Facing the Challenge of AI

Artificial intelligence (AI) is advancing at an extraordinary rate. While the field has been steadily progressing over the years, the actual acceleration from a user standpoint came with the introduction of ChatGPT in 2022. Since that time, new capabilities have been emerging almost weekly. Organizations are left with a nearly constant perception of FOMO (fear of missing out).

Today’s professionals, especially those in analytics, are accustomed to a fast-changing environment. However, AI evolves at a pace far surpassing traditional analytics, demanding unprecedented agility and adaptability from leaders.

We are at a pivotal moment in history, comparable to the dawn of the Industrial Revolution. The very nature of work is undergoing a dramatic transformation. To capitalize on the opportunities created by AI, organizations must move quickly and decisively to experiment and implement in ways that challenge the old “play by the rules” handbook.

Clayton Christensen, in his seminal book The Innovator’s Dilemma, identified why successful companies often fail when confronted with disruptive technologies. Those disruptive technologies are often introduced with a limited feature set and priced at a reduced margin. The feature set and margin reduction often prevent larger organizations from identifying and reacting to the challengers before it is too late.

The introduction of AI lowers the barriers to entry for this disruptive technology. The low cost and speed of development present meaningful challenges that ignoring the issue will not solve.

Effective AI integration begins at the strategic level. Today’s executives must cultivate a deep understanding of AI’s capabilities and its alignment with their company’s goals. More than ever, corporate board directors expect AI to be embedded into business strategies to leverage its potential for competitive advantage. This process involves understanding the benefits, such as cost savings, revenue enhancement, improved customer experiences, and its pitfalls.

Regardless of its form, AI strategies’ success hinges on the quality of the underlying data. You can only perform effective analytics with reliable data, and the same is true for AI. Over the past five years, there have been numerous instances where companies have invested millions of dollars into AI initiatives only to abandon them due to poor data quality. When consulting or teaching, I emphasize a “data-first” approach. The integrity of one’s data underpins any successful AI strategy.

Developing a successful AI strategy requires change management and senior-level endorsement. Educational programs in strategic business AI are designed to provide leaders with a framework to construct preliminary AI strategies tailored to their organizations. These strategies outline specific use cases and potential financial upsides, giving organizations the necessary momentum to start their AI journey. This process involves continual testing, learning and adapting.

One of the most transformative applications of AI to business is generative AI. Tools like ChatGPT, Google’s Gemini AI, and Meta AI revolutionize how ideas and content are generated, enabling creativity and efficiency. Understanding how to leverage such tools can provide immediate benefits, from improving content strategies to refining business planning processes.

For example, generative AI addressed longstanding issues like spam filtering by integrating natural language processing capabilities. AI can enhance teaching materials and provide iterative feedback in educational contexts, leading to more effective and engaging learning experiences.

AI’s impact extends beyond IT or data science departments; it touches every aspect of business, including operations, marketing, finance, legal and sales. Understanding and integrating AI into these functions is now essential. For mid to senior-level executives, proficiency in AI is an asset and a critical component for career advancement and organizational success. As AI reshapes the business landscape at an unprecedented speed, leaders must keep pace with this rapid revolution.

Embracing and embedding it into strategic planning is not just beneficial – it’s vital for effective leadership in this new era.

Mark Price is an adjunct professor at the University of St. Thomas, teaching analytics, AI and courses in the Master of Science in Management, a new program from St. Thomas. Before teaching at St. Thomas, Price was an adjunct professor at St. John’s University in Collegeville, Minnesota, where he taught brand management and digital marketing. Price also writes about the impact of technology on customer experience, having recently published an article on the impact of AR/VR in the online newsletter www.retailwire.com.

Before his role as an adjunct professor, Price was also the chief data officer of CaringBridge, responsible for identifying and developing data-driven insights that lead to actions to improve user experience across authors, commenters, visitors, and donors. He led the team from 2021-23, leveraging such approaches as machine learning and predictive modeling for innovation.

Before CaringBridge, Price led his analytics firm, LiftPoint Consulting, for 16 years before selling the company to ThreeBridge Consulting in 2018, where he consulted on analytics for for-profit companies such as Exxon Mobil, Bass Pro Shops, Advanced Auto, and nonprofit organizations such as the Minnesota Zoo and Minnesota Public Radio. Price’s business experience includes brand management at General Mills and Ralston Purina.

Price has an MBA from the Darden School of the University of Virginia and a bachelor’s degree from Haverford College. He is also a member of the Braintrust at RetailWire, an online thought leadership forum, and a board member at the Friends of the St. Paul Public Libraries and Jewfolk LLC.