The damage from pandemic-induced lockdowns, office and school closures and consumer retrenchment continue to reverberate through the economy. As the crisis drags into its seventh month, it has left businesses facing hard choices in adjusting to what now seems like many permanent changes. Deloitte studied the way global enterprises respond to the crisis and developed a three-stage model for categorizing short-, medium and long-term practices it describes this way (emphasis added):
Required actions to address the COVID-19 crisis can be divided into three major stages: Respond, Recover and Thrive. These three stages are interspersed with two additional interim stages, and culminate in a long-term operating environment we call the 'next normal'.
The early months were focused on business survival through a series of reactionary changes, which was followed by mid-term operational stabilization in a world with diminished demand, continued socio-political restrictions and unpredictable events. The last stage of the pandemic response focuses on strategic actions to maximize revenue, profitability and competitive positioning.
Respondents to Deloitte's latest biennial survey of global cost management practices and transformation trends found that most expect stage one ("respond") to last about three months, stage two ("recover") to take about six months and stage three ("thrive") to last about 10 months. As autumn begins, it puts us in the middle of stage two, however, nearly one-quarter of respondents say their organization is already in or approaching the last stage, requiring strategic assessments and decisions. A continued state of economic unpredictability, business caution and societal unease has many organizations looking to AI automation to reduce expenses and improve responsiveness.
Automating the way to post-pandemic success
The pandemic was unusual in the pantheon of economic shocks because lockdowns that initially created a sudden loss of supply in many goods and services morphed into demand shortfalls as persistent unemployment, continued fear and a dearth of effective medical treatments had consumers tightening their belts. Many businesses initially responded by temporarily furloughing workers, some with pay, some without, but regardless, companies needed to drastically cut other costs as sales quickly dried up.
Unfortunately, long-term uncertainty about the level of consumer demand and behaviors persists in many industries, leading most to make strategic expense reduction measures part of their long-term plans. Automation technology has proven to be one of the most popular avenues for lowering costs without harming quality or customer experience. As Deloitte puts it:
Automation is the top transformation action arising from the COVID-19 crisis. Globally and across all regions, roughly two of three companies expect to pursue automation in all three stages of Respond-Recover-Thrive.
Automation is part of a broader strategic theme of IT investment to accommodate the sudden criticality of online systems to digitized business operations and a persistently remote workforce (see my earlier column discussing an emerging permanent class of WFH employees). Although the broader imperative is to use IT investments to build and enhance digital sales channels, supply chains and remote work environments, Deloitte sees technology-enabled cost reductions as enabling these investments. According to Deloitte (emphasis in the original):
Decisions that companies make today to cope with the COVID-19 crisis can help or hinder their positioning for the future. By using cost reduction and performance improvement strategically to transform the enterprise and improve competitiveness — which includes investing in key capabilities such as automation and remote work that align with the new realities of a post-crisis business environment — companies can leverage their cost savings and improvement efforts to not only transform how they operate, but to position themselves to thrive in the next normal.
AI is the foundation of next-generation automation
There are winners and losers coming out of the pandemic and as I've detailed several times, the disruption has accelerated several business and technological trends that had already been established. One of the most significant for its ability to reshape business processes for years to come is using machine and deep learning, aka AI, to analyze and act upon the enormous quantity of data generated in every organization.
AI-enhanced automation is pivotal to several strategies to make businesses more resilient, agile and efficient in the face of future disruptive events, including:
- Reshoring manufacturing and customer support operations to highly automated facilities that substitute data and software for employees.
- Streamlining labor-intensive processes by replacing manual processes with data-driven algorithms.
- Maximizing the utilization and uptime of capital equipment and IT resources via optimization algorithms and predictive analytics.
As I detailed in an earlier article, automated supply chain management (SCM) is another area where AI can thrive from a post-pandemic emphasis on improving the reliability, efficiency and scalability of critical business processes. I noted that:
A recent survey of supply chain professionals found that a majority of respondents believed that robotics, automation, predictive analytics and AI all have "potential to disrupt or create competitive advantage." Despite the promise for AI to significantly enhance SCM and logistics performance, adoption remains small. While 59% of these SCM practitioners currently use cloud infrastructure, only 28% have deployed predictive analytics systems and a mere 12% use AI technologies.
A recent post by the VP of Azure's AI operations highlighted several other areas where AI can improve enterprise processes, including:
- Scaling crisis management through chatbot-based systems allowing healthcare providers to automate symptom assessment and response.
- Cost reduction through automated financial fraud analysis, knowledge mining and form processing such as insurance claims.
- Accelerating decision making by analyzing massive quantities to data to inform decisions about store locations, plant management and farm operations.
- Improving the customer experience through personalized shopping or support interactions and self-surface interfaces using chatbots and natural language processing (NLP).
Pandemic is reshaping attitudes towards AI investment
A recent survey about enterprise AI/ML initiatives by Algorithmia probed how the pandemic changed how IT leaders prioritize AI budgets and projects. Echoing themes cited by Microsoft, Algorithmia found the areas of emphasis coming out of the crisis are:
- Cost optimization (58.8% of respondents)
- Customer experience (57.8%)
- Financial insight analysis (52.9%)
- Fraud detection (47.1%)
Overall, the Algorithmia survey found that organizations tended to lower the overall priority of AI projects in the pandemic's wake. However, the reaction can't be read as an indictment of AI's strategic importance, since, if organizations remain in the early-to-mid stages of Deloitte's recovery model, they would remain focused on tactical responses, not strategic repositioning.
Indeed, another question found that most respondents said the pandemic heightened their appreciation of AI/ML initiatives, with a majority responding that AI projects should have been a higher, if not their highest priority.
Pandemic response and strategy will be a topic of management courses and MBA case studies for years to come since, in such an extreme event, the cream always rises. How organizations react to the pandemic will not only affect their immediate competitive positioning, but is a predictor of how well they will cope with future calamities. Indeed, a recent Aon survey of business executives found that two-thirds believe the pandemic isn't a black swan event, but "exposed new risks and vulnerabilities that require a significant change in how businesses like mine think about the future.
Next-generation process and workflow automation backed by data and machine learning are critical to allowing businesses to efficiently operate in a less predictable environment where being able to quickly adjust costs, production and capacity in response to events can be the difference between bankruptcy and prosperity.
Unfortunately, a primary goal of many automation efforts is the replacement of employees with algorithms, which will result in the current unemployment crisis spreading to job segments previously thought to be recession-resistant. Indeed, the Wall Street Journal recently detailed the plight of professionals losing six-figure jobs in the pandemic's wake. It noted that the unemployment rate for compute- and math-based occupations have more than tripled this year and includes this ominous quote from the CEO of Discover Financial:
The pain so far in the economy has largely been at the lower end of the pay scale, [but] the white-collar layoffs are still to come.
Many of these jobs will be permanently replaced by machines and software, a fact that is central to NVIDIA's strategy and behind the Arm acquisition I discussed in my last column. One area of employment growth will be in jobs designing and developing automated systems. Indeed, Gartner predicts that by 2025 virtually every enterprise will have an automation architect, versus fewer than 20% currently.
Coming out of the pandemic, one message for both executives and technologists is to jump on the AI automation train before it runs over you.