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.
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
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.
5. How AI is revolutionizing financial services
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.