat purchases and bury anything that does not already have velocity. The result is a system that amplifies what already works and ignores everything else - including the... - [What is predictive analytics and how do ecommerce brands use it?](https://rodan.io/insights/what-is-predictive-analytics-and-how-do-ecommerce-brands-use-it.md): Most ecommerce marketing budgets are allocated based on what happened last quarter. That is not strategy - it is extrapolation dressed up as planning. The brands that consistently outperform their category do not just analyse historical data. They use it to make probabilistic statements about... - [How to build a customer data strategy for an ecommerce brand](https://rodan.io/insights/how-to-build-a-customer-data-strategy-for-an-ecommerce-brand.md): Most ecommerce brands do not have a data strategy. They have a data accumulation problem dressed up as one. They collect everything, activate very little and spend considerable budget connecting tools that were never designed to talk to each other. The result is a marketing function that is... - [AI for ecommerce pricing: a practical introduction](https://rodan.io/insights/ai-for-ecommerce-pricing-a-practical-introduction.md): Most ecommerce businesses think they have a pricing strategy. What they actually have is a pricing habit - rules set months ago, margins defended instinctively and competitor checks done manually when someone remembers to do them. The cost of that habit compounds quietly. You leave margin on the... - [How to use AI to reduce customer acquisition costs in ecommerce](https://rodan.io/insights/how-to-use-ai-to-reduce-customer-acquisition-costs-in-ecommerce.md): Most ecommerce marketing teams are running harder to stand still. Paid media costs have risen consistently for years. Attribution is broken. Third-party audiences are degrading. And yet the default response from most growth teams is to spend more, optimise the creative, and hope the ROAS holds. - [What marketing data do you need before AI adds any value?](https://rodan.io/insights/what-marketing-data-do-you-need-before-ai-adds-any-value.md): Most marketing teams buying AI tools are doing it in the wrong order. They are investing in models before they have anything worth modelling. The result is expensive, slow and quietly embarrassing - a lot of dashboards that do not drive decisions, a lot of vendor promises that did not survive... - [How AI is changing ecommerce personalisation at scale](https://rodan.io/insights/how-ai-is-changing-ecommerce-personalisation-at-scale.md): Most ecommerce personalisation programmes are more sophisticated in the pitch deck than in production. You have a CDP, a recommendations engine, maybe a handful of segmentation rules that someone built in 2021 and nobody has touched since. You call it personalisation. Your customers experience... - [AI attribution modelling vs last-click: what ecommerce brands need to know](https://rodan.io/insights/ai-attribution-modelling-vs-last-click-what-ecommerce-brands-need-to-know.md): Most ecommerce marketing teams already know last-click attribution is wrong. They say it in meetings. They write it in strategy decks. Then they keep optimising to it because it is the number that is easy to pull and easy to defend. That is the real problem - not ignorance, but inertia.... - [How to use AI for demand forecasting without a data science team](https://rodan.io/insights/how-to-use-ai-for-demand-forecasting-without-a-data-science-team.md): Most marketing directors and heads of ecommerce have the same quiet frustration: they know their demand forecasting is wrong, they know it costs them money, and they have no clear path to fixing it that does not involve hiring a team of specialists they cannot justify or afford. The mistake most... - [AI for customer segmentation: what actually works in ecommerce](https://rodan.io/insights/ai-for-customer-segmentation-what-actually-works-in-ecommerce.md): Most ecommerce businesses think they are doing segmentation. They have RFM models, cohort reports, maybe a few lifecycle stages configured in their ESP. They send different emails to different groups. They call it personalisation. It is not segmentation. It is bucketing. And the difference costs... - [Why Cyber Security Is Now a Board-Level Priority for SMEs - Insights](https://rodan.io/insights/sme-cyber-security-strategic-imperative.md): When Jaguar Land Rover's production lines went dark in September 2025, the £5 million daily losses sent shockwaves far beyond the automotive sector. The incident - alongside similar disruptions at Marks & Spencer and Asahi Group Holdings - signals a new reality for organisations ... - [What does a successful AI transformation look like at scale?](https://rodan.io/insights/what-does-a-successful-ai-transformation-look-like-at-scale.md): Most senior leaders at this revenue tier have already run an AI pilot. Some have run several. A proof of concept delivered a promising result, someone wrote a slide about it, and then - quietly - nothing changed. The organisation moved on. The pilot sat in a drawer. This is not a technology... - [How to calculate the value of your company's data](https://rodan.io/insights/how-to-calculate-the-value-of-your-companys-data.md): Most senior leaders know their data is worth something. Very few can say what. That gap - between intuition and number - is where bad decisions live. The mistake organisations at this scale make most often is treating data as an IT asset rather than a commercial one. It sits on an infrastructure... - [How Rodan is Powering Modern Marketing in Financial Services - Insights](https://rodan.io/insights/how-rodan-is-powering-modern-marketing-in-financial-services.md): Marketing in financial services has long relied on assumptions - age, income, location and a handful of demographic signals. In 2025, these no longer tell the full story. People are not just data points. They are shaped by culture, habits and values that influence how they make d... - [Rodan Partners with UK Risk Consulting to Deliver Next-Generation GRC Solutions - Insights](https://rodan.io/insights/rodan-ukrc-partnership.md): Rodan is pleased to announce a strategic partnership with UK Risk Consulting (UKRC), one of Europe's leading independent networks of Governance, Risk and Compliance (GRC) specialists. - [AI for financial services: what mid-market firms need to know](https://rodan.io/insights/ai-for-financial-services-what-mid-market-firms-need-to-know.md): Most mid-market financial services firms are neither behind nor ahead on AI. They are stuck in a more dangerous position: busy. Busy running pilots that never scale. Busy evaluating vendors who built products for firms ten times their size. Busy explaining to the board why the transformation... - [How to build internal AI capability without hiring a full data science team](https://rodan.io/insights/how-to-build-internal-ai-capability-without-hiring-a-full-data-science-team.md): Most mid-market organisations facing pressure to move on AI arrive at the same flawed conclusion: we need to hire. A head of data science. A machine learning engineer. An analytics lead. Maybe all three. They write job descriptions, post them, and then wait - sometimes for six months - while the... - [Why your AI project needs a data strategy first](https://rodan.io/insights/why-your-ai-project-needs-a-data-strategy-first.md): Most AI projects fail before a single model is trained. Not because the technology is wrong, not because the team lacks capability, but because the organisation trying to run the project has no coherent view of its own data. The pattern is familiar. A senior leader approves a budget. A vendor is... - [How to select and manage an AI consultancy as a mid-market business](https://rodan.io/insights/how-to-select-and-manage-an-ai-consultancy-as-a-mid-market-business.md): Most mid-market businesses have already made at least one AI investment that did not deliver what was promised. A proof of concept that never reached production. A vendor relationship that consumed six months of internal resource and produced a dashboard nobody uses. A strategy document that... - [The five stages of AI maturity - and where most mid-market businesses actually are](https://rodan.io/insights/the-five-stages-of-ai-maturity-and-where-most-mid-market-businesses-actually-are.md): Most organisations at your scale are not behind on AI because they lack ambition. They are behind because they have been solving the wrong problem. They invested in tools before they had the data foundations to use them. They ran pilots that never scaled. They hired a head of data and expected... - [How to turn your existing data into a strategic asset](https://rodan.io/insights/how-to-turn-your-existing-data-into-a-strategic-asset.md): Most organisations at your scale are sitting on more data than they know what to do with. CRM records, ERP transactions, customer behaviour logs, finance reports, operational dashboards - the data exists. The problem is not collection. The problem is that none of it is connected, governed or... - [AI and competitive advantage: what mid-market firms can do that enterprises can't](https://rodan.io/insights/ai-and-competitive-advantage-what-mid-market-firms-can-do-that-enterprises-cant.md): Most mid-market leaders look at AI and see a resource gap. They see hyperscalers spending billions, global consultancies fielding armies of data scientists and enterprise competitors with dedicated AI labs. The natural conclusion is that they are behind, and that catching up requires investment... - [How to evaluate your current data infrastructure before investing in AI](https://rodan.io/insights/how-to-evaluate-your-current-data-infrastructure-before-investing-in-ai.md): Most organisations at this revenue scale have already spent serious money on data. A data warehouse here, a BI platform there, maybe a data lake that nobody fully trusts. The question is not whether you have data infrastructure - you do. The question is whether it is fit for what you are about... - [What does a responsible AI policy look like for a mid-market company?](https://rodan.io/insights/what-does-a-responsible-ai-policy-look-like-for-a-mid-market-company.md): Most mid-market companies are already using AI. The question is whether anyone has written down what that means - what is permitted, what is prohibited and who is accountable when something goes wrong. The mistake most organisations at this stage make is treating responsible AI as a compliance... - [How to make AI a board-level priority without overpromising](https://rodan.io/insights/how-to-make-ai-a-board-level-priority-without-overpromising.md): Most boards are not ignoring AI. That is not the problem. The problem is that they have seen three flavours of it already: the breathless pitch from a vendor, the POC that never scaled and the strategy deck that sat on a shelf. When the next senior leader walks in and asks for budget and... - [The difference between analytics, business intelligence and AI - and why it matters](https://rodan.io/insights/the-difference-between-analytics-business-intelligence-and-ai-and-why-it-matters.md): Most senior leaders at this scale have already spent money on at least one of these things. A data warehouse. A BI platform. Maybe a pilot with a large language model. And most of them are quietly unsure whether they got the return they expected. That uncertainty usually points to the same root... - [How to move from data reports to AI-driven decisions](https://rodan.io/insights/how-to-move-from-data-reports-to-ai-driven-decisions.md): Most finance and operations leaders at mid-market firms can tell you exactly what happened last quarter. They have dashboards. They have analysts. They have reports that land in inboxes every Monday morning, colour-coded by RAG status, attached to slide decks nobody reads past slide four. - [What is a chief data officer and does your business need one?](https://rodan.io/insights/what-is-a-chief-data-officer-and-does-your-business-need-one.md): Most mid-market firms have a data problem they have misdiagnosed. They think the problem is tooling - the wrong BI platform, an underpowered analytics team, reports that arrive too late to influence decisions. So they buy new software, hire a data analyst or two and wait for things to improve. - [How to avoid the most expensive AI mistakes mid-market firms make](https://rodan.io/insights/how-to-avoid-the-most-expensive-ai-mistakes-mid-market-firms-make.md): Most mid-market firms do not have an AI problem. They have a sequencing problem. The board has approved a budget. A vendor has been selected. A pilot is running. And somewhere in that chain, nobody asked whether the underlying data is clean enough to learn from, whether the use case connects to... - [AI for operational efficiency: where to start in a complex business](https://rodan.io/insights/ai-for-operational-efficiency-where-to-start-in-a-complex-business.md): Most senior leaders at this scale know they should be doing something with AI. The problem is not ambition - it is orientation. Everyone is being pulled toward the most visible use cases: chatbots, dashboards, co-pilots. Meanwhile the decisions that actually drive operational cost are being made... - [How to structure your data function as you scale beyond £500m](https://rodan.io/insights/how-to-structure-your-data-function-as-you-scale-beyond-500m.md): Most organisations at this stage have data. What they do not have is a data function that scales with the business. They have analysts scattered across departments, a central team that spends most of its time on reporting, and a data infrastructure that was designed for a company half the size.... - [What mid-market businesses get wrong about AI adoption](https://rodan.io/insights/what-mid-market-businesses-get-wrong-about-ai-adoption.md): Most mid-market businesses are not behind on AI because they lack ambition. They are behind because they are copying the wrong playbook. They watch what large enterprises announce, read the same vendor whitepapers and conclude that AI adoption means buying a platform, standing up a Centre of... - [How to choose an AI vendor: the questions to ask before you sign](https://rodan.io/insights/how-to-choose-an-ai-vendor-the-questions-to-ask-before-you-sign.md): Most organisations at your scale have already had the first AI conversation. Some have run a pilot. A few have signed a contract. Almost none have done adequate due diligence before committing. The mistake is understandable. AI vendors are well-funded, well-rehearsed and very good at... - [The real cost of building an AI team vs using an AI consultancy](https://rodan.io/insights/the-real-cost-of-building-an-ai-team-vs-using-an-ai-consultancy.md): Most senior leaders underestimate what it actually costs to build an internal AI capability. Not because they are careless - because the visible costs are only part of the picture. The mistake organisations at this stage typically make is treating AI hiring as a straightforward headcount... - [How to run an AI pilot that actually makes it to production](https://rodan.io/insights/how-to-run-an-ai-pilot-that-actually-makes-it-to-production.md): Most AI pilots fail quietly. Not with a dramatic collapse, but with a slow fade - the project that produced a promising demo, got featured in a board presentation and then stopped being talked about six months later. Nobody cancelled it formally. It just stopped mattering. This is the dominant... - [What is a data strategy and why does your business need one before AI?](https://rodan.io/insights/what-is-a-data-strategy-and-why-does-your-business-need-one-before-ai.md): Most organisations at this scale have already spent money on AI. A pilot here, a vendor contract there, perhaps a proof of concept that the data team is still trying to operationalise six months after it was declared a success. The results have been underwhelming - not because the technology... - [AI governance for mid-market companies: a practical guide](https://rodan.io/insights/ai-governance-for-mid-market-companies-a-practical-guide.md): Most mid-market organisations are already using AI. The problem is they do not know exactly where, who authorised it, or what decisions it is influencing. Someone in finance is running analysis through a large language model. A marketing team built an automated scoring tool eighteen months ago... - [How to build a business case for AI investment in a mid-market company](https://rodan.io/insights/how-to-build-a-business-case-for-ai-investment-in-a-mid-market-company.md): Most AI business cases fail before they reach the board. Not because the technology is unproven, but because the person building the case framed it wrong. They led with capability - what the AI can do - rather than with commercial impact - what the business will gain, or stop losing. - [How AI is changing the operating partner role in private equity](https://rodan.io/insights/how-ai-is-changing-the-operating-partner-role-in-private-equity.md): Most operating partners are carrying a portfolio that has quietly outgrown their model. Ten years ago, an operating partner with four or five assets could hold the material performance questions in their head, run the quarterly cadence and still have bandwidth to go deep on the one company that... - [The most common data problems acquirers discover post-close](https://rodan.io/insights/the-most-common-data-problems-acquirers-discover-post-close.md): There is a version of this story most PE firms have lived through at least once. The deal closes. The hundred-day plan starts. And within the first few weeks of working inside the business, the operating team realises that the data underpinning the investment thesis does not quite match reality. - [How to evaluate whether a portfolio company's data team is fit for purpose](https://rodan.io/insights/how-to-evaluate-whether-a-portfolio-companys-data-team-is-fit-for-purpose.md): Most PE operating partners spend the first hundred days looking at commercial performance, management quality and operational efficiency. The data team gets a cursory glance - a headcount number, maybe a conversation with the CFO about reporting capability. That is a mistake. A data function... - [Rodan sponsors Trapeze event on the future of food and drink advertising restrictions - Insights](https://rodan.io/insights/rodan-trapeze-lhf-2025.md): With the UK’s new restrictions on advertising "Less Healthy Food & Drink" (LHF) products set to come into effect in October 2025, many hospitality brands are rightly asking: what will we still be allowed to say, promote or even show online? - [AI transformation playbook for PE-backed businesses](https://rodan.io/insights/ai-transformation-playbook-for-pe-backed-businesses.md): Most PE-backed businesses arrive at the AI conversation the wrong way. A portfolio company CEO reads something, attends a conference, or gets sold to by a vendor. Suddenly there is a pilot running in one corner of the business, disconnected from any value thesis, with no clear owner and no... - [What operating partners should ask about data before signing an LOI](https://rodan.io/insights/what-operating-partners-should-ask-about-data-before-signing-an-loi.md): Most operating partners walk into a deal with a clear view on EBITDA, management quality and market position. They have a thesis. They have a model. What they rarely have is a clear view on whether the business can actually generate the data-driven performance improvements that thesis depends on. - [How to write an AI and data value creation plan for a board pack](https://rodan.io/insights/how-to-write-an-ai-and-data-value-creation-plan-for-a-board-pack.md): Most PE-backed businesses have a slide about AI in their board pack. It says something like "exploring opportunities in AI" or "AI strategy under development." It is, in effect, a placeholder. It tells the board nothing about where value will come from, on what timeline, or what it will cost to... - [Benchmarking AI maturity across a private equity portfolio](https://rodan.io/insights/benchmarking-ai-maturity-across-a-private-equity-portfolio.md): Most private equity firms now ask about AI in due diligence. Fewer know what to do with the answers. A portco says it uses machine learning for demand forecasting - is that a competitive asset or a retrofitted spreadsheet with a new label? Another says it has "an AI roadmap" - who wrote it, and... - [The role of the CDO in PE-backed growth businesses](https://rodan.io/insights/the-role-of-the-cdo-in-pe-backed-growth-businesses.md): Most PE-backed businesses at the growth stage have more data than they know what to do with. CRM systems, finance platforms, operational tools, ecommerce stacks - each generating signals that nobody is connecting. The result is a leadership team making consequential decisions on instinct,... - [How PE firms are building AI capability across their portfolio](https://rodan.io/insights/how-pe-firms-are-building-ai-capability-across-their-portfolio.md): Most private equity firms have accepted, at least in principle, that AI matters. The harder question - the one that separates firms generating real value from those generating slide decks - is how you actually build AI capability across a portfolio of ten, fifteen or twenty businesses with... - [AI and EBITDA: how to link technology investment to financial outcomes](https://rodan.io/insights/ai-and-ebitda-how-to-link-technology-investment-to-financial-outcomes.md): Most PE-backed technology investments get justified with a slide full of capability descriptions and a vague promise of operational efficiency. The investment committee approves it. Twelve months later, someone asks what it delivered - and nobody has a clean answer. This is not a technology... - [What a strong data infrastructure looks like at exit](https://rodan.io/insights/what-a-strong-data-infrastructure-looks-like-at-exit.md): Most PE-backed businesses spend years building commercial value and eighteen months scrambling to explain it. By the time advisers are preparing the information memorandum, the data is inconsistent, the metrics are contested and management is spending more time in Excel than running the business. - [How to run an AI audit on a newly acquired business](https://rodan.io/insights/how-to-run-an-ai-audit-on-a-newly-acquired-business.md): You have just closed a deal. The data room told you about revenue, margins and customer concentration. It almost certainly told you very little about the target's actual AI and data capability - or the technical debt sitting underneath it. That gap is now your problem. Most PE operating partners... - [AI governance for PE-backed businesses: what operating partners need to know](https://rodan.io/insights/ai-governance-for-pe-backed-businesses-what-operating-partners-need-to-know.md): There is a version of AI adoption happening across PE portfolios right now that looks like progress but is not. A portco spins up a pilot. The CEO reports green shoots. A few teams start using AI tools. Nobody has defined what data those tools can access, who approved the use case or what... - [Why most PE portfolio AI projects fail - and what to do differently](https://rodan.io/insights/why-most-pe-portfolio-ai-projects-fail-and-what-to-do-differently.md): There is a pattern that repeats itself across PE portfolios with uncomfortable regularity. A portco management team pitches AI as a value creation lever during the hold. The operating partner nods. A vendor gets selected, often quickly. Six months later, the project is either stalled, descoped... - [How to identify AI value creation opportunities in the first 100 days](https://rodan.io/insights/how-to-identify-ai-value-creation-opportunities-in-the-first-100-days.md): The first 100 days after acquisition set the trajectory for everything that follows. Most operating partners know this. What fewer acknowledge is how often AI ends up on the wrong side of that window - deferred to the transformation roadmap, handed to a workstream that starts in month four, or... - [The hidden data risks in mid-market M&A deals](https://rodan.io/insights/the-hidden-data-risks-in-mid-market-ma-deals.md): Most M&A due diligence processes are built to catch the problems that look like problems. Revenue concentration, customer churn, working capital cycles, legal exposure. The checklist is long and well-rehearsed. What the checklist misses is data. Not data in the abstract sense - everyone knows... - [Announcing Quantsole: A Simpler Way to See and Use Your Data - Insights](https://rodan.io/insights/announcing-quantsole.md): Announcing Quantsole: A Simpler Way to See and Use Your Data - [How AI is reshaping operational improvement in PE-backed businesses](https://rodan.io/insights/how-ai-is-reshaping-operational-improvement-in-pe-backed-businesses.md): Most PE-backed businesses arrive at the hundred-day plan with the same playbook: cut costs, stabilise management, rationalise the portfolio, improve EBITDA margins. It works - until it does not. The problem is not the playbook itself. The problem is that operational improvement has a ceiling... - [What is a data strategy and why does it matter at due diligence?](https://rodan.io/insights/what-is-a-data-strategy-and-why-does-it-matter-at-due-diligence.md): Private equity firms are getting better at spotting technical debt in software. They are getting worse at spotting data debt - and the two are not the same problem. A business can run on creaking infrastructure and still be fixable. A business whose data is fragmented, ungoverned or structurally... - [How to measure the ROI of AI investment in a portfolio company](https://rodan.io/insights/how-to-measure-the-roi-of-ai-investment-in-a-portfolio-company.md): Most AI investment in portfolio companies gets measured the wrong way, or not measured at all. Operating partners approve a budget line, a system gets deployed, and six months later someone is asked whether it "worked". By then the baseline is gone, attribution is impossible and the answer is... - [AI readiness assessment: what to look for before acquisition](https://rodan.io/insights/ai-readiness-assessment-what-to-look-for-before-acquisition.md): Most acquisition theses now include some version of "AI opportunity" in the value creation plan. The problem is that very few deal teams have a structured way to assess whether that opportunity is real, accessible or priced correctly. The gap shows up at portfolio review, twelve months... - [When to build vs buy AI capability in a portfolio company](https://rodan.io/insights/when-to-build-vs-buy-ai-capability-in-a-portfolio-company.md): Most operating partners approaching AI in a portfolio company ask the wrong question first. They ask "what AI tools should we be using?" before they have answered "what problem are we actually solving, and what does winning look like in our hold period?" That sequencing error is expensive. It... - [How PE firms are using AI for portfolio value creation](https://rodan.io/insights/how-pe-firms-are-using-ai-for-portfolio-value-creation.md): Most private equity firms talk about AI. Few have worked out what to do with it. The pressure is real. Hold periods are longer. Exit multiples have compressed. LP expectations have not. In that environment, every operating partner is looking for ways to accelerate value creation between... - [What does AI maturity look like in a mid-market business?](https://rodan.io/insights/what-does-ai-maturity-look-like-in-a-mid-market-business.md): Most mid-market businesses will tell you they are "exploring AI." A handful will say they are "investing in it." Very few can tell you what it has actually changed - in revenue, margin, decision speed or operational capacity. That gap between activity and maturity is where value gets destroyed.... - [The AI due diligence checklist every PE operating partner needs](https://rodan.io/insights/the-ai-due-diligence-checklist-every-pe-operating-partner-needs.md): Most private equity operating partners are walking into management presentations where the word "AI" appears on every third slide. Some of those claims are real. Most are not. The problem is that very few deal teams have a structured way to tell the difference. The mistake firms make at this... - [How to conduct an AI due diligence on a portfolio company](https://rodan.io/insights/how-to-conduct-an-ai-due-diligence-on-a-portfolio-company.md): Most private equity firms now ask some version of the AI question during diligence. The problem is most of them ask the wrong questions - and the management teams they are interrogating know it. The typical exchange goes something like this: the deal team asks whether the company uses AI, the... - [AI strategy in 2026: what's changed and what still matters](https://rodan.io/insights/ai-strategy-in-2026-whats-changed-and-what-still-matters.md): Most organisations that invested in AI over the past two years have something to show for it. A chatbot. A dashboard. A pilot that worked in the demo. What they often do not have is a business that operates meaningfully differently because of AI. - [What is model fine-tuning and when does it make sense for a business?](https://rodan.io/insights/what-is-model-fine-tuning-and-when-does-it-make-sense-for-a-business.md): Most organisations experimenting with AI are using foundation models out of the box. They plug in a general-purpose model, write a prompt, and wonder why the outputs feel slightly off - too generic, wrong in tone, occasionally wrong in fact. The instinct is to keep tweaking the prompt. Sometimes... - [How to brief an AI consultancy: a guide for business leaders](https://rodan.io/insights/how-to-brief-an-ai-consultancy-a-guide-for-business-leaders.md): Most AI engagements fail before they start. Not because the technology is wrong, not because the consultancy is incompetent, but because the brief was vague. The business leader walked in with a problem they had not fully defined, the consultancy shaped the engagement around what they could... - [What is structured data injection and when should you use it instead of RAG?](https://rodan.io/insights/what-is-structured-data-injection-and-when-should-you-use-it-instead-of-rag.md): You have a language model. You want it to answer questions about your business - your revenue, your customers, your operations. So your team reaches for the obvious tool: retrieval-augmented generation. They build a pipeline, chunk up some documents, embed them, store them in a vector database... - [How to run an AI proof of concept that stakeholders will trust](https://rodan.io/insights/how-to-run-an-ai-proof-of-concept-that-stakeholders-will-trust.md): Most AI proofs of concept fail not because the technology does not work, but because nobody agreed on what "working" meant before they started. The business lead wanted revenue impact. The technical team measured model accuracy. The CFO wanted a payback period. The PoC delivered all three things... - [The hidden costs of AI that most vendors don't talk about](https://rodan.io/insights/the-hidden-costs-of-ai-that-most-vendors-dont-talk-about.md): Most organisations buying AI right now are making a decision based on incomplete numbers. The vendor has shown them a compelling demo. The pilot produced encouraging results. The commercial team has put together a business case built around productivity gains and headcount efficiency. It looks... - [How to measure AI performance beyond accuracy metrics](https://rodan.io/insights/how-to-measure-ai-performance-beyond-accuracy-metrics.md): Most organisations deploying AI are measuring the wrong things. They ask their data science team whether the model is accurate. The team says yes. Everyone moves on. Six months later, the business cannot explain why the AI system is not delivering the expected value - and nobody has the... - [AI hallucination: what it is and how businesses should manage it](https://rodan.io/insights/ai-hallucination-what-it-is-and-how-businesses-should-manage-it.md): You have deployed an AI tool. It is generating output. And some of that output is confidently, plausibly wrong. That is the hallucination problem. Not a glitch, not a temporary limitation - a structural feature of how large language models work. Most organisations discover it the hard way: a... - [What is the context window in an LLM and why does it matter?](https://rodan.io/insights/what-is-the-context-window-in-an-llm-and-why-does-it-matter.md): Most business leaders evaluating large language models focus on the wrong things. They ask about accuracy, speed, cost per query. These matter. But the constraint that most often determines whether an LLM deployment actually works in practice is one that rarely appears in vendor conversations:... - [How to get your data ready for AI in six practical steps](https://rodan.io/insights/how-to-get-your-data-ready-for-ai-in-six-practical-steps.md): Most organisations pursuing AI are not held back by the technology. They are held back by the data underneath it. The typical pattern looks like this: a leadership team approves an AI initiative, a vendor gets selected, and then the project stalls. Not because the model is wrong, not because the... - [AI and data privacy: what every business leader needs to understand](https://rodan.io/insights/ai-and-data-privacy-what-every-business-leader-needs-to-understand.md): Most business leaders treating AI and data privacy as a compliance problem are solving the wrong question. Compliance is a floor, not a strategy. And the organisations that treat it as a ceiling are the ones that either stall their AI programmes waiting for legal sign-off or, worse, ship... - [How to structure an AI centre of excellence in your organisation](https://rodan.io/insights/how-to-structure-an-ai-centre-of-excellence-in-your-organisation.md): Most organisations that are serious about AI make the same structural mistake: they create a team, give it a name, and assume the rest will follow. Eighteen months later, that team has produced several proofs of concept, a governance framework nobody reads and a growing backlog of requests it... - [What is prompt engineering and why does it matter for business?](https://rodan.io/insights/what-is-prompt-engineering-and-why-does-it-matter-for-business.md): If you have experimented with AI tools and found the results inconsistent, underwhelming or simply not fit for purpose, the problem is probably not the model. It is how you are talking to it. Most organisations treat large language models like a search engine. They type a vague question, get a... - [How to think about AI risk in a regulated industry](https://rodan.io/insights/how-to-think-about-ai-risk-in-a-regulated-industry.md): Most organisations in regulated industries approach AI risk backwards. They wait for the regulator to publish guidance, then retrofit compliance onto systems that were already built. By the time the policy lands, the architecture is set, the vendor is contracted and the risk is already embedded. - [The difference between building AI and buying AI](https://rodan.io/insights/the-difference-between-building-ai-and-buying-ai.md): Most organisations approaching AI adoption are asking the wrong question. They are asking "which tool should we buy?" when they should be asking "what problem are we actually trying to solve, and what kind of AI commitment does that require?" The confusion is understandable. The market is full... - [What is a large language model and how do businesses use them?](https://rodan.io/insights/what-is-a-large-language-model-and-how-do-businesses-use-them.md): Most business leaders have spent the last two years being told that large language models will transform their industry. Many have run a pilot. A few have deployed something into production. Almost none have a clear picture of what these systems actually are, what they can and cannot do, and... - [How to write an AI policy for your organisation](https://rodan.io/insights/how-to-write-an-ai-policy-for-your-organisation.md): Most organisations that have started using AI tools have not written a policy governing them. They have relied on acceptable use guidelines borrowed from IT security, verbal guidance from managers, or nothing at all. Meanwhile, their employees are feeding customer data into public LLMs, using... - [What is AI governance and how do you implement it?](https://rodan.io/insights/what-is-ai-governance-and-how-do-you-implement-it.md): Most organisations deploying AI are doing so faster than they are governing it. That is not an accusation - it is a structural problem. The pressure to ship something, to show the board a working prototype, to not fall behind competitors, consistently outpaces the work of deciding who is... - [How to protect sensitive data when using AI tools](https://rodan.io/insights/how-to-protect-sensitive-data-when-using-ai-tools.md): Most organisations using AI tools are exposing data they do not realise they are exposing. Not through a breach. Not through negligence in the traditional sense. Through normal, daily usage by well-intentioned employees who have no idea where their inputs go once they hit send. The mistake most... - [What is RAG and why does it matter for enterprise AI?](https://rodan.io/insights/what-is-rag-and-why-does-it-matter-for-enterprise-ai.md): Most organisations deploying large language models hit the same wall. The model is impressive in a demo. It answers questions fluently, drafts content quickly, reasons through problems with apparent confidence. Then someone asks it something specific - about your product margins, your customer... - [The difference between AI, machine learning and data science](https://rodan.io/insights/the-difference-between-ai-machine-learning-and-data-science.md): Most business leaders have sat through a presentation where someone used "AI", "machine learning" and "data science" interchangeably - then left the room more confused than when they arrived. The confusion is not accidental. Vendors benefit from it. Consultants who lack rigour perpetuate it. - [What is agentic AI and why does it matter for business?](https://rodan.io/insights/what-is-agentic-ai-and-why-does-it-matter-for-business.md): Most organisations are still treating AI as a sophisticated autocomplete. They feed it a prompt, get an output, copy it into a document and move on. That is not a transformation. That is a faster way to write emails. The more significant shift - the one most leadership teams have not yet fully... - [Why most AI projects fail - and how to do it differently](https://rodan.io/insights/why-most-ai-projects-fail-and-how-to-do-it-differently.md): Most AI projects do not fail because the technology does not work. They fail because the organisation was not ready for what success would require. The pattern is consistent across sectors. A board approves a budget. A vendor is selected. A proof of concept runs for twelve weeks. The results... - [How to evaluate an AI vendor: the questions every business should ask](https://rodan.io/insights/how-to-evaluate-an-ai-vendor-the-questions-every-business-should-ask.md): Most organisations approach AI vendor selection the wrong way. They issue an RFP, receive polished decks, sit through demos built on their own data, and then make a decision based on which vendor told the most compelling story. That is not evaluation. That is buying a presentation. - [What is an AI audit and does your business need one?](https://rodan.io/insights/what-is-an-ai-audit-and-does-your-business-need-one.md): Most organisations adopting AI are flying partially blind. They have bought tools, run pilots, perhaps pushed something into production - and they have no reliable picture of whether any of it is working, whether it is creating risk, or whether the money they have spent is doing anything useful. - [askKira and Rodan Partner to Drive AI for Educators - Insights](https://rodan.io/insights/askkira-rodan.md): askKira, AI platform designed to make educators more efficient is delighted to announce its strategic partnership with Rodan, consultancy and advisory firm, specialised in data analytics & AI. - [Introducing Eclipse: Our Enterprise LLM Framework - Insights](https://rodan.io/insights/eclipse.md): Artificial intelligence is advancing rapidly, with new large language models (LLM) emerging that are bigger, better, and more capable than ever before. Enterprises need AI solutions that can keep pace with this innovation and adapt to leverage new models as they are developed. Ec... - [Introducing Rodan Labs - Insights](https://rodan.io/insights/introducing-rodan-labs.md): In an era where data is the new gold, mining it effectively becomes the alchemy of the 21st century. Welcome to Rodan Labs, the innovative arm of Rodan and your go-to destination for groundbreaking, data-driven solutions. - [Data Analytics Demystified: A Guide - Insights](https://rodan.io/insights/data-analytics-beginners-guide-demystified.md): Data analytics is the systematic process of examining, cleaning, transforming, and interpreting data to discover meaningful patterns and draw valuable conclusions. In today's digital age, organisations generate massive amounts of data through various channels, from customer inter... - [Introducing The Data Advantage: Unlocking Business Potential with AI & Analytics - Insights](https://rodan.io/insights/the-data-advantage-introduction.md): In today’s rapidly evolving business landscape, data is more than just numbers - it’s the foundation for smarter decisions, strategic growth, and lasting competitive advantage. At Rodan, we specialise in transforming raw data into actionable insights, helping businesses across indu... - [Introducing Rodan - Insights](https://rodan.io/insights/introducing-rodan.md): Welcome to Rodan, where innovation isn't just a buzzword - it's our modus operandi. Unlike traditional advisory firms, partnering with Rodan grants you access to a team as dedicated as you are. We are laser-focused on solving complex data issues, tapping into untapped opportunities...