Technology innovation in artificial intelligence (AI) is accelerating at a breakneck pace, and the ability to innovate, adopt and integrate AI techniques to evolve business models will separate those businesses that recover from the COVID-19 pandemic from those that will fail. Four artificial intelligence technologies are poised to lead the global economy out of the pandemic-induced recession. Applications for these technologies across verticals abound. Smart strategic and financial investors are scouring the market for new ways to digitally disrupt established businesses.
Professors Andreas Kaplan and Michael Haenlein have defined AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” Others dismiss artificial intelligence as just that: artificial, and not intelligent at all.
To leverage AI, software and hardware engineers program agents into devices that can perceive their environment and take actions to achieve specified endpoints, or at least increase the probability of success. Some of the tools for AI include search and optimization, logic, probability-based methods, statistical-based methods, neural networks, and evaluation.
The surge in development of AI over the past decade has been enabled by exponential advances in computer power, at the same time as devices that capture unfathomable quantum of data proliferate throughout the enterprise and consumer economies. Software and hardware engineers have increasingly leveraged AI as the natural tool to connect these two worlds and solve problems and automate solutions. Advancements in AI have increasingly been driving disruption and creating new business models. As we experience a global pandemic and strive to adapt to the “new normal,” artificial intelligence will only accelerate.
Throughout the COVID-19 pandemic, the importance of sharing critical information across countries about the spread of coronavirus has been emphasized. However, much remains unsaid about how COVID-19 could have been managed more efficiently by using advanced data technologies that have transformed businesses.
Here are four areas where AI could change the face of the post-COVID economy:
Augmented Analytics
Now more than ever, data scientists have immense amounts of information to analyze. This makes exploring every single possibility virtually impossible, and businesses can easily miss important insights. So, as data scientists use automated algorithms to explore more hypotheses, augmented analytics represents a fourth wave for data and analytics capabilities. As of late, data science and machine learning platforms have completely transformed how businesses generate analytics insight, while augmented analytics identify key hidden patterns.
Augmented analytics is the use of enabling technologies such as AI to assist with data preparation, insight generation, and insight explanation to increase how people explore and analyze data in analytics and B.I. platforms. As we launch into this new decade, the number of citizen data scientists is likely to grow 5x faster than professional data scientists because businesses will need citizen data scientists to scale data science capabilities. Between data scientists and augmented analytics, data insights will broadly be available across the business, including analysts, decision-makers, and operations workers this year.
As this pandemic drags on, businesses will have to pivot their strategies and figure out how to keep a healthy cash flow. How much cash is required to see a business through the next 12 months? By utilizing the power of compiled company data, companies can create cash flow simulations, plan business operations, and liquidity analysis to get a clear picture of future financial capabilities.
Data can help businesses streamline internal operations and look productivity through analysis, measuring the time individual and collective tome invested in meetings and tasks and compare and contrast this data to pre-pandemic performance. Are employees doing better than before? What factors might be able to improve productivity?
Blockchain
Blockchain is a unique technology made up of a decentralized, distributed, and often public digital ledger. The ledger is used to record transactions across so many computers that a recorded transaction could not be altered retroactively, at least not without the alteration of all subsequent blocks. It allows businesses to trace a transaction and work with untrusted parties without the need for a centralized party. Blockchain reduces business friction and has applications that began in finance, but have expanded to government, healthcare, manufacturing, supply chain, and more.
Blockchain during this pandemic is being used in many ways. The U.S. Department of Homeland Security (DHS) published guidelines listing blockchain managers in food and agricultural distribution as ‘critical infrastructure workers. The Cybersecurity and Infrastructure Security Agency has developed an initial list of sectors and workers who should continue their regular schedule. Healthcare blockchains are being developed with a focus on patient data management, insurance, worker credentialing, and other data.
While blockchain is often criticized for sacrificing efficiency for security, AI can optimize energy consumption by improving algorithms. Reducing the consumption of energy for blockchain transactions benefits all of the industries that rely on it for data transfer and supply-chain management, and fosters mainstream adoption. AI also reduces the storage needs for blockchain transactions. AI introduces new sharing techniques that make the size of the blockchains smaller and their storage more efficient.
Empowered Edge
With the thought that keeping traffic local will reduce latency, edge computing is a topology where information processing and content collection are placed closer to the information sources. According to Gartner Group, today, most of the focus of this technology is a result of the need for IoT systems to deliver disconnected or distributed capabilities into the embedded IoT world, which will address challenges.
In addition, the empowered edge will enable the specifics of digital business and I.T. solutions. Technology and thinking will even shift to a point where the experience will connect people with hundreds of edge devices. Intelligence will move toward the edge in a variety of devices – from industrial to screens to smartphones to automobile power generators.
In the age of the COVID-19 crisis, edge data centers and edge computing are more critical than ever before. For example, The Internet of Things (IoT) healthcare market is expected to grow to $534.3 billion by 2025. A considerable percentage of now households subscribe to streaming services like Netflix or Hulu n and continues to increase even more due to recent social distancing during the COVID-19 crisis. The number of people who work from home has increased, and because of social distancing and the COVID-19 crisis, the number of work-from-home jobs has unexpectedly surged. Edge computing makes working from home that much easier by improving network performance for end-users.
Smart Spaces
A smart space can be a physical or digital environment where humans and technology-enabled systems interact in open, connected, coordinated, and intelligent ecosystems. Essentially, smart spaces are developing as individual technologies come out of silos to work together to create a collaborative environment. One example of smart spaces is smart cities – where areas that combine business, residential, and industrial communities are being designed using intelligent urban ecosystem frameworks, linking to social and community collaboration.
As technology becomes a much more integrated part of our daily lives in the global pandemic, smart spaces will become more and more popular for businesses to adopt.
Before this pandemic, we saw a move to office-sharing and open spaces, and after the pandemic, the appetite for shared office space is likely to soften. People are increasingly sensitive to personal space and safety, and we could see a move away from densely shared office space going forward.
Market Trends in AI
According to the PwC/CBInsights MoneyTree report for Q1 2020, venture capital firms deployed over $4.0 billion of fresh capital into 148 deals for AI companies. The report goes on to identify five companies where venture capital firms put in at least $200 million, which are referred to as “mega-rounds:” pony.ai, Netskope, Berkshire Grey, SambaNova Systems and SentinelOne. Many of these companies are targeting the applications outlined above. The data shows that strategic and financial investors are looking for companies that are combining emerging, connected and smart technologies to digitally transform their industry.
Applications for AI
AI has applications in healthcare, allowing users to analyze their own health data to identify anomalies, diagnose disorders and prescribe solutions. In the automotive realm, AI is helping cars to drive autonomously. In finance and economics, AI is helping fund managers deploy assets and harvest dividends and returns. In e-commerce, AI is assisting e-tailers in predicting what products consumers will want to buy and propose it to them. In cyber-security, AI is helping identify threats and eliminate them. In law, AI is being used to crunch terabytes of data in seconds and identify discoverable evidence and conduct due diligence, identifying potential liabilities. In video gaming, AI is being used to predict player behavior, identify anti-social conduct, and increase the sale of virtual goods. In the military, AI is being used to identify threats and increase security.
Innovating Inside and Out
The archives of bankruptcy courts and other corporate cemeteries around the world are littered with examples of companies that failed to anticipate new technology by innovating and evolving their business models. For any doubters, simply compare the value of a New York taxi medallion in 2010 (before the proliferation of ride-sharing companies such as Uber and Lyft) and now, or the number of smart phones powered by the once-ubiquitous Symbian operating system against the number of smart phones using it today (close to zero).
Companies do well when they accelerate their digital transformation plans and reinforce their corporate innovation and venturing programs. The most successful technology companies have dual platforms to innovate, both internally and externally.
To innovate organically from the inside, forward-thinking companies set up internal skunk labs and R&D programs. By way of example, Google asks engineers to spend 20% of their time on new ideas.
Looking outside the four walls of their corporations, innovation can be achieved through licensing programs, accelerator and incubator initiatives, commercial agreements, joint ventures, and strategic investments. In the last decade, many corporations have set up corporate venturing groups to identify new technologies and seed them with capital in the form of minority investments, and connecting them with business units to accelerate their go-to-market strategy. Corporate development arms seek to acquire adjacent or complementary technologies or teams of engineers in strategic acquisitions and “acqui-hires.”
Together, the combined internal and external efforts to innovate can lead to new products, new partnerships, new distribution channels, new revenue streams, and new higher-paying, value-added jobs for our economy.
The potential applications of AI to automate processes, develop operations, improve security, promote commerce, and protect against fraud may enable what some call the “singularity.” While some companies are struggling to stay afloat during a COVID19 induced economic coma, the key to recovery may lie in leveraging AI technologies and applications to innovate and evolve their business models. Forward-thinking businesses will simultaneously innovate internally and externally. As Marc Andreesen recently wrote, “it’s time to build.” He might have added that it’s time to do so with artificial intelligence.
