  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. 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.
