AI Globe

The intelligent enterprise leaps forward in 2021

The many disruptions in 2020 effectively illustrated the fragility of life, work, and society at large. People and organizations alike scrambled to solve problems in ways not considered before the COVID-19 pandemic. Many businesses suffered hefty losses while others survived, and even thrived in some cases, with increased agility and a move toward modern technologies.

In reflecting upon last year’s events and the use of advanced technologies, including artificial intelligence (AI) and machine learning, we observed promising activity in several key sectors such as healthcare, manufacturing, retail, finance, and education.

In healthcare for instance, while COVID-19 drastically shifted life for everyone, many essential healthcare workers were on the frontlines to help overcome the global pandemic with assistance from machine learning. One particularly promising example came from MIT researchers who developed an AI model to help them diagnose asymptomatic COVID-19 patients through the sound of their coughs. The difference between a healthy cough and an asymptomatic cough cannot be heard by the human ear, but when “the researchers trained the model on tens of thousands of samples of coughs,” the AI system discerned asymptomatic coughs with 100% accuracy. As we continue to keep physical distance from each other, a widely available test like this for asymptomatic patients could help the world flatten the curve.

Across the pond in the UK, a research project is being funded to track side effects related to COVID-19 vaccines as they are distributed. With several companies vying to deliver their vaccines to the market as quickly as possible, this tool is used to track adverse side effects. The awarded government contract for this purpose indicates “that the AI tool will ‘process the expected high volume of Covid-19 vaccine adverse drug reaction (ADRs) and ensure that no details . . . are missed.’” Other use cases that leverage AI to combat the pandemic, can be found on our previous blog, “How machine learning will help us outsmart the coronavirus.”

Companies outside of healthcare also took advantage of machine intelligence to showcase new capabilities or streamline their operations. For example, in select major cities across the U.S., driverless cars performed additional road testing. Case in point, during last quarter, Cruise introduced its very first driverless car hitting the asphalt in San Francisco. While there was a human in the passenger seat to experience the ride, this was the company’s first step toward securing permits to launch a commercial service using its autonomous vehicles.

For many who had never heard of Zoom prior to the pandemic, virtual video communication technology became nearly ubiquitous for those who could no longer communicate or collaborate in person—at home, at work, in education. This means more people relied on these types of technologies to perform functions they would normally handle face-to-face. People began to think of video communication as the virtual water cooler for happy hours, birthday celebrations, and other meet ups. Even visits to Santa went viral. AI models are taking virtual communication to the next level with chatbots, improved personalization, smart replies, and more.

While much was accomplished with AI last year, 2021 promises to do even more. Here are some of the trends we expect to unfold this year:

AI spend will break through previous records

To adapt to the major disruptions caused by the pandemic and the ensuing social and economic shifts, businesses and governments worldwide have begun increasing technology spends while lowering budgets in other departments such as HR and marketing. According to Gartner, 67% of board of directors (BoDs) surveyed foresee expansion to the technology budget and part of that budget belongs to advanced technologies with AI and analytics “expected to emerge stronger as game-changer technologies.”

Competition in the coming years will require organizations to adopt AI at a faster rate. Machine learning will help augment human power by unearthing new insights otherwise hidden in data and by automating a series of workflows, tasks, and processes that consume too much human time and effort. This can be consequential in many areas of operations including finance, sales, product development and delivery, security, and IT. These needs will push technology spending to new heights. Over the next four years alone, global AI spending is forecasted to double from “50.1 billion in 2020 to more than $110 billion in 2024.”

CIOs will help lead the productivity revolution

More enterprises will implement AI strategies by leaning on their CIOs to achieve real business results. This year, experimentations with machine learning will accelerate but that alone will not be sufficient. Enterprise CIOs will be under increasing pressure to explore, select, and implement suitable technologies that can power the intelligent enterprise. Their focus will remain on maximizing productivity by streamlining the many facets of internal operations.

As of 2019, “only 8% of firms engage in core practices that support widespread adoption. Most firms have run only ad hoc pilots or are applying AI in just a single business process.” Focusing on AI core practices as opposed to ad hoc implementations will not only enable stronger adoption within these organizations, but will also foster additional cross-team collaboration for better results. CIOs encouraging adoption of these new technologies will empower employees to explore and test AI projects so that they are used as efficiently as possible. This will help drive business success as in-person workflows remain disrupted by the pandemic, with an accelerated secular push toward remote work.

AI will become more widespread  

A natural byproduct of increased C-suite adoption of AI deployments within the enterprise is efficiency via automation, speed, and scale. Widespread adoption of intelligent applications and process automation simply translates into cost reductions and time savings. According to Gartner, “organizations want to reach the next level by delivering AI value to more people.” More internal stakeholders being exposed to a company’s AI initiatives will eventually bleed into other areas of business, internally and externally. “In the enterprise, the target for democratization of AI may include customers, business partners, business executives, salespeople, assembly line workers, application developers and IT operations professionals.” With more people realizing the benefits of machine learning in particular, we can expect potential for more AI-related learning, problem-solving, and even jobs.

Cybersecurity will enhance the remote workforce

Last year, many organizations were forced into a decentralized workforce in a matter of days. This unanticipated shift pushed these organizations toward new technology implementations that ensured information security in a very short time. More than ever, safety protections are essential for physical employees as well as remote operations. McKinsey notes that “as employees became comfortable working from home, companies began standardizing procedures for remote work environments and explored technologies to reduce long-term risk.” This year, enterprises will further strengthen their cybersecurity efforts in response to the increased vulnerabilities exposed via use of non-secure networks and devices by the growing size of the virtual workforce.

Ethical and responsible AI gain attention

As machine learning becomes more prevalent in day-to-day business, the conversation around data privacy and ethical uses of AI gains momentum. The topic of AI ethics is no longer a subject of discussion for only major universities or nonprofit organizations. Enterprises are becoming fast aware of the issues pertaining to mass aggregation and analysis of personal and sensitive data. The benefits of unlocking data to make smarter business decisions or reduce errors in operations comes with the added responsibility of protecting data in ways that do not cause reputational, moral, or regulatory harm. Major companies have already had to face backlash for not providing a clear outline of their data collection and processing standards. “Companies need a plan for mitigating risk — how to use data and develop AI products without falling into ethical pitfalls along the way.”

At Entefy, we are bullish about AI and how it will transform the way we work and live. 2021 promises to be an important year in our collective journey toward the intelligent enterprise. Be sure to read our previous blogs on enterprise AI and the “18 important skills” you need to bring it to life.