Harnessing agile concepts for the development of Intelligent Systems

Sharif, Amir M.

doi:10.1007/bf03037243

Cited by 4 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Agility Looking at implementation strategies, an iterative, agile approach may be best suited for developing AI systems (Hoda, Salleh, & Grundy, 2018;Leijnen, Aldewereld, van Belkom, Bijvank, & Ossewaarde, 2020;Sharif, 1999). This involves creating prototypes first (Shore et al, 2007), which are then tested and analysed without spending too many resources.…”

Section: Organizing Ai Developmentmentioning

confidence: 99%

aiSTROM -- A roadmap for developing a successful AI strategy

Herremans

2021

Preprint

View full text Add to dashboard Cite

A total of 34% of AI research and development projects fails or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies (Rackspace Technology, 2021). We propose a new strategic framework, aiSTROM, that empowers managers to create a successful AI strategy based on a thorough literature review. This provides a unique and integrated approach that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, we start by identifying the top n potential projects (typically 3-5). For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas), or outsourcing development. Looking at new technologies, we have to consider challenges such as bias, legality of black-box-models, and keeping humans in the loop. Next, like any project, we need value-based key performance indicators (KPIs) to track and validate the progress. Depending on the company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, we should make sure that our strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a valuable, literature supported, tool for managers and lead developers.

show abstract

Section: Organizing Ai Developmentmentioning

confidence: 99%