Historical events such as the attack on Pearl Harbor and the Yom Kippur war are historical reminders for the impact of unexpected surprises. Although relevant information was available in these cases, the ability to analyze and act upon it was lacking, resulting in far-reaching consequences. To avoid being caught off guard by unexpected events, government agencies are investing significant resources in collecting and analyzing relevant data to provide early warnings and alerts.
In the business world, strategic surprises come in the form of disruptive events that can alter the competitive landscape. These can include rapidly advancing technology or new opportunities that transform business practices. As in the case of government agencies, many companies are doing their best to be prepared for these events to remain competitive in an ever-changing business landscape. In order to stay competitive in a dynamic business environment, larger companies allocate significant resources to their CTO and CIO offices, while smaller businesses rely on their top executives to closely monitor emerging trends and patterns.
In early 2023, as seen in the Google trend figure below, Generative AI had formed a disruptive wave of excitement and interest that caught many businesses by surprise. Its impact on various industries is still being analyzed, and it has the potential to fundamentally change business practices. In future posts, we will explore what may become obsolete and the opportunities it presents.
the result for searching "generative AI" at Google trends
In this post, I would like to discuss why many companies failed to anticipate the rise of generative AI, and suggest ways for organizations to become more resilient to changes in technology. By examining these issues, we could learn from past mistakes and become more resilient in the face of change. I acknowledge that such analysis seems to rely on the "wisdom of hindsight", however, the purpose of this analysis is not to judge past decisions but to break down past patterns in order to enhance future actions.
In the following sections, we will examine five key reasons for the strategic surprise in the context of generative AI. These include the crying wolf effect, underestimating technological advancements, decluttering noise from signals, overemphasis on short-term gains, and narrow focus on traditional business mental models.
The crying wolf effect - The initial hype around chatbots in 2016 led to high expectations from stakeholders about the potential of chatbots to revolutionize customer service and user experience. However, the reality of building a chatbot that could understand complex queries and provide helpful responses proved to be more challenging than anticipated. As a result, many chatbot projects failed to live up to their promises, leading to disappointment among stakeholders and a loss of trust in the technology. The high-profile discontinuation of chatbot projects, such as Facebook's M, further contributed to the "cry wolf effect," where stakeholders became skeptical about the potential of new technology to deliver on its promises. This led to a decline in news coverage and funding for chatbot startups, as well as a shift in focus towards more proven technologies. More importantly, when initial signals for actual improvements in generating AI for natural text arised, they were often dismissed as “we've been there before..”. In these situations, establishing conviction for a genuine signal involves two key elements: demonstrating the presence of the signal as well as articulating why the current circumstances are different. This level of conviction demands a technical expertise, which brings us to the next point.
A profound understanding of technological advancements - The remarkable achievements of large language models, as reflected in ChatGPT, can be attributed to substantial advancements in three technical domains over the past decade. The first pertains to the capacity to convert natural language into a machine-readable format. This has evolved from the utilization of word2vec for words and then the progression from recurrent neural networks (RNNs) to attention based models employing transformers, which enable the capture of the semantic nuances of sentences. The second domain involves progress in deep neural networks, which have transitioned from producing outstanding outcomes in image classification to generative models. The third one, is the progress made in engineering capabilities, such as training on more extensive datasets and utilizing larger models. For instance, GPT-3 possesses 175 billion parameters that demand 800GB of storage and a considerable infrastructure and preparation for its training. To the untrained eye, these numbers may seem unremarkable, but the extensive investments in GPT-3 - encompassing significant human effort and expensive computing resources - reflect a pervasive confidence that larger models can catalyze a transition from classification to generation
Decluttering noise from signals - Businesses rely upon ongoing feedback in order to address current and future customers' needs. Most companies develop internal feedback loops that allow them to gather insights from a variety of sources, including their employees, customers, industry partners, as well as external consulting. According to Reuters, ChatGPT reached a record of 100 million monthly active users within less than two months of its launch. The maturity, adoption, and impact that generative AI is having on the industry landscape seems unprecedented. While analysis reports were available, it is not trivial to induce a strong “call for action” from such resources as in Gartner's 2022 hype cycle. Strategic planning in the context of disruptive technology is a broad topic that requires covering many aspects ranging from a “skin-in-the game” to the ways that models should and shouldn't be presented for decision making. Therefore, I intend to dedicate a future post solely to this subject, which I hope to share soon.
The role of AI in production - For certain sectors, AI was seen as a supplementary feature, restricted to delivering insights or recommendations, or as a specialized tool confined to a single area like computer vision. As a result, deploying and planning the AI strategy was often left for later phases in the product life cycle. This approach was largely driven by the startup mindset of focusing on minimal viable products (MVPs) and incremental bootstrapping. Part of the surprise element in the generative AI revolution is the sudden observation that the usage of large language models is transitioning from an incremental enhancement to a "must have" within the competitive landscape for staying relevant. This shift highlights the importance of understanding the role of AI in production and its potential impact on businesses. Therefore, even if AI components are scheduled for later phases in the development cycle, their conceptualization and design should be incorporated as an essential component of product and feature planning on early stages.
First-hand experience - As the number of available technologies is constantly growing, it is getting harder to keep track of emerging capabilities, and to a greater extent distinguish between production-ready technology from an immature marketing effort. While there is no substitute for our first hand experience, the next best thing is to maintain a dialog with trusted professionals to help us better understand the core of the technology and ring the alarm when needed. As previously highlighted, different businesses have different approaches to handling swift changes proactively. Nevertheless, even companies built on the premise of being agile must be alerted to changes that could cause significant upheaval.
In conclusion, the rise of generative AI caught many businesses by surprise, and it has the potential to fundamentally change many business practices. This is an example for the ongoing need to evaluate disruptive technologies with high potential for an impact on our industries. With a growth mindset, businesses can learn from past events and become more resilient in the face of change.