Article | March 3, 2020
Technologies like Artificial Intelligence and Machine Learning will enable wider audience to get access to space analytics and insights. Assisted processes through software created by Machine Learning will enable users to run sophisticated models on their data. That is what I see happening in the next few years. It’s not about replacing, it’s about assisting new types of users to get access to the type of analysis that wasn’t accessible before. Automated Machine Learning is what we are going to be seeing in the coming years. It will expand the customer/ user base of spatial analytics by making it more accessible.
Article | May 20, 2020
Article | August 19, 2020
The main objective of demand planning is to help businesses prepare to meet future demand. The forecasts are largely based on historical, seasonal demand patterns, not current demand signals. In fact, the main driver for future forecasting is historic sales during the same time period in prior years. The inaccuracy of demand forecasts that are based on historical data alone has often resulted in gaps between past and present situations, which, in turn, create significant business challenges. For example, seasonal supply needs based on the events of previous years and on marketing trends can lead to overstocks and increased inventory costs, stock-outs and missed sales opportunities.
Article | December 20, 2020
COVID-19 has impacted every aspect of our lives including the way we do business. In fact, according to a recent survey by McKinsey, COVID has accelerated companies’ digital transformation journeys.
In a post-COVID world, there will be an even-greater acceleration of AI adoption by enterprises. AI business applications will be centered around automating tasks, forecasting supply disruptions, and enhancing customer behavioral analytics. There will be a rise in industry and sector specific AI applications where business domain knowledge and business content data are the main differentiators. However, increases in AI adoption rates do not necessarily translate into higher success rates. To avoid failure, business executives need to develop robust AI strategies and metrics, enhance data quality, and focus on AI integration and governance.
Key trends and applications for 2021 and beyond are as follows:
AI and Healthcare
Artificial intelligence played a crucial role in the detection of COVID-19. Indeed, we have seen the emergence of the use of AI at hospitals to evaluate chest CT scans. With the use of deep learning and image recognition, COVID patients could be diagnosed thus enabling the medical team to follow the necessary protocols. Another important application was the triage of COVID-19. Once a patient has been diagnosed with COVID, AI has been used to predict the likely severity of the illness so the medical staff can prioritize resources and treatments.
In a post-COVID world, we will see increased use of AI in detection of illnesses, triage of patients, and drug discovery. According to a recent market research reported by PRnewswire, the market size for global healthcare IT is expected to reach $270 billion by 2020. The increase will be driven by COVID-19, government policies, and the use of technologies such as artificial intelligence and big data.
AI and Supply Chains
Coronavirus has highlighted the need to re-think traditional supply chain models. There will be an increase in the use of technology such as artificial intelligence, Internet of Things, and 5G to make supply chains more efficient.
Artificial intelligence applications will focus on improving end to end visibility, analyzing data to detect anomalies, and forecasting supply and demand outlooks thus making supply chains more resilient.
AI and Retail
The pandemic has changed what and how consumers buy, with retailers forced to grow their online presence. E-commerce has been put at the forefront: in the first six months of 2020 consumer spending with US retailers increased by about a third compared for the same period in 2019 according to Digitalcommerce360.
According to new market research reported by PRnewswire, AI in retail will be worth about $20 billion by 2027. When it comes to retail and ecommerce, we can find AI applications in several areas including customers analytics for product recommendations, targeted marketing, and price optimizations.
For the latter, AI is applied to analyze patterns and data on customer profiles, their purchase power, product specification, timing of purchase, and what the competition is offering. The outcome of the analysis will set the pricing strategy. Several companies use AI to set their pricing strategy on a frequent basis, for example Amazon’s average product’s cost changes about every 10 minutes according to Business Insider source.
AI and Intelligent autonomous agents
COVID has highlighted the need to deploy intelligent autonomous agents that cannot catch diseases to fight against the pandemic. We have seen both robots used at hospitals to diagnose COVID-19 patients and drones deployed to monitor if the public is adhering to social distancing rules.
An ABI research showed that mobile robotics applications market size will increase to $23 billion by 2021. This increase is mainly due to applications that disinfect, monitor, and deliver materials.
The integration of AI with drone technology and robotics will create new application opportunities and will make them mainstreamed across several sectors.
AI and Education
Education is another sector that was badly hit by COVID. According to Unicef more than 1 billion children are at risk of falling behind due to school closures. The pandemic has highlighted the need for educators to adopt digital solutions to minimize learning vulnerabilities across the globe.
AI application in education will mainly focus on personalized learning where the technology is used to design and tailor training materials that matches the student’s ability and learning preferences. Other applications include the deployment of voice assistants to interact with educational material and the use of AI to support teachers in administrative tasks.
AI and Digital Twins
The pandemic has accelerated the adoption of digital twin technology. Digital twins are replicas of physical assets such as cities, offices, and factories. This technology became crucial in testing pandemic scenarios and emergency plans.
Digital twins technology is expected to reach a global spend level of about $13 billion by 2023 fueled by AI and machine learning according to Juniper Research.
When integrated with artificial intelligence and IoT, digital twin technology becomes very powerful when trying to test scenarios and predict bottlenecks, breakdowns, and productivity.
AI and Ethics
Over the last year, we had several prominent examples of AI ethics issues. The first example relates to facial recognition: after several calls against mass surveillance, racial profiling and bias, and in light of Black Lives Matter movement starting in the United States, several tech companies such as Microsoft banned the police from using its facial recognition technology. The second example relates to the use of an algorithm to predict exam results during COVID-19 period: after accusations and protests that the controversial algorithm was biased against students from poorer backgrounds, the United Kingdom government was forced to ditch the algorithm.
In the absence of regulations and tightened frameworks, ethics will continue to be the main concerns surrounding the use of artificial intelligence.