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Risk shadows every business move but companies can employ enterprise risk management (ERM) to address the problem of risks.  However, there are formidable challenges in employing ERM.  This article explains how AI technology and our invention revolutionize ERM implementation to unleash the value creation power of modern enterprises.

Challenges in ERM Practices

The risk register can determine whether an organization’s ERM practice is effective, much like financial accounts can indicate whether a business is practicing good accounting.  It often reveals that the registered risks are events that have occurred, or potential occurrences conjured from the experiences of a few.  Therefore, it fails to instil organizational alertness.  Imagine a ship where only the captain keeps a lookout for icebergs.  Despite the captain's expertise, the ship remains vulnerable.  Now, imagine a scenario where every crew member is vigilant, scanning the horizon; the ship's safety odds would soar.  Similarly, cultivating a sense of alertness throughout an organization is not solely about constructing a risk register; it involves instilling a collective and conscientious approach towards risk.  The challenge is how to achieve this ubiquity in risk awareness.

The risk criteria table is used to quantify risks and standardize the risk assessment process with specific thresholds for classifying the impact and likelihood of risks.  The framework ensures consistent and uniform risk evaluation across the organization.  However, while such a table is initially an asset, it can transform into a roadblock.  The challenge arises when the organization evolves and circumstances change, yet the table remains static.  Changing a risk criteria table can be a monumental task, and reevaluating risks based on revised criteria becomes a Herculean effort.  The challenge lies in maintaining the relevance of a risk criteria table in ever-changing circumstances.

Risk analysis extends beyond simple quantification.  A risk event is usually multifaceted, and fitting it into predefined classifications can be arbitrary.  Take, for example, geopolitical risks: a change in leadership in a key trading country could provoke a myriad of consequences, from regulatory shifts to alterations in cultural perceptions.  Determining the event’s impact level based on predefined criteria demands finesse beyond a simple quantification judgment.  When such a judgment becomes too intricate, it might dissuade broader participation in the organization and potentially sideline a wealth of diverse insights.  The challenge here is how to establish quantification where inclusive participation is highly desirable to reflect the multifaceted nature of risks.

Risk identification requires venturing beyond known horizons.  The financial crash of 2008 and the unforeseen challenges posed by the COVID-19 pandemic highlight the problem of "unknown unknowns" – risks that we cannot foresee because they lie beyond our current scope of understanding.  When adhering to predefined risk criteria, we inadvertently become blind to subtle, unconventional threats or misjudge their magnitude.  The challenge is how to avoid overlooking these very elements and risks that could be of paramount importance.

Risk assessment relies on human judgment, particularly when the prevailing context is crucial.  While human instincts can sense danger, expressing these intuitions, quantifying them, and then communicating them often poses considerable challenges.  These limitations in language, mathematics, and communication may indeed be the most substantial barrier to cultivating risk alertness within an enterprise.  However, within these limitations also lies an opportunity to harness innate risk-detecting human instincts and create an environment where everyone feels empowered.  The ultimate challenge is how to capitalize on the human element in the risk assessment process to overcome those other challenges as discussed.

The innovative solution

Our solution brings together the synergy of human insight and AI.  The real magic happens when we combine human intuition with AI’s articulate communication, transforming instincts into actionable insights, and addressing the problems discussed above.  Our approach draws inspiration from groundbreaking concepts like relativity in physics.  Let us term the ERM practice now commonly used as the "Fixed Scale Approach," which is characterized by fixed-scale risk criteria tables.  The revolutionary model we propose can be referred to as the "Relative Approach."

Why “relative”?  ERM's core challenge is the prioritization of risks in a resource-constrained environment.  Prioritization is the key, but is it essential to rigidly quantify risks if we can comparatively assess them without quantification?  When making comparisons, we inevitably introduce subjectivity, which often carries the negative connotation of inherent bias.  However, bias has merits when channelled correctly and becomes an asset if such biases arise from insights of those directly confronting the risks.  In such cases, these biases encapsulate unique, ground-level understandings that a sterile, objective approach might overlook.  By sidestepping the daunting task of quantifying and categorizing risks, we simplify and decentralize the risk assessment process, embedding the organization and enriching it with a wider spectrum of experiences and insights.

Where does AI fit into this paradigm?  While AI possesses powerful analytical and linguistic capabilities, it lacks the context-driven, experiential intuition that humans possess.  Hence, relying on AI for risk assessment without considering the full context of the situation at hand is myopic.  Instead, AI can serve as an augmentative tool, amplifying the insights derived from human intuition.

The solution we introduce adopts our proprietary patent-pending technology to channel human-derived insights into an AI system.  Rather than taking over decision-making, the AI focuses on meticulously documenting these insights, articulating them with precision, and then running immersive simulations.  These simulations, rooted in real-world scenarios, not only serve as a bridge between abstract risks and tangible outcomes, fostering a deeper understanding and engagement but also act as catalysts, inspiring fresh ideas and perspectives.  Furthermore, the AI system enhances communication by translating these insights into a format that is comprehensible and actionable for stakeholders.

This synergy between human intuition and AI's computational strength forms a holistic, agile, and robust risk assessment model that revolutionizes ERM practices with the following features:

• Dynamic over static: In a rapidly changing business environment, static tools and methodologies can hinder more than help.  Adaptability is key.


• Human intuition is irreplaceable but augmented: While tools and technologies evolve, human insight remains invaluable.  The future of ERM hinges on balancing human intuition with technological advances.


• AI as a complementary force: AI should not replace the human element; instead, it should augment our capabilities.  Think of it as a magnifying glass, amplifying our insights.


• Preparation over prediction: It is not about foreseeing every risk but being agile enough to respond when they arise.  ERM is not about predicting the future with absolute certainty but about being best prepared for it, come what may.

As we sail into the intricate waters of risks, the above acts as our compass, guiding our strategies and actions.