Key Takeaways on Generative AI for CEOs: Revolutionizing Business with Speed and Trust

July 5, 2023

By Noam Harel

Generative AI stands out from other technological breakthroughs due to its remarkable velocity and unprecedented speed. In a matter of mere months since its initial emergence in the limelight, this cutting-edge innovation has already achieved scalability, aiming to attain substantial return on investment. However, it is imperative to effectively harness this formidable technology, ensuring that it can deploy on a large scale and yield outcomes that garner trust from your business stakeholders.

When deploying and adopting Generative AI in your organization, you need to take into account that it’s on the brink of revolutionizing business knowledge work, transforming innovation, and paving the way for new business models. Embracing the principles of responsible AI for generative purposes goes beyond mere business risk management; it becomes a competitive advantage. While generative AI offers greater accessibility compared to traditional AI, it still demands the acquisition and adoption of new skills by your existing workforce, considerations for fresh future talent, and the cultivation of an innovative culture across your business.

Generative AI presents a significant hurdle for CEOs. While the current emphasis lies on enhancing productivity, efficiency, cost reduction, ethicality, and addressing technical constraints, a wave of business-model innovation revolution is on the horizon. Just as Mosaic, the pioneering free web browser, propelled the internet era and transformed our work and lifestyle, Generative AI possesses the potential to disrupt virtually every industry. It promises both a business competitive innovation edge and the prospect of creative destruction with potential “hallucinations.” CEOs must recognize the implication: the frenzied activity of today must evolve (and be distilled into the must-have key use cases) into a generative AI strategy that is embraced by the C-suite, Department heads, business units and knowledge workers.

That undertaking is no small feat, however, and CEOs, who are often distanced from the technology itself, may feel unsure about their next steps. However, from our perspective, the CEO’s primary focus should not be on immersing themselves entirely in the technology itself. Instead, they should concentrate on understanding how Generative AI will influence their organizations and industries. By making strategic choices, they can effectively seize opportunities and overcome challenges.

Generative AI poses a formidable challenge for CEOs, and that’s the harsh fact. Although the current emphasis centers on productivity gains and technical limitations, a paradigm shift in business-model innovation is on the horizon. Generative AI has the power to disrupt nearly every industry and that impact is certainly not reserved to tech giants alone. This calls for CEOs to devise a new adoption plan, one that leans on their own department level business leaders to recognize the need and use cases for a Generative AI strategy. By making strategic choices, they can effectively capitalize on opportunities and address challenges.

CEOs are faced with critical decisions centered around three fundamental pillars: Potential, Human Capital, and Policies. These pillars raise pressing questions that demand their attention. How will the availability of Generative AI’s vast memory empower every employee and unlock new realms of business innovation? How will this transformative technology redefine employee roles, skill set requirements, and their management? And how can leaders effectively address the potential for false or biased output from Generative AI models?

Undoubtedly, the realm of GenAI is constantly advancing, and each pillar mentioned above carries both short- and long-term implications, along with numerous unanswered inquiries. However, CEOs must proactively prepare for the inevitable obsolescence of their current business models. Here’s a strategic approach to navigate the future:

Embrace Potential: Explore the limitless possibilities that arise when leveraging generative AI’s vast memory. Encourage employees to harness this technology to drive innovation, fuel creativity, and discover new solutions.

Empower People: Embrace the changing landscape of employee roles and management. Provide necessary training and resources to adapt to the evolving nature of work in a generative AI-powered environment. Foster and empower a culture of continuous learning and growth.

Govern Policies: Establish robust frameworks to address the challenges associated with generative AI. Prioritize transparency, fairness, and accountability to mitigate the risks of false or biased output. Regularly review and update policies to align with evolving industry standards.

By strategically focusing on these pillars, CEOs can navigate the complexities of the future with confidence. Embracing Generative AI and its potential while addressing the associated challenges will position businesses for continued success in an ever-changing landscape.

Certainly, the realm of Generative AI is rapidly progressing and evolving, and each of the aforementioned pillars entails both short- and long-term considerations, along with numerous unanswered queries. However, it is imperative for CEOs to prepare themselves for the time when their existing business models become outdated and get their house in order, data management and classification included. Let’s delve into strategies for navigating this future.

Think Big

Never before has AI been as accessible as it is now, and new tools like ChatGPT, Bard, DALL-E, Midjourney, open source LLM models, and Stable Diffusion are being released on a faster cadence than ever before. These “low-code, no-code” GenAI tools will also facilitate widespread adoption of AI capabilities with business users and knowledge workers within your organization. As a CEO, you’re certainly assessing the functional attributes of generative AI in your organization while you contemplate the best strategy of adoption. For example, you might be looking at short- and long-term gains in productivity and efficiency that can significantly reduce costs such as in the use case of summarizing a large corpus of business data (e.g., support documents) with remarkable accuracy in a matter of seconds, while a researcher or customer support might spend hours on the same task (with an estimated cost of $40 to $60 per hour, pending on geo location).

The democratizing influence of generative AI on your business implies that your competitors will have comparable access and capabilities as they evaluate numerous use cases that depend on existing large language model (LLM) applications, such as enhancing productivity for programmers utilizing GitHub Copilot in their R&D or Engineering teams or aiding their marketing content developers through As you evaluate the right strategy, think big  —  merely keeping pace with other competing organizations might not be enough to outsmart the competition. Be careful not to boil down your GenAI innovation and differentiation to a singular edge use case. If you want to transform your business, you need to think about adopting Generative AI at a much larger and customizable scale, leveraging your own internal data across multiple use cases. 

Identify the Perfect Use Cases

For CEOs, the crucial task is to discover the company’s low-hanging fruit and ideal use cases to start with — ones that provide a genuine competitive edge and yield the greatest impact compared to existing solutions.

These use cases can be found at any stage along the business value chain. Some companies can achieve growth by enhancing their offerings, others by reducing support costs or internal product innovation cycles. Growth opportunities also lie in reducing TTM and cost-savings reductions, as well as fostering imagination and generating fresh ideas from your employees. 

Once you enable your leaders to identify their critical use cases, they will collaborate with their technology teams to make strategic decisions on whether to fine-tune existing large language  models (LLMs) or train a custom model to address your unique use case(s). 

Strategize Your Business Investments

Evaluate the timing of your investment, carefully weighing the potential business risks of moving too quickly on a complex project when the necessary talent and technology might not be fully ready against the risks of falling behind or outsmarting your competition. Present-day generative AI is still limited by its error-prone nature and should primarily be implemented for use cases that can tolerate variability and be forgiving by nature. 

Additionally, CEOs must consider new funding mechanisms for data and infrastructure. For instance, they may need to determine whether the budget should come from Data Science, IT, R&D, CIO office or another source if they deem custom development to be a critical and time-sensitive requirement.

The Era of Generative AI and the Expanding Market for LLMs

As research gains momentum and becomes increasingly proprietary, and as the algorithms grow more intricate and complex, keeping up with cutting-edge and open source models poses a significant challenge. Your data scientists and data engineers will require specialized training, advanced skills, and deep expertise to comprehend the inner workings of these models—understanding their capabilities, limitations, and applicability to novel business scenarios. Consider investing in upskilling your technical engineering teams to maintain your competitive edge.  

Redefining Business Roles, Scope, and Responsibilities

The revolution is already here. Certain Generative AI shifts have already transpired, and your competition is certainly adopting, or in the process of adopting Generative AI elements. Traditional AI and ML algorithms employ powerful logic or statistics to analyze data sets, automating processes or enhancing decision-making workflows. With its remarkable ability to generate initial human-like content, Generative AI will augment numerous knowledge worker and business roles by boosting productivity, performance, efficiency, mundane tasks and creativity. 

However, this tectonic change should not occur in isolation. CEOs must remain cognizant of the impact that AI has on employees’ emotional well-being and professional identities. It is common for productivity improvements to be erroneously associated with overall staff reduction, and AI has already instilled that concern among employees. Yet, it is also conceivable that AI will create as many jobs as it displaces.

Therefore, the influence of AI represents a pivotal cultural and workforce issue, necessitating CEOs to collaborate closely with HR to comprehend the evolving nature of roles. As generative AI is implemented across business units and departments, make sure to assess adoption, progress and employee sentiment. The overarching message should emphasize the indispensability and supercharging of human capabilities and their pivotal role in effectively and ethically deploying AI to drive innovation, not human capital replacement. 

As AI becomes more prevalent, CEOs must adapt and continuously learn to develop a strategic workforce plan that adapts to the current technological advancements. CEOs must craft this plan today and remain flexible and agile as technology evolves and morphs. As you consider your strategic plan to adopt Generative AI, think big and plan beyond the hype cycle and the next AI feature your organization must release to keep up with your competition. The focus goes beyond anticipating changes in job skills, roles, scope, and descriptions; it’s about ensuring that the company has the right forward-looking and adaptable personnel as well as an effective and well-versed management layer in place to maintain quality, growth, competitiveness, and to maximize your company’s Generative AI investments. 

Given the growing significance of data science and engineering, many companies will benefit from designating a senior executive, such as a Head of AI, Head of AI Transformation, CDO, or CDAO, to oversee the business and technical aspects of AI initiatives. 

In order to achieve success, it is imperative for this executive to establish small and agile data science or engineering teams within every business unit adopting generative AI. These dedicated teams will power, tailor, and refine models, unleashing their true potential for specific use-driven tasks and applications. 

This approach will ensure that technical teams possess domain expertise and direct contact to support individual business contributors or knowledge worker end users, thereby minimizing the gap between Generative AI low-code technology and applications and the general workforce.

Safeguarding Your Business from Generative AI’s Challenges

Generative AI, despite its remarkable capabilities, faces a fundamental obstacle—the absence of a dependable truth function. This limitation, known as “hallucination,” gives rise to various consequences, spanning from entertaining blunders to potentially harmful errors. It is imperative for businesses to recognize the risks that generative AI poses, including copyright violations, leaks of confidential information, and the emergence of unforeseen functionalities post-product launch, known as capability overhang.

Prepare Yourself for Risk

It’s crucial for companies to implement policies that promote the safe and responsible use of generative AI by employees. These policies should outline specific cases where its performance aligns with established guidelines. While encouraging experimentation, it’s equally important to ensure that all experiments are tracked across the organization, avoiding any “shadow experiments” that may jeopardize sensitive information. Additionally, these policies should emphasize clear data ownership, establish thorough review processes to prevent the publication of incorrect or harmful content, and safeguard both the company’s proprietary data and that of its clients.

An important task in the near future is to provide comprehensive training, such as training on prompt engineering, to your employees on effectively utilizing Generative AI within their areas of expertise. The properties of low code in Generative AI might lead employees to feel overly confident in their ability to accomplish tasks quickly and “efficiently” even if they lack the necessary background or skills or critical judgment to assess the output resulting in subpar results and company output. You must encourage all employees to maintain a healthy skepticism towards AI-generated insights, content, and responses. It is essential for company policies to stipulate that employees should only utilize data they fully QA, comprehend, and that all content generated by AI must undergo thorough review by the business owners of the data – call it the new quality assurance. 

Ensuring Quality and Security: A Human Approach

Leaders must caution their employees against utilizing public generic LLM models and public chatbots to share business information and data. It is important to note that any data entered into generative AI tools is stored and utilized for further model training. Even Microsoft, a prominent investor in generative AI, has advised its employees against sharing confidential data with ChatGPT.

In the current landscape, companies face limited options to leverage Large Language Models (LLMs) without compromising data privacy, unless they use a secured Generative AI enterprise platform like ClearGPT. Storing the complete model on dedicated servers or on-premises is one approach to address data privacy concerns. As LLMs evolve, solutions to safeguard sensitive information will become more sophisticated. It is crucial for CEOs to regularly update security protocols and policies to align with these advancements.

Generative AI presents unparalleled business opportunities, but it also presents CEOs with significant unknowns and business risks. Navigating this uncharted territory may feel unfamiliar and uncomfortable. To separate valuable insights from the noise, it is essential for leaders to develop an effective strategic business approach to generative AI that is tailored to their unique organization and use cases. This might involve reimagining your business models, identifying the right or wrong opportunities to capitalize on, organizing and possibly reorganizing the workforce and operating and revenue models to foster generative AI innovation at scale, and ensure that your organization’s business experimentation and future adoption upholds principles of security, productivity and ethics. By embracing these practices, leaders can create a sustainable competitive advantage in the long run.