Why GenAI’s greatest potential is also its greatest challenge

Although we are in the early stages of Generative AI and Large Language Models (LLM), many are anticipating a significant impact on talent, including new work opportunities and the reshaping and transformation of how work gets done. Since the introduction of ChatGPT, a Generative Pre-trained Transformer (GPT) chatbot, in autumn 2022, we’ve already seen the creation of a new role, the prompt engineer. We have also seen early AI adoption by sales and marketing companies to generate content and build relationships. Unlike other technology innovations, generative AI's greatest potential is that it will help virtually everyone in their job. At the same time, generative AI’s greatest potential is also seen as its greatest challenge.

Generative AI makes the case that it will assist virtually everyone in their job, which is far different from the more traditional AI that requires deep technical expertise in data science, machine learning, and years of training. For Generative AI and GPTs to impact so many roles, they must go from Generative Pre-trained Transformer to General Purpose Technology and from technology-focused to business-focused.

The journey to general-purpose technology is nothing new. There’s a long history of technology innovations moving from the super-hyped to the almost invisible general-purpose commodity. Consider how the steam engine that powered the Industrial Revolution led to electricity becoming available in every home, and then mainframe computers becoming desktops or even mobile devices.

A recent Harvard Business Review study, The New Decision Makers, points out the benefits of empowering frontline workers with digital tools that enhance their operational capabilities.

• 87% of survey respondents say their organization will be more successful when frontline workers are empowered to make important decisions.
  
• 72% say productivity has increased through empowering frontline workers.
  
• While 86% say frontline workers need better technology-enabled insight.
  

An MIT study of call center agents reinforces the HBR article. The study examined real-world contact center agents using AI assistants and a control group without AI assistants. The authors found that agents with access to a conversational AI assistant increased their productivity by an average of 14%, and lower-skilled workers saw a boost of 35%. This model bridged the digital skills gap, and workers were upskilled, not replaced, thanks to the generative AI technology.‍

Building a culture for growth‍

In a world where Generative AI enables natural language processing and true self-service analytics that empower data analysts, business users, and all non-technical users, the digital skills gap is substantially narrowed. Without traditional technical skills, workers from diverse backgrounds can engage with data more intuitively, and people can query and analyze data using natural language. The conversational approach reduces the inherent “tax on curiosity” imposed by the need for technical expertise. It lowers the barrier to entry for data-driven insights. It increases the speed of action, empowering a broader audience to make data-driven decisions, ultimately leading to a strong data culture.

As business executives look to build this strong data culture, they must also consider the critical role of data literacy within that strategy. Data literacy initiatives infused with conversational and generative AI built within a specific business context will provide much greater value than traditional technology-focused learning programs, like learning SQL or Python.

Data literacy initiatives that leverage AI to increase the understanding of the business, educate employees on how to use data for decisions, and empower frontline employees to act quickly will significantly impact building a lasting data culture.

Upskilling and Reskilling

LLMs and Generative AI facilitate a common business language and consistent terminology for business terms and data that will expedite the upskilling and reskilling of employees. When employees can quickly and effectively learn to navigate and analyze company data, it boosts their productivity and confidence in handling data-driven insights. This will also aid companies in keeping pace with the rapid evolution of technology and more easily ensure that their workforce's skills remain relevant and up to date.

By making data analytics more accessible and conversational, individuals who may not have had the opportunity or resources to gain technical skills can now actively participate in data-driven roles. Well-organized data literacy programs coupled with generative AI will lead to more diverse and inclusive workforces, bringing various perspectives and ideas that can drive innovation and problem-solving. We are already seeing sales and services organizations identifying and realizing the benefits of generative AI.

The Challenges

However, there are challenges to leveraging generative AI across the organization. In a recent Salesforce survey¹, 63% of sales professionals and 53% of customer service professionals say employees expect generative AI training. In both areas, over 60% report not receiving the necessary training.

Clear benefits have been identified in obvious areas, such as sales, marketing, and customer support, but adoption remains low. Why is that? Generative AI continues to suffer from a lack of trust and the fear of the unknown. Individuals and organizations may need more time to be ready to turn over long-standing and important relationships with customers to AI-generated responses and content.

Security and privacy are also an ever-present concern for every organization. Using company data to train AI brings concerns about data exposure and leakage. Cindi Howson, ThoughtSpot’s Chief Data Strategy Officer, highlighted the security concerns in her article on how GPT will change data & analytics. She said, “The GPT euphoria got doused with some reality recently as Samsung employees realized they were sending false information to customers, and Italy outright banned ChatGPT.”

In short, attention must be paid to ensuring data security and privacy while promoting foundational data literacy needed to ensure the responsible and effective use of tools, technologies, and insights.‍

Looking Ahead‍

The potential for Generative AI to diminish the digital skills gap is quite evident while opening access to self-service data analytics. As organizations move forward, it is essential to approach this frontier with a balanced perspective, prioritizing not only the use of new technologies but also the foundational data literacy needed by workers and the security and privacy needs of the entire business.

The journey towards realizing the full potential of Generative AI and LLMs is littered with promise and complexity. As organizations and individuals navigate this path, the focus should remain on empowering people, enhancing skills, and fostering a culture of innovation. In doing so, organizations get ever closer to the future, where the digital skills gap is not a gaping chasm but a bridged connection that fosters growth, innovation, and collaboration.

The age of Generative AI is not just on the horizon; it's unfolding right now. Are you ready?