Your AI transformation journey

Any large technological adoption agenda is a long journey, especially when adopting a technology that is rapidly evolving, such as AI. While transformation and adoption journeys are highly individual to the organization, we have observed patterns of successful AI adoption. Therefore, to de-risk this journey for customers, the AWS CAF-AI provides the following observations learned from thousands of customers as best practices. Still, each organization’s AI journey remains a unique one.

When embarking or advancing on your AI transformation journey, consider four critical elements, which are also illustrated in Figure 3:

1. The destination of your journey, namely the business outcomes that you seek to achieve and from which you work backwards.

2. The AI flywheel as the motor of your journey. The AI flywheel is a virtuous cycle where initial high-quality data (which is timely, relevant, valuable, and valid) is used to train or tune an AI system that then delivers predictions. These predictions positively impact business outcomes that in turn lead to more or deeper customer relationships, sparking the creation of more or higher quality data (network and flywheel effect).

3. Your data and data strategy is what keeps the AI flywheel in motion.

4. Your foundational capabilities that, above all else, drive success or failure when adopting AI.

Figure 3: The AWS CAF-AI cloud transformation journey

When approaching this journey, base it on iterative and incremental improvements. We also suggest you reach out to your AWS contacts (for example, your account team) to get assistance from AWS ML strategists, enterprise strategists, and ML advisors. After an initial assessment, the adoption cycle begins, and it is based on four stages:

• Envision: This first phase focuses on envisioning how AI can help accelerate your business outcomes. This means identifying and prioritizing transformation opportunities in line with your business objectives. Associate your transformation initiatives with key stakeholders (that is, senior individuals capable of influencing and driving change) and measurable business outcomes. Be sure to also identify in this early phase what data assets and sources these initiatives and opportunities rely upon. Work backwards from your opportunities towards data requirements.

• Align: In this second phase, you focus on the foundational capabilities. You identify cross-organizational dependencies and surface stakeholder concerns and challenges. AI adoption is a cross-functional effort, much more so than this is the case for other technologies. Aligning internally on the goals set in the envision phase is critical. Doing so helps you create strategies for improving your cloud and AI readiness at large, ensure stakeholder alignment and future buy-in, and facilitate relevant organizational change management activities.

• Launch: In this phase, you focus on delivering pilot initiatives from early proofs of concept to production and demonstrate incremental business value. Pilots should be highly impactful on the organization and the business, as well as meaningfully benefit from AI being applied to it. Regardless of whether they are successful or not, they can help influence your future direction. Learning from them helps you adjust your approach before scaling to full production.

• Scale: This phase focuses on scaling pilots in production to achieve broad, sustained value. Scaling here can mean not only the technical capabilities of solutions or initiatives, but also the reach of them through the business and towards your customers. This activity translates your activities into customer value.

While you iterate through these cycles, recognize the limits of what you can achieve in a single cycle. It is important to be ambitious and aim high, but trying to do everything in the same cycle can lead to discouragement in the organization. This is why pairing a larger picture with many pragmatic and actionable steps and measurable KPIs on these smaller steps is crucial. Every step then brings the organization closer to its goal. Do not try to do everything at once. Rather, evolve the foundational capabilities and improve your AI readiness as you progress through your AI transformation journey.