Datacentric Leadership

In this transformative era, leaders must adapt their mindset and skill set to embrace the potential of AI while managing its challenges and risks.

I heard a gentle, soft voice with music, filling me with positive energy. It gently woke me up on a weekday and greeted me.

Good morning Kaitki; it’s a gorgeous sunny day. I hope you are energized to start your day. I am here to help you start your day. Let me know when you are ready. Oh, BTW, I applaud you for your workout last evening and for exceeding your exercise goal.

Hearing positive words to start my morning routine pleased me, and I began to get ready. I told my virtual assistant to go ahead with my schedule. She responded and told me my breakfast menu, my car-servicing schedule and my plan for the day. She also mentioned my son’s school event that I was required to attend.

My virtual assistant takes care of many tasks of my daily activity, saving me time that I can use for essential activities. I took my auto-driving car to work, and during my ride, I started thinking about how leadership had evolved over the years and how it is changing in this era of AI.

AI has given us many tools, automated many manual aspects and presented us with enormous amounts of data. We have gathered many insights from the data to improve processes and workflows to improve organizational efficiency. We have also saved capital and operational expenses and are getting the expected return on investment in AI.

At the same time, AI has improved things, simplified our lives and removed low-key and mundane manual tasks. It created opportunities to upskill and train our staff. If you think it replaces the job functions, I don’t agree. It gives new tools to all the functions, and we need to train the staff on using those tools.

I was listening to a David Letterman interview with Bill Gates from 1995, where Gates tried persuading Letterman to get a computer. Letterman did not appreciate the value of the computer and asked Gates, “Do you know this internet thing?”

Gates responded with several ways the internet could be used, such as being able to watch and listen to a baseball game on your computer. Letterman said, “so you can listen to a baseball game on your computer; does the radio ring a bell?” Gates responded, “well, Dave, in 27 years, you’ll publish this video clip on YouTube.”

With the advent of the internet and personal computers, there was much concern about people losing jobs and many security-related issues. Now, with AI, we seem to be reliving that discussion. Computing transformed the world for everyone — think about the number of jobs created when computers came in, providing many new opportunities and industries. It did not take away jobs, it changed them. It brought in more jobs, and it was great for the economy and humankind.

Since AI is coming into our lives in a big way, it needs a new mindset, and we will have to see how to use its full potential.

It also brings several challenges, and we must proactively manage those aspects.

AI is becoming an integral part of our business, and large language models (LLMs) have emerged as the most versatile and powerful AI models available. Generative AI models — such as generative pretrained transformers, or GPTs — are a type of LLM, which are trained using self-supervised or semisupervised learning on large quantities of unlabeled text.

Some well-known LLMs are ChatGPT-4, Google’s Bard, BLOOM and LLaMA. Generative AI has several potential applications that we can think of today, and many will evolve. Some applications are in education, art, music and writing, as well as in health care, finance and gaming — essentially in every field we can think about.

Despite all the excitement, generative AI comes with significant challenges and risks. The models are trained on the unproven repository, basically the internet. Training data has a lot of biased and misleading information, so can we trust the output generated by generative AI models?

Generative AI models, such as ChatGPT-4 and Google Bard, are intriguing, but only as good as the data used to train them. They unfold many issues and concerns around governance, copyright, intellectual property, ownership rights, data sharing, security and data sovereignty.

Data used to train AI models shouldn’t infringe on intellectual property. AI algorithms should be unbiased and shouldn’t discriminate based on attributes like age, ethnicity, gender, etc. Organizations must disclose the data source they use to train the model. AI brings many questions and complexities around ethics, trust and the need for new policies. Data generated from machine-learning models can’t be trusted if the data used in AI algorithms are biased.

Organizations and governments intend to separate high-risk use cases of AI, like legal, hiring and financial applications, from low-risk use cases, such as AI-based itinerary generation or gaming. Cyber security and external AI-based threats are critical areas of focus for organizations.

AI transformation needs significant mindset change at all levels, like what we did when we entered the era of PCs. Change management will become critical to organizations.

Leaders must prepare and train themselves to make the best out of the AI opportunity while handling its challenges and risks. They should:

Become Datacentric Leaders
Most leaders today focus on their function. Now, they must learn about other aspects that may influence the data. Organizations and leadership shall become more datacentric than workflow-centric. Leadership must get more proactive by using the data and insights generated by AI tools.

In other words, leaders will have powerful tools to continuously improve. Data and insights will allow innovation at all levels and in many areas. Leaders should recognize and capitalize on that. Leadership will become more global than just functional. Leaders will have views and insights at every level beyond the functional aspects.

Become Adept at Change Management
Today’s leaders do use change management, but they need to become much better at it. We already have many policies and regulations in place, and organizations are functioning based on those norms, so there is a level of comfort in everyone’s minds, e.g., legal structure, copyright laws, human resource policies and patent laws are all in place.

Organizations are mainly focusing on their product and business using these norms. With the changes called out by generative AI, these fundamental building blocks will change, and leaders will need to build that awareness. They must make sure that these new norms get into practice and, in some cases, contribute to evolving these changes.

They will be dealing with two dimensions of change management. One dimension enables change in the organization most effectively; another is participating in the change, keeping an open mind and collaborating industry-wide to define and refine that change.

Become Strategic and Proactive Leaders
Leadership shall evolve to be more strategic and proactive to identify the transformations needed in the organization, both in bringing new AI tools and changing the roles and responsibilities of the individuals to get the best out of the AI transformation.

There shall also be a balance between technology and business leadership. Technology leaders will expand their perspective on business aspects, macro and microeconomic environments, business challenges and how technology and innovations impact and influence those aspects.

Similarly, business leaders shall become more technology conversant. This convergence will create more strategists with a broader perspective to connect the dots in different dimensions. It will allow a better cross-functional view and reduce silos within the organizations.

My thought process was interrupted by the voice in my car. You have reached the office, Kaitki. Have a great day!

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This article originally appeared in the Fall 2023 issue of In the Lead magazine, from Buccino Leadership Institute. The bi-annual magazine focuses on leadership perspectives from the field of health care, with content that is curated from leaders across the industry who share lessons learned from real-world experiences.

Categories: Business