I am the founder and CEO of Apriorit, a software development company that provides engineering services globally to tech companies. Upgrading your product using top-notch technologies like artificial intelligence (AI) is often considered the key to gaining a competitive advantage. Even during the pandemic in 2020, 47% of organizations left their investments in AI unchanged and 30% decided to increase their AI funding, according to Gartner. AI can enhance processes across various industries. For instance, when applied in healthcare, AI can analyze thousands of MRIs and X-rays in minutes, helping therapists quickly identify abnormalities. In industrial production, AI solutions can improve quality control by processing a wide range of data from production lines, maintenance records and customer complaints.
Artificial intelligence continues to gather steam in the enterprise. But it hasn't reached majority status among enterprises. Several roadblocks to more widespread AI adoption remain -- some linked to talent and data and some to perception– says a recent report from Cognilytica, a research firm. By 2025, according to "Global AI Adoption Trends & Forecast 2020," 40% will have deployed AI in some form, compared with 12% in 2020. These numbers indicate that AI in production is still tentative, and enterprises are incrementally making their way toward the technology only as they see immediate return.
The emergence of artificial intelligence has completely transformed the workflow of several companies and factories. Society is reaping the benefits of smart functionalities of artificial intelligence through multiple different IoT devices. But it is interesting to know how non-AI technologies are helping in AI development efficiently and effectively. Developers are building AI with non-AI technologies to provide more time-efficient and cost-efficient artificial intelligence models. Let's explore the top four non-AI technologies that are essential for AI development in 2021.
During Aragon Research's research community meeting this week, Jim and I were discussing what it will take to manage your workforce in the future; a hybrid mixture of humans and digital labor. The discussion got us thinking about what it means to manage technology versus what it takes to manage people, and how this will change as organizations introduce AI-enabled technologies. In this blog, we explore the differences and similarities between managing humans and technology to understand how management will change as we introduce technologies that can learn, recognize patterns, and change/respond. Digital labor is a term that applies to the automation of tasks that are performed by computer applications. Our future workforce will be a hybrid combination of humans and AI-enabled technologies (i.e., bots, assistants, robotics, etc.), supported by traditional non-AI technology.