Embracing Generative AI: How Should the C-suite Approach Adoption?

As part of Solutions Review’s Contributed Content Series—a collection of contributed articles written by our enterprise tech thought leader community—Pandurang Kamat, the Chief Technology Officer at Persistent Systems, outlines how the C-suite should approach their generative AI adoption strategies.

Until recently, AI has mainly operated behind the scenes. People didn’t realize they were using AI when their email subject line auto-filled or asked Alexa to order whatever was in their Amazon cart. In late 2022, OpenAI’s release of its transformative ChatGPT chatbot powered by Generative AI marked the beginning of a cultural and societal shift in how non-technical people interact with AI. For many, it was their first real hands-on opportunity to utilize any AI technology, exposing them to its ability to provide human-like responses to nearly every possible query imaginable.  

In business, Generative AI (GAI) promises to be an impetus for innovation, an accelerator of productivity, and a tool that gives employees back their most valuable asset: time. It prominently and rightfully dominates the business media headlines today. 

In the near term, there are several ways that GAI will advance workflows, accelerate processes, and create new efficiencies for enterprises. Many are racing to adopt and implement GAI as quickly as they can. However,  as enterprise generative AI adoption picks up, there are questions about data confidentiality, privacy, and transparency, alongside ongoing debates about avoiding harmful biases and ethical violations from creeping into the foundation models used to power GAI. These concerns have led some companies, especially in highly regulated industries like banking and finance, to take an enthusiastic yet cautious approach to mapping out potential use cases for their generative AI adoption efforts.

It is critical to understand where the technology is headed and what it looks like in practice for an enterprise.  

Productivity and Go To Market Advantages 

While the full long-term impact of GAI will take time to determine, what’s clear is that companies can utilize the technology for some rapid, shorter-term benefits while adjusting use cases and deployments to account for new or unexpected technological and organizational developments.  

1) Reimagine Digital Engineering 

Quality technical talent has been hard to come by for several years. New data from EY suggests that more than 80 percent of organizations do not have enough skilled tech workers on staff. Alleviating the pressure on tech teams while still having the right talent to drive innovation forward remains a top priority for executive leadership. Generative AI may bring much-needed relief. Two core functions comprise the core of digital engineering teams: application development and legacy modernization. Embedding GAI into the development lifecycle for both will increase speed-to-market and uptime because much of the legwork will be supported by AI.  

2) Redefine Enterprise Access to Data, Search, and Virtual Assistants to improve workplace productivity. 

Custom solutions, trained on an organization’s data, will unlock a new workflow efficiency. With a single conversational query, teams can gain insights and analyses on customer data, trends, transactions, and actions that would take significant time to research and compile manually. What’s more, Generative AI will further democratize AI usage, as it opens access to data and insights without any dependence on tech or support teams to create dashboards or report templates, as GenAI can perform those functions independently.

By removing these business barriers, GenAI will fundamentally change how service, marketing, and sales teams interact with customers—bringing a new level of personalization to the customer journey. Beyond the customer, GAI-based tools will be applicable for any use case that involves accessing and analyzing massive volumes of company data for new insights and trends.  

3) Expanding Horizontal and Vertical Capabilities with GAI 

One of the most substantial benefits that GAI will bring to the enterprise is efficiency in a company’s ability to serve external customers and audiences and improve internal processes. Customer and employee support roles can view a comprehensive picture of a user’s history at a glance. Human Resources will likely rely on GAI to draft policies and companywide emails, giving time back to their teams to dedicate to improving employee experiences.

Perhaps most critically, vertical and functional solutions can be completely reimagined with GenAI-powered experiences powered by the technology’s ability to quickly tap into data and execute queries and commands—from clinical trials to supporting financial advisors, processing insurance claims, and onboarding for customers and employees.

Consider Security and Confidentiality 

But before running ahead, C-level leadership should consider where GAI implementation could go wrong. With the technology’s relative newness, the use case creation and adoption playbook is written in real-time across different companies and industries. The following should be kept in mind as leaders establish guardrails to guide how GAI is used in their organizations and under what circumstances.  

1) Security

Security is critical to consider when onboarding third-party GAI solutions. Whether an organization is building a solution in-house or partnering with a solutions provider, defining how the data is being used is essential. How is one organization’s sensitive data protected from being shared with other organizations? How is the model trained over time? Before investing in either solution, clearly understand what happens to the data once it’s disseminated.  

2) IP Infringement Exposure

It’s unclear how protected AI-generated work—particularly images, code, and content—is from intellectual property infringement violations. According to Dr. Lance Elliot, Stanford University Fellow and AI expert, GAI vendors cannot guarantee that the content generated is free of copyright infringement. By the nature of how consumer-based large language models are trained, there is no way—at present—to certify that what’s created isn’t a replica of another’s work. Though unlikely, it could happen.

For enterprises, Generative AI presents a different kind of risk, as models utilize enterprise data and other sources versus the vast array of data available via the Internet. There are important considerations that enterprises need to consider on the lineage of training data, licensing and permissions, and security, privacy, and compliance.  

3) Data Ethics

Many have called into question the ethicality of AI. Experts caution that it could lead to biased decision-making in critical situations, such as medical or safety scenarios, and in others, such as hiring or loan approval, where bias can lead to inequity among different consumer populations. There’s also ongoing concern about which data is used to train Generative AI models and ensure proper consent and attribution to clarify which data and information the model generates outcomes.

Caution remains, and governance over the ethical use of AI will remain a priority for both business leaders and governments. In June, the European Parliament took a major legislative step to address the potentially harmful effects of AI when it released a draft law aptly named the AI Act. According to The New York Times, “The European bill takes a ‘risk-based’ approach to regulating AI, focusing on applications with the greatest potential for human harm.” Others will likely follow suit soon.  

Generative AI has tremendous potential to reimagine the future of work and digital experiences. From digital engineering and data insights to customer service and creative production, GAI will bring speed and efficiency to mundane, repeatable work. But as with any new technology, guidelines will be necessary to ensure that it is used predictably and fairly. Business leaders should investigate the technology’s potential benefits for their companies while managing risks and understanding how it will impact employees and customers. Given the lightning-quick generative AI adoption path, customers and employees will soon expect organizations to embrace GAI to leapfrog the competition.