IBM Cloud Podcast Miniseries: The Challenge of AI
5 min read
We're excited to bring you a seven-part miniseries on unlocking the value of data and AI in a multicloud world.
Artificial intelligence (AI) is a $16T opportunity, the largest ever. 83% of all businesses say AI is a key initiative for the immediate future, yet 81% don't understand the data required for AI, 60% see compliance as a barrier due to a lack of trust in AI outcomes, and 65% of businesses do not fully trust their own organizations analytics. As a result, only 1 in 20 companies have extensively incorporated AI into their offerings or processes.
In this seven-part podcast miniseries, we talk with a collection of subject matter experts on unlocking the value of data and AI in a multicloud world.
Episode 1: The Challenge with AI
Deborah Leff kicks off this seven-part series, exploring this disconnect in between the importance companies see in AI and actually incorporating it into their offerings and processes. We discuss the the drive to AI, the gaps hindering adoption, and how can they be addressed.
Episode 2: The AI Ladder
Data fuels digital transformation, and AI unlocks the value of that data. Companies seeking to transform their business with artificial intelligence run into 3 major challenges:
- Data is the lifeblood of AI, but complexity slows progress. 80% is locked in silos or not business-ready.
- AI skills are rare and in high demand. 62% are challenged to acquire talent [and build skills].
- Skepticism of AI systems and processes. 62% indicate that they need an approach to AI production readiness.
Until these challenges are addressed, businesses will find that operationalizing, sustaining, and scaling AI is challenging—in other words, an experiment.
In the second of our seven-part series on the challenges of AI, Russ Milano discusses the IBM AI Ladder—IBM's prescriptive approach on how to solve the three challenges so that AI can transform your business.
Episode 3: Make Your Data Simple and Accessible
81% of businesses don't understand the data required for AI, and 80% of that data is locked in silos or not business-ready.
In this third episode of our miniseries on the The Challenges of AI, we chat with Matthias Funke about the first rung of the AI Ladder: Collect.
Before you can start doing AI, you need to know where your data resides across multiple clouds in a hybrid environment, the quality and value of the data, and its readiness to be ingested into analytics and AI tools. The Collect rung of the ladder addresses these challenges by employing AI models for all types of data—structured, semistructured, or unstructured—and providing access to this data through a common SQL engine.
Episode 4: Create a Business-Ready Analytics Foundation
In this fourth episode of our seven-part miniseries on the The Challenges of AI, we chat with Jay Limburn about the second rung of the AI Ladder: Organize. The second rung details how clients moving to AI need to cleanse, integrate, and catalog all types of data; ensure governance and lineage of all your data; and deploy virtual data pipelines so data doesn't have to be moved as it is used.
Episode 5: Build and Scale AI with Trust and Transparency
In this fifth episode of our miniseries on the The Challenges of AI, we chat with Chris Zobler about the third rung of the AI Ladder: Analyze. 65% of businesses do not fully trust their organizations' analytics well enough to base their business on the output of machine learning models and AI software. To address these concerns, clients want to instill trust with built-in explainability, bias remediation, and policy compliance as well as build, run, and manage AI models in a unified experience.
Episode 6: Operationalize AI Throughout the Business
In the sixth episode of our miniseries on the The Challenges of AI, we chat with Susann Ulrich about the top rung of our AI: Ladder: Infuse. Simply stated, this rung is about operationalizing AI throughout your business—leveraging AI to automate processes, improve business outcomes, reduce costs, make employees more productive, and rapidly innovate with new business models.
Episode 7: Unlock the Value of Data and AI in a Multicloud World
In the final episode of our seven-part miniseries on the The Challenges of AI, we chat with Clarinda Mascarenhas about an item that isn't part of the AI Ladder but is still needed by enterprises: Modernize. The average enterprise has five to six clouds, in addition to a wealth of data scattered across on-premises and legacy systems. That data must be integrated, governed, and leveraged across the full AI lifecycle. With Modernize, customers can make their data ready for an AI and hybrid cloud world by automating the end-to-end data and AI lifecycle management, integrating and governing data across hybrid cloud and data settings, and virtualizing all data, regardless of where it lives.