The role of AI in a post-COVID world
Almost twenty years ago, the SARS outbreak made a profound mark on East Asia. Much like the crisis today, hundreds of millions of people stayed at home to avoid exposure to the virus. Unwilling to go out for anything but the essentials, many Chinese began shopping online. And so began the rise of Alibaba, then in its infancy, to one of the biggest colossi of Asia.
Some say there is opportunity in adversity in every business. It is a controversial statement for sure, yet it certainly holds some truth. With the current dust settling and people are going back to their ‘normal’ lives, it may come as a shock to many companies that not all will be business as usual. When disaster strikes, slumbering business trends tend to accelerate at a higher level. C-levels have the obligation to look ahead, to anticipate, to evolve their strategies and organizations at a faster pace.
In every crisis, some companies make bold moves, and there is no indication this crisis will be different from others. For example, it’s fair to think that some companies will aim to be less dependent on global supply chains by manufacturing closer to their end-user market. The trend of online shopping won’t go away and will likely further increase. Of course, it also expected an increasing number of people will work remotely, now that we have become more proficient at communicating online.
At ML2Grow, we believe that artificial intelligence is an important enabler for companies trying to adapt to these trends. Manufacturers are using computer vision to recognize objects and handle quality control with less human intervention. Workers then have the opportunity to focus on less repetitive and more rewarding tasks, in a safer environment. Through machine learning, AI will also increasingly help companies to detect and reach new customers and offer them personalized products and promotions.
Some of the companies that are at the forefront of AI adaptation will thrive in the post-COVID world. Other companies will have to act quickly to acquire the skills, capabilities, and research to begin the AI journey.
However, we at ML2Grow are here to help you in every step of the process.
Why AI is continuing to grow
Business, in general, are evolving to the next level, from digital native to having systems in place that can analyze enormous amounts of data, learn and adapt to make complex decisions. To say these abilities bring value is an understatement.
Many companies in Belgium already have adopted ERP systems and basic data analytics. BI tools help visualize and make business processes clearer. A big disadvantage of BI is that it only shows and transforms the data as it is. While it makes it easier for humans to process and interpret the data, the BI itself does not make any interpretations or predictions. AI on the other hand enables machines to really solve problems and take actions that seemingly appear intelligent.
What you can do or are doing with data is often described in four levels in literature or blogs. AI is a technology which can be used to achieve all four levels. The levels are visualized below in a graphic that we created for the Pack4Food ‘Intelligent Packaging’ closing event in December 2019 and was later highlighted in the VMT Food magazine. The figure displays a popularized use-case in which a breach in the cold chain of a frozen pizza occurs with different levels of understanding why, how or when it happened.
Everyday companies create immense volumes of data. To really unlock the true potential of your data and reach the desired level of analytics described above, you will need a clear understanding of your data sources. Our experts are ready to current data to help you identify opportunities for improvements, increased productivity, and cost savings.
For SME’s we have developed an AI Roadmap in which we describe guidelines and good practices to activate your data. This roadmap can be implemented by you, us, or we might suggest solutions provided by third parties. Under certain conditions, this data audit advice can be subsidized by the ‘KMO Portefeuille’ (SME e-wallet) program for Flanders based companies. In such trajectories, we will often construct machine learning model(s) identifying the connection between the data and the operational goal. We will then use these models to assess the potential impact of the solution and which integration(s) are needed to impact the business process. The result is a complete implementation plan which defines the necessary steps in data collection, data infrastructures, software development, and integration of AI applications and their costs and benefits. This plan takes away much of the uncertainty that AI projects inherently have at the start.
Proof-of-concepts are frequently used to reduce uncertainty in AI projects but often they are not the right choice. The uncertainty we encounter is mostly related to the operational impact the solution will have while proof-of-concepts mainly address technological feasibility. Those two things are not the same: quite often we know the model can be created, we just don’t know if it’s worth doing it. In these settings, we advice going for a data audit, which results in an implementation plan and if the conclusion is that an operational model is beneficial, no work is wasted.
– Joeri Ruyssinck, CEO ML2Grow
Bolstering the supply chain
Something that became clear during the covid crisis is that many manufacturers depended on global supply chains for resources and parts. In fact, many European goods are produced in high-volume factories in new developing economies. The national bank of Belgium recently stated that many Belgian companies are now confronted with disrupted supply and rising purchasing costs for raw materials.
Now, with disrupted global supply chains, companies have moved redundancy as a top priority on their agendas as a means of reducing risk.
Half of the companies that depend on supplies indicate that they have been moderately or severely disrupted. Supply problems are particularly widespread in wholesale (64% of respondents), construction (58%) and manufacturing (57%). Medium-sized companies have been particularly affected as they have less leverage to press demands.
AI offers the potential for companies to regain competitiveness in countries where manual labor is costly. It also enables manufacturers to optimize cost in each factory through predictive maintenance and better planning. For example, by deploying advanced manufacturing technologies such as 3D printing and autonomous robots. This use-cases allow them to operate more efficient facilities worldwide.
A great example of a company that uses the potential of AI is BMW. Computer vision brings intelligence to the quality control and monitoring process of the BMW factory. A deep learning model analyses images of goods on the production line, minimizing the number of production faults and bad products reaching the market.
Changing consumer patterns
During the pandemic, we drastically altered our consumption habits. We made more purchases online and consumed food and beverages exclusively at home. E-commerce’s share of global retail trade went from 14% in 2019 to about 17% in 2020. What’s more, long periods of forced isolation, combined with anxiety about an economic recession, could cause consumers to cut back on luxury items in favor of essentials.
This makes it increasingly harder for companies to discover emerging trends and identify changes in consumer preferences. That’s why more and more consumer-based companies are converting from BI to AI capabilities. Amplifying weak signals and detect trends early on will be primordial to improve sales and brand engagement.
With recommender systems, marketers can deliver intuitive search, relevant recommendations, and behaviour-based personalization for every part of the shopper’s journey on your website. Or what about predicting customer churn to effectively target customers, who are going to stop buying your goods and services, with personalized offers.
More remote working
During the pandemic, there was a massive shift to remote working. Many think this trend is here to stay as more people than ever experienced the benefits of avoiding hourlong traffic jams and managing their household from the comfort of their office. Companies that are already implementing AI have a big advantage in remote work situations over companies that are lagging. They are more modular and agile in comparison with their competitors.
The survey of the national bank of Belgium also confirms that many companies struggle to fill vacancies. A third of companies looking to hire say it is more difficult to find staff for all staff categories. Another third says it is more difficult to recruit specific profiles. Last week, the Flemish employment agency VDAB reported that the number of new vacancies has risen to a record high.
By augmenting HR with AI companies can forecast labour needs, supply disruptions or find more qualified people to support the growth of their business. Learn more about the importance of AI in HR in our previous blog post.
It’s time to put AI at the core of your business
AI is transitioning from early adopters to wider use and will have a potential impact across many industries (commerce, healthcare, automotive, financial services, etc.). Winners will be companies who adjust their business models to the new reality. It will no longer be sufficient to be a digital native, business leaders will need to put AI at the core of their organization to really unlock the value of their data.
It’s time for companies to step away from reactive measures and take bold, transformative action. Read our previous blog post about how you can implement a profitable AI system, and also sustainably anchor this within your business.