Skip to main content

Cognitive System Users will Reach One Million by 2022

Large enterprises have an asset that many SMBs do not. They are in possession of massive quantities of historical and current data. In addition, the business case for employing cognitive systems to analyze that data is more compelling, when compared with smaller companies.

ABI Research predicts the number of businesses adopting artificial intelligence (AI) technologies worldwide will grow significantly, up from 7,000 this year to nearly 900,000 in 2022 -- that's a CAGR of 162 percent.

AI is now making significant strides in cloud processing, storage capacity, and machine learning algorithms to enable cognitive systems and robotics to surpass people in performing some key tasks in manufacturing and other industries.

AI and Machine Learning Market Development

Increasingly, businesses are applying these emerging technological advancements to deliver various forms of automation and innovation that will eventually equal or exceed human capabilities.

"Even though nearly one million businesses will adopt AI by 2022, it will not be a great fit for every company," says Jeff Orr, research director at ABI Research. "Many businesses will have to adapt their corporate governance policies to deal with the lack of a guaranteed outcome when implementing machine learning."

While most enterprises start using machine learning to analyze their existing business data for insights, the technologies have far-reaching application in specific industries -- ranging from reduction of false positives in fraud detection, to powering conversational interfaces for chatbots and virtual assistants.

While some of the world's largest and innovative enterprises -- such as American Express, Coca Cola, Netflix, PayPal, and Uber -- already deploy projects powered by machine learning, ABI Research finds that not all organizations will likely benefit from these cognitive system technologies.

According to the ABI assessment, progressive organizations that are comfortable with uncertainty in outcomes and measuring changes in key performance indicators (KPIs) will find the most to gain from enacting machine learning projects.

On the other hand, more traditional companies that focus only on ROI timetables will find emerging technologies -- including machine learning, cybersecurity, and IoT -- to be somewhat frustrating to implement and difficult to measure.

Outlook for Machine Learning Apps

Several SaaS solutions are available for machine learning and businesses looking to experiment will have many vendors to choose from in the near future.

Best practices include starting off with a pilot project, and requesting case studies about enterprises that have already gone through their first operational deployment.

"It is the companies that choose to ignore AI entirely that will quickly find themselves at a competitive disadvantage," concludes Orr.

Popular posts from this blog

Hybrid Work: How to Enhance Employee Productivity

When you hire qualified talent for a key role and trust them to perform, you'll likely achieve the best outcome. Skilled and experienced people will deliver results, regardless of the challenges. That's a key lesson learned from the pandemic experience as most knowledge workers were asked to work from their homes. However, some resist returning to an open-plan office. It's unacceptable. Meanwhile, forward-thinking leaders decided a "return to normal" is undesirable, and in hindsight, everyone should aspire to be more accomodating than before. Therefore, location flexibility is okay. Hybrid Workforce Market Development How will people adapt to these changes? They'll apply the modern IT tools at their disposal. They'll learn new skills and thrive. Nearly 80 percent of employees are now successfully using online collaboration tools for work in 2021 -- that's up from just over half of workers in 2019, according to the latest market study by Gartner. This g

Mobility-as-a-Service Creates Disruptive Travel Options

Building on significant advances in big data, analytics, and the Internet of Things (IoT), more innovative transit service offerings aim to increase public transport ridership and reduce emissions or congestion within metropolitan areas. By providing these services through smartphone apps, the transit services also significantly increase user convenience, providing information on different human mobility offerings -- including public transport, ridesharing, and autonomous vehicles. Mobility-as-a-Service Market Development According to the latest market study by Juniper Research, Mobility-as-a-Service (MaaS) subscribers will generate $53 billion in revenue for MaaS platform providers by 2027 -- that's rising from $5.3 billion in 2021. Let's start with a basic definition. MaaS is the provision of multi-modal end-to-end travel services through single platforms, by which users can determine an optimal route and price. The study identified a monthly subscription model as key to incr

Upside for New 5G Network Transport Infrastructure

The global mobile communication sector is in the midst of a significant network infrastructure upgrade to support the introduction of new high-bandwidth and low-latency broadband service offerings.  Telecom service provider data centers have an important role in fifth-generation (5G) network deployments. Providers undergoing their transition to Stand-Alone (SA) 5G must understand the technical demands of telco data centers and the key enablers of those offerings. According to the latest worldwide market study by ABI Research, the major prerequisites of 5G and the emerging transport solutions would help operators position themselves to successfully capitalize on the new revenue opportunities from delivering differentiated 5G connectivity services. 5G Transport Network Market Development "The rise of the telco data center has a high degree of confluence with the requirements of SA 5G architectures. SA 5G and its increasing reliance on telco data centers can be attributed to the incr