We’ve discussed the need
to redefine enterprise intelligence and what the future of intelligence
will look like for technology and business leaders, and we know that
organizations are paying attention. In fact, enterprises spent $200 billion on
data, analytics, and AI software, hardware, and services last year. That
doesn’t even include the investment in external data and internal labor costs
to further fuel intelligence initiatives.
What comes to mind when you think of intelligence within your organization? Is it having access to the latest information on key metrics, such as revenue, costs, and profit? Is it a broader view of ‘all information’ you need to make a decision?
IDC has been using the phrase “data intelligence software” to describe a category of capabilities that provide intelligence about data, and the term “data intelligence” has caught on in the industry. But not all definitions of data intelligence are equal. Let’s take a closer look at how IDC defines the term, and some permutations that have emerged.
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.
