For companies that are committed to creating Digital Transformation (DX) within their organizations, artificial intelligence (AI) is a critical component. The data that is created in DX initiatives has limited value if an organization can’t extract valuable, accurate, and timely insights from it. That’s why enterprise organizations are using AI technologies to pull actionable value from its data; in fact, by the end of 2019, 40% of all DX initiatives will be related to AI.
Four months ago, IDC launched its IT/OT Convergence Strategies program, and since then both end users and technology vendor engagement around the topic has been outstanding. These engagements have happened across the board: with IT leadership, operational technology (OT) leadership, and relevant business leaders all in some manner participating in the IT/OT convergence enablement ecosystem.
AI is at the heart of digital disruption; By end of 2019, 40% of DX initiatives will use AI services and AI will be the technology that will propel DX. By 2021, 75% of commercial applications will use AI; By 2022, 75% of IT Ops will be enabled by AI; by 2024, By 2024, AI-enabled interfaces will replace 30% of today’s screen-based applications; By 2024, 7% rise in AI-based automation will drive new wave of business processes.
Face it, you are paying attention to robotics. Maybe it’s as a curiosity, maybe it’s out of due diligence, or perhaps your interest stems from a realization that robotic technology has become useful in ways well beyond the expected use. Regardless of your interest in robots, the fact is, robotic technology is quickly expanding beyond the realm of industrial automation and has steadily been making its way into new industries and use cases. As this technology expands into new areas, it is important for companies developing business applications, IoT and analytics platforms, and systems integration to pay attention and look for opportunities to capitalize on a new and growing market.
In October 2018, a Reuters article informed the world that Amazon had scrapped an AI–based recruitment application that turned out to be biased against women. Most headlines about this story highlighted the company’s failure in developing an actionable and fair solution for one of the most important processes of the HR team.
However, what this and similar examples of today’s AI “failures” neglect to acknowledge is the complexity of end-to-end process automation based on AI technology. This complexity stems not only from current technical limitations but also from the immaturity of corporate policies, government regulations, and legal systems to deal with machines that automatically analyze, decide, and act.
No one questions that companies should do more to get a better Return on Investment in Data (ROID) – but…
With PTC’s $70M acquisition of generative design software vendor Frustum announced today, and the continued focus on expanding generative design capabilities by Autodesk, Dassault Systèmes, and Siemens, manufacturers have multiple options for AI and machine learning-infused CAD and CAE (i.e. simulation).
More than 3 years ago, I wrote a IDC Community post, “Using Robots to Curb Labor Shortage in Chinese Manufacturing” highlighting a factory in China that replaced 90% of the people in the factory with automation and robots. In that case the workforce was reduced from 650 employees down to only 60 people, those remaining were doing drastically different work than those jobs that were replaced. The jobs shifted from manual labor to oversight, maintenance, and support of the automation and robotics systems.
The proliferation of data types and quantities should be a major advantage for enterprise organizations. More data and more types of data should offer complex insights into challenges and opportunities in how the business runs and should lead to better decisions and business outcomes. However, ask any data analyst, and they’ll share this reality: data analysts spend a bulk of their time on search, data preparation, management, and governance activities, and not on data analytics where the true value lies.