Five Ways an AI Strategy Creates Value in Your Organization, and How to Lead Each One.

This is an article written by Steven Nooijen, Managing Director at Xebia Transformation, and was originally published on Steven’s blog. 

Why Most AI Strategies Create Confusion 

Ask ten executives what their “AI strategy” is and you’ll get ten answers that sound similar but address different things. One is rolling out Copilot licenses. Another is speeding up the software delivery lifecycle. A third is automating an invoicing process. The fourth is evaluating LLM platforms for a customer-facing chatbot.  

AI enables four different value plays. If you don’t distinguish between them, the chances are that your AI conversation creates confusion. Each play has its own benefits, challenges, operating model, and hence investment logic. This article describes the four plays, so that you can create a clear and actionable AI Strategy for your organization. 

1. AI for Leaders 

This is the play of the CEO, the board, the ExCo and business unit leaders. It is about separating what is truly valuable from the noise, deciding where AI creates value for your organization, and how that is funded and governed. And it is about leadership itself: how leaders lead their people through the change, how they grow as leaders along the way, and how they leverage AI themselves.  

Questions we hear from leadership: 

What makes it hard. AI initiatives start bottom-up across the organization, sometimes each with its own budget, vendor and risk profile. Leaders must make investment and risk decisions about technology most have barely used themselves. Governance swings between two extremes: so loose that risk and spend go unchecked, or so tight that every experiment needs a committee. The human side is often underestimated, starting with leaders themselves. AI transformation is a self-awareness test: employees mimic their leaders, so change-ready leaders make initiatives stick. The real test comes once it’s underway. What do leaders do when adoption stalls, people resist, or key leaders don’t lead by example? 

Investment logic. The spend in this play is modest compared to the other four: assessments, strategy and operating model work, governance and policy design, leadership development. Yet its leverage is the highest, because it decides whether the money spent in the other four plays is well spent. Track success through time-to-decision on AI investments, adoption across the other plays, governance maturity and realized ROI from the other four value plays. The ExCo owns this play. Don’t delegate it to IT or a single Chief Data or AI Officer. 

2. AI for Everyone 

Tools that enable individual knowledge workers in their day-to-day jobs through assistants like Microsoft CoPilot, ChatGPT, Gemini or Claude CoWork. AI tasks cover writing emails, summarizing meetings, analyzing documents and spreadsheets, or building slide decks. 

Questions we hear from leadership: 

What makes it hard. This category looks easy because the tools are cheap and abundant, but extracting real value from them is much harder than buying licenses. In many organizations adoption stays shallow as training and change management are an afterthought. Anxieties about data security and compliance block the use cases that would matter most, with the biggest risk being confidential information leaking into the public domain. 

Investment logic. The economics of this play are about adoption at scale. Licenses are cheap on a per-seat basis, but you’re rolling them out to hundreds of people, which means most of the real spend goes into training, support, and change enablement rather than the technology itself. Having said that, you should keep track of and limit token spend carefully as it can quickly spiral out of control. The return shows up as hours saved across a large population with all kinds of activities, notoriously hard to tie back to revenue. Track success through adoption rates and cost-per-seat. Run the programme as an IT enablement effort, not an engineering one. 

3. AI for Development 

Code assistants and LLM-powered development frameworks change how your engineers create software. These tools help developers with tasks like generating code, debugging, and creating documentation. This significantly speeds up the software delivery lifecycle (SDLC). 

Questions we hear from leadership: 

What makes it hard. The main metric in this play is speed, but the real challenge is the trade-off between speed and quality. Code that ships faster but fails under heavy load, or that introduces subtle security vulnerabilities, can erase the apparent productivity gains overnight. Security, IP, and compliance risks multiply once a model has access to your proprietary codebase. And there’s a softer challenge undermining adoption underneath all of this: engineers are losing trust in other people’s (AI-generated) code and are worried about lagging behind or even losing their jobs. 

Investment logic. Costs in this category come in two layers: a per-engineer license fee for the tools themselves, and the often-underestimated cost of redesigning engineering workflows around them. The good news is that ROI is genuinely trackable through metrics like cycle time, deployment frequency and change-failure rate. If engineering leadership commits to measuring this deliberately, the spend becomes visible on the books and so do the gains. 

4. AI for Operations 

This is AI to automate or improve internal processes yielding operational excellence. Common use cases include: ticket triage, R&D, invoice processing, demand forecasting or knowledge assistants. We are already doing this for years with “classical” AI techniques like machine learning and neural networks, but Generative AI and Agents are creating new opportunities or can enhance existing solutions. 

Questions we hear from leadership: 

What makes it hard. This is where the loudest “where’s the ROI?” conversations happen, because every use case is expected to pay back within a budget cycle. This becomes especially tricky when cross-department processes are redesigned. These processes hold the biggest ROI promise but are also the most organizationally challenging in terms of ownership and governance. The real gains rarely come from patching an existing process with automation. They come from redesigning the process around AI. A new technical challenge introduced by agentic systems is that they don’t behave like deterministic software. This means that testing and monitoring them reliably require techniques most teams haven’t developed yet. 

Investment logic. Investment in this category is use-case driven, with each initiative funded against a specific business process. The ROI is also the most concrete of the five plays because every project has a defined scope and a clear baseline to compare against: FTE savings, increased throughput, lower error rates. That same precision is what makes scrutiny so high. If you can’t show proof of value within twelve months, the use case might be killed before it gets a chance to scale. 

5. AI for Products 

AI for Products is where AI interacts directly with your customers. It’s embedded in your products, apps, and digital channels and owned by teams who are accountable for your brand’s positioning and propositions: product, digital, CX, sales or marketing. Customers expect more than traditional products can deliver: products should now understand intent immediately, solve needs completely, and do so without hassle. Applications range from AI features in existing products, such as smart search or personalized recommendations, to entirely new AI-native products and business models that drive a competitive shift. 

Questions we hear from leadership: 

 
What makes it hard. AI products behave differently from traditional products. Their outputs are probabilistic. They can fail silently, returning a confident but wrong answer that nobody notices. In addition, the core component is often a third-party model whose updates you don’t control, in a field where solutions age fast. As a result, security, regulatory, and ethical risks effectively turn every release into a compliance event. Success with AI products requires clearly defined acceptable error levels, AI evaluations, trust and safety metrics and cost monitoring next to the usual business KPIs. Without a strong product operating model, AI products rarely make it past the pilot stage. 

Investment logic. The return is measured in commercial outcomes: conversion, retention, NPS, revenue uplift and new revenue streams, with trust and brand reputation on the other side of the ledger. The investment has two halves that depend on each other. One is finding the opportunity: test early and scale only what proves its value. The other is building the capability: the operating model, skills and culture to deliver AI products again and again. Possibility without capability remains a pilot. Treat this as a strategic investment rather than IT spend, with the strategic decision sitting with product and commercial leadership. 

How to Build an Effective AI Strategy. The two steps every leadership team should take 

Concluding: “AI strategy” is actually five strategies: four places where AI creates value, and one leadership play that holds them together. How should you proceed? 

Step 1: Be explicit about where AI creates value for you. Which of AI for Everyone, Development, Operations and Products is your priority? Which is secondary? Which aren’t yours to play in right now? Vague answers here cause misaligned investment downstream. Making this choice, and revisiting it, is the core of AI for Leaders. If you want a structured way to get to a sharp answer, our AI strategy consulting team runs exactly this diagnostic with leadership teams. 

Step 2: Build a modern Data & AI operating model that offers real flexibility across architecture, procurement, deployment, change enablement, and governance. Each play will stress one or more of these dimensions differently. Read more in our companion piece on how to establish an adaptive and resilient operating model that’s built to support all plays at once. 

Thank you for reading. We hope this gives you a sharper lens for the next AI strategy conversation in your organization. 

If you would like to explore which AI priorities are most relevant to your organization, and what operating model is needed to turn them into measurable outcomes, schedule a complimentary strategy consultation with Steven Nooijen, Managing Director, Xebia Transformation, and author of this article, or another member of our transformation team.

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