AI is here and is only going to grow. Almost two thirds of businesses have either implemented or are implementing AI, with spending on it expected to hit over $47 billion by 2020. Indeed, former Google executive chairman, Eric Schmidt, has suggested that machine learning or AI will be the one commonality between all big start-ups in the next five years. In other words, AI will become as commonplace as computers themselves.
That means businesses that haven’t yet made a case for implementing some form of AI risk getting left behind. However that doesn’t mean jumping in blindly. That only leads to disaster. Implementing AI needs to be done carefully and with meticulous preparation if it is to add real value.
Where AI can help
It helps to start by considering what systems and processes AI can have the most effective impact on within your business. The most common implementations so far can be divided into three categories.
- The first is process automation. Automating back office administrative and financial tasks saves people-hours and streamlines processes, ultimately saving money and human error.
- The second is big data analysis — using machine-learning algorithms to detect patterns in vast volumes of data to provide new insights.
- The third is natural language processing (NLP) in the form of chatbots and virtual assistants.
Implementing — the groundwork
Once you have decided which of these areas your business will most benefit from, it’s time to consider a plan for implementation. First you need to set clearly defined goals or problems you want AI to solve. Once you have set a clear goal that will add real business value, you need to make sure your data is ready. In order for AI to go to work effectively, data needs to be cleaned and integrated, not siloed across disparate departments and legacy systems. If you don’t have one, you might consider bringing in a data scientist for this step.
It’s also essential to consider how and where data can be accessed and how this data is organised. Better and larger training sets of data results in more accurate models for the AI to learn from. Your data needs to be well-organised to enable your investment to make a return quickly.
First steps and full integration
When you have the groundwork in place, it’s time to start implementing. Begin with a small pilot project. Bring in external AI experts to work with a dedicated internal team. Focus on a small straightforward goal that is a stepping-stone towards larger implementation. If this is successful you can move onto full implementation, but again start small. Use AI incrementally to build value and provide feedback before expanding into daily processes across departments or even the business as whole.
Automation is the future
AI is already saving half a billion people an estimated two hours a day. With a possible 43% of processes ripe for automation in finance alone, that is only set to increase. With the combination of new advanced sensors and the IoT, the potential for AI is almost limitless.
At Sciant, we have been working with industry system providers to implement AI and Machine Learning to automate data processing. The hospitality and travel sector has improved the performance of data to optimise revenue through more personalised consumer offerings. This has been achieved by connecting data from across revenue management, property management, central reservation and customer relationship management systems — where AI can identify patterns of behaviour and market activity and present more accurate predictions.
In transport and logistics, we are seeing the benefits of applying AI and Machine Learning to a sector working with disparate platforms that work within a single ecosystem across different businesses and locations — placing greater need on businesses to update and transform their technology to remain agile.
We need to start implementing now to make sure we are in a position to take advantage of that potential, not left behind wondering how we missed the boat.