Ubiquitous Artificial Intelligence

Photo by Michael Dziedzic on Unsplash

As I make my way into the kitchen to make my first coffee of the day, I ask Alexa to play Gabriella Quevedo. A moment later, gentle guitar music fills the room.

In that fraction of a second, the software in my Bose smart-speaker detected the wake word Alexa, recorded my voice and sent it over the internet to Amazon’s servers, where Amazon Voice Service converted my speech to text, interpreted my request, correctly identified what I’d asked for and contrived for Spotify to play Gabriella’s music for me, on my speaker, in my kitchen.

It’s nothing short of miraculous that Alexa gets this one right. There are an infinite number of ways to form a sentence and I’ve yet to meet another human being who can understand my pronunciation of Quevedo, let alone intuit what I’m on about.

Alexa pulls off this amazing feat through the magic of AI. Well, OK, it’s not magic. It’s a combination of Natural Language Processing and Natural Language Understanding (inferring not just what I said, but what I meant). But as Arthur C. Clarke said, “Any sufficiently advanced technology is indistinguishable from magic.”

The magic of AI has become ubiquitous. It targets us with ads in our Facebook feeds. It powers Alexa and Siri. It recommends films for us to watch on Netflix and music to enjoy on Spotify. It helps drive our Teslas, optimises the routing of our Ubers and supervises our Amazon deliveries.

Clever automation no longer surprises us. We’ve come to expect a level of intelligence. From mobile banking to developing vaccines, AI is powering new ways of working and new business models that would previously have been seen as too complex to contemplate.

Machine Learning

The flavour of AI at the heart of almost all commercial applications is Machine Learning. This is the process whereby computers are trained on multiple sets of data in order to improve their decision making, in much the same way we improve our own decisions.

This training may be done using specially created data sets or through iterative exposure to real world examples. It may be supervised or unsupervised:

  • In supervised learning, the computer is trained using data which has already been labelled. This generally produces the most accurate results and is particularly useful when using data from prior experiences to reinforce or improve the learning.
  • In unsupervised learning, the computer is left to find its own patterns in the data. This is useful when dealing with large amounts of unstructured, unlabelled data in real time. It requires less human intervention but is generally less accurate than supervised learning.

Whatever the approach, the end result is a computer system that can do four things really well – faster and better than any human:

  • Ingest vast amounts of complex data from multiple sources;
  • Analyse that data using powerful algorithms;
  • Develop actionable insights from this analysis;
  • Learn from the results achieved and improve the analysis.

Intelligent Automation

Over the last three decades, our business processes have become increasingly automated, starting with simple workflow and evolving through robotic process automation to the point where machine learning can be used to optimise processes in ways that far exceed our own abilities.

This latest form of AI-enabled process automation is referred to as Intelligent Automation. What sets it apart from simpler forms of automation is the way information is used to optimise the process and make decisions earlier than ever before. To give one simple example (a project carried out by one of our member firms):

  • A major bread manufacturer uses Intelligent Automation to dramatically reduce food waste. Information on express orders from supermarkets is combined with other predictive data to provide an early demand signal to the bakery, reducing the amount of excess bread produced by more than 50%.

More generally, wherever there is a large amount of complex data that can be used to inform high-value decisions, there is an opportunity to use machine learning to improve those decisions and drive value for the business and its customers. For example (again from one of our member firms):

  • One of the world’s largest online shopping sites used Intelligent Automation to build a recommendation engine that ingests more than 1.2 billion listings each month and generates more than 200 million recommendations that help sellers on the site optimise their listings. The result is a three-fold increase in sales for those sellers.

More advanced forms of machine learning, such as neural networks (collections of algorithms modelled loosely on the human brain), can be used to automate processes that rely on recognition of images or patterns. These are particularly useful in applications that involve natural language processing or image recognition. For example:

  • A major telecoms provider (again working with one of our member firms) has built and trained a neural network to encode customer enquiries and determine their underlying emotional content, with an accuracy rate of 75%.
  • A horticultural grower in Holland (working with an innovative agritech start-up) is using a neural network to generate insights into early-stage plant growth from an automated imaging system. These insights help the growers increase yields.

Even more advanced forms of machine learning involve the use of layers of neural networks each extracting a higher level of insight. This is known as deep learning. These techniques are applied in a range of applications, from the machine vision used in self-driving cars, to drug design, to the famous AlphaGo board playing AI. They are also being used in a business context, where they provide the ability to process ever more complex data with increasing accuracy. For example:

  • A leading B2B CRM vendor uses deep learning to generate sales forecasts for its clients, with greater than 95% accuracy.
  • A major international bank uses deep learning to identify financial fraud and money laundering.

Where Should You Apply AI in Your Business?

The short answer is wherever you have processes that involve large amounts of complex data that can be used to drive critical, high-value decisions.

If you can see areas of your business where you have high volumes of data and high-value decisions then there is most likely an opportunity to generate value from data and machine learning. A good example would be the use of real time claims data to inform dynamic policy pricing in the insurance industry.

A good starting point, when looking for the best opportunities to apply AI, is to ask the following questions:

One. Where do we have people using logic or judgement to make decisions?

Two. Are these decisions high value or high impact for us or our customers?

Three. Do these decisions involve the use of data from multiple sources?

Four. Do other sources of data have potential to improve these decisions

Five. Is there a high cost to mistakes or bad decisions?

Six. Are the processes high volume or repetitive?

Seven. Do we have bottlenecks in terms of capacity or skills?

How should you get started?

Many successful AI projects start with a data study or proof-of-value experiment to test the underlying hypothesis and build the first iteration of the data model and algorithms. Indeed, one reason why so many AI projects flounder in their early stages is a failure to get to grips with data that is largely siloed and unstructured.

The skills and experience needed to do this are in very short supply. A combination of machine learning and data science expertise is a prerequisite for successful AI projects and people with these skills command a hefty premium in the job market. There is also a very real risk of reinventing the wheel if the team are not experienced with the extensive libraries of datasets, models and algorithms already in use.

For this reason, we strongly advise our clients to engage with niche firms who specialise in Applied AI (data and machine learning) and Intelligent Automation and who have these skills and experiences in depth, across many industries and use-cases. Many of these firms also have proven platforms on which to build solutions, rapidly and reliably.

Conclusion

There is a paradox inherent in artificial intelligence: once a difficult AI challenge has been mastered, it is no longer considered an AI challenge. Over the last few years, we have become so accustomed to the use of AI in our lives that we no longer regard it as exceptional.

In much the same way, AI is becoming embedded in our business systems and processes both directly and indirectly, because so many of our processes are now carried out by SaaS software in the Cloud, the natural home of data and machine learning.

Nonetheless, AI remains something of an enigma to many executives and the overwhelming majority of in-house AI projects fail to deliver. The solution is to reach out to the experts for help and advice.

If you would like to learn more about the examples in this article or the expert firms involved, please do contact us at innovation@clustre.net

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