AI readiness is your organization’s ability to effectively integrate AI into day-to-day operations and workflows in a responsible, scalable, and outcome-focused way. In the roadmap, AI readiness is shaped by five practical drivers: 1. Business strategy – How AI supports your business goals. AI projects should be tied to clear objectives like improving customer experience, reducing costs, or increasing efficiency, not run as isolated technical experiments. 2. Technology and data strategy – The data and infrastructure you need to run AI solutions at scale. This includes treating data as a strategic asset, ensuring quality and consistency, and having the right platforms in place. 3. AI strategy and experience – The expertise and repeatable processes that let you move from pilots to sustainable value. This is about building skills, patterns, and ways of working with AI. 4. Organization and culture – The vision, operating model, skills, resources, and culture that support adoption of AI-powered tools and workflows. 5. AI governance – The processes, controls, and accountability structures that help you maintain privacy, security, compliance, and responsible use of AI. These insights are based on 104 in-depth interviews with business and IT leaders who have already deployed AI at scale. When you develop these five drivers in parallel, you can move faster, manage risk more effectively, and get more tangible business value from AI rather than just running disconnected experiments.

To align AI projects with your business strategy, focus on three things: leadership support, clear problem definition, and a shared vision of success. 1. Secure leadership buy-in Involve senior executives and department heads early. Leaders should do more than verbally support AI—they should actively sponsor initiatives, allocate resources, and make it clear that AI is part of the company’s strategic direction. Interviewed leaders noted that without this, AI efforts often stay siloed in technical teams and fail to scale. 2. Clearly define the problem and use case Start with the business problem, not the technology. Successful organizations: - Identify specific use cases (for example, reducing churn, improving forecasting, or automating repetitive tasks). - Ask whether AI is the right tool for that problem. - Require a clear hint of ROI, such as faster task completion, better user experience, or lower costs. This keeps AI initiatives practical and grounded in measurable value. 3. Establish a shared vision of success Agree on what success looks like—both qualitatively and quantitatively—and make sure all stakeholders are aligned. Leaders in the study emphasized the importance of: - Defining metrics and KPIs (for example, total cost of ownership, productivity, efficiency, quality, opportunity cost). - Clarifying whether the initiative is primarily a productivity, efficiency, or quality play—or a combination. When everyone understands the “why,” the expected outcomes, and how success will be measured, AI projects are easier to prioritize, fund, and scale across the organization.

To support AI at scale, you need two things in place: high-quality, well-managed data and a deliberate approach to buying versus building AI solutions. 1. Prioritize data quality and structure Leaders interviewed consistently described data as foundational to AI readiness. Key practices include: - Ensuring consistent, up-to-date data so AI outputs are accurate and reliable. - Treating data as a strategic asset: identifying valuable sources, breaking down silos, and maintaining integrity over time. - Cleaning, annotating, and validating data to avoid “garbage in, garbage out.” - Building semantic data models or data dictionaries that align data to business concepts. Common techniques they use: - Labeling and annotating data to reduce bias and improve output quality. - Connecting siloed data sources so AI can access a complete, coherent view. - Incorporating real-time data where relevant to keep outputs current. - Transforming raw data (text, images, etc.) into structured formats AI can process. Data preparation is not a one-time project. Organizations that do this well treat it as a continuous discipline, supported by long-running data governance efforts and data literacy initiatives across teams. 2. Decide when to buy vs. build AI solutions The roadmap highlights that buying prebuilt AI tools and building custom models are both valid options, and the right choice depends on your context: Buying prebuilt AI (for example, for summarization, content generation, or productivity): - Faster to deploy and show early wins. - Lower upfront investment and less technical overhead. - Good fit for general, repeatable use cases. Building custom AI: - Higher investment in time, talent, and infrastructure. - Deeper customization and tighter alignment with your unique needs. - Better fit when you need differentiation or must work within a very specific or proprietary tech stack. Leaders in the study use questions like these to guide the decision: - Do we have the talent to build this right, or would we need to hire/contract? - Are we under time pressure to deliver results quickly? - Is this a standalone solution (often better suited to buying) or part of an existing internal app (often better suited to building)? - Can our current technology stack easily integrate third-party AI tools, or is it highly specialized? Because AI technology is evolving quickly, organizations that regularly reassess these choices—and stay open to both buying and building—are better positioned to adapt and keep their AI investments aligned with business priorities.