Artificial Intelligence and Machine Learning Industry Trends Post-COVID and What to Expect in 2021
There are two fundamental artificial intelligence paths for companies facing the pressures of COVID-19. The first path involves companies whose numbers are down, and they don’t see the value in doing research or have the money for research projects. They haven’t embraced AI or machine learning in the past, and they don’t see this as the right time to dive in. However, this will likely lead to problems over time.
The second path is to dive into AI and machine learning to make every dollar invested pay off in the short and long term. This means finding efficiencies in sales, operational processes, logistics, and other areas where enhanced data can find improvements.
Most firms will land in the first group, especially those crunched for cash due to the pandemic; however, there’s no better time than right now to get a return on investment through AI initiatives. When everything is slowing down during a global recession, it’s a good time to find differentiators. Businesses that can improve their workflows and run all their operations more smoothly and efficiently can outperform their competitors even during turbulent times.
According to a McKinsey report, “While investments in analytics are booming, many companies aren’t seeing the ROI they expected. They struggle to move from employing analytics in a few successful use cases to scaling it across the enterprise, embedding it in organizational culture and everyday decision-making.”
As leaders dive deeper into AI, they should consider several trends and ongoing use cases for AI and machine learning that will persist throughout the COVID-19 pandemic and into the following years.
The shifting data scientist role
When you put an elevated premium on the role of a data scientist, they have the power to decide how long they take to do their job. They are largely in control of what they do because there’s very likely another opportunity around the corner that may come with a pay raise. This isn’t to mean data scientists are not capable of producing valuable insights, but in the past few years, much of their research hasn’t seen the light of day.
Companies investing in AI during and after COVID-19 want to see more measurable returns from AI. It’s no longer a dynamic where AI is presented as a beneficial public relations announcement or something to show off to investors. Now, companies are taking an “invest today and see results today” approach because they need impactful data to help them make the right decisions to get through these tough times.
This shifting attitude puts pressure on data scientists. There are more expectations for them to produce quality work quickly. At the same time, the job market is not nearly as attractive and open. Their hand is forced into being more in line with their employer’s goals and delivering measurable impacts. Data scientists might push back on this new situation, but it’s actually a positive development as it encourages them to deliver more AI value to their companies. It will provide opportunities for highly trained data scientists to shift into upper management roles. They will be more involved in transformation and providing guidance to other staff who will increasingly leverage user-friendly AI platforms that don’t require advanced technical knowledge.
AI accessible to everyone
The proliferation of advanced user-friendly AI platforms will continue to shift how and who uses AI to make impactful decisions. Currently, many firms employ a centralized team of data scientists that helps every company division, from marketing to finance. This creates bottlenecks where a department is excited about the promise of AI and orders a project but might end up waiting for years for the results. Firms cannot wait that long, especially in a COVID-19 world where margins are thin, and companies need to aggressively find competitive advantages.
With a powerful, self-service AI platform, many projects can be completed in tandem by members of each department. The company might still need a resident leadership-level data scientist to translate and structure a business problem with data, but the company thrives because they can drastically shorten AI production cycles, turning ideas into data-driven reports, and those reports into action. Processes and workflows are dramatically improved by cutting out the data scientist silo, and instead giving power to individual departments to make changes at scale.
This “democratization” of AI will pay off tremendously in the next year for many industries, especially retailers. The shift toward online buying that was already progressing is now accelerated with COVID-19, and AI is needed to improve the quality of the shopping experience. Data from IBM’s U.S. Retail Index states that the pandemic moved this online shopping shift forward by five years, noting the first quarter of 2020 saw a 25% drop in department store sales, followed by a 75% drop in the second quarter.
This change to primarily online shopping is a tremendous opportunity for AI. Firms can use AI and machine learning to sharpen forecasting and logistics and remove workflow obstacles. They can make data-driven changes to these and other areas that will make online shopping easier, more engaging for consumers and more profitable for retailers.
No big breakthroughs are required
Advances in AI and computing power are needed over time to improve accuracy and results. However, the current AI and machine learning tools and platforms are more than adequate. Through the end of 2020 and for the next few years, there are no technological advances really necessary in order for AI to continue to provide valuable innovation in industry. Of course, there are important advancements every year in terms of accuracy, better computer vision, and faster computing; it’s just that the current 2020 platforms are already exceedingly powerful.
Leaders simply need to shift how their teams use AI (if they use it at all) and change their expectations to get more out of the technology. This will require cultural changes in the way leadership views AI and how they can push their teams to find the most impactful results, which may end up being multiple fast, small wins.
Managing the pandemic requires innovation
The pandemic accelerated the use of AI and machine learning within several industries due to the necessity of saving lives, improving logistics, and managing and tracking infections. Through 2021 and beyond, many of these AI-driven use cases will continue to evolve and provide organizations and people with measurable benefits. Some of the COVID-19-specific implementations include:
• Improving models to detect infections, especially those that could otherwise go undetected.
• Assisting political leaders in making better decisions regarding supply management, hospital capacities, and other related issues with machine learning. Improvements in this arena will see AI streamlining processes and improving the flow of information between decision-makers.
• Continuous forecasts on COVID-19 risks and developing conditions.
• Disrupting logistics and supply chains throughout the pandemic to advance the use of AI-powered autonomous machines that can process orders and encourage distancing among human workers. This trend will continue, along with AI’s usage in managing inventory dynamically, with the technology looking at current conditions (COVID-19’s impacts) instead of just relying on past data.
The pandemic exposed the need for greater collaboration and information sharing between various groups. Thankfully, this exposure is driving change, as healthcare organizations, PPE suppliers, and policymakers are embracing AI tools to streamline processes, build more accurate models, and promote the use of AI in making decisions that impact people’s lives.
AI continues to improve workflows
As COVID-19 pushes companies to streamline their operations and get the most out of every dollar spent and earned, AI will play a continued role in workflow management. AI’s improving applications will help firms to offload routine tasks such as document tracking and team member reminders. It will also continue to reduce costs by eliminating human errors, especially for scenarios that require situation analysis that is free from human-related biases. Risk analysis with AI will further help project managers gain needed perspective about their current work and find problems early in the workflow cycle. The continued trend of increasing data collection, especially in text and image, is essential for the healthy growth of AI, given its need for data.
On the customer service side, more AI is needed to improve chatbots and other virtual customer service tools. With such a dramatic increase in online-based shopping during the pandemic, firms are struggling with frustrated customers who are confronted with long phone or chat wait times. This provides an opportunity for AI to improve the accuracy, speed, and resolutions with chatbots, so some customer inquiries are offloaded away from overworked live agents. A Gartner report from summer 2019 found that “By 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25%.” The report discussed the need for conversational AI programs that could handle customer service inquiries and serve as proactive communication tools for companies and government agencies. For example, these tools are screening people for COVID-19 symptoms and providing self-service information platforms for people seeking the latest developments about the pandemic.
After COVID-19’s end or ongoing management, there’s a tremendous long-term opportunity for AI and machine learning. A report titled “PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution” estimates “AI could contribute up to $15.7 trillion to the global economy in 2030.” It’s an extraordinary sum that points to the technology’s promise to bring efficiency, accuracy, and faster speeds to a wide variety of environments.
Pedro Alves is the founder and CEO of Ople.AI, a software startup that provides an Automated Machine Learning platform to empower business users with predictive analytics.