The excitement about artificial intelligence (AI) is contagious. Our imagination has been ignited to its potential in a wide range of fields. In marketing, however, many have concluded that the hype is just that: hype. There may be potential, they say, but it is far too hard to implement AI
On a recent trip I bumped into some old colleagues from a previous company, and we began to discuss what we were working on and spoke of how quickly – in only a few years – innovation and technology have come on. And this is because innovation waits for no
Since the rise of the industrial revolutions, there has always been a very active debate among the academic community confronting “technology innovation optimist” and “technology innovation pessimist”. Likewise, there is debate whether artificial intelligence (AI), like many previous technologies could have a positive or a negative impact on the economy,
In the Cologne district of Mülheim, not far from Keupstrasse with its oriental charm of doner restaurants, baklava, cafés and mosques, and next to old brick halls, concert halls and television studios, lies Carlswerk, a large former industrial area that has been transformed by the arrival of both new and
Beyond the wow around artificial intelligence is the reality. If you don’t use AI to make actual business decisions, it’s probably a waste of time, effort and money! From recommendations for your playlist to the next-best offer to decisions about customer targeting and company strategy, we are all looking for
Twelve years ago, in 2006, Time magazine made an interesting move in deciding on its “Person of the Year.” Instead of one of the many eminent scientists, world leaders, or stars of stage and screen who had influenced the world that year, Time chose to recognise a social phenomenon. It
When I ask people what they think the Internet of Things (IoT) is all about, the vast majority will say “smart homes,” probably based on personal experience. If I say that it is also about industries making using of data from sensors, then most people’s immediate reaction is to think
It is a truth universally acknowledged – if not always acted upon – that advanced analytics, and AI in particular, need data. What’s more, it needs lots of data, and it needs to be relevant and of good quality. Companies with access to more data tend to perform better, which
In my previous blog post, I talked about how both retailers and consumers can benefit from applications of artificial intelligence and discussed some compelling use cases. I would like to take this post a step further and delve into a particular bugbear of mine: the struggle to find the right size. In
Much of the discussion about AI centres on what the algorithms can do, as well as the potential for change and/or disaster. But what about the team behind the algorithms? Few discussions have focused on this most essential group, and not just the modellers, but the data sources, the trainers,
