Navigating the path of AI adoption

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Navigating the path of AI adoption

Capturing business value by identifying Critical Success Factors for AI adoption

The key question for Artificial Intelligence (AI) investments and applications is: What critical success factors contribute to the successful adoption of artificial intelligence? This blog explores the answers to this question. The outcomes are a result of an extensive research done by 3 MBA students from Nyenrode Business Universiteit, as part of graduation project, supported by Nyenrode and Deloitte experts.

The rise of Artificial Intelligence (AI) is gaining pace: AI
applications are growing exponentially and investment in AI is forecasted to
triple between 2019 and 2023 to nearly $100bn, a trend that has further
accelerated during the COVID-19 pandemic. The ‘early-adopter’ phase is coming
to an end; however, most companies that are investing in AI are not getting it
right and are struggling to derive tangible business value from their
investments1.

The ‘early-adopter’ phase is coming to an end; however, most companies that are investing in AI are not getting it right and are failing to generate tangible business value from their investments.

This research identifies critical success factors that contribute to the successful adoption of AI at an overall level and across a three-stage adoption model. At an overall level, three factors emerge as cross-cutting:

  1. Businesses should not engage with AI for the sake of AI but use it to solve a specific business problem.
  2. More data is not always better, but rather quality data is needed.
  3. Moreover, AI’s transformational character requires targeted investment in upskilling and reskilling throughout the organization.

At the beginning of the AI exploration phase, organizations should start by getting the organizational stage right: identify a clear business case, ensure top management support, foster a culture that allows the freedom to experiment develop iteratively, provide access to the necessary resources, and identify a champion, while already making sure that AI development aligns with company values and regulation. In the Implementation phase focus shifts to getting the technology right: the right systems architecture and an accurate algorithm. Explainability or controllability are critical for user trust. Engaging users in the design will ensure the solution remains focused on what the business needs. At the same time, the company should start adapting business processes readying for roll-out. To successfully scale, value must be created, captured, and protected. Critical to creating value is ensuring the algorithm remains accurate when confronted with real data. As the scaling is likely to face bumps, executive management support is vital while protecting the business value means emphasizing AI & data governance, navigating the regulatory environment and strong cybersecurity.

Navigating the path of AI adoption

In addition to these success factors, our research reveals several additional context-specific success factors. Companies that are adopting AI solutions or working with AI projects are encouraged to consider both generic and contextual factors and prioritize their attention and effort given the criticality of factors across different stages of adoption. And by doing so, develop a holistic view and a comprehensive action plan on their business, sector, and environment. To help guide this process, the overall results of the research are summarized into a graphical model.

1 Why Is It So Hard to Become a Data-Driven Company? (hbr.org)

Co-written with Thomas Harrison-Prentice, Michael Kerr, Edward Ellis and Sam Solaimani (Nyenrode Business Universiteit)

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