Oftentimes, businesses come to us not knowing where to begin when they want to implement new, innovative technologies. With the dawn of AI solutions like ChatGPT, a generative AI text tool, or DALL·E 2, ai generated art tools. The idea of using artificial intelligence (AI) to engage their subscribers is daunting.
So what do we tell them? Just start. Experiment. Take the first step toward trying something new.
In many cases, it makes sense to turn toward data to make complex business decisions. But whether or not to employ AI predictive technology so you can take the best course of action using a combination of existing data, training data, and real-time input is still a tough choice to make. However, referring to a step-by-step framework can be an excellent place to start.
What does making tough decisions have to do with AI?
Every organization faces the challenge of making tough decisions in the face of uncertainty. If you knew the exact, potential outcome of each branch of a decision tree, the choices you face in your daily life would not be as nerve-racking. But AI can help you make better predictions, and the AI canvas provides a straightforward framework to decide whether or not to use AI.
Ironically, artificial intelligence provides the decision-making path to follow, which can help an organization decide whether implementing an AI prediction machine even makes sense in the first place. The concept is fully fleshed out, along with a step by step example, in this recent HBR article, A Simple Tool to Start Making Decisions with the Help of AI, by Ajay Agrawal, Joshua Gans and Avi Goldfarb. They are also authors of the acclaimed book, Prediction Machines.
How does the AI Canvas work?
The first four steps in the top row of the framework are prediction, judgment, action and outcome. They describe the four most important dimensions of any given decision. The bottom row of the AI Canvas is input, training and feedback. It lays out the concluding data-centric considerations in the entire decision-making process. In the article by Agrawal et al, they lay out an interesting example relating to home security that demonstrates a real-life application of the AI Canvas.
Step 1: Prediction with a purpose
The first step is to define what needs to be predicted. In other words, what do you need to know? Do you need to increase your email open rate and click rates? Do you want to save time curating content for your current and potential clients? All these questions you ask should relate to what insights you need.
Step 2: Judgment of value
The second step is to determine the impact of the predictions. Are the results of the predictions made what you expected? Do the insights from your customer data provide value? It is up to your own judgement to decide.
Step 3: Take action to see results
The third step is to take action. With these predictions, what can you do with it? Are you planning to create new types of content based on AI-selected topics? Are you looking at the customer data to score leads? The action can be as simple or complex.
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Step 4: Outcome of your AI strategy
The last step is to evaluate. Based on the results of your actions, you can decide if you made the right choice or not. If so, what changes need to be made? What other predictions are needed? The point of any strategy is to continue to test, measure, and evolve it.
With AI, don’t wait for the opportunity
There is a wide range of possibilities. We have all heard that AI will become a more and more powerful force in business. That means predictive decision-making is an AI category that any organization should begin to explore. Get started with rasa.io, your very own AI-powered newsletter, to get a jumpstart on your AI strategy.
