Artificial Intelligence, and Machine Learning in particular, have without doubt become the hottest topics of discussion throughout the business and technology worlds.
Development, breakthroughs, benefits, ethics, data privacy – there is so much information being published almost daily about AI.
Which naturally makes it confusing for those taking their first steps into discovering how and where AI might make a difference to their business.
To help, we have tried to distil all the AI commentary from the last few months to just the five most useful articles. These handpicked stories provide an overview on AI, guidance on how to implement and scale it across your business, and then some specific examples of how it is transforming financial services – one of the industries that we feel stands to gain the most from the use of machine learning.
Combined with our material on how to discover where in your business would stand to gain the most from AI, these five articles give you a complete overview on how to use machine learning to catch up with the competition.
- The State of AI in 2019
AI is exciting; it’s evolving by the day; the capacities are seemingly endless. Has all the noise around AI softened the impact of this great revolution?
James Vincent’s article dispels the misunderstandings surrounding AI and shows how broad the term is, from smart homes to healthcare, all the way to improving business processes. He explains the process of machine learning and how it is a fundamental subfield of AI, and highlights the amusing misunderstandings of its power and dangers.
Moreover, while The Verge shamelessly re-uses the much-repeated example of using machine learning to recognise images of cats, the point James is making is serious – automated decision-making can give businesses a competitive edge. But only provided it is deployed correctly, with suitable respect to data privacy, bias and sensible business objectives.
This useful article highlights the dangers of not addressing these fundamentals, and concludes by quoting Kai-Fu Lee, a renowned AI researcher, that we are currently in the “age of implementation” – only emphasising that those who are not investigating how and where to implement AI to improve business processes’ accuracy and effectiveness will soon be left behind.
- Seven ways to jump-start AI
With all the PR about how AI can change your business, it’s unsurprising that businesses are jumping headfirst into this technology. However, for many, this haste has meant the projects have not all been plain sailing, resulting in them meeting more challenges than advantages.
We see this problem in many of the businesses we speak to about AI, where they have previously tried to deploy machine learning into business processes, but have not seen the benefits they expected. This is why we liked this article from Information Week, as it describes a business’ more pragmatic route to jump-starting an artificial intelligence project. All the speed, but less of the haste.
The article provides seven key lessons, based on the barriers organizations have faced when implementing AI without sufficient prior consideration; from ensuring you have mapped out exactly the business challenges you want AI to address, to ensure you have the right team in place, the right data, and buy-in across the company.
These steps echo exactly our own thoughts and practices for deploying AI, which is why our AI Value Discovery Service has been designed to discover where the technology will be most impactful to your business, whilst addressing the obstacles that would otherwise derail the project. For more on the thinking behind this service, and a sneak peek of the process it goes through, download our free white paper here.
- Five takeaways on scaling machine learning
According to a recent Gartner survey, 37% of organizations have already implemented AI into their day to day business, with many other businesses looking to introduce the technology. This article from InfoWorld highlights the ways that large organizations like Facebook and Twitter have maintained the advantages machine learning first gave them by scaling its use from a small number of uses cases far wider across the business.
Whilst it sounds daunting especially for SMEs who do not have the same resources as these two tech giants, or have even deployed their first project, it also shows smaller businesses how to make sure their first use case is not simply a “point solution”, is inherently scalable, and that maximum value is planned for from the outset
- How Artificial Intelligence is helping financial institutions
AI and machine learning has the ability to transform businesses within the financial sector, and this Forbes article discusses the competitor advantages the technology has to offer. From chatbots and personalised customer service to providing 24/7 banking services and preventing and detecting fraud and money laundering, AI is in widespread use protecting and serving the financial services industry.
However, the article does touch on a key barrier within smaller financial companies: the high salaries of AI expertise. This creates two trends – a tendency to look outside the business for experienced support, plus a lack of tolerance for AI projects that fail to add value.
Our artificial intelligence and machine learning services not only give smaller financial institutions access to this expertise, but our practical approach ensures that no technology is deployed before a clear financial case is scientifically discovered.
- How AI is revolutionizing financial services
Building off the Forbes article above that looks at where AI is currently proving valuable for financial services, McKinsey Global Institutes predicts that from the $5.6 billion that banks are expected to spend on implementing AI in 2019, the financial industry could see a return of upwards of $250 billion.
The additional angle this article covers is the potential compliance challenges businesses face when deploying AI, especially if machine learning is to determine credit risks for potential new customers. The main question being asked is whether AI’s output is transparent enough given regulators’ requirements for fully explicable decision-making – the so-called black box problem – which in turn leads to concerns over whether AI can truly be unbiased if it naturally dependent on the data it is given. Or more accurately, data that humans have chosen to give it. There is no silver bullet to this, but some solutions include ensuring the team managing the AI project is diverse, although this inherently requires an even greater salary spend.
These five stories provide an excellent primer for businesses investigating the opportunity that AI presents to their business. And the key theme across them all is clear: finding the right use case for your AI project is more than half the battle.
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