Introduction to the AI ladder
We all know data is the foundation for businesses to drive smarter decisions. Data is what fuels digital transformation. But it is AI that unlocks the value of that data, which is why AI is poised to transform businesses with the potential to add almost 16 trillion dollars to the global economy by 2030. You can find the relevant source here: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.
However, the adoption of AI has been slower than anticipated. This is because many enterprises do not make a conscious effort to lay the necessary data foundation and invest in nurturing talent and business processes that are critical for success. For example, the vast majority of AI failures are due to data preparation and organization, not the AI models themselves. Success with AI models is dependent on achieving success in terms of how you collect and organize data. Business leaders not only need to understand the power of AI but also how they can fully unleash its potential and operate in a hybrid, multi-cloud world.
This section aims to demystify AI, common AI challenges and failures, and provide a unified, prescriptive approach (which we call "the AI ladder") to help organizations unlock the value of their data and accelerate their journey to AI.
As companies look to harness the potential of AI and identify the best ways to leverage data for business insights, they need to ensure that they start with a clearly defined business problem. In addition, you need to use data from diverse sources, support best-in-class tools and frameworks, and run models across a variety of environments.
According to a study by MIT Sloan Management Review, 81% of business leaders (http://marketing.mitsmr.com/offers/AI2017/59181-MITSMR-BCG-Report-2017.pdf) do not understand the data and infrastructure required for AI and "No amount of AI algorithmic sophistication will overcome a lack of data [architecture] – bad data is simply paralyzing."
Put simply: There is no AI without IA (information architecture).
IBM recognizes this challenge our clients are facing. As a result, IBM built a prescriptive approach (known as the AI ladder) to help clients with the aforementioned challenges and accelerate their journey to AI, no matter where they are on their journey. It allows them to simplify and automate how organizations turn data into insights by unifying the collection, organization, and analysis of data, regardless of where it lives. By climbing the AI ladder, enterprises can build a governed, efficient, agile, and future-proof approach to AI. Furthermore, it is also an organizing construct that underpins the Data and AI product portfolio of IBM.
It is critical to remember that AI is not magic and requires a thoughtful and well-architected approach. Every step of the ladder is critical to being successful with AI.
The rungs of the AI ladder
The following diagram illustrates IBM's prescriptive approach, also known as the AI ladder:
Figure 1.2 – The AI ladder – a prescriptive approach to the journey of AI
The AI ladder has four steps (often referred to as the rungs of the ladder). They are as follows:
- Collect: Make data simple and accessible. Collect data of every type regardless of where it lives, enabling flexibility in the face of ever-changing data sources.
- Organize: Create a business-ready analytics foundation. Organize all the client's data into a trusted, business-ready foundation with built-in governance, quality, protection, and compliance.
- Analyze: Build and scale AI with trust and explainability. Analyze the client's data in smarter ways and benefit from AI models that empower the client's team to gain new insights and make better, smarter decisions.
- Infuse: Operationalize AI throughout the business. You should do this across multiple departments and within various processes by drawing on predictions, automation, and optimization. Craft an effective AI strategy to realize your AI business objectives. Apply AI to automate and optimize existing workflows in your business, allowing your employees to focus on higher-value work.
Spanning the four steps of the AI ladder is the concept of Modernize from IBM, which allows clients to simplify and automate how they turn data into insights. It unifies collecting, organizing, and analyzing data within a multi-cloud data platform known as Cloud Pak for Data.
IBM's approach starts with a simple idea: run anywhere. This is because the platform can be deployed on the customer's infrastructure of choice. IBM supports Cloud Pak for Data deployments on every major cloud platform, including Google, Azure, AWS, and IBM Cloud. You can also deploy Cloud Pak for Data platforms on-premises in your data center, which is extremely relevant for customers who are focused on a hybrid cloud strategy.
The way IBM supports Cloud Pak for Data on all these infrastructures is by layering Red Hat OpenShift at its core. This is one of the key reasons behind IBM's acquisition of Red Hat in 2019. The intention is to offer customers the flexibility to scale across any infrastructure using the world's leading open source steward: Red Hat. OpenShift is a Kubernetes-based platform that also allows IBM to deploy all our products through a modern container-based model. In essence, all the capabilities are rearchitected as microservices so that they can be provisioned as needed based on your enterprise needs.
Now that we have introduced the concept of the AI ladder and IBM's Cloud Pak for Data platform, let's spend some time focusing on the individual rungs of the AI ladder and IBM's capabilities that make it stand out.
