This week Sundar Pichai, the CEO of Google, announced that his company's internet search engine -- the way the vast majority of humans interact with a near-total corpus of human knowledge -- is about to change. Enter a query, and you'll get more than pages and pages of links, along with a few suggested answers. Now you'll get an assist from artificial intelligence. "Soon," a Google blog post under Pichai's byline declared, "you'll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web." A chatbot named Bard will deliver search results in complete sentences, as a human might.

The nature of a successful recruiter has always been the ability to match the right candidate to the right role. Being able to spot the key skills, and knowing if a candidate is the right fit for a companies' culture, is what sets the top billers apart from their colleagues. As humans, we're able to make these judgements based on facts which are often combined with a recruiter's second sense. Is Artificial Intelligence capable of replicating this very human trait? The simple answer is we don't yet know what AI will eventually become capable of, but for my money, humans will remain the key cog in recruiting, and AI isn't yet, able to teach itself without human input and development.

Regularly receiving queries about Artificial Intelligence is of course flattering, but can also be bothersome, as some of the most regularly asked questions could easily be answered via a quick Google search. A number of people are however wary of doing so, partly because of movies' unrealistic image of A.I (among other unrealistic expectations), and the current startup culture which venerates coders and techies, putting them and their knowledge on a pedestal. With that in mind, below is a simple, quick and no-nonsense look at A.I through the lens of the questions I most frequently get asked. A.I is not a mere constellation of processes and technologies, but also a business topic and an academic matter, while also being an ethics concern (among other things). I'd argue that to keep things very easy you could define A.I in three ways: I mean, yes, of course.

Since neuromarketing is such a new concept, most marketing and data people are still asking "What is neuromarketing?". When a person with a marketing background asks me this, I try to answer with an example instead of a technical definition. I might say something like, "Well, with a real-time neuromarketing strategy in place, you can use web data to identify how your customers feel in response to a stimulus. Then with this information, you can create marketing assets that evoke positive emotional responses within your customer base, in order to prompt your audience to fulfill your call to action." To be more precise, however, neuromarketing is a marketing approach that's aimed at capturing customer behavior from huge volumes of data taken from sources like: Neuromarketing requires big data resources to support both batch and real-time analytics.

Access to relevant customer and industry information is the primary competitive advantage businesses have over their direct and indirect competitors today. It's the smartest approach to remaining vigilant in a business environment where competition is at an all-time high. That's where data warehousing comes in. Data warehouses are central repositories of integrated data from one or more disparate sources used for reporting and data analysis, which--in an enterprise environment--supports management's decision-making process. Digitalization is integrated into the foundations of today's business landscape, and there is no going back from here.