Artificial Intelligence cloud transformation value chain
Artificial Intelligence has evolved from niche technologies into a powerful and broadly available business capability. Machine Learning is by now fueling a new wave of innovation, where data is the genesis of invention, and where ML is a net-new capability for organizations not only to describe the past but also to predict the future and prescribe meaningful actions. Because of the impact this capability has on all markets and businesses, organizations across all industries are increasing their investment in AI. This investment can create a competitive advantage through improved customer insight, greater employee efficiency, and accelerated innovation. This is driven by the applicability of AI to a vast problem space that spans both vertical and horizontal use cases.
Notably, the business problem space to which AI can be applied is not a single function or domain, rather there is significant potential across all functions of businesses and all industry domains with the opportunity to reset the playing field in markets where AI does make an economical difference. As AI enables solutions and solution paths to problems that have remained uneconomical to solve for decades or simply technically were impossible to tackle without AI, the resulting business outcomes can be profound.
As an example, the emergent capabilities of large AI models to perform domain-specific functions with little additional data are taking organizations by storm and help business to differentiate. The discipline that these mainly fall into is generative AI, which has captured widespread attention and imagination. However, developing, applying, and tuning such models can be complex.
Figure 2: The AWS CAF-AI transformation value chain (all pink and magenta parts of it are dimensions taken from the original CAF that we build upon here).
The preceding figure provides an orientation on how to think about Artificial Intelligence adoption in the face of a changing market landscape and the rapidly accelerating field.
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AI provides new capabilities to your organization.
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With these new capabilities, you and your organization strive to create tangible business outcomes. These business outcomes can be many, such as reduced business risks (for example, by detecting broken or faulty parts in a production chain), by improving the environmental, social, and governance (ESG) performance (for example, by automatically summarizing and flagging environmental protection compliance reports), growing new and existing revenue (for example, by personalizing product and service recommendations to customers) or by increasing the operational efficiency (for example, by classifying and mapping travel receipts to internal booking codes). However, creating these business outcomes relies on your ability to adopt AI.
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To adopt AI, your organization needs to transform along at least four domains:
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Technology: A domain that focuses on establishing the technological capability and then enabling the usage and adoption of AI.
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Process: A domain that focuses on digitizing, automating, optimizing, and innovating on your business operations through the power of AI.
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Organization: How your business and technology teams orchestrate their efforts to create customer value and meet your strategic intent, driven by AI.
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Product: Reimagining your business model by creating new value propositions (products, services) and revenue models that capitalize on the capabilities of AI.
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Transforming these domains and enabling them to use AI depends on your foundational capabilities in business, people, governance, platform, security, and operations.
To adopt AI successfully, plan out your journey:
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Work backwards from your understanding of what AI enables you to do.
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Define what your expected business outcomes are over time.
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Carve out the transformation that your business has to go through.
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Develop the foundational capabilities that enable this journey.
