Artificial intelligence will be the most impactful technology to affect businesses since groundbreaking inventions like the telephone, the personal computer, and the internet. It represents a seismic shift that has the potential to transform every aspect of modern business for the better, and we’re already witnessing some of those effects today.

And while this is a fascinating time for businesses, the question I always get is this: how do I start using AI in my business today?

So let me answer it. In this post, I’ll offer some practical ways that your business could leverage AI immediately. Not only that, but I’ll also discuss the kinds of skill sets and approaches that you’ll need to make it a success.

Ignore the major players in AI

We all know the technology giants who are leading the way with artificial intelligence. For companies like Amazon, Google, and Facebook, AI is crucial to their continued success. However, they also have billions of dollars to invest in research and development and are expected to be on the bleeding edge of innovation.

So one of the big questions is how does your business – which unfortunately doesn’t have billions in cash and a talent pool of the best and brightest minds in tech – actually make use of this game-changing innovation?

First thing’s first: stop comparing yourself to the giants. Even small businesses can leverage AI in a big way, but you need to adjust your expectations in-line with your resources and scale. Throughout the rest of this post, I’ll be looking at strategies any business can adopt.

Always start with the end in mind

This might sound obvious, but you must start with a clear goal in mind.

It’s easy to spend a lot of time and money racing down rabbit holes and not really getting anywhere. If you’re planning to leverage artificial intelligence, you need to have a concrete goal in mind: what is the tangible result you expect to gain? For example, this could be doubling your number of customers or reducing the time to purchase by a certain percentage.

While artificial intelligence can be applied in various ways within your business, it’s not a magic wand: it won’t instantly cure your business problems or teleport new customers to your doorstep. There’s a lot of hard groundwork to do which will lead to core changes in how your business operates – and then come the benefits.

It’s also important to have a strong awareness of the actual business benefits that similar companies have gained from artificial intelligence. With so much hype and buzz around AI, you need to ensure your goals are realistic and attainable. Tied in with this is the fact that there are considerable limitations on what can actually be achieved by leveraging AI.

So let’s take a look at the technology. There are two main types of AI used today: supervised and unsupervised.

Unsupervised Learning

Unsupervised learning involves mining existing data sets in ways which weren’t possible with previous data mining techniques. This uses algorithms like ‘clustering’ and ‘nearest neighbour’ to find new patterns (such as buying patterns or lucrative customer segments) that are surfaced using this AI technique.

Supervised Learning

Supervised AI is used to make predictions about new data. It leverages historical data to train the algorithm which, when pointed to new data, can infer key business insights.

For example, algorithms can be trained to detect whether or not a transaction is fraudulent. It can also be used for more open-ended applications, like the products that a customer is more likely to purchase based on various elements of historical data.

Organise and validate your data

The effectiveness of the algorithm is heavily dependent on the quality and integrity of your data. If the data is incomplete or full of errors, then the algorithm won’t be able to find trends and those it does find will be unreliable.

In other words, you need to learn to walk before you can run. Make sure you have processes in place to guarantee the integrity and quality of the data that you’ll be feeding into the algorithm.

Here are a few suggestions on how to do this.

Data profiling

Performing data profiling ensures there are no unexpected gaps and that the columnar types all have valid data. For example, making sure a string is always a string and that an integer is always an int will help considerably.

Automated data reconciliation

Having formalised data reconciliation in place will also help ensure the continued quality of your data. Reconciliations can be done on a daily, weekly, or even monthly basis, with extracts from the source system to ensure that no divergence occurs between the data in the source and reporting system.

Spotting and preventing duplicates

Using fuzzy magic logic and having automated processes in place to remove duplicates will help ensure that such data doesn’t cause distortions to your algorithms.

It’s also crucial to make sure you have access to the right data. For example, you won’t be able to predict the future purchases of a specific customer without complete historic purchase information.

If you use flaky data, you’ll get flaky results.

Don’t make AI a black box

To use artificial intelligence successfully in your business you need team members who understand how the AI was built and integrated with the rest of your infrastructure. If you don’t already have the expertise in-house, then you should hire experts or train up your staff: a strong skill base is a crucial foundation of any AI-based initiative.

If you choose to outsource all your AI requirements, you can become beholden to consultancies and third-party companies that do all the work and can almost hold you to ransom on a now-strategic part of your business – a situation you desperately want to avoid.

There is a lot to consider when trying to leverage AI in your organisation. The most important thing is to do your research: understand what leveraging AI really means and be prepared for the challenges it presents. Hopefully this article has given you a taste of what’s required and left you with realistic expectations for moving forward.