For Data and AI-Driven Success, invest in the “The Missing Think”

Introduction

In today's rapidly evolving business landscape, the influence of artificial intelligence (AI) is undeniable. According to the World Economic Forum , 44% of the world's workforce will need to be reskilled in the next three years to adapt to this new era. Moreover, AI is poised to augment as many as 87% of roles across various sectors, including Customer Service, Procurement, Risk Management, and Finance.

However, there is a glaring gap in the workforce: Critical thinking may become the most important skill .

Today, it is estimated that as high as 96% of occupations require critical thinking and that 70% or more lack the problem solving and critical thinking skills essential for success in this AI-driven world.

In this article, we explore why investing in employee training for critical thinking is essential for your organization's future success. We also assert that computational thinking is essential for applying critical thinking in the context of making decisions with data and for prompt engineering to be used effectively.

Defining Critical Thinking

The World Economic Forum defines critical thinking as "a combination of deductive reasoning, to reach logical conclusions, and inductive reasoning, to infer a broader understanding, to make sound judgments, including those related to decision-making and comparisons of potential outcomes of hypothetical scenarios."

Critical thinking is a quintessentially human trait, fundamental to effective problem-solving.

The Critical Thinking Gap

AI's capabilities will encompass tasks such as math/stats, data visualization, and content generation for storytelling. Unfortunately, these are the areas where 67% of companies have been investing in training for the past several years.

AI may handle tasks, but humans must still provide the critical judgment and decision-making that drive innovation and progress.?

AI's Demand for Critical Thinking

As AI becomes increasingly integrated into job roles and processes, critical thinking emerges as a non-negotiable skill, especially for leaders and decision-makers across all functions.

The power of data, technology, and process automation can only be harnessed effectively when individuals make judgments and decisions in new ways. The pandemic exposed vulnerabilities in decision-making practices, highlighting the need for a permanent shift towards data-driven decision-making.

Does AI possess Critical Thinking?

According to AI experts, the answer is a solid no. If you are still not sure, this is how ChatGPT 4.0 answered the question:?

“AI, like Gen-AI or ChatGPT, does not possess critical thinking in the way humans do. It generates responses based on patterns and information learned from its training data, lacking independent beliefs and opinions.”

To harness AI's potential, we must adopt a new mindset that emphasizes how we go about understanding the business problem we seek to solve before engaging AI.

The Missing "Think"

Computational Thinking—a problem-solving approach that involves breaking down complex problems into smaller, manageable parts and using logical and algorithmic thinking to solve them.

It is a mindset applicable to various disciplines and problem domains, not limited to math or data science expertise. Without Computational Thinking, we struggle to bridge the gap between AI-generated insights, data, and complex problems, akin to deciphering a vast spreadsheet of data without context.

Critical and computational thinking yield better AI Prompts: Industry-specific AI prompts may be abundant, but their value is not in just the ability to copy and paste them. Creating or using prompts should follow understanding your unique business problem and highest value use cases to ensure you are working on the right things.

Apply critical and computational thinking through problem diagnosis, decomposition, and reframing, looking for ways in which data can assist with problem solving. It is essential to align AI outputs with task objectives and foster effective collaboration with AI systems—a practice rooted in critical thinking.

As explored in the HBR article “AI Prompt Engineering Isn’t the Future” , by Oguz Acar, Prompt Engineering may go away. “Problem formulation becomes the more enduring skill that will keep enabling us to harness the potential of generative AI.”

Risks of Ignoring Critical and Computational Thinking: Neglecting critical and computational thinking carries several risks.

First, the Dunning-Kruger effect , where limited skills lead to overestimated competence. Second, the Asleep at the Wheel risk where AI is highly effective and people stop paying attention and accept what AI tells them. Third, in an age where algorithms are accessible to anyone, understanding and evaluating AI outputs is imperative.?

Conclusion

In conclusion, computational thinking is a mindset that equips non-technical individuals to execute by working collaboratively with AI. The question is whether we continue down the old path of data literacy, or we quickly embrace the fact that effective decision making with data, whether or not it is assisted by AI, begins and ends with critical and computational thinking.?

Investing in employee development to foster critical and computational thinking in problem solving and decision-making with data is not merely an option; it's a strategic imperative.

By cultivating these skills, your organization can unlock the full potential of AI technologies, enabling innovation, informed decision-making, and sustainable growth.?

Authored by Susan Stocker , Principal Solution Consultant, Aryng

About Susan:

At Aryng, Susan is responsible for corporate Data Literacy solution consulting and training programs. She became an Aryng advisor in 2018 and Consultant in 2020.Susan has 20 years of experience in all aspects of global enterprise learning strategy. For the last 12+ years she has specialized in reskilling the workforce in support of digital transformation. She served as the global learning leader for GE Capital, GE Energy, and GE Software where she designed digital competency models and assessments.She also taught a range or courses including inferential statistics and business agility to thousands of business leaders and employees around the world. Susan is also a hands-on practitioner and has become a certified Citizen Business Data Analyst, Certified Scrum Master, and Certified Lean Six Sigma Master Black Belt, delivering business impact throughout her career. Susan holds a B.S. degree in Economics from the University of Kansas and is lives in the Atlanta, GA area.