In Greek mythology, Cassandra was a Trojan princess, daughter of King Priam and Queen Hecuba. She was so beautiful the godApollo fell in love with her and granted her the gift of prophecy. When she rejected him, he couldn't take the gift back, so he cursed her so that her prophecies would never be believed. In the modern world, the term "Cassandra" is used to describe someone whose warnings of disaster are ignored. For a business, ignoring warnings about impending disasters can be a fatal mistake. Today’s chaotic business environment requires someone who can give voice to impending disasters. Who is the right person to sound those warnings? Risk managers are busy acting like firefighters trying to beat down the flames of the latest disruption. They often don’t have the time to peer into the future looking for black swan events that could prove catastrophic for businesses. A Chief “What Could Possibly Go Wrong” Officer could prove valuable in helping companies predict and prepare for future disasters. However, there is always the risk that such a person could become a C-level Cassandra.
The Need for a Chief “What Could Possibly Go Wrong”Officer
During a podcast about crisis management and black swans, Sharon Robson, a partner with Core State Consulting, insisted that companies need to ask some serious “what if” questions, such as: “Is your organization prepared for the CEO to go missing, the CIO to be stuck somewhere and unable to communicate with you for a period of time? … What could go wrong with our infrastructure? How many times do we talk about fires in data centers? Well, we know that's a likelihood, so we have fire prevention systems in our data centers, but what else could happen?”[1] The point she was making is that asking “What could possibly go wrong?” is an important ongoing exercise. Rich Weissman, a consultant and adjunct college professor, agrees. He notes, “After a black swan hits, we often reflect that it was predictable and we should have seen it coming.”[2] He believes that building more “robust supply chain profiles” requires companies to ask some serious “what if” questions. He explains, “Supply chain managers need to pay attention to risks that are bubbling under the surface. … There are a flock of black swans just over the horizon.”
For risk managers fighting the fires at hand, looking over the horizon is a luxury they often don’t have. And, in 2026, there will be plenty of fires to put out.Supply chain journalist Nick Bowman explains, “Following a chaotic year marked bytariffs, geopolitical instability and persistent supply chain disruptions, 2026is shaping up to be more of the same for global manufacturers and logistics operators.”[3] He reports that in the Materials Handling Institute’s “Top Supply Chain Trends of 2026” report the Institute’sCEO, JohnPaxton, observed, “2026 marks a turning point where supply chains are not just reacting to disruption — they’re anticipating it.”
With risk managers fully engaged in current events, a Chief “What Could Possibly Go Wrong” Officer could be tasked with focusing on events over the horizon and with asking the right “what if” questions. The staff at Logility notes, “What-if analysis is a valuable tool helping to meet service goals while staying on budget or even improving the financial picture overall. By studying both historical and near-real-time data, companies canidentify and mitigate potential demand and supply risks and improve decision-making as well as forecasting. Traditionally done with spreadsheets, artificialIntelligence (AI) and other tools can uncover data to support suspected yetunproven factors. Performing a what-if analysis involves predicting specific outcomes based on different data: specific events that may or may not be favorable to the business.”[4]
The Way Ahead
Bowman reports, “Firms [are] expected to double down on the search for tech-savvy supply chain professionals, as well as artificial intelligence systems capable of providing complex scenario planning and predictive analytics.” A company’s Chief “What Could Possibly Go Wrong” Officer would be the person in charge of these tech-savvy individuals and would work closely with risk managers. As Bowman points out, because today’s business landscape is so complex, companies need AI systems to help them understand it and explore possibilities. At Enterra Solutions® we have focused on advancing Autonomous Decision Science™ (ADS®)to help clients do just that. ADS technology can autonomously analyze data, generate insights and make subtle, contextually informed, judgment-based decisions quickly, accurately and with limited human intervention, and then learn from the results of those decisions. It can effectively reshape the way companies structure and optimize their value chain. The Enterra ADS platform powers the Enterra System of Intelligence™ and brings together three previously siloed technologies:
1. A Semantic Reasoning and Vector SymbolicLogic-based Artificial Intelligence that enables human-like reasoning, decision-making and learning. This unique capability combines common-sense and industry knowledge with inference reasoning to create a system that can make decisions with subtle, human-like reasoning and then learn from the outcomes.
2. Glass-Box, explanatory, transparent machine learning in the form of the proprietary Representation Learning Machine™(RLM). The basis of the RLM is high dimensional mathematics and functional analysis. RLM uniquely identifies a function that describes the combination and contribution of variables in the data set that describe the observable effects through multiple layers of interaction with a high degree of precision. This is classified as a “glass-box”, explanatory algorithm that generates a function, whose output is visible as opposed to “black-box” algorithms that merely generate patterns, but do not offer any explanatory description of the dynamics of system/data set, nor have any substantive “understanding” of what the pattern means.
3. Constraint-based, non-linear optimization capability that incorporates the RLM derived formula, along with semantic reasoning constraints and logic, to perform fast optimization that reflect the complex multi-dimensional real-world considerations to derive highly actionable recommendations. This capability breaks the dimensionality barrier that is associated with linear models.
The unique combination of these techniques has enabled Enterra®to provide clients with significantly differentiated capabilities and created a highly defensible chasm in the competitive landscape — with both large AI technology platforms and point solution players. The Enterra System ofIntelligence includes a set of interconnected business applications that span across the value chain of an enterprise and work in concert to perform end-to-end optimization, planning, and decision-making at scale and at the speed of the market.
This Sense, Think, Act, and Learn® system works alongside existing client systems by sending instructions to and coordinating the actions of client Systems of Record and then learns from the outcomes. The business application modules included in the Enterra System ofIntelligence are:
• Enterra Consumer Insights Intelligence System™.This System allows clients to quantitatively uncover and logically understand the inter-relationships that lead to heightened consumer understanding, hyper-personalized product recommendations, and new product innovation.
• Enterra Revenue Growth Intelligence System™ (ERGIS™).ERGIS systemically performs holistic revenue growth optimization (including optimizing strategic and tactical pricing, trade promotion, trade architecture, price pack architecture, media mix, customer segmentation, and assortment).
• Enterra Demand and Supply Chain Intelligence System™.This System concurrently performs non-linear demand and supply planning optimization.
For the Chief “What Could Possibly Go Wrong” Officer, however, one of the System’s most important modules is Enterra BusinessWarGaming™. Business WarGaming enables organizations to leverage their data to make strategic decisions by anticipating the moves of their competitors and taking direct action to beat the competition, mitigate risk, navigate uncertainty, and maximize market opportunity. Part of Enterra BusinessWarGaming is the Enterra Global Insights and Decision Superiority System™(EGIDS™) which can help business leaders rapidly explore a multitude of options and scenarios.
Concluding Thoughts
When the Chief “What Could Possibly Go Wrong” Officer is fully engaged in what-if scenario planning, a company will soon discover he or she is also the company’s Chief “What Could Possibly Go Right” Officer. A few years back, supply chain journalist Patrick Burnson wrote, “It is critical to future-proof our supply chains to make them resilient in the face of disruptions by allowing quicker pivots and best plans of actions in times like these.”[5] He believes leveraging artificial intelligence solutions is essential to achieve the resilience today’s supply chains need. Malinka Waliyadde, co-founder and CEO of AKASA, agrees. He writes, “There are three tiers on the scale of knowledge and ignorance: the ‘known knowns,’ or the problems you’re aware of and know how to solve; the ‘known unknowns,’ or the problems you’re aware of but the solution may be unclear; and the ‘unknown unknowns,’ or the problems you simply can’t anticipate.”[6] AI-assisted what-if scenario planning can help with all three levels. Waliyadde concludes, “The AI must be designed to continuously observe, learn and adapt. ...This learning and adaptation must continue indefinitely as the AI becomes more and more capable of handling complex tasks, persistently transforming the outlier into the ordinary — the unknown into the known. Today’s outliers become tomorrow’s built-in solutions.” Companies need to know what could wrong and what could go right as they traverse the modern business landscape.
Footnotes
[1] Shane Hastie, “Crisis Management, Black Swans and Resilience,” InfoQ, 3November 2023.
[2] Rich Weissman, “A flock of black swans hovers — and supply chains need to getready,” Supply Chain Dive, 1 April 2021.
[3] Nick Bowman, “Supply Chain Uncertainty Likely 'Here to Stay' in 2026,”SupplyChainBrain, 17 December 2025.
[4] Staff, “The Benefits of What-If Analysis for Durable Goods Companies,”Logility Blog, 20 October 2021.
[5] Patrick Burnson, “Can AI Mitigate Black Swan Supply Chain Events?” SupplyChain Management Review, 14 July 2021.
[6] Malinka Waliyadde, “Automation For The Things You Can’t Possibly Anticipate,”Forbes, 9 August 2021.
