Essential Competencies for AI/GenAI Leaders

In the digital age of AI/GenAI we are currently living in, customer needs change. Industry dynamics change. Business models change. Competence requirements change. Project parameters change. However, the leadership required to drive these changes has not changed much. Most leaders continue to drive AI transformation using the old pre-digital style of leadership. This has resulted in a failure rate as high as 70% as per a recent study by McKinsey.

Leadership is extremely crucial in the digital era of AI/GenAI. Whether AI will benefit or harm the humanity will rely on the key decisions leaders make today. And decision-making is dependent on the approach of the leaders toward Generative AI.

There are three types of approaches:

1. Optimistic
2. Skeptic
3. Pragmatic

In an optimistic approach, leaders are the enthusiastic early adopters of GenAI, believing in its potential to revolutionize industries, transform businesses, and create significant value for customers. They are willing to take risks with the expectation that the technology will yield significant benefits in terms of innovation, efficiency, and competitive advantage. However, they downplay the ethical part of AI, weighing profits more than ethics.

In a skeptic approach, leaders approach GenAI with caution, adopting a "wait-and-see" attitude. They monitor the technology's progress and demand concrete evidence of its value before considering its adoption. They worry about the potential job loss and rise in unemployment caused by its large-scale adoption. They focus more on its ethical implications such as the potential for bias and misuse, than profits.

In a pragmatic approach, leaders are neither too enthusiastic and nor excited about the promises of GenAI, nor doubt its potential to enable transformation. They normally reserve their opinions about the speculations around the job prospects or job losses likely to be caused by it. They take a measured approach to GenAI adoption, carefully evaluating the benefits (profits), while ensuring that GenAI is used responsibly and ethically.

To enable AI/GenAI transformation, leaders should adopt a pragmatic approach, balancing profits and ethics. It is the most difficult challenge for the leaders.

To help leaders develop a pragmatic approach, I have provided a Digital Leadership Framework, in my book.

It begins with building a AI/GenAI Leadership brand ― a perception that you can successfully drive AI transformation. It is demonstrated through the following four key behaviors essential to handle the unpredictability and uncertainty of AI transformation projects:

1. Learning new Skills
2. Connecting with People
3. Leveraging Data
4. Delivering Results

To demonstrate them successfully, one must develop the following four key competencies:

1. Growth Mindset
2. Empathy
3. Informed Decision-making
4. Fast Execution

Each of these competencies uniquely maps to a behavior:

Growth Mindset enables Learning.

Empathy enables People Connections.

Informed Decision-making enables Leveraging Data.

Fast Execution enables Delivering Results.

To be relevant and competitive in this dynamic and fast-paced environment enabled by AI, it is important to reskill. Reskilling means learning completely new AI skills outside of your comfort zone, to which you have no previous exposure. Leaders should be the champions of AI Reskilling. They should themselves learn and inspire others to learn. To step into a new uncomfortable space, you need a Growth Mindset.

As per a study by Boston Consulting Group, only 10% of enterprises have scaled generative AI. 90% of them are still lagging. Out of these 90%, only 50% have begun piloting whereas remaining 40% have taken no action. I had interviews with few employees belonging to these "no action" firms. It was observed that only 10% of the top management wanted to drive generative AI. 76% of the middle management wanted to implement proof-of-concept (PoC). 91% of the lower management wanted to work on generative AI projects. However, the remainder 90% top, 24% middle, and 9% lower management had a skeptic “wait-and-see” approach. Unfortunately, they comprised the key decision makers and decided to take no action. This led to higher frustration and low motivation in employees, mostly in the middle and lower management. Had the leaders at the top demonstrated Empathy and connected better with middle and lower management, they could have been made some progress with the AI transformation.

In 90% of the lagging firms, 84% of the generative AI innovative ideas originated from lower management and external consultants. Out of which, only 15% were considered for piloting. A staggering 95% had neither cross-functional involvement nor support from leadership. This siloed decision-making would result in 90% of the pilots kicked-off in 2023 and 2024, not moving to production. Informed decision-making based on leveraging the available data would have improved the chances of pilots being scaled.

There was a large time gap observed between decision and implementation of pilots. Out of the 15% pilots under consideration, 35% were not implemented at all, 40% were implemented after 6 months, 20% were implemented within 3 months, and only 5% were implemented within a month. The contrast is striking ― on one hand, implementing a pilot can take up to six months, while on the other, new large language models (LLMs) are being launched nearly every month. Fast execution would have enabled quick actions, fast learning from mistakes, and rapid results.

Thus, cultivating these competencies is essential for a leader to drive AI/GenAI Transformation.

You can find more information on how to cultivate them in my book, Digital Leadership Framework: Cultivating the Four Essential Competencies.

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