There is no doubt that the need for companies to get serious about digitally transforming their business and production processes is critical to maintaining their competitive edge. The customer is driving the need to be able to get to market faster with products that are competitively priced with the features and capabilities they demand. Artificial intelligence (AI) is at the core of being able to realise success. The results of a Gartner global survey of over 3,000 CIO's confirms the importance of AI focused initiatives. Compared to a baseline established in 2015, this decision to implement AI is up 10%.

Over 1,000 attendees, 50 speakers and 30 exhibitors; this is a brief summary of what I was lucky enough to take part in during the first AI Europe 2016 conference held in London on the 5 and 6 December. The attendee list boasted the biggest names from the world of artificial intelligence such as Microsoft, Dell, Uber, Samsung and Nvidia, as well as several innovative start-ups, the likes of Blippar and DreamQuark whose innovations are based on machine or deep learning models. Even if we can say with a degree of certainty that further advances in artificial intelligence are yet to come, leading players are in agreement that most AI techniques and technologies are now well-advanced. Therefore, their major preoccupation today is more about the quality of the data sets being used to train and validate their machine and deep learning models. Whether it's Dell or Uber, Microsoft or Blippar, they all have one thing in common: they all agree on the fact that as of now, the quality of the data used in AI for machine learning is of the utmost importance.

Artificial Intelligence is a technology that uses human-like learning to perform tasks. The idea of Artificial Intelligence or AI is nothing new. As a concept it has been in our literature and art for centuries. But these ideas had no foundation other than as philosophies of nature and science fiction. Connectionist paradigms of Artificial Intelligence have a somewhat more flexible approach than their rule-based cousins, but both have applications in modern technologies. Machine learning is based on artificial neural networks, which are a much-simplified version of how our brains work.

So how could scalable retail artificial intelligence in the cloud – Brand AI – potentially turn these challenges into unique opportunities for competitive advantage during the next five years? But unlike today's arguably bland, soulless smartphone versions that focus on delivering simple functionality, Brand AI would have a unique, human character that reflects the retailer's values to inform its interactions and maturing relationship with an individual customer. Intended to be more than another'digital novelty', this disruptive form of customer engagement builds on and enhances a B&M's traditional brand as a trusted long-term friend throughout the entire customer journey by offering compelling, timely presale insights, instant payment processing and effective after sales support and care. A customer is empowered to select what personal data they choose to share (or keep private) with the Brand AI to enrich their relationship. Social, location, wearable or browsing and buying behaviour data from complementary or even competing retailers could, potentially, be shared via its cloud platform.

What is the relationship between artificial intelligence and cybersecurity? One certainly strengthens the other. Thanks to "machine learning", which is already well known among researchers, AI makes it possible to tackle security in a different way, better suited to the changing context of cybercrime, with greater anticipation and through behavioural analyses. Applications have already been released by laboratories, particularly via open-source libraries, however new skills are required. "By improving its knowledge and understanding of the phenomena by itself, machine learning enables an attack to be detected even if it is not familiar with the signature."