Innovation: Staying ahead of the curve
Businesses are born out of innovation, but today with disruptive technologies, the average lifespan of a company has dropped by 77%, from 67 years in the 1920’s to just 15 years.
Innovators that defy this often talk about their culture – highly trained staff that are empowered to make good decisions (and no expectation of perfect ones). What they don’t talk about is how they integrate longer term views of socio-economic trends, technology innovation and market changes into their solutions that give competitive advantages.
Kainos, celebrating it’s 30th birthday last year, recognises that looking forward for our customers is important and one way that we do this is through investment in our Applied Innovation team. The team works with a wide variety of customers to bring new innovative technologies to market quicker. One technology strand that has become an increasingly important part of our innovation strategy is Artificial Intelligence.
Can manual tasks be replaced by intelligent machines? Can user experience be enhanced in real time by an algorithm? Or can businesses learn from their usage patterns to cut energy bills by 40%? Here, I want to explore the technologies that make all of this possible and how we have utilised them.
With interest accelerating over the past few years, these are among the most popular buzz words at the minute on every media platform, as shown by their popularity on Google Trends:
But are they just buzzwords? No. We all come into contact with these technologies every day from music recommendations on Spotify to something so simple as searching on Google.
Ok, but what do they mean?
Artificial Intelligence (AI) is the simulation of human intelligence by a computer system/machine. They are programmed to mimic human actions and thoughts, but how? This is where machine learning comes in. Machine learning (ML) is a subset of AI, allowing computers to learn by themselves, without being explicitly programmed. This is a different approach to traditional software systems where us humans would define a set of rules for a system to follow in order to turn an input into an output. E.g. For an insurance system, if the user inputs an age under 18, multiply the premium by 2.
Now, with ML, systems can take in inputs and expected outputs and learn from these to create these rules by themselves.
AI and ML are being used by the big tech companies to develop cool new tools: from facial recognition for overlaying puppy ears on your face to stitching multiple concert videos from different perspectives into one.
But what can it do for businesses outside of the technology sector?
All business in all sectors, be it retail, financial or insurance, have large amounts of data about their products, services and customers, and that data is growing exponentially. Many companies are performing analytics on their data, finding trends in past data in order to aid strategic planning for the future. However, we can take this one step further by harnessing machine learning techniques to predict future trends and identify non obvious patterns in the data.
Are your Facebook photos data?
Although sometimes internal data isn’t enough on its own. With many comparison sites around, it’s more likely that a user will shop around first before visiting a single provider’s site. Therefore, it becomes increasingly hard for a provider to build a user profile for each of its customers.
Facebook holds a lot of data about you, but possibly the most valuable data that could be overlooked is your photos. One idea our innovation team worked on is a travel recommendation tool. By analysing a user’s Facebook photos (with their permission of course), it can curate a list of keywords from the photos such as ‘city’, ‘beach’, ‘mountains’ etc. Feeding these into an ML algorithm can then predict the type of holiday best suited to this user. This allows a travel agency to display more tailored recommendations to the customer when they visit their site.
In fact, keyword searching is becoming a thing of the past now. It doesn’t give as much context to express our intent as searching by voice or photos (like ASOS’s visual search functionality to find similar clothes), or in the case of this solution – not having to search at all!
We are about to, if not already, experiencing a technological shift with the rise of Artificial Intelligence and Machine Learning. It will transform technology, society and all industries, so it is vital that businesses incorporate these technologies into their strategies now.
Being aware of data is one thing and analysing data is another, but harnessing AI and ML techniques to improve customer experience, cut costs, improve efficiency (the list of solutions goes on…), is the key to utilising the mass of data we have available.
Businesses reacting now will be the industry leaders, staying one step ahead of competitors.
Kelly joined Kainos in 2014 in her placement year as a Software Engineer. She has worked as part of the Applied Innovation team and has lead Kainos AppCamp and AICamp. Currently, she is working with machine learning technologies.
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