*November 10, 2021* It’s now a decade since Nassim Taleb formalized the powerful concept of *Antifragility -* the ability to gain from disorder. The author noted that while most things break when subjected to shocks (fragility), some thrive and grow when exposed to volatility, randomness, and disorder. The Greeks encoded these concepts in their mythology. The bird Phoenix, whenever destroyed, is reborn from its own ashes back to where it was (robustness). But Greek mythology has a more powerful metaphor: the Hydra. The Hydra is a serpent-like creature with numerous heads. Each time one is cut off, two grow back. This results in a very odd creature that actively seeks out disorder - an obvious choice for a system benefiting from harm (antifragile). We can recognize the same phenomena in our daily lives: our teacups don’t like turbulence, and (most) politicians won’t survive a minor scandal (fragile); but rockstars will see their Spotify plays increase with every controversy. As the world turns the page on the pandemic, we recognize a similar effect: some companies had to close their doors; others were resilient to the changes; yet a third group benefited disproportionately from the disorder and increased its market share. It might be an interesting exercise to reflect on the conditions that allow some companies to become stronger when facing adversities. This post discusses how AI is becoming an essential part of the puzzle and why you should consider antifragility as a guiding principle (beyond cost reduction and efficiency gains) that motivates and shapes how your AI journey will help you achieve your strategic goals. ### How can an organization become Antifragile? Antifragile companies embrace volatility and errors. These stressors include disruptions and disorders such as natural disasters, financial crises, customer demand randomness, volatile customer tastes, and other unanticipated factors. What’s the mechanism behind this process? Firstly we need to recognize that stressors carry *information*. The fact that your supplier is taking twice as long to deliver your order tells you something about the environment. Every plane crash makes the design of the next one more robust. So we hit here the first characteristic of organizational antifragility. A company with *static* business processes and workflows won’t be able to learn with the information contained in the stressor. We need dynamic, adaptive capabilities to improve when exposed to faults. From the above discussion, it is evident that agility - the ability of a business to mobilize and respond quickly - is a required but not sufficient property of antifragility. A truly antifragile company *explicitly* *wants* randomness and disorder (remember the Hydra). So what is the necessary ground for a company to be in a place where it can gain from disorder? Here I’ll assume that antifragility manifests itself in a *closed system*, i.e. the antifragility of one thing may come at the cost of the ultimate fragility of something else (it’s an interesting question wether this is true in general). In light of the above, we see that an antifragile company is one that can *read and absorb* the information contained in stressors *more effectively* than its competitors. With suitable mechanisms in place, it can learn from shocks and build a stronger competitive advantage. Such a company will *desire* volatility in the environment - since it will benefit disproportionately from the disorder relative to its competitors. While AI is not a silver bullet for every business problem, it certainly helps embrace volatility in operational conditions - by allowing the integration of disparate data sources into decision-making. **Example: Supply Chain Management** During the pandemic, consumer sentiment has changed dramatically, with a marked shift to value and a greater focus on essential products. This created unique challenges in forecasting demand. Moreover, the volatility felt in supply chains all over the world aggravated the situation by an increase in lost sales due to product unavailability and increased costs in inventory inefficiencies. From early in the global pandemic, Nike, the sportswear giant, responded quickly to a consumer shift to digital engagement and transformed its supply chain to serve consumers more directly. Nike is now using AI and machine learning technologies to predict and order the products that will be popular among consumers and to deliver products faster and more accurately. This capacity enables fast, agile actions because the model anticipates demand changes rather than just responding to them. As reported in March this year, Nike’s sales have shown accelerating numbers as the company capitalizes on the benefits of its digital businesses, effectively seizing the pandemic disruption to accelerate and transform its operations. A digital-first supply chain with a global scope needs to integrate the entire end-to-end supply chain to synchronize the majority of processes and decisions through real-time, autonomous planning. Forecast changes in demand can be automatically factored into all processes and decisions along the chain, back to inventory, production planning and scheduling, and raw-material procurement. Novel algorithms, powered by the relevant contextual data, can perform decision-making at a global level rather than at a local level, enhancing its ability to address potential disruptions in real-time. Demand spikes are predicted accurately, and the routes and volumes of material flows can be adjusted automatically. Many companies still rely on manual forecasting. And note that much forecasting is done - understanding demand is common to several internal functions like risk assessment, capital-expenditure planning, and workforce planning. Traditional approaches to demand forecasting require constant manual updating of data and adjustments to forecast outputs. These interventions are typically time-consuming and do not allow for agile responses to immediate changes in demand patterns. ### AI as a driver for Antifragility The previous example illustrates how AI can be used to absorb and learn from information-loaded stressors. To reinforce the connection between AI and Antifragility, we note the following: - In general, AI systems *benefit* from exposure to novel data, which helps improve its performance. - AI-driven processes can become capable of handling non-stationary contexts by using dynamic models that continuously integrate new stressors. - Errors (or incorrect predictions) become a first-class object of analysis and integration within the organisation. Overcompensation is encouraged by focusing resources on the operational conditions where the process is more vulnerable. - AI-driven approaches offer a natural context where a great variety of disparate data sources are integrated using powerful algorithms - enabling the prediction of events that were not possible to model before. Capturing the proper data context is essential in extracting information from stressors.