The onslaught of the pandemic was proof that there could no longer be “business as usual” for companies and industries. The industry demanded innovation and transformation for businesses to survive and stay afloat.
However, businesses still in the midst of post-pandemic recovery will once again need to adapt to today’s unstable economy. On top of rises in inflation and supply-chain issues, an article on Fortune predicts that a recession will most likely hit in the first quarter of 2023.
These looming economic threats do not mean that companies must surrender and accept an inevitable decline in their growth. Companies must use the technologies at their disposal to remain competitive and profitable.
Now is the time to maximize the advent of artificial intelligence (AI) and automation and become resilient for what comes next—but how can businesses do this?
Capturing more value through AI-based supply chain management
While businesses already recognize data’s importance, most do not know how to utilize and convert it into value. AI changes the game by integrating data across all parts of the supply chain and generating data-driven insights.
Faster data analysis
Humans do not have the capability to sift through and analyze the vast data sets captured and stored by companies. Through machine learning, AI can make connections between diverse data points and use special algorithms to detect any anomalies that may affect operations or incur losses. In other words, AI processes and contextualizes data so it can inform business decisions.
In our post challenging the misconceptions about AI, we emphasize that businesses of all kinds can and must utilize AI, considering there are custom-built solutions which align with a company’s specific needs. The challenge comes with knowing how to prioritize the use cases where AI can be deployed to optimize the process.
To illustrate, our company works with manufacturers to automate and streamline their quality control processes through AI. By training the AI to perform image classification based on historical people-based judgments, it can automatically detect flaws in the product or equipment. This improves the speed and efficiency of quality testing, while also lowering the risk of errors common to manual inspection.
Supply and demand forecasting
The ability of AI to identify patterns between historical and new data can be harnessed to project future trends, particularly in areas vulnerable to shocks and disruptions like supply and demand. Swedish furniture brand IKEA developed a demand forecasting tool that uses statistical sales for each product to estimate demand more efficiently and accurately. This tool factors in the influence of shopping preferences, seasonal changes, and in-store visits to avoid inadequate supply or overstock, both of which can impact sales and logistical costs.
Leveraging AI for workforce productivity
With the help of AI and automation, industries can now diversify workforce skills and enhance overall productivity in various ways: through substitutes, complements, and additives.
A widely observed example of AI substituting human labour is in customer service, where businesses adopt AI-driven chatbots. Chatbots can automate responses to standard and predetermined inquiries, e.g. product details, store locations, or parcel tracking. They can also route and escalate issues to the right team to ensure better responses. Companies are thus able to manage a volume of support requests and offer 24/7 customer assistance without bogging down their service teams.
By automating time-consuming activities, AI complements workers’ efforts and allows them to focus on the more strategic and essential tasks at hand. Healthcare companies can remove bottlenecks and at the same time ease the administrative workload of their professionals by adopting AI solutions that can pre-authorize insurance, manage bill payments, and maintain electronic patient health records.
There are also instances where the adoption of AI creates new roles. From the discussion of Diamandis and Kotler’s The Future Is Faster Than You Think in the podcast The Human Upgrade, the convergence of AI with existing technologies transforms the division of labour between humans and machines, leading to the need for jobs with new or higher skills. Applying this context in the manufacturing industry, the combination of AI and 3D printing does not entirely replace machine operators, but simply requires upskilling and reskilling in design and engineering in order to work with the new technologies.
The emergence and subsequent application of AI across industries compels business leaders to rethink and embrace new ways of working. As the World Economic Forum reports that job creation will likely surpass the number of job displacements, there lies a bigger incentive for companies and organizations to prepare a clear roadmap that at once adopts AI and takes full advantage of these new roles.
Written exclusively by Juliet Barnes for Gradient Ascent
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