--- title: "AI strategy and readiness" description: "Use case selection, data and infrastructure readiness, build or buy decisions and a roadmap a board can approve." url: https://www.expandware.com/services/ai/ai-strategy-and-readiness/ section: "Services" topics: ["AI", "NIST AI Risk Management Framework", "ISO/IEC 42001", "EU AI Act", "ISO/IEC 23894"] publisher: "Expandware Private Limited" --- # AI strategy and readiness Use case selection, data and infrastructure readiness, build or buy decisions and a roadmap a board can approve. Choose the AI use cases worth building. The first AI decision is not which model, it is which problem. We assess candidate use cases against the value they would create, the data they would need, the risk they would carry and the infrastructure they would run on, then produce a roadmap that starts with something measurable. The people writing the recommendation also build and operate AI systems, so the plan survives contact with delivery. Where it usually starts: - The board wants an AI strategy and the organization has a list of ideas rather than a plan. - Several pilots are running and none has a path to production. - Nobody can say whether the data a use case needs exists, or may be used. - A vendor is proposing a platform before the problem has been defined. What is included: - Use case discovery: Working sessions with the business to find tasks where AI changes a measurable outcome. - Value and feasibility scoring: Each use case scored on value, data availability, risk and effort, on the same scale. - Data readiness: Where the needed data lives, its quality, who owns it and whether it may be used. - Infrastructure options: Hosted models, cloud accelerators or owned GPUs, compared on cost, control and timeline. - Risk and policy: An acceptable use policy and a risk classification for each use case. - Roadmap: A sequenced plan with the first production use case, its success measure and its budget. Where it sits: Strategy sits with the people and oversight side of the AI architecture: the users whose work would change and the governance that decides what may be built. It sets which parts of the rest of the diagram are worth building first. Decisions to settle early: - Build, buy or wait. Many AI needs are met by features already in software you own; some justify a build; some are better left until the technology or the data matures. Each use case gets one of the three. - Hosted models or your own. Commercial models are quickest to start; private models give more control over data and cost at volume. The data and the regulation usually decide. - Central team or federated. A central team keeps standards and avoids duplicate spending; business teams know where the value is. We recommend a model that suits your size. How the work runs: Discover, Candidate use cases from the business. Assess, Value, data, risk and infrastructure. Decide, Build, buy or wait, with reasons. Plan, A roadmap with the first measurable win. How it is bought: - A fixed-fee assessment: We hold short sessions with the teams whose work could change, review candidate use cases and the data behind them, and give you a scored shortlist with a fixed price for the full readiness program. - A fixed-price build: Use case discovery and scoring, data readiness checks, infrastructure options, an acceptable use policy and a costed roadmap, delivered at a fixed price for the departments in scope. - Running it afterwards: Once the roadmap is agreed, the first use case moves to delivery under its own price, and a periodic review keeps the portfolio current as models and regulation change. Worked example, a list of ideas and no plan. Say an insurer's leadership has collected AI ideas from every department, two pilots are running with no route to production, and the board has asked for a strategy. - Discover: We run short working sessions with claims, underwriting, finance and service teams to find tasks where AI would change something measurable. - Score: Each idea is scored on value, data, risk and effort. Document triage for claims rises to the top, while a customer-facing chatbot is deferred. - Check data: For the leading use cases we confirm where the documents live, who owns them, and whether policy and law allow their use for this purpose. - Plan: The roadmap starts with one production use case, its success measure and its budget, and each pilot is either folded in or stopped. At the end, the board has a plan it can fund and measure, staff have a policy for AI tools, and the first project starts with its data question already answered. (An illustration, not a client account.) What you have at the end: - Scored use case portfolio: Every candidate use case scored on value, data, risk and effort on one scale, so they can be compared fairly. - Data readiness assessment: For each shortlisted use case, where its data lives, its quality, its owner and whether it may be used. - Infrastructure and sourcing options: Hosted models, cloud accelerators and owned GPUs compared on cost, control and timeline for your use cases. - AI acceptable use policy: What staff may and may not do with AI tools and company data, written to be followed rather than filed. - Roadmap with budgets and success measures: The first production use case and the sequence after it, each with a budget and a measure of success. Standards and platforms worked with: NIST AI Risk Management Framework, ISO/IEC 42001, EU AI Act, ISO/IEC 23894. Where it goes wrong, and what we design out: - Starting from the technology. Choosing a model or platform before the problem leads to pilots that prove nothing. Every use case starts from a business measure it would move. - Pilots without an exit. Experiments that never reach production consume budget and goodwill. Each one gets a success measure and a decision date, and is stopped if it misses. - Ignoring the data question. Many use cases fail because the data is missing or may not be used. Data availability and permission are checked before anything is scored as feasible. What it changes: - A short list, not a long one. Effort goes into the few use cases with clear value and usable data. - Decisions with reasons. Build, buy and wait decisions are written down with the evidence behind them. - A first win on a date. The roadmap starts with a use case small enough to ship and important enough to matter. How you will know it is working: - The board can see which AI projects are funded and why. - Each pilot has a measure, an owner and a date on which it continues or stops. - Staff know which AI tools they may use and with what data. - The first production use case is measured against the outcome it promised. Who you work with: - AI lead. Runs the discovery sessions, scores the use cases and writes the roadmap. - Data specialist. Checks where the data for each use case lives, its quality and whether it may be used. - Risk and policy adviser. Drafts the acceptable use policy and the risk classification for each use case. What we need from you: - An executive sponsor who can decide what is funded. - Time from the teams whose work could change. - Details of AI pilots and tools already in use. - Your risk appetite, or the people who set it. Common questions: Q: How long does an AI readiness assessment take? A: Typically a few weeks, depending on how many business areas are involved and how quickly data owners can be reached. It ends with a written roadmap rather than a presentation. Q: Will you ever recommend not using AI? A: Yes, where a rules engine, a better report or a process change would do the job more cheaply and more reliably. A recommendation not to build is a useful result. Q: Do you favor particular vendors? A: No. We compare hosted and open-weight models, and cloud and owned infrastructure, on the same terms, and the comparison is written into the report so you can check it. Q: What do we need to prepare? A: The ideas already in circulation, access to the people who own the processes and the data, and any constraints you already know about, such as data that may not leave the country. Q: How do you estimate the value of an AI use case before building it? A: From the business measure it would move, estimated from how the task is done today: the time spent, the error rate, the cost of delay. Where the estimate is uncertain we say so, and the first build is designed to test the assumption cheaply before a larger investment is made. Q: What happens to the pilots we are already running? A: They are scored like every other use case. A pilot with a clear measure and a path to production joins the roadmap; one without either is stopped, and what it taught is recorded so the next attempt starts further on. Q: Does an AI strategy need its own budget line? A: The roadmap budgets each use case separately, because the costs differ: data work, integration, model usage and ongoing operation. A single AI budget hides which projects are paying for themselves and which are not. Q: Who in our organization should be involved? A: An executive sponsor who can make decisions, the leads of the teams whose work would change, the owner of the data in question, and someone from risk or legal. The technical team matters too, but most early questions are about the business, not the model. Often paired with: Generative AI and agents, AI governance and security, Data integration and analytics. --- Canonical page: https://www.expandware.com/services/ai/ai-strategy-and-readiness/ Site index for machines: https://www.expandware.com/llms.txt Full site text: https://www.expandware.com/llms-full.txt Expandware Private Limited. Inquiries: solutions@expandware.com, +92 (333) 32 11011.