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Generative-AI Opportunities & Risks Table Discussions

Roundtable Forum:

April 17, 2024


Generative AI presents unprecedented opportunities and risks. Currently it is forcing Enterprises into the data and operational unknown, within an unfamiliar space. Crafting an effective strategic approach to generative AI can help mitigate risks while taking advantage of its highly effective capabilities. Leaders who are prepared to reimagine and enhance their business models—identifying the right opportunities, organizing their workforce and operating models to support generative AI innovation, and ensuring that experimentation doesn’t come at the expense of security and ethics—can create a long-term competitive advantage.


How to Assess Risk?

Companies need policies that help employees use generative AI safely and that limit its use to cases for which its performance is within well-established guardrails. Experimentation should be encouraged; however, it is important to track all experiments across the organization and avoid “shadow experiments” that risk exposing sensitive information. These policies should also guarantee clear data ownership, establish review processes to prevent incorrect or harmful content from being published, and protect the proprietary data of the company and its clients.


Another near-term imperative is to train employees how to use generative AI within the scope of their expertise. Generative AI’s low-code, no-code properties may make employees feel overconfident in their ability to complete a task for which they lack the requisite background or skills; marketing staff, for example, might be tempted to bypass corporate IT rules and write code to build a new marketing tool.

“About 40% of code generated by AI is insecure, according to NYU’s Center for Cybersecurity—and because most employees are not qualified to assess code vulnerabilities, this creates a significant security risk.”



AI assistance in writing code also creates a quality risk, according to a Stanford University study, because coders can become overconfident in AI’s ability to avoid vulnerabilities.


Leaders therefore need to encourage all employees, especially coders, to retain a healthy skepticism of AI-generated content. Company policy should dictate that employees only use data they fully understand and that all content generated by AI is thoroughly reviewed by data owners. Generative AI applications (such as Bing Chat) have already started implementing the ability to reference source data, and this function can be expanded to identify data owners.


How to Ensure Quality and Security?

Ethical & Diversity Standards & Guidelines for Publications will become a factor for Leaders regarding releases of generative AI content and code. Comprehensive Documentation may take center stage as a beginning step along with review boards to consider possible stringent impacts. Suggested Licensing for downstream uses, such as the Responsible AI License (RAIL), presents another mechanism for managing the generative AI’s lack of a truth function.


Finally, leaders should caution employees against using public chatbots for sensitive information. All information typed into generative AI tools will be stored and used to continue training the model and therefore not private. Today, companies have few ways to leverage LLMs without disclosing data. One option for data privacy is to store the full model on premises or on a dedicated server. This may limit the ability to use state-of-the-art solutions, however. Beyond sharing proprietary data, a keen eye must be on protecting personally identifiable information. Leaders should consider leveraging cleaning techniques such as named entity recognition to remove person, place, and organization names. Enterprises should regularly update their security protocols and policies.

How to capture "quick wins"?

Enterprise Leadership must identify the low-hanging fruit—the areas where GenAI can deliver quick wins.

a) How can it streamline task-intensive and other repetitive, and mentally draining processes to boost productivity?

b) How can we attract and keep customers by embedding GenAI into external and internal touchpoints?

c) How do we identify the use cases for quick wins and prioritize them?

Small wins give stakeholders confidence to apply budget and continue to implement both short-term and long-term GenAI plans. Utilizing early adopting third-party technology companies and platforms, Enterprises not only will demonstrate the efficacy of GenAI but also build a culture of innovation and adaptability that will entice investors, employees and customers. Therefore, increasing an Enterprise’s transformation and relevance. How can we swiftly and successfully build GenAI capabilities throughout the Enterprise?

a) Technology Alliances:  Choosing the right technology platforms and partners will enhance and expand your possible capabilities’.

b) Bridging the Skills Gap for upscale initiatives. Pioneering organizations like AT&T recognize the critical role of talent and are actively upskilling, hiring, and acquiring new AI skills. They have over three times more full-time employees using AI on a daily basis than other companies. “AT&T launched an internal GenAI assistance program for employees to

enhance creativity and effectiveness. And, underscoring the intense battle for

talent, the right people across companies are now commanding salaries of

$900,000 or more.”

How can we foster cross-functional collaboration?

c) Break down silos: Enable diverse expertise including third-party expertise to unite around GenAI initiatives?

d) Democratize GenAI: Implementing across the organization is critical to achieving greater GenAI maturity.


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