A Simple Framework for Enterprise AI

Picture this: 

It’s Monday morning, you arrive at work, and you get an email with a list of customers that are set to churn from your business this week. You send them a marketing promotion and… BAM, they renew for another year instead of churning! 

This used to be fantasy. With AI, It’s quickly becoming a reality. Businesses can take their raw data and turn it into unprecedented insights to get ahead of their competition. 

But let’s be real – it’s difficult to do!  

Enter the AI Ladder. A 4-step framework to get from messy, siloed data – to AI powered business insights across your company. Each step of the AI ladder involves data. So before we jump into this framework… let’s quick answer the question – What exactly is data in the age of AI 

What is Data?  

We know data as the information that flows through our digital world. There are various types of data, which fall into three categories:  

• Structured – neatly organized into tables and rows, like a spreadsheet  

• Semi-structured – think web documents, JSON files, etc. 

• Unstructured – like the freeform text you find in emails or social media posts   

People tend to think of data as just the structured kind. That is far from true! The companies that will get ahead are those that utilize ALL available data. 

Ok, so you have your data. It’s in its raw format, siloed and scattered.  How do we get from raw, messy data – to actionable business insights (Real AI)? Enter our framework – The AI Ladder.  

The four rungs/steps of the AI ladder are, which we will go through one by one, are: 

1. Collect your data

2. Organize that data

3. Analyze the data (This is the AI part)

4. Infuse the results throughout your organization

**-Disclaimer – I did not invent this framework. This is IBM’s AI framework that has resulted in successful AI projects at thousands of businesses worldwide. 

Collect  

Data is the lifeblood of AI. If a business wants to predict churn, learn more about their customers an industry, or understand which trends to invest in, it all starts with their data.  Data is harvested from various sources, like:  

• Databases storing customer information  

• Social Media posts about your company 

• Enterprise Resource Planning (ERP) systems  

• Customer Relationship Management (CRM) systems  

Each piece of data is a clue, a tiny fragment of the puzzle that AI systems aim to solve.   

Collecting relevant data from all available sources is the first step toward AI. But collecting data isn’t enough. We have to make sure our data is high quality and accessible before it becomes useful. 

Organize  

Data, in its natural state, can be messy and unruly. Cleaning and preparing data Is the artisanal craft of data scientists, ensuring that AI algorithms can work their magic effectively.  

Businesses have to make sure that their data is:

• Protected 

• Accessible 

• High quality 

• Trustworthy

• Traceable (Lineage) 

If your data is lacking in any of these departments, you’re just wasting your time. You can have the most talented data scientists in the world working with the most modern machine learning algorithms available… but if the data is bad? It’s all a waste. 

Garbage in = Garbage out. 

Tools like data catalogs, data warehouses, and other integration tooling are helpful when it comes to creating a trusted, accessible, business ready data foundation. 

Once data is organized and accessible, the real fun begins.  

Analyze 

Now it’s time for the magic. Here is where we apply AI to our data to give us insights and answers we couldn’t find ourselves. The thing is, AI has turned into a catch all term… but what is it really?  

Summed up – Artificial Intelligence is when machines have the ability to process information like humans.  

When most people think of AI, they think of SkyNet and the Terminator.  

But you aren’t most people. You are a technologist! You likely think of AI as ChatGPT and the Transformer architecture (A type of advanced AI model used for understanding and generating human language).  

In reality, AI is more of a concept. The Transformer is just a more relevant deep learning architecture that has been popularized in the last year. ChatGPT, the Transformer architecture, and most data science as a whole, can be accurately captured as Machine Learning. 

Machine learning is a branch of AI which focuses on using data and algorithms to imitate the way humans learn. These algorithms are typically built using advanced tools like TensorFlow and PyTorch.  

Some great examples of machine learning in action are Netflix recommendation engine, or Self-driving cars. 

Using data and statistics, algorithms are trained to make insights and predictions about the subject at hand. For us at home, it’s the right Netflix movie. For enterprises? It’s scenarios like:

• Who is the next customer to churn from my business? 

• How much should we budget for advertising in the coming fiscal year?  

• How much should we charge for our products to meet next year’s revenue targets? 

These insights subsequently drive decision making within different segments of the business, ideally impacting key growth metrics. All because of the data! 

Infuse 

The journey up the AI Ladder reaches its pinnacle in the ‘Infuse’ stage – where the true value of your efforts comes to life.  

Imagine this:

Data is not just analyzed; it’s woven into the very fabric of your enterprise, driving innovation and efficiency at every level.  

From enhancing customer experiences to streamlining operations, from bolstering risk management to revolutionizing financial strategies – Infusion is the critical leap from potential to reality. Infusing results ensures that the insights gathered from your data don’t just remain a theoretical exercise.  

Summary 

By following the AI ladder – Collecting and organizing your data, analyzing your trusted data using Machine Learning and AI, and infusing the insights across your business… 

You and your business doesn’t just adapt to the future; you actively create it. 

In the game of business, you either adapt or get left behind. When it comes to data, where does your business stand?