As part of the “Win with AI” summit I attended recently, we want to talk about creating an AI strategy. How do you how do you put together a strategy for introducing artificial intelligence?
Full disclosure, I have an IBM champion and my company Trust Insights is a registered IBM Business Partner. Any purchases you make from IBM may benefit us financially.
What is the Business Goal of AI
When it comes to creating a strategy for AI the first and most important thing to ask is “what is the business goal?” and not “what is the technology goal?” You want to understand how this effort will align with your business goals. Is it improving profits? Is it reducing workforce costs? What is the business outcome you’re after? If you don’t know that answer, do not attempt to apply artificial intelligence to the problem. The reason for that is that AI is not a magic wand. It’s math, statistics and probability algorithms. As good and as powerful as these algorithms are, they are still fundamentally math, not magic. You need to be very clear about what your goal is and what you want to achieve.
Goal Alignment with 3 As of AI
Second, does the goal that you’re trying to achieve and be specific about it does it align with the core benefits of artificial intelligence?
The three core benefits are: Acceleration, Accuracy, and Automation
Acceleration: you want results faster.
You can accelerate if you know where you’re going and you know what you’re trying to do, you want to get there faster. AI can help with that.
Accuracy: you want higher quality results.
AI is math so accuracy is something that machines are extremely good at finding and improving upon.
Automation: you want the process to be machine-driven.
If everything is already laid out, you have the processes in place, and you the process executed by machine where and when possible. Automation is that third benefit, and you will be able to do that with artificial intelligence.
If the business goal you’re after is one of those three, artificial intelligence is going to be a useful road to travel towards achieving your business goal.
The third layer you need in your AI strategy is to map out the Rumsfeld matrix.
What are your known knowns? This is the information that you know. This is what your dataset contains, what your anomalies are, and what your processes entail. There are no surprises.
What are your unknown knows? What are the silos in your company? Where is the data stored? Have you had a complete audit and inventory of your data? What is your governance structure? Do you have your processes and procedures and everything documented out? Those your unknown knowns. Companies have more of these than they think. If you don’t have these things in place you need to solve these problems first, and that is not an AI function. That is a data science, data architecture, or information architecture function. Make sure you have those pieces in place before moving forward with implementing AI.
What are your known unknowns? Where are the gaps in your data – what parts of your data are unclean, what data you don’t have that you might need to go get externally? These are the things that you’ll need to solve with data science first then can implement artificial intelligence. A big part of your AI strategy is converting those unknowns into knowns by doing your audit inventory, documenting your processes and governance, and your unknown unknowns.
What are the unknown unknowns? You don’t know what you don’t know and there can be a lot of these. There can be things like biases in your data, severe flaws in your data that can be macro conditions that are impacting your data that you may not even know about. The unknown unknowns are something you have to keep an eye on throughout your AI implementation. What are those things you didn’t know that you didn’t know? Once you start digging in that transitions into known unknowns and unknown knowns. Use information architecture or data science to solve for those you know what you’re working with.
Implementation of AI
Sorting out and organizing your data, infrastructure, and processes are probably 80 to 90% of an AI implementation. You want to start by fixing your data and getting it ready for training the algorithms. You would then choose the tactics, the execution, the vendors, the algorithms, the methodologies to solve the unknowns. Then you would build training data sets on the knowns. For the execution portion of an AI strategy, you need people to implement the process, you need a platform, like IBM Watson, and you need participation and buy-in from the levels of the company that are going to be impacted by the system.
Unless you are a company that is, is entirely staffed by people who only ever copy-paste, you’re not going to be getting rid of people with an AI implementation. In your early days of an AI implementation, you’re going to be adding people because you need to bring in new talent and specializations that you don’t necessarily have available. By having participation abuy-in in from the top level executives all the way down to the day-to-day workers is going to be essential for making an AI implementation work well.
That’s the strategy for creating an AI strategy for your business. Start with understanding your goals and align those goals with the benefits of AI to make sure that AI is the right choice for accomplishing those goals. Next, identify your knowns and identify your unknowns. Once you know that information you can solve your unknowns and start rolling out the AI implementation to people, to process, and to the platform with company-wide participation. The best option is to start with something small and achievable. Do something that is has a measurable benefit and that is relatively straightforward and relatively low risk. For example, implementing AI can help automate and accelerate the drudgery of categorizing receipts. Image recognition is a very simple a function that artificial intelligence is very good at, specifically optical character recognition. Nobody loves reading and categorizing receipts. You’ll see immediate benefits in a very small contained way. Don’t try to automate the entire enterprise all at once. Use AI at a tactical level to begin to show some early wins get some immediate results and then elevate to bigger and bigger functions.
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