Chief technology officers are on point for advising the C-suite on generative AI. Here are five ways CTOs can address the impact of this technology on their organization.

Jed Maczuba, Chief Technology Officer

March 4, 2024

4 Min Read
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Vitalij Sova via Alamy Stock

If 2023 was the year generative artificial intelligence went mainstream, 2024 is shaping up to be the year it will take hold and transform business. The good news is that CTOs and other tech specialists are ready to move from ideas to implementation when it comes to AI. That’s not necessarily true of their C-suite counterparts, who may lack knowledge about how to get the most out of this emerging technology.  

By default, CTOs have landed in the driver’s seat for advising executive leadership and other organizational stakeholders about how to put AI into practice to grow the business.  

Here are five steps that CTOs can use to help their organizations develop an enterprise-wide generative AI strategy: 

1. Build a foundation of knowledge. Most C-suite executives are conversant in AI, but not much more, so it is up to the CTO to help them discern fact from fiction. To make informed decisions, the C-suite needs to move past the hype and focus on the valuable insights that AI techniques can offer the organization through adoption and execution.   

For example, CTOs can clarify the differences between generative and discriminative AI. It’s an essential distinction because each represents a vastly different approach to learning from data. They are also at separate points on the adoption curve. Generative models can be used for tasks like data generation, while discriminative models excel at classification and prediction tasks.  

2. Talk in terms of business value. AI can help any business create efficiencies and generate new ideas, designs, and concepts. The possibilities may be endless, but they start with outlining the potential applications in your industry. Remember, your C-suite colleagues will be most interested in ways that AI can enhance efficiency and increase shareholder value.  

For instance, in wealth management, use cases such as summarizing meeting notes, drafting meeting agendas, scheduling client review meetings, and interpreting policies are table-stakes for the first generation of AI-powered assistants. More advanced use cases have greater impact on firm operations and client engagement, including using AI to proactively resolve case queries, recommend next best actions, initiate home-office actions, build custom workflows, and gain insights into portfolio performance.   

3. Increase muscle-memory in-house. From marketing and sales, to operations, legal and human resources, the potential applications of generative AI are boundless. Encouraging employees across disciplines and functions -- including the executive team -- to engage with this technology will advance their understanding of its potential value and risk to the organization. After that, move on to phase two of experimentation, which can include licensing trained generative AI models built by others to ease the organization into implementation. This phased approach will not only help organizations formulate their AI strategy, but for some, it also can serve as a gateway to building applications in-house.   

4. Form a steering committee for acceptable AI use and quality control.  Like other technologies that have altered the way we transact, a thoughtful enterprise approach to AI is essential. At minimum, acceptable use policies for AI should follow standard protocols for internet usage -- covering intellectual property protection and compliance restrictions, including classification of data and personal identifiable information. Legal counsel, compliance, HR, and communications should all be part of a steering committee that establishes the organization's policies for appropriate AI usage and what may cause exposure. Likewise, every company should have mechanisms in place for continuous monitoring and improvement of its AI initiatives and for enforcement of policy. Companies need to stay up to date with advancements in AI technology, using the latest versions with enhanced security, safety and performance features.  

5. Make the data connection. Across all industries, CTOs are well-positioned to lead the C-suite conversation about how data and AI applications are inextricably linked. CTOs understand that data is what is driving AI adoption and yet, data protection is paramount. This means getting the C-level to commit to investing in robust security for generative AI-enabled tools and work to protect data and prevent unauthorized access to sensitive information.  

Very quickly, we will see data scarcity drive searches for new sources of data to train the models that power generative AI. Copyright infringement, confidential data leakage, data poisoning and a rising tide of data obfuscation technologies will tax the availability of high-quality data that feeds large language models (LLMs). Multimodal AI is in its infancy with many established LLM providers lacking capabilities. The drive to develop smaller, more cost-effective models and open-source LLMs will present new opportunities for sectors with sensitive data and mount a challenge to proprietary LLMs.  

As AI usage and adoption proliferate, it is imperative that organizations be able to flex their thinking and strategy around the technology. CTOs can take a lead role in guiding their colleagues about the impact of AI on their organization by developing enterprise-wide guidelines and protections and identifying the less-obvious use cases -- ones that will move the needle in terms of efficiency and competitive advantage. 

About the Author(s)

Jed Maczuba

Chief Technology Officer, Advisor360°

Advisor360° builds and delivers deeply integrated technology for enterprise wealth management firms. As the company’s Chief Technology Officer, Jed Maczuba leads a globally distributed team of technologists responsible for building and operating the company’s market-leading products and platform. He oversees Advisor360°’s Data, Platform, and Product Engineering teams and is responsible for accelerating research and development across the platform and spearheading development of future product capabilities.

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