41 percent of the 3,000 executives surveyed in a recent McKinsey Global Institute study are unsure about how to use AI (Artificial Intelligence) in their organisation.
This is an article on where to start (and where not to!), based on my experience in the field over many years on a variety of AI projects. I present some high-level ingredients to help you kick off your first AI business initiative. An understanding of the steps involved in this approach will help you to build a picture of potential solutions to business challenges that can be validated in the future with others in the field.
Get Informed enough to be dangerous
You don’t need to understand the intricacies of every AI technology. Nor do you need to identify a set of use cases to match to each. In fact, doing so can be counter-productive. The technologies can be overwhelming, let alone mapping them to your specific business challenges.
I will not cover basic AI terminology here, as there are already loads of learning resources on the internet that I would recommend. If you need a refresher, here are a few overviews to get started on some of the more common terms you’ll hear mentioned: Machine Learning, Deep Learning, Robotic Process Automation, Natural Language Processing. Gaining an understanding of these high-level concepts, will help you to form a picture of what they can and can’t do, and the types of business problems they are capable of solving.
Business First
AI projects should be started with some sort of business benefit or value in mind. Sounds obvious? Less than half of the projects I have observed started with this business focus. But, where they did, the project was far more likely to achieve the project goals and anticipated benefits.
By way of an example, I was bought in after the start of one such AI project, where the customer wanted to do some ‘cool AI’ stuff that their users in the field. They wanted to deliver material benefits to the organisation and, let’s face it, to demonstrate the organisation’s ability to innovate and disrupt with AI.
Their goal was to use AI and ‘tech’ to make their field worker’s jobs easier and more efficient while they were on the road. This all sounded great – in theory.
As the project progressed, my team discovered the real business problem. We found that in fact, the field workers were inefficient on the front line because they were spending so much time searching for information while back in the office. Their front-line vs back-office work days should have been at a ratio of 4:1. Instead it was the opposite. They didn’t need some flashy AI in the front of their business. Rather, they needed some powerful, time saving AI in the back.
So instead, we recommended a Natural Language Processing capability that allowed them to ask questions of their large corpus of unstructured documentation to get the answers quickly. This meant they would only need to spend 2 days a week in the office; a vast improvement on spending 4 days a week on unproductive back-office tasks.
While the new AI approach was not as ‘flashy’ or ‘visible’ to the public as their original selection (an iPad AI app essentially), it has actually solved a real business productivity issue.
Start by: Focussing first on a high value business problem to solve and then worry about which AI technology (or cool flashy tech idea) to implement.
Data + Data Scientist alone = Failure
‘I have all this data, how much will it cost me do some AI on this and get an outcome using a data scientist?’.
This type of question is one that I am asked regularly.
In my experience, simply throwing a data set at a data scientist will seldom return much value. It seems this approach is often taken when a project goal or business outcome hasn’t been well defined. In an effort to address this omission, an organisation will take a purely data driven approach in the hope of finding a ‘diamond in rough’.
I witnessed this on one such project, where a data scientist was instructed to ‘go find something’ in the data. Yes, produce magic! After several weeks (and expense) nothing was returned, despite the fact the data scientist was highly skilled and experienced.
In this instance, it was found that the data sets did not contain the elements required to produce a reliable model. I liken this to committing to baking a cake before taking a moment to first check if you have all the ingredients.
We spent some time and found a more relevant set of data to solve the problem. While this came with Its own set of challenges to obtain, it was a far more profitable path to pursue than the original ‘dump and hope’ approach.
To address this, I recommend that you engage your business domain experts early to help identify information that will typically be needed to solve the business problem. This isn’t intended to be an exhaustive exercise, but rather an initial, broad-brush assessment. In my experience, your business domain SMEs will be surprising well equipped to undertake this initial information assessment with minimal input from data scientists.
Start by: Performing due diligence on the data sets and make sure they relate to the business issue or proposed value before committing weeks and dollars.
Build the right team
So, you have selected the business problem that you want to solve. You have found the information that you will likely need, and you have identified the right technology to address the issue (or had someone help you do this). What now?
It’s time to build the right team to get it done.
AI + Technical IT + Business domain
In my experience, organisations who are looking to start their first AI project will be missing one or more of these skills. Typically, you will have the business domain skills and you will have an existing pool of IT skills that you can draw upon, but you will be missing the AI skills.
So, one of the first elements that you will need to consider is how you will get access to the skill sets that
you need. An effective way for business leaders to achieve a sense of direction on this might be to partner with experts or to build first pilots in partnership. Start small and use the process to inform an in-house capability down the track once the organisation has cross skilled in the new AI domain.
I find that small teams with complimentary skills work best when looking to adopt a new technology such as AI. Typically, a technical team of around 3-6 people, with a complimentary number of subject matter experts or team members who understand the target business domain intimately. These usually already reside in your business. This blend is critical to the success of the project.
You should also be aware that all “data scientists” are not created equal. In fact, this rather broad term covers a huge set of different skill sets. Depending on the type of AI project you are taking on, you will need a different mix of skills. For example, there is no point hiring a data scientist with Machine Learning skills, if the business problem you are trying to solve relies on an understanding of Natural Language Processing.
Once the business problem is mapped to the right technology, selecting the required skill sets is straight forward.
Start by: Partner if you need to. AI isa huge domain and it will take time to learn which parts are right for your set of business challenges. In house teams can come when the capability matures.
I’d love to hear your thoughts on this topic or if you have any other questions or advice for others please comment –
About the Author: Lisa Bouari is an Owner and Director of outThought.co. Lisa has delivered roadmaps and solutions to a wide range of industries and executives to make Cognitive and AI technology possible in their organisation. With a focus on Natural Language Processing, Conversational Agents and Robotic Process Automation. Lisa enjoys the never-ending learning and excitement of her domain, meeting new clients and the odd salsa dancing night out.