As artificial intelligence’s (AI’s) potential grows, so does the need for a cohesive AI strategy to leverage AI to prioritize and execute the enterprise’s goals. Aside from articulating business goals and mapping out the ways organizations can use AI to achieve those goals, there is another extremely important element that every AI strategy needs: a code of ethics.
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.
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.