As the prospect of a protracted recession looms large, you’re likely evaluating what you can do to help reduce operating costs and increase profits.  Solutions of the past won’t suffice – consider new strategies such as using artificial intelligence (AI) to drive profitability in a recession. 

Debates about the offsetting value of a comparably robust labor market aside, two consecutive quarters of declining GDP have transpired. The IMF baseline forecast for 2022 continues to predict a net contraction of 6.1% to 3.2% for the year. Current trends across U.S. markets reflect this gloomy outlook, with the S&P 500 down 7% for September and widespread losses recorded in the retail, banking, and healthcare industries. 

With inflation remaining high – at 8.3% – for August, 73% of U.S. consumers will continue to delay non-essential large purchases and curb overall discretionary spending. For retailers, whether brick-and-mortar or online, restrained consumer spending will severely limit their ability to rely on customer acquisition growth for the time being. 

So long as top-of-the-funnel growth remains unfeasible, retailers will need to seek new solutions based on technologies most companies are uninitiated in to become more profitable. Learn what artificial intelligence is and how innovative developers are leveraging it to create recession-hardened business strategies. I love this.

Key Takeaways

• The recession has arrived. etailers are particularly exposed and must take quick and decisive action to drive profitability. 
• The complexity of global economic conditions has rendered most businesses ineffective at making holistic real-time strategy adjustments. 
• AI-based technologies are changing the game and giving businesses of all sizes and industries the insights they need to stay agile and profitable.

Understanding Recession

In economics, a recession refers to a sustained business cycle of reduced economic activity. Generally, widespread declines in spending precipitate recessions. Recessions have various potential causes, such as:

• External trade shocks
• Adverse supply shocks
• Bursting of a misvalued economic bubble
• Geopolitical events and natural disasters

What economic condition exactly constitutes a recession varies from country to country and among economists. Nevertheless, the most common definition cites two consecutive quarters of GDP decline. In the U.S., the National Bureau of Economic Research (NBER) lists GDP, real income, employment, industrial production, and retail sales as constituent indicators of recession. While some recessions are brief – the shortest being the recession caused by the COVID-19 pandemic in March and April 2020 – the average duration for recessions that fit all notable definitions since World War II is ten months.

The Design and Function of AI

Business leaders have hailed 2022 as a banner year for new AI developments and business applications. Nevertheless, AI remains an opaque concept to most non-specialists, conjuring conflicting notions drawn in varying degrees from both popular science and science fiction. To bring the picture into focus and begin tackling questions of what AI can do for business strategy, it helps to describe AI from the perspectives of design and function.

• Design: AI strives to mimic human thought processes and decision-making capabilities to solve problems. Unlike automation, AI is dynamic and changes over time as it integrates more data. In effect, AI learns. 
• Function: To the degree that it succeeds in mimicking human decision-making processes, AI technologies enable users to offload tasks from human workers to machines, freeing up skilled labor hours and radically speeding up analytical processes.

Within the field of AI, there are subset algorithm types that differ in the degree of what computer scientists call supervision. Algorithms that require a layer of human intervention to structure datasets constitute the supervised subfield of machine learning. AI programs that employ multi-layered neural networks capable of ingesting unstructured data and recognizing categories within it make up the unsupervised field of deep learning.

The Challenges of Making Data-Driven Adjustments

Economic forecast disagreement and macroeconomic uncertainty indices have risen nearly tenfold since the outbreak of the COVID-19 pandemic. Examples of far-reaching and ongoing unforeseen effects abound. To list just a few examples:

• The rapid shift to remote work during temporary gathering restrictions accelerated digital transformation and cloud service adoption rates exponentially and, in the process, brought millions of new remote access data points online, precipitating a 600% spike in cybercrime and data breaches.
• The temporary closure of production facilities in countries like China and India coincided with an overnight increase in demand for devices that enable remote work, resulting in supply chain disruptions around the globe and typically insulated industries. Within just the shipping industry alone, the average cost of a 40-ft. cargo container jumped from $1,331 in February 2020 to $11,109 by September of the same year.
• The outbreak of a major land war on the periphery of Europe during a period of already heightened uncertainty has compounded existing conditions. Just as manufacturing economies and production were reopening in crucial regions, a geopolitical twist has destabilized E.U. and U.S. relations with China.

These limited examples from just the last two years illustrate the vulnerability of companies and entire economies to black swan-type events. Even large international enterprises lack the time and resources to predict and interpret such events – much less formulate solutions in times of crisis.

How AI Can Help Companies Drive Profitability in a Recession

Companies have more data than ever before. The global datasphere doubled in size in the last three years, ballooning from 41 zettabytes in 2019 to 97 in Q2 of 2022. Nevertheless, the overwhelming majority of this data – 80-90% – is unstructured and opaque for most operational purposes. 

This is where innovators in AI and deep learning services enter the picture. 

With AI capable of interpreting large volumes of unstructured data, companies have a force multiplier for turning their data lakes and disparate storage systems into actionable insights that adapt in real time without human intervention or supervision. Deep learning systems unburden companies from throwing human resources at problems fundamentally too large and complex for workable solutions. 

With AI-powered Recommendations, businesses can:

• Act upon highly probable and optimized profit/inventory/cash based treatments everyday  
•  Have the front seat view to predictive opportunity and risks occurrences and preemptively take action in order to put your company in the favorable position daily.  
• Maximize the vast amount of data that has been accumulated to find the hidden patterns that lead to highly accurate business outcomes.

Make Swift, Data-Driven Decisions With Hypersonix

Hypersonix has developed an AI-based Product  that not only helps you get “back in the black” but also stay there with heightened responsivity, allowing you to move swiftly in times of crisis and rapid change.

See how Hypersonix can help you unlock your data to drive profitability today.