How to Get Started on your AI Journey

Overcome the AI Challenge

For many businesses, AI adoption is a big sticking point. It’s generally seen as being too difficult to implement due largely to four main challenges: a lack of AI-ready infrastructure, data volume and complexity, a lack of expertise, and a general fear and mistrust of AI systems.

The AI Ladder

The easiest way to overcome these challenges and get started on your AI journey is by completing the four steps of IBM’s AI ladder.

Step 1: Collect (Make data simple and accessible)

The first rung of the ladder is all about making your most valuable asset, data, as simple and accessible as possible. You need to unlock data hidden away in corporate silos and non-interoperable databases and bring it back into the light, so everyone in the organisation can see, analyse, and utilise it.

• Do a data census – sometimes called “data archaeology” this involves tracking down all internal data and finding out where and how it’s stored and how it’s structured. Then look at public and private data and even data on social media. Once you have all this information you can start building a comprehensive data catalogue.
• Put data in a business context – think about your organisation’s specific business goals and how you plan to use these new data insights. If you’re finding this stage difficult, partner with subject matter experts (SMEs) to help you put the data into context, so you can make sense of how you can use the data and start building your next AI project.
• Set up a corporation-wide information architecture policy – keeping data freely available while adhering to strict data regulations and compliance can be challenging. Collecting metadata will help make this easier, as will being aware of the various rules and regulations for data usage.

Step 2: Organise (Create a business-ready analytics foundation)

To create high-quality AI models, you need high-quality data, which is why data standards for AI application are so high. On this rung of the ladder you need to:

• Ensure data is accurate, complete, and compliant
• Record and catalogue all your data sources
• Make sure the right data governance processes are in place
• Having all your data is properly organised and compliant, you can move up the AI ladder to step 3.

Step 3: Analyse (Build and scale trusted AI)

This stage of the journey sees theory put into practise, as you create a solid and reliable framework for your AI operations. Now is the time to start building, scaling and leveraging AI and machine learning (ML) so you can analyse your data and start putting those insights to good use.

To do this you need an end-to-end AI lifecycle, which has three distinct stages:

• Build: ensure the right algorithms, tools, and techniques are in place to build the best model for your business.
• Run: Deploy and run the model in real life and retrain when necessary.
• Manage: Solutions like IBM Watson OpenScale can help you continue to manage your model and ensure that results align with the business goals you set at the start of your journey.

Step 4: Infuse (Operationalise AI throughout your business)

Your new AI projects will be more successful if you infuse AI into all workflows across your entire organisation. This is the best way to gather the most data intelligence and gives a complete data picture for your model, so it can ultimately unlock more value for your business for use in areas like financial, IT, and business operations, as well as risk and compliance, and customer service.

An important point to bear in mind, is that you can complete the four rungs of the AI ladder in any order you like. Many organisations start at the top and work their way down, but at the end of the day there is no right or wrong way, just choose a strategy that works best for your business.