"If trouble comes when you least expect it, then maybe the thing to do is to always expect it."
For most organizations, today is business as usual! However, the clock is always ticking; predicaments like barbarous competition, IT hurdles, cyber-attacks, data breaches, supply chain disruptions, natural calamities, and whatnot can land any business in an awkward state of affairs. Furthermore, if the companies don't have a strategy, the aftermath can be far more dreadful than it needs to be.
Oftentimes, we take business continuity for granted, and the pandemic has set a substantial precedent for it.
According to a Mercer survey, an astounding 51% of businesses worldwide lack a business continuity plan.
Even the cybersecurity landscape continues to evolve, presenting challenges and new possibilities.
According to Veenam's 2024 Data Protection Trends Report, out of 1200 enterprises, 76% had at least one ransomware attack.
So, to combat such issues in a structured manner, it's imperative to identify bottlenecks, modernize the business model, optimize processes to achieve operational excellence, and, most importantly, ensure business continuity.
Business continuity is how a business continues to operate in the event of an unforeseen service disruption; though not synonymous with disaster recovery, the difference lies in comprehensively.
A Business Continuity Plan (BCP) includes contingency plans for business processes, assets, human resources, collaborators, and any other aspect of the business that may be impacted.
Strategic technology investments are vital to ensure seamless business continuity, and with artificial intelligence (AI) becoming almost omnipresent, unlocking new possibilities and challenges, it would not prove wise not to explore AI's transformative potential in enhancing business continuity.
We have now attained a level of technological sophistication that enables us to take advantage of advanced real-time analytics. By pairing it up with AI, we can simulate real-time scenarios, automatically allowing us to identify correlations for risks eliminating sections of business working as silos, and providing a panoramic view of interconnections and dependencies.
AI algorithms are incredibly sharp at analyzing trends, patterns, and variances in data, providing a level of comprehension that exceeds human sight. AI can analyze historical data to detect emerging trends & patterns, allowing businesses to plan for potential challenges and take precautionary measures. For instance, predictive maintenance with AI can enhance equipment's dependability and act as an indispensable asset in many industries, ensuring seamless operations and cost savings with just-in-time maintenance.
AI can automate crucial operations like incident response and recovery systems. For example, AI chatbots can offer clients 24x7 support even during a crisis. AI security can be programmed with multiple business continuity scenarios and contingency management algorithms to mathematically arrive at the best possible course of action and execute it autonomously with minimal human assistance, greatly enhancing business survivability and continuity.
Noteworthy to mention, AI uses data to develop predictive trends, guidelines, and decisions. As a result, if such patterns reflect underlying biases, algorithms can further amplify them, resulting in outcomes where biases are more pronounced, known as algorithmic bias.
According to IBM's Institute of Business Value, in 2023, businesses that have fully integrated AI in security and automation saved an average of $3 million on information security costs.
AI's adaptive learning capabilities can become critical in identifying and responding to real-time risk hazards. These systems analyze activity patterns to detect inconsistencies, responding to new threats as they evolve while securing endpoints without requiring significant human intervention, resulting in both cost & time savings. For instance, if it takes 230 days for an organization to identify, act, and recuperate from cyber calamities, with AI, it comes down to just 99 days, a 57% reduction.
AI offers striking visibility into prior sourcing and spending data, unraveling success factors and critical voids in business processes. Spending analytics can evaluate supplier performance, pick up historical demand trends, identify patterns with specific suppliers, ensure optimal inventory and efficacious governance, and help make more informed decisions to keep supply chain disruptions at bay. Nevertheless, it's imperative to note that AI is as good as the data we feed it.
According to UT Austin, an average human makes at least three errors (averaging at 5-6) per hour, which can be brought down to near 0 levels with AI integration, but AI is not error-proof. McKinsey's State of AI Report in 2023 indicated that only 32% of businesses tried mitigating AI inaccuracies; even Gartner's Hype Circle 2023 placed Gen AI at the peak of inflated expectations.
Ignorance can threaten business continuity, and from a business standpoint, confusing AI use cases is a prescription for catastrophe, but nothing comes close to the growth potential that AI has to offer, and a healthy amount of human oversight is all it takes to keep AI in check.
Humans will continue to be the primary drivers of organizational success, but AI co-working will soon become cardinal in ensuring long-term success and business continuity. Also, with an ever-increasing number of businesses tending to AI to solve complex business issues like critical talent shortages, there's no better time than now to start integrating AI.
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