Did you know that in December 2019, the Canadian company Blue Dot used AI to predict and report the news of the COVID-19 outbreak? Technology has come a long way since the last pandemic. In the years leading up to COVID-19, industries have invested in predictive technology and this investment is paying off bigtime. COVID-19 singlehandedly makes the case for big data, analytics and artificial intelligence.
The healthcare industry, for example, has applied artificial intelligence, robotics and machine learning algorithms to improve diagnoses and medical outcomes. A tool like remote patient monitoring makes a huge difference during a time like COVID-19.
With improvement in business intelligence, we have seen sophisticated use of infrastructure, all adding up to the effective collection and use of data pools. Automation becomes more airtight and predictive with more information at its disposal. Data forecasting truly is the star of automation and can revitalize a business during unprecedented challenges like the one we are facing today.
The latest report by Boston Analytics Group tells us how and why companies who are already leveraging on AI and predictive analytics will thrive and even do well in a postcrisis world. The figure shows how 14% of businesses succeeded in increasing sales and improving profit margins. The report also says that the remaining 86% of businesses are yet to make a start and this could slow down their progress. (Please refer to exhibit 1.)
We all have tons of data from various sources, and all of them are useless without AI at the core. Artificial Intelligence assists machines in solving problems and making decisions. It helps unearth patterns, forecasts human behavior, renders operations and logistics intelligent and contactless, and finally, is able to self-learn, adapt and improve to different situations.
Here’s how artificial intelligence can drive business.
Building productivity models
AI can drive sales, performance and productivity in multiple ways. Productivity and propensity models can help businesses detect which customers are likely to buy from a company and what they are going to buy.
Demand and supply
With COVID-19 demanding businesses to understand and know exactly what their customers want, demand forecasting using AI can test innumerable mathematical models. Precision in this department can drive supply chain and logistics.
Retail and logistics
Consumption and buying patterns are completely off-kilter thanks to COVID-19. Retail and logistics will increasingly depend upon AI to help set their supply chains right. Predictive analysis will help companies figure out patterns in customer behavior and prioritize supply. Advanced modelling techniques help with scheduling, inventory, supplier behavior, and work on improving retail networks. Optimizing travel routes and product flows really helps businesses control their inventory and anticipate demand.
Rapid decision making and productivity
With AI, businesses can use data to make complex decisions by putting together a fact framework and analyzing data sets more accurately. During a pandemic like COVID-19, scenarios are vastly unpredictable and there is little room for human error. AI can eliminate these errors and also fast-track the decision making process. The more data at your disposal, the more accurate the analysis is, and businesses also find that there are cost-effective data storage solutions at their disposal.
We should remember that AI is technology that is programmed but trained, and it adapts to different scenarios. Machine learning algorithms help AI-powered machines self-improve and learn as they go along. Production down time, lead time and errors can be monitored using analytics, coupled with machine learning.
According to a report by McKinsey, machine learning “Reduces supply chain forecasting errors by 50% and it can also reduce costs related to transport and warehousing significantly”. Mechanics and operations that run on ML algorithms outperform businesses that don’t have this capability. ML can mine data and help data science teams build successful business models. Companies like Google and Amazon are investing in AutoML or automated machine learning, which automates the process of applying machine learning algorithms to processes and problems. We may need AutoML in light of the current crisis, when companies have little time to adopt AI.
Machine Learning helps with sales forecasting, inventory control, product customization, and business productivity.
Big Data proves to be very useful to businesses in the post-COVID scenario. Big Data algorithms can assess customer behavior and spending patterns during the pandemic. They can also help evaluate risks. Businesses can also use Big Data to test different models, products and services before launching them.
Winston Churchill famously said, “Never let a crisis go to waste.” With a new way of living and survival, we have new opportunities in business that present themselves. Big Data can be used to drive innovation and to assess risk in various scenarios.
Predictive analytics and forecasting
Predictive analytics and forecasting are useful in driving ‘what if’ scenarios and in understanding variables. With COVID-19 casting uncertainty on the immediate future, predictive analytics can use historical data to make predictions and to develop statistical models that drive favorable outcomes. Companies can forecast trends and customer behavior. The predictive analytics market is poised to grow to $10.95 billion by 2022.
In the time of COVID-19, predictive analytics can be used to forecast the impact of the pandemic on both the health of the workforce as well as the scope of business during these uncertain times. It is also a great tool to prevent fraudulent practices and cyberattacks. The automation industry can use predictive analytics to mine past data to inform future manufacturing plans. Predictive analytics can be used in the energy space to predict prices and to understand demand. In finance, we can put together models to ascertain credit risks.
With predictive analytics, you can discover business trends much earlier than your competitors. You can share important information with all players in your business. For example, you can help your distributors identify alternative routes for goods and services.
Automation is already being used in such diverse ways to fight the pandemic and to protect businesses. Hospitals, banks, governments, retailers, manufacturers and even call centers are witnessing automation in important ways.
For instance, if a bank uses DR automation to process its business applications, it can still operate remotely during a pandemic lockdown. Digitization and automation are the key to business survival during uncertain times. Foundries and manufacturing units can use automation to keep their businesses on track.
Robotics process automation (RPA) can do wonders for a business. It can very quickly deploy a digital workforce for an organisation and ensure that it’s business as usual. Digital workers can, for instance, be deployed overseas. They can work remotely and can perform tasks that humans find mundane and repetitive.
Here are 7 ways you can use analytics to make your business smarter during the pandemic.
1. Use it to boost sales
Did you know that Airbnb has grown exponentially because they hired a data expert early in their journey? This is a business that uses data analytics extensively for decision making. Businesses that build these capacities early in the day will survive the post-COVID world.
2. Manage workforces using chatbots, robots and drone systems
Chatbots powered by AI can truly be game-changers for businesses now. A lot of businesses have transitioned to using chatbots and they already have a competitive edge. During this time, we see machine learning, AI and robotics process automation intersecting in exciting ways. The SARS outbreak in 2002 led to the proliferation of online marketplace platforms in China. COVID-19 is going to have a bigger impact. From robot deliveries to robots deployed in hospitals, robots can truly change the landscape across various industries.
3. Automation of call centers
Chatbots can make customer support simpler. Companies can use intelligence and analytics to drive their chatbot interactions and to address call center volume. Businesses can apply automation to call centers using data analytics, voice-based agents and chat bots. Call centers are already witnessing widespread automation and the pandemic has only speeded the process. Natural language processing, for example, is used by many call centers to interpret customer queries, especially figurative or open-ended speech.
4. Understand changing customer profiles and consumption landscapes
Big Data and analytics can help companies assess and develop multiple business scenarios. Companies can then plan their logistics, supply chains and risk within these frameworks. With COVID-19 disrupting customer behavior and spending, companies can understand changing patterns in behavior and optimize their data and analytics to deliver better products and solutions. Data and analytics also help companies understand customers better and use this to streamline their decision-making process.
5. Retool predictive models
The scenarios and models that companies used pre-COVID 19 are not applicable to our current world environment. While the pre-COVID 19 data is valuable, companies need to rejig their predictive modelling strategies to suit the new normal. Data scientists, for instance, need to reframe the model to suit the current environment and also advise data teams to assess data keeping the current crisis in mind. Retraining learning modules is important to reset analytics to understand current fluctuations and trends. Data scientists advise retooling once a month at least to put analytics to good use.
6. Predict scenarios and manage risk
A good example of predictive forecasting for business is when the head of sales uses data to project sales revenue for the upcoming quarter. By using data analytics, companies can predict trends, cycles, scenarios and fluctuations. They can plan adequately by using different data sets.
7. Make data work for you
Using data analytics, companies can work on past data. They can find out what they want to know based on past data, including key business decisions and insights. Once companies get answers about the past data, they can train their data analytics system to learn from these previous patterns and predict outcomes. Most companies also don’t put their data-driven decisions to action. Actionable data will be most helpful for future outcomes.
Data and analytics help you look inside your business. With the pandemic causing so much uncertainty, it is important to also look outside at factors like geographic trends, shifting audiences, opportunities to pivot, and key shifts.