The adoption of AI to drive greater automation provides impactful and measurable results, but that doesn't mean it comes without challenges. More often than not, your systems may be dated and not quite compatible with the visions of artificial intelligence dancing in your head. There is also the ongoing concerns that bringing AI and related technologies into businesses, people will be out of work. So, to ensure your organization has fully thought through the future adoption of AI and automation, here are five considerations to start you on the path to success. It goes without saying, but the adoption of AI will not only change the technology your team is using but also how they operate together and with your technology stack.

The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, Data Scientist on Oracle's Information Management Platform Team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that lies with using machine learning in a big data environment.

ORGANIZATIONS have been obsessed with artificial intelligence (AI) for the past 18 to 24 months, believing it can provide them with a strong competitive advantage and a key differentiator in a crowded marketplace. As a result, the world has spent nearly US$24 billion on AI and cognitive technologies -- and forecasts suggest that spending will reach US$77.6 billion by 2022, growing at a CAGR of 37.3 percent between 2017 and 2022. Obviously, with all the attention and investment in AI, you tend to think that executives would have a clear idea of what AI really is. A new report suggests, however, that most executives don't. AI is a disruptive technology because it has the potential to boost efficiencies and cut down costs almost overnight.

The rewards of machine learning can be compelling, and it may make you want to get started, now. At the same time, however, you'll want to consider machine learning challenges before you start your own project. This article isn't meant to scare you away; rather, it's meant to ensure you're prepared and that you're carefully thinking about what you'll need to consider before you get started. We spoke with Brian MacDonald, data scientist on Oracle's Information Management Platform team, about the pitfalls he's seen and what companies can do to avoid them. The biggest difficulty, of course, is the skills gap that comes with using machine learning in a big data environment.

Today, many businesses have entered the "innovation imperative" -- an executive mandate for the company to launch the next wave of innovation in their industry, all while maintaining and improving business with their current customers. I work on a team at Epsilon tasked with corporate innovation within our agency services discipline. Across the agency, our innovation team is charged with leveraging research and data in all its shapes, sizes and types to figure out what makes consumers tick so we can help our clients connect with them through smarter marketing and more effective creative. For example, we've recently introduced machine learning as a new accelerant to our data-infused creative process. Innovation really is key for any company seeking to proactively thwart disruption to their business model, operations or products.