Artificial intelligence (AI) is poised to transform the way that marketing professionals work, and how organizations target, engage and connect with customers and prospects. Just like how marketing automation created new tasks and job functions, AI will revolutionize the way marketing is performed – and dictate a new set of job needs and skills.
We’ve discussed how the term artificial intelligence (AI) covers a wide array of applications; just like many of these functionalities, affective computing is beginning to see some growth in the market. Spanning across computer science, behavioral psychology, and cognitive science, affective computing uses hardware and software to identify human feelings, behaviors, and cognitive states through the detection and analysis of facial, body language, biometric, verbal and/or vocal signals.
Artificial intelligence (AI) adoption is at a tipping point, as more and more organizations develop their AI strategies for implementing the revolutionary technology within their organizations. However, there are still major challenges to AI adoption; in fact, cost of the solution and lack of skilled resources are cited as the top inhibitors of adopting AI.
While we’ve discussed the disruptive power that artificial intelligence (AI) applications bring to enterprise organizations, the truth is that AI adoption is still low for these businesses. However, adoption level is at a tipping point; investment in AI has tripled, and recent technical innovations promise to make AI not just an underlying technology capability, but a fundamental business tool. Here are just three of the technical innovations that enterprises can use to better leverage the disruptive power of AI:
In an earlier blog about the Future of Work, and in a recent IDC Perspective, we presented IDC’s view of the Future of Work and offered a framework that provides a way to approach and scope the organizational, policy, and technology changes required to leverage this opportunity in a holistic manner. In this blog, we’ll take a closer look at the growing role of technologies like artificial intelligence (AI), robotics, IPA, and augmented and virtual reality (AR/VR) in automating and augmenting the tasks and processes traditionally accomplished by human workers. We’ll also explore how organizations are planning to acquire the skills required to leverage the opportunities for automation and human-machine collaboration.
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.
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.