Artificial Intelligence (AI) is growing in prominence as a beneficial tool for business strategy. AI’s capabilities are expanding, making it an even more attractive resource to drive business outcomes.
While exciting, if your business jumps in too soon, you’ll be spending money on implementing tools, with no promise of return. This makes evaluating your present business intelligence and analytics activities and goals for implementing AI a necessary step to take before diving into implementation.
This article will take a deeper dive into why your organization needs to take an AI readiness assessment and help you to understand where you need to start so your business can make the leap to prescriptive analytics.
What are the different levels of analytical capabilities?
The maturity model for analytics looks like the graph below. In order, the three levels of analytical maturity can be defined as descriptive, predictive, and prescriptive. Each different level provides a more advanced degree of analytical capabilities, helping your organization to answer different questions about your data.
It’s typically assumed that organizations evolve from left to right as they get more mature, though, in reality, it’s more nuanced than that. The larger issue is that many organizations struggle to cross the chasm to get to predictive and prescriptive analytics, that provide disproportionate business value due to limited readiness and skills.
Descriptive Analytics - What and why did it happen?
This is the preliminary stage of data processing, basic reporting, and dashboard capabilities. Descriptive analytics provide insight into historical data.
Example: Any company report or dashboard that provides a historic review of the organization's sales, operations, financials, or other data points.
Predictive Analytics - What could happen in the future?
This is used to identify future probabilities and trends to better position yourself in the market.
Example: You anticipate an influx of sales for September based on past data demonstrating a correlation between the start of the quarter and sales.
Prescriptive Analytics - How should we preemptively respond to these predictions?
This is leveraging the predictions of historical data and suggest decision options for avoiding potential risk, changing the future for the better.
Example: You decide to scale up operations during the first month of each quarter to coincide with the influx of sales.
You can use AI to make predictions. How will making these predictions drive significant business outcomes? Identify your goals for implementing AI. Getting started without a goal is the perfect formula for siloed and ineffective efforts.
Consider what information your business would like to be able to predict or gain more insight on. This should be something that can have an impact on business outcomes.
What are your use cases?
Now that you’ve identified your end goals for implementing AI, you can explore potential use cases. Consider what data you have access to and how it’s related to accomplishing your end goals.
Identifying what you’re hoping to accomplish can ensure you’re spending on the right technology and capabilities of the future.
Evaluating the current state of your business
What does your data infrastructure look like currently? Who accesses it and how? Are you empowered to make the best decisions for your business? If your business doesn’t have access to your raw data, you’re not ready for AI.
This is where the AI readiness assessment comes in. This resource will help you evaluate where you currently stand, so you can make changes to move towards prescriptive analytics for your organization.
AI can be a powerful tool for impacting business outcomes when implemented strategically. When implemented haphazardly, it’s costly and ineffective.
Is your business ready for AI? Grab our AI readiness assessment to evaluate.