Insight: Integrating Generative AI Within Organizational Systems Comes With Challenges. Here Is Why Businesses Should Still Invest In This Space, Now More Than Ever

The following insight is derived from a recent ‘Insights from the Field’ event featuring QuHarrison Terry on the topic of Operationalizing Generative AI. 

Meet Our Guest Contributor:

QuHarrison Terry | International Bestselling Author | LinkedIn Top Voice in Tech 

QuHarrison Terry is the bestselling author of The NFT Handbook (Wiley, 2021) and The Metaverse Handbook (Wiley, 2022). He is a four-time recipient of LinkedIn’s Top Voices in Technology award, is a notable entrepreneur, and currently leads growth for Mark Cuban Companies.

For more information, please visit QuHarrison’s LinkedIn profile.  

Introduction: Why does this matter?

Following the abrupt uptick of generative AI innovation in the past couple of years, organizations have ramped up research and development efforts to meaningfully explore the value of integrating generative AI into organizational systems, with a particular interest in achieving operational hyper-efficiency. This effort requires a deep understanding of the steps toward operationalizing generative AI—from development to adoption to deployment—analyzing benefits and risks that must be carefully considered by businesses to augment performance and financial gains. 

The Problem: What is the key question and what specific problem does this question attempt to address?

The challenge at hand is integrating and operationalizing artificial intelligence within organizational systems, and the need to carefully consider the steps from development or adoption of generative AI tool all the way to deployment. Leveraging his experience from navigating the early days of blockchain and NFT technologies to currently leading the growth handle of the Mark Cuban Companies, QuHarrison discusses the challenges that organizations face when considering utilizing emerging technologies like artificial intelligence to enhance business operations as well as the short and long-term implications of doing so from a financial, workforce, and ethical perspectives.

The Solution: What recommendations are stated to address the problem?

As a first step, QuHarrison stressed the importance of deeply understanding how generative AI tools like ChatGPT work and embracing the mindset of an early adopter for organizations and individuals alike. Organizations in particular need to allocate resources for a dedicated and open experimentation of such tools in real-world settings. This approach will help organizations to quickly recognize limitations and capitalize on the affordances of artificial intelligence. For example, the food startup Truffle Shuffle uses AI to provide ‘Michelin Star’ cooking class experiences for its customers. The media and entertainment industry is another one that’s well-positioned to benefit from integrating AI into its operations. A quintessential example is Open AI’s Sora that has taken the movie industry by storm in terms of the quality of content it is able to generate, even prompting a certain Hollywood star to halt production plans for a physical multi-media studio. When more organizations take on the challenge of implementing AI into their operations, the more credibility and trust this garners especially to the benefit of generative AI skeptics, and the more empowered we will all be to effectively collaborate with even more advanced generative AI tools in the horizon.  

The Implications: What are the broader implications of this topic for strategy and practice, and what could be done to augment positive outcomes and mitigate negative consequences?

The speed with which the artificial intelligence and machine learning fields are advancing leaves little time for companies to adopt and train their workforce. This challenge brings with it legal and ethical conundrums for organizations in terms of potential impact on brand and operational integrity. As a result, we could observe a ‘natural’ ordering in terms of openness for experimenting with operationalizing generative AI. Organizations in the business and technology sectors will continue to strongly advocate for its pervasive use, while organizations in the healthcare industry will have to employ a more cautious strategy when it comes to operationalizing AI into their operations, such as by carefully segmenting aspects of their operations in which AI could easily and beneficially be integrated. To use the healthcare sector as an example, such operational aspects may include those that do not involve direct interactions with patients.  

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