CEO and cofounder of Cyber Leadership Institute, a fast-growing community of cyberleaders from more than 50 countries.
The idea of generative AI, which exploded onto the tech scene toward the end of 2022 with the introduction of GPT-4, ChatGPT, AlphaCode, Midjourney, Bard and many others, has transitioned from hype to reality much faster than anticipated.
Dubbed the most important technological breakthrough since social media, generative AI generates text, images and sounds in response to short user prompts. The technology continues to unleash waves of innovation across several industry verticals, responding to emails, preparing tax returns, recording metal songs, writing pitch decks, debugging code, sketching architectural blueprints and providing health advice.
Predictably, a staggering 85% of the respondents to ISACA’s Generative AI Survey, polling more than 2,000 digital trust professionals’ perspectives globally, confirmed that generative AI would boost human productivity. A separate study predicts generative AI will contribute up to $15.7 trillion to the global economy in 2030, more than the current outputs of China and India combined.
But as organizations tap into potential benefits, they may likely introduce a new host of significant risks. The ISACA survey highlighted dozens of challenges posed by generative AI. Below, I will explain how senior business leaders can mitigate three key obstacles: policy ambiguity, unclear regulations and cultural resistance.
Establish a strong tone at the top.
A starting point is for senior executives to establish clear guidance to safely embrace generative AI. Unfortunately, this is not the case. The ISACA survey exposed a significant gap regarding the strategic adoption of generative AI. Specifically, only 28% of respondents said their organizations expressly permit generative AI, while 35% are unsure.
Turning a blind eye or banning this transformative technology may create significant fault lines. First, employees might be tempted to bypass established governance processes and experiment with sensitive data on shadow computing platforms, creating deeper security and privacy issues. Second, when ambitious employees feel constrained by lethargic cultures, they join forward-leaning competitors.
As the linchpin of the enterprise, the CEO must send a clear and unequivocal message that clearly articulates how generative AI can be safely harnessed to amplify the organization’s mission. But executives must go beyond one-off blogs and develop clearcut generative AI usage policies aligned to their risk appetite. At a minimum, these policies should cover the following.
1. Set rules governing the protection of intellectual property, copyrights, patents and trademarks.
2. Establish governance processes to ensure qualified human review of all generative AI outputs to eliminate inaccuracies, biases, exaggerations and plagiarism before making strategic decisions or publicly distributing the information.
3. Prohibit the use of confidential information on public generative AI platforms beyond the organization’s ability to protect.
4. Articulate acceptable generative AI usage to promote lawful, ethical and unbiased use.
Understand legal ramifications.
Once the policy requirements have been established, the next step is carefully working with your legal experts to understand the implications of applicable laws governing the use of generative. Blindly embracing generative AI can open serious legal issues. Worryingly, several lawsuits have already been filed in the U.S. against companies that allegedly trained their models on copyrighted data without authorization.
Crafting the appropriate legal responses can be a complex undertaking for organizations trading in multiple jurisdictions because, until recently, there have been very few global laws governing the use of generative AI. Regulators are, however, starting to act, albeit in disjointed and sporadic approaches. The Chinese top internet regulator was one of the first to make a bold move, proposing rules that require government review of AI chat tools for protecting personal data and require that developers train their GenAI models using only data compliant with Chinese law.
The European Union has issued extensive top-down prescriptive rules, including prohibiting uses of AI that it says pose an unacceptable risk; it’s in the final stages of passing. The U.S. White House, on the other hand, has invited public input on GenAI, which will be considered by the President’s Council of Advisors on Science and Technology (PCAST) working group on GenAI.
As these policy considerations unfold, these strategic questions can help executives manage generative AI-related legal risks.
1. What are generative AI-related legal risks in jurisdictions we do business in or markets the organization plans to penetrate?
2. How can we translate legal jargon into straightforward business rules and embed those principles deeply into operational business processes or software development lifecycle?
3. Does our internal legal team have the depth to dissect overlapping or incoherent laws, or should we enlist the services of a suitably qualified external legal firm?
4. Have we adjusted our risk appetite statement to align with emerging generative AI laws to ensure the benefits of innovation outweigh the potential downsides?
Create opportunities for generative AI to augment human cognition.
One concerning finding from the ISACA survey relates to deep uncertainty around generative AI. Only 6% of respondents said their organizations are providing training to all staff on AI. This is worrying, given that at least 45% of respondents expressed concerns that generative AI will also replace many jobs. Left unaddressed, employees fearing generative AI may replace their jobs may actively resist change, derailing program success.
To cope with this potential disruption, management must deliberately invest in programs to upskill their workforce. Getting this right requires rigorous workforce strategic planning—mapping the skillsets needed to maintain competitive advantage and managing a fluid pipeline to meet that demand. A critical starting point is identifying opportunities where employees can work alongside generative AI and formulate strategies to maximize those synergies. But as the adage goes, you can lead a horse to water, but you can’t make it drink. Sustained change requires management to foster a continuous learning culture and create reward systems that encourage upskilling.
Look ahead.
Given the significance of generative AI, it’s no wonder that the World Economic Forum recently predicted it would cause significant shifts in global economies and labor markets. But to maximize its benefits, management must establish measures to ensure generative AI adoption aligns with risk appetite.
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