Four non-tech conditions for AI to improve performance

Over the last few years, numerous reports have highlighted the gap between the potential of artificial intelligence to produce value and the actual gains achieved by firms. Recent research has looked into what makes AI initiatives successful, and identified four drivers that guarantee a positive impact on performance. Interestingly, they are not directly related to technology but more to culture and organisation.

In their article, Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance, Patrick Mikalef and Manjul Gupta identify specific resources that create an AI capability. They also examine how this capability impacts organizations’ creativity and performance.

An AI-led transformation is also a cultural transformation.

The findings of the study provide evidence that an AI capability can result in increased creativity and performance. However, it also helps to better understand what the conditions are for artificial intelligence to generate performance.

Here are the four main takeaways:

1. ‘Soft’ factors matter. Data, infrastructure, and techniques used to develop artificial intelligence solutions have been the main focus of practice-based literature. But other elements have an impact on AI success as well: developing the structures and culture that enable value generation from artificial intelligence investments.
   “For example, interdepartmental coordination is found to be a necessary condition to enable flow of information and data, as well as a means to develop AI solutions that correspond to the business requirements,” they write. “Developing an AI orientation within the firm is a necessary precondition for successful deployments.”
2. Skills are paramount. “Our results indicate that practitioners should focus not only on purely technical skills associated with AI, but also on the managerial competencies to direct AI initiatives toward priority areas that generate business value,” they add.
   These findings show that training technical and business staff on new artificial intelligence techniques and their applications is important.
3. A bold mindset is necessary. Adopting an organizational culture that embraces risk-taking and making bold, radical actions is critical: “This is a necessary mind-set when it comes to AI projects, as in many successful business cases using AI, going forward with uncertain initiatives that can possibly yield high returns has proven to be instrumental,” they specify.
   Findings from their study indicate that when it comes to artificial intelligence, it is important to embrace a logic of “high risk high gains”.
4. Internal benchmark to fuel resources allocation. Being able to self-assess the organization’s strengths and weaknesses is an important component of becoming an AI-ready organization.
   “This could show imbalances within the organization and units that are not on par with the others or have major weaknesses that could potentially inhibit overall attainment of goals,” they explain. “Such benchmarking attempts could help direct financing and resource allocation more efficiently and help generate business value with fewer uncured costs.”

This is an interesting perspective on the necessary transformation of the incumbents’ resource stack, at both a cultural and organisational level.

After all, an AI-led transformation is also a cultural transformation.

This article is based on a post published on Prof. Benyayer’s blog.

This post gives the views of its author, not the position of ESCP Business School.