The Secrets of AI Value Creation by unknow

Author:unknow
Language: eng
Format: epub
ISBN: 9781394233632
Publisher: Wiley
Published: 2024-02-20T00:00:00+00:00

Management System

An AI management system encompasses the definition and review of an AI strategy with a guiding and supporting coalition, communicating the AI strategy effectively, creating the core capabilities, and continuously measuring to ensure the strategy is on track. Without such a management system, even the best strategy is likely to fail.

An AI initiative requires organisational support from various areas. The best way of gaining that support is by creating a guiding coalition that can contribute in creating and reviewing an AI strategy. That AI strategy is supposed to be an evolving document that allows for understanding of value creation gaps, builds trust, and inspires enterprise commitment. It should capture the learnings from failures and successes on the AI journey, and be crafted with the input of key coalition members from, for instance, IT, data management, HR, and finance, who together provide the capabilities, knowledge, and experience to support the AI strategy's objectives.

The communication of an AI strategy requires a two‐way dialogue: internally to the organisation and externally to the customers. Internally, it is essential to provide stakeholders with knowledge transfer, while also building trust and demonstrating how their job relates to the value AI provides and the goals of the enterprise. Externally, AI aspirations and value should be communicated to all stakeholders, including the enterprise (board and management), employees, and customers.

Every AI initiative necessitates support to facilitate the development and upkeep of its AI capabilities. The task is to identify what resources are necessary and create them in alignment with the core components of the AI strategy. For instance, a financial sponsor for the AI investment is needed, with the initial costs typically covered by the budgets for digital transformation, IT, or business intelligence. Securing the appropriate funding is key, because it grants the initiative the power and resources to hire and retain AI talent, a task for which HR must be consulted to understand their incentives, demands, and salary expectations. However, resources and support cannot only be gathered internally; in many cases external partners might be a wise choice for building and sustaining the core capabilities of and supporting the AI initiative.

Last, it is essential to measure and show the progress of the AI initiative. Measurement plays a critical role in providing focus, feedback, learning, and incentives. According to Lafley and Martin, the key is to measure the progress towards a future outcome when it comes to innovation. For measures to be effective, they emphasise the importance of prespecifying the expected outcomes. In the context of AI, the definition of an expected outcome and its assessment has to be done in alignment with the business stakeholder.

But determining the right metrics to measure can become a challenge in an innovation area such as AI, where the project failure rate is high, and business outcomes might take a while to realise or are dependent on success factors that are not in control of the AI initiative. Thus, it is essential to measure progress by tracking actions taken and gauging their success/failure.

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