Navigating Gen AI landscape: Strategies for CEOs

The public-facing iteration of ChatGPT achieved a remarkable milestone, welcoming a staggering 100 million users in merely two months. This achievement marked a watershed moment, democratizing AI in an unprecedented manner and earning the distinction of being the fastest-growing app in history. What sets this generative AI apart is its inherent accessibility, obviating the need for users to possess a background in machine learning. Virtually anyone with the ability to pose questions can harness its capabilities, fostering an inclusive user base regardless of age, educational background, or geographical location, as long as they have internet access.

With the appropriate safeguards in place, generative AI stands poised not only to unlock innovative applications for businesses but also to expedite, scale, and enhance existing ones. Consider, for instance, a customer sales call. Traditionally, salespersons relied on static customer data—such as demographics and purchase history — before the call for upselling opportunities. Now, a specially trained AI model can dynamically suggest such opportunities during the interaction.

CEOs at crossroads

CEOs find themselves at a crossroads, grappling with the decision of whether to act swiftly or exercise caution regarding generative AI’s integration. Some perceive the potential to leapfrog competitors by revolutionising how work is accomplished through generative AI applications. Others advocate for a more measured approach, opting to experiment with a few use cases before making substantial investments. Additionally, companies must evaluate their technical proficiency, technology infrastructure, data architecture, operating model, and risk management protocols to accommodate the transformative potential of generative AI.

Generative AI transcends the realm of chatbots, promising to automate, augment, and expedite a spectrum of tasks. While text-based chatbots, like ChatGPT, have garnered considerable attention, generative AI extends its influence to various content types, including images, videos, audio, and computer code. Its utility encompasses functions such as classification, editing, summarization, question answering, and content creation, thereby redefining work processes across business functions and workflows.

The generative AI ecosystem is rapidly evolving

The nascent generative AI ecosystem is rapidly evolving, with foundation models serving as its core. Specialized hardware provides the computational muscle required for model training, while cloud platforms offer accessibility to this hardware. MLOps and model hub providers furnish the essential tools, technologies, and practices for organisations to adapt foundation models and integrate them into end-user applications. Many companies are entering the market to offer specialised applications built upon foundation models, catering to specific tasks and service issues resolution.

CEOs must prioritise the exploration of generative AI, recognizing its potential to add value across various use cases. The economic and technical barriers to entry are surmountable, while the repercussions of inaction could lead to falling behind competitors. Collaboration with the executive team is essential to pinpoint where and how to engage with generative AI. Some may perceive it as a transformative opportunity, touching every facet from research and development to marketing and sales. Others may prefer a gradual approach, commencing with limited use cases before scaling up.

To navigate the generative AI landscape effectively, CEOs must adopt strategic considerations. Organisational readiness is paramount, necessitating a deliberate and coordinated approach that acknowledges unique risk factors and the foundational role of these models. Rather than sporadic deployment, organizations should envision a comprehensive approach that identifies the most transformative use cases by domain, streamlining business functions through generative AI.

A robust technology infrastructure is indispensable, with the chief technology officer playing a pivotal role in assessing the adequacy of computing resources, data systems, tools, and model access. Speed is of the essence, given the rapid evolution of generative AI technology. Establishing a “virtual expert” to empower frontline workers and internal experimentation can accelerate the adoption process.

Balancing risk and value creation is imperative. Generative AI carries its share of risks, including the potential amplification of biases from training data and hallucination. CEOs must stay attuned to evolving regulations related to consumer data protection and intellectual property rights to mitigate potential liabilities. Regulatory approaches may vary across countries, necessitating adaptability in process management, culture, and talent management.

Building the requisite talent and skills is vital for businesses seeking to harness the potential of generative AI. Leadership must identify the necessary capabilities, extending beyond technical roles to encompass engineering, data, design, risk, product, and other business functions. This concerted effort ensures a workforce capable of unlocking generative AI’s full potential for business value.

Courtesy: McKinsey Technology Council and QuantumBlack, AI by McKinsey