The AI Ladder : IBM’s Prescriptive Approach
Digital transformation is impacting every industry and business, with Data & AI is playing a prominent role. The AI ladder is IBM’s prescriptive approach and entails 4 simple steps or rungs of the ladder. In this blog, we will discuss the market dynamics, IBM’s perspective and a detailed overview of the AI ladder - what it entails and how IBM offerings map to the different rungs of the ladder.
Market Dynamics & IBM’s Data & AI portfolio
Data is the fuel; Cloud is the vehicle and AI is the destination. The intersection of these 3 pillars of IT is the driving force behind digital transformation disrupting every company and every industry. To be successful, enterprises must modernize their portfolio and re-tool their Data, AI and application workloads. While the benefits of cloud are becoming obvious by the day, there are still a number of enterprises reluctant to embrace public cloud right away. This doesn’t mean that they don’t see the value of cloud and the benefits derived from embracing a cloud architecture. These enterprises are in some cases constrained by regulatory concerns which make it a challenge to operate on public clouds and are hence embracing private cloud. In summary, Hybrid cloud is the ultimate destination and taking the necessary steps to modernize is not an option, but a survival necessity.
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 Artificial intelligence (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. However, adoption of AI has been slower than anticipated. Business leaders not only need to understand the power of AI, but how they can fully unleash its potential and operate in a hybrid, multi-cloud world.
Our objective to demystify AI, present common AI challenges and failures, and finally, 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. According to a study by MIT Sloan Management Review , 81 percent of business leaders 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, we’ve built a prescriptive approach (known as The AI ladder), to help clients overcome these 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 ladder to AI, enterprises can build a governed, efficient, agile, and future-proof approach to AI. Furthermore, it is an organizing construct to the Data and AI products and services offered by IBM and our business partners, and it is the technology foundation to unify how those products and services work together.
What we have learned from AI pioneers is that every step of the ladder is critical. AI is not magic and requires a thoughtful and well-architected approach. 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 first with how you collect and organize data.
The AI ladder has four steps (often referred to as “rungs” of the ladder). They are:
- Collect: Make data simple and accessible
- Organize: Create a business-ready analytics foundation.
- Analyze: Build and scale AI with trust and explainability.
- Infuse: Operationalize AI throughout the business.
Spanning the four steps of the AI ladder is the concept of Modernize, which is how clients can simplify and automate how they turn data into insights by unifying the collection, organization and analysis of data, regardless of where it lives, within a multi-cloud data platform, Cloud Pak for Data.
IBM’s approach starts with a simple idea. Run anywhere. The platform can be co-located where your infrastructure investments have been made or will be made: Google, Azure, AWS and IBM Cloud. You can also deploy this on-premise which is extremely relevant for customers focused on a hybrid cloud strategy.
The way we support all these infrastructures is by layering Red Hat OpenShift at the core. OpenShift is a Kube based platform that also allows IBM to deploy all of our products through a modern container-based model. In essence, all of the capabilities are re-architected as micro-services, so that they can be provisioned as needed based on your enterprise needs.
IBM enjoys a strong Data & AI portfolio with 100+ products developed and acquired over the past 40+ years including some marquee offerings such as Db2, Informix, DataStage, Cognos Analytics, SPSS Modeler, Planning Analytics etc. The depth and breadth of IBM’s portfolio is what makes it stand-out in the market. With Cloud Pak for Data, IBM is doubling down on this differentiation, simplifying and modernizing its portfolio as customers look to a hybrid, multi-cloud future.
Summary
It’s clear that in today’s hybrid multi-cloud world, for organizations to succeed with AI, it’s imperative that they modernize the architecture in which information is ingested, stored, organized, accessed, and analyzed.
As organizations seek to adopt AI, it’s important to remember three things. First, to start with your problem. Whether you’re just starting on your AI journey or you’re well underway, always come back to the core business problem that you’re trying to solve. In a lot of cases, it begins with your needs and pain points. Think about how AI can help build exceptional, personalized customer experiences.
Second, remember that you can’t have AI without IA. Organizations need a modern information architecture to connect data from all necessary sources, to make it accessible to users across teams, to dynamically build and deploy AI models, and to simplify and unify data and AI services across cloud environments. The AI Ladder was developed to help organizations build an information architecture and ultimately reach their AI goals.
Finally, AI is not magic. It’s hard work. It requires the proper tools, methodologies, and mindset, to overcome the gaps that companies are facing (data, skills, and trust) to truly embrace an AI practice and put it to work across your organization.
AI is the biggest opportunity of our time, and yet there’s still a certain fear in the market that AI is going to replace jobs. However, the reality is this: AI is not going to replace managers. Rather, the managers who use AI will replace the managers who do not.
