The infinite monkey theorem asserts that given enough time, a monkey randomly typing would eventually produce the whole works of Shakespeare. Enter OpenAI and ChatGPT, which seem to have unleashed a digital version of this curious primate.
Generative AI, such as ChatGPT, feels like magic. Ask any question and receive a coherent answer. Picture something in your mind and watch it materialize. Suddenly, people declared generative AI either humanity’s greatest existential threat or the most critical technological advancement ever.
Historically, experts reached a consensus on the capabilities and limitations of technological waves like machine learning (ML). However, generative AI has even AI scholars disagreeing. A recent leak of a Google researcher’s memo implying that early GenAI pioneers had “no moat”stirred up a heated discussion about the nature of AI.
AI’s trajectory seemed to follow earlier trends like the internet, cloud, and mobile technology. Overhyped by some and dismissed as “old news”by others, AI has impacted fields like healthcare, automotive, and retail. But the game-changing effect of interacting with an AI that appears to understand and respond intelligently has led to unprecedented user adoption. For instance, OpenAI attracted 100 million users within two months, sparking a frenzy of both passionate endorsements and strong rebuttals.
It’s clear that generative AI will bring significant changes to enterprises at a pace that surpasses previous technological shifts. As CIOs and technology executives wrestle with aligning their strategies with this unpredictable trend, a few guidelines can help navigate the evolving currents.
Encourage AI experimentation
Understanding AI’s potential can be daunting due to its vast capabilities. To simplify, focus on encouraging experimentation in specific, manageable areas like marketing or customer service. Prototype and pilot internally before defining complete solutions or handling every exception case (e.g., workflows to manage AI hallucinations).
Avoid lock-in, but buy to learn
The rapid adoption of generative AI means that long-term contracts with solution providers carry more risk than ever. Traditional category leaders in HR, finance, sales, support, marketing, and R&D might face a seismic shift due to AI’s transformative potential. Vendor relationships should be flexible to avoid the potentially disastrous cost of locking in solutions that don’t evolve.
Meanwhile, the most effective solutions often come from providers with deep domain expertise. A select group will leverage AI in agile and inventive ways, producing returns beyond those typically associated with enterprise applications. Engaging with potential revolutionaries can address immediate practical needs and shed light on AI’s potential impact.
Expect to see a wave of startups launched by veterans who’ve left their motherships as current market-leading applications may struggle to pivot fast enough.
Enable human + AI systems
Large language models (LLMs) will disrupt sectors like customer support that rely on human responses. Incorporating human + AI systems will offer immediate benefits and create data for further improvement. Reinforcement learning from human feedback (RLHF) has been crucial to these models’ advancements and will be critical to how well and how quickly such systems adapt to and impact business.
Believe in a hybrid strategy this time
With cloud computing, I mocked hybrid on-premise and cloud strategies as mere cloud washing. The economies of scale and the pace of innovation made it clear that any applications trying to straddle both realms were destined for obsolescence. Yet, as we enter the generative AI era, the diversity of opinions among experts and the transformative potential of information suggest that it may be risky to entrust all efforts to public providers or any single strategy.
Unlike cloud applications, AI makes information the product itself. Every AI solution craves data and requires it to evolve and progress. The struggle between public and private AI solutions will depend on context and the technical evolution of model architectures. The generative AI future will likely be hybrid—a mix of public and private systems.
Repeatedly validate AI’s limitations
Generative AI that can craft essays or create presentations differs significantly from predictive AI technology that drives autonomous vehicles or diagnoses cancer via X-rays. Defining and approaching the problem requires an understanding of the scope of capabilities that various AI approaches offer.
It’s also crucial to anticipate shifting boundaries. The generative AI of the future may draft the first—or final—versions of the predictive models you’ll use for production planning.
Leadership must iterate and learn together
In fast-moving situations, leadership is essential. Hiring a management consultancy to create an AI impact study for your firm is more likely to hinder your ability to navigate change than prepare you for it.
CEOs should drive engagement from their technology leaders to scale learning across the organization and assess leadership efficacy. This collective and iterative learning approach is a compass to navigate the dynamic and potentially disruptive AI landscape.
Conclusion
For centuries, inventors fixated on mimicking bird flight in their quest for human flight. Success came when the Wright brothers shifted focus to fixed-wing designs and principles of lift and control. A similar reframing is crucial for every industry and function in the AI realm.
Companies that view AI as a dynamic field for exploration, discovery, and adaptation will see their ambitions soar. Those who cling to strategies that worked during earlier platform shifts (cloud, mobile) will remain grounded, watching the evolution of their industries from afar.