AI First versus Business-First Approach to Digital Transformation

Digital Transformation is no longer a choice but a necessity for businesses to thrive in today’s technology-driven world. As organisations navigate the ever-evolving Digital Transformation landscape, a growing number have turned to an “AI-first” approach, placing artificial intelligence at the heart of their innovation efforts. While AI undoubtedly holds immense potential, it is crucial to critically examine whether this strategy is always the most effective path to organisational success. (Holmström & Hällgren, 2021).

This begs us to ask if we should approach Digital Transformation AI-First or business-First, but what does this mean? The former prioritises integrating AI technologies into every facet of a business and positions AI as the central driver of innovation and efficiency. The latter takes a more pragmatic approach that focuses on solving specific business problems and delivering measurable results not necessarily guided by the latest technology. A business-first approach prioritises the needs and goals of the business, and if there is a genuine use case for AI, this is integrated strategically.

The allure of AI-first is understandable, and no business wants to be left behind. By leveraging AI’s data analysis, automation, and predictive modelling capabilities, organisations can unlock new efficiencies, improve decision-making, and enhance customer experiences. There are specific industries that may benefit from an AI-first approach. These companies likely have a robust data infrastructure, strong technical expertise and AI talent. However, for most businesses that are not further along the curve, a singular focus on AI can overlook the more fundamental business problems that must be addressed (Leech et al., 2024).

A key concern with an AI-first strategy is the risk of technology-driven innovation, where AI solutions are developed without a precise alignment to specific business problems. Catlin et al. (2018) argue that organisations must avoid the “shiny object syndrome,” where they chase the latest technologies without clearly understanding their strategic implications. Not every organisation possesses the dynamic capabilities of companies like Netflix or Amazon. The truth is most businesses are still grappling with basic fundamental challenges such as departmental silos, resistance to change, legacy operating models and processes.

Before embarking on an AI-first journey, organisations must first articulate their core business objectives and identify the specific challenges they seek to overcome. Understanding these issues is essential to ensure that AI is applied strategically and delivers tangible value. Without a well-defined business problem, AI initiatives may become mere technology projects without meaningful impact.

A business-first approach, where AI is used to solve specific business problems, is often more effective. AI can be applied strategically to deliver tangible benefits by focusing on the organisation’s core challenges. This approach is supported by research from McKinsey Global Institute (2018), which highlights the importance of identifying clear use cases for AI to maximise its impact.

Furthermore, an AI-first strategy can lead to a narrow focus on technical capabilities, neglecting the broader organisational context. Strategically managing an artificially intelligent firm requires a robust understanding of technology, economics, and management interconnections. (Wagner, 2020). Successful AI implementation requires a holistic approach that involves people, processes, and technology. Organisations must invest in training and development to build the necessary skills and competencies to leverage AI effectively.

In conclusion, while AI offers immense potential to drive innovation and growth, it is essential to approach AI-first strategies with caution. By focusing on business problems and leveraging AI to address these challenges, organisations can maximise the benefits of this powerful technology and achieve sustainable competitive advantage.

References

Holmström, J., & Hällgren, M. (2021). AI management beyond the hype: exploring the co-constitution of AI and organizational context. In AI & Society (Vol. 37, Issue 4, p. 1575). Springer Nature. https://doi.org/10.1007/s00146-021-01249-2

Leech, G., Garfinkel, S., Yagudin, M., Briand, A., & Zhuravlev, A. (2024). Ten Hard Problems in Artificial Intelligence We Must Get Right. In arXiv (Cornell University). Cornell University. https://doi.org/10.48550/arxiv.2402.04464

Wagner, D. N. (2020). Strategically managing the artificially intelligent firm. In Strategy and Leadership (Vol. 48, Issue 3, p. 19). Emerald Publishing Limited. https://doi.org/10.1108/sl-08-2019-0119

Catlin, T., LaBerge, L., & Varney, S. (2018, October 18). Digital strategy: The four fights you have to win. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/digital-strategy-the-four-fights-you-have-to-win