# aiSTROM -- A roadmap for developing a successful AI strategy

arXiv: https://arxiv.org/abs/2107.06071 · HTML: https://mlsanity.com/paper/2107.06071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 115 references that are in the map.
- First posted on arXiv 25 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [A Functional Taxonomy of Music Generation Systems](https://mlsanity.com/paper/1812.04186/) (2018)
- [MorpheuS: generating structured music with constrained patterns and tension](https://mlsanity.com/paper/1812.04832/) (2018)
- [A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks](https://mlsanity.com/paper/1909.13568/) (2019)
- [Regression-based music emotion prediction using triplet neural networks](https://mlsanity.com/paper/2001.09988/) (2020)
- [Towards robust audio spoofing detection: a detailed comparison of traditional and learned features](https://mlsanity.com/paper/1905.12439/) (2019)

## Abstract (arXiv)

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.