# Playbooks
*Intelligence from the Field — practical guides*
- [How to build a customer data strategy for an ecommerce brand](/insights/how-to-build-a-customer-data-strategy-for-an-ecommerce-brand): 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...
- [How to build internal AI capability without hiring a full data science team](/insights/how-to-build-internal-ai-capability-without-hiring-a-full-data-science-team): 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...
- [How to evaluate your current data infrastructure before investing in AI](/insights/how-to-evaluate-your-current-data-infrastructure-before-investing-in-ai): 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...
- [How to run an AI pilot that actually makes it to production](/insights/how-to-run-an-ai-pilot-that-actually-makes-it-to-production): 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...
- [AI governance for mid-market companies: a practical guide](/insights/ai-governance-for-mid-market-companies-a-practical-guide): 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](/insights/how-to-build-a-business-case-for-ai-investment-in-a-mid-market-company): 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.
- [AI transformation playbook for PE-backed businesses](/insights/ai-transformation-playbook-for-pe-backed-businesses): 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...
- [How to write an AI and data value creation plan for a board pack](/insights/how-to-write-an-ai-and-data-value-creation-plan-for-a-board-pack): 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...
- [How to identify AI value creation opportunities in the first 100 days](/insights/how-to-identify-ai-value-creation-opportunities-in-the-first-100-days): 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...
- [How to brief an AI consultancy: a guide for business leaders](/insights/how-to-brief-an-ai-consultancy-a-guide-for-business-leaders): 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...
- [How to run an AI proof of concept that stakeholders will trust](/insights/how-to-run-an-ai-proof-of-concept-that-stakeholders-will-trust): 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...
- [How to get your data ready for AI in six practical steps](/insights/how-to-get-your-data-ready-for-ai-in-six-practical-steps): 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...
- [How to structure an AI centre of excellence in your organisation](/insights/how-to-structure-an-ai-centre-of-excellence-in-your-organisation): 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...
- [How to write an AI policy for your organisation](/insights/how-to-write-an-ai-policy-for-your-organisation): 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...
- [How to protect sensitive data when using AI tools](/insights/how-to-protect-sensitive-data-when-using-ai-tools): 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...
- [How to evaluate an AI vendor: the questions every business should ask](/insights/how-to-evaluate-an-ai-vendor-the-questions-every-business-should-ask): 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.
## Curated resources
- [Private Equity Governance](/insights/playbooks/private-equity-governance): A practical governance overlay for private-equity operating partners and portfolio leadership teams.
- [Resources](/resources): Browse practical checklists, question banks and example outputs.
- [Guides](/guides): Browse Rodan's practical guides for data, AI and technology leaders.
- [Data Governance Toolkit](/governance): A practical route into proportionate data governance.
- [Data Maturity Pyramid](/maturity): Assess the foundations needed to make data useful in operations.
- [Insights Framework](/frameworks/insights): A framework for moving from reporting to decision-ready intelligence.
- [Technology Due Diligence Question Bank](/resources/tech-dd-question-bank): Questions for reviewing technology capability and delivery risk.
- [Data Room Checklist](/resources/data-room-checklist): A practical checklist for assembling decision-ready evidence.
- [Governance Starter Pack](/resources/governance-starter-pack): Templates and prompts for establishing practical governance.
- [AI Readiness Checklist](/resources/ai-readiness-checklist): A checklist for testing whether an AI opportunity is ready to progress.
HTML: https://rodan.io/insights/playbooks