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Written by: Daniel G. Hernandez – VP, Offering Management, Analytics, IBM

New innovations go through an evolution of dismissal, avoidance, fear and finally acceptance. AI is no exception – but it’s not magic and it’s not science fiction. It’s computer science fused with business reality – and it can help you, your business, your employees, and your customers through better prediction, automation and optimization.

Business leaders are striving to get more from their data but data remains unanalyzed, inaccessible or untrusted. Yet we are in a digital world, and it is data that fuels digital transformation. As some in the industry have predicted, models will run the world. And that brings us to why almost every enterprise on the planet is ramping up its investment in AI.

Why AI? Well, it’s the key to unlocking the value of data in totally new ways:

  • Predict and shape future outcomes
  • Optimize people to do higher value work
  • Intelligently automate decisions, processes and experiences
  • Re-imagine new business models
My team is seeing almost every business we engage with getting on the journey to AI. But what we have learned from the leaders is that AI is not magic. It takes a prescriptive approach – a methodology – to successful reap benefits from the journey ahead. At their core, enterprises must start by making their data ready for AI – and do this in a manner that provides trust and transparency for the people that will rely on it. That is, enterprises require trustworthy AI that is explainable, bias-free, robust, fair, and auditable. And that starts with the data that fuels AI.

Some 85 percent of responding enterprises in an MIT Sloan survey1 view AI as a strategic opportunity to unlock the business value hidden in their data. However, the same study cites that 81 percent do not understand the data required for AI. MIT’s conclusion was right on:

 

No amount of AI algorithmic sophistication will overcome a lack of data [architecture]

AI success starts with a simple principle: There is no AI without an IA (information architecture). That’s why we’ve put together a prescriptive approach composed of 5 core imperatives we call the ladder to AI. It is designed to help our enterprise clients with a thoughtful and well-architected approach.

 

Step 1: Modernize all your data estates in a multicloud environment

Given the dynamic nature of AI, your data estate needs a highly elastic and extensible multi-cloud infrastructure to unify capabilities within a fully governed team-platform. Clients are also looking to automate their AI lifecycles across an array of contributors through collaborative workflows. To modernize your data means building an information architecture for AI that provides choice and flexibility across your enterprise. As clients modernize their data estates for an AI and multicloud world, they will find that there is less “assembly required” in expanding the impact of AI across the organization.

Step 2: Collect data to make it simple and accessible

Enterprises need to establish a strong foundation of data, making it simple and accessible, regardless where that data resides. Since data used in AI is often very dynamic and fluid with ever-expanding sources, virtualizing how data is collected is critical for clients.

Step 3: Organize data to create a business – ready analytics foundation

Just because you can access your data doesn’t mean that it’s prepared for AI use cases. The organize rung is about creating a business-ready analytics foundation designed to ensure your data is ready for AI. Bad data is paralyzing to AI, so clients must integrate, cleanse, catalog, and govern the full lifecycle of their AI data.

Step 4: Analyze – scale AI everywhere with trust and transparency

Once your data is accessible and AI-ready, then you are better prepared to apply advanced analytics and AI models. This rung provides the business and planning analytics capabilities that are key for success with AI. It also provides the capabilities needed to build, deploy and manage AI models within an integrated portfolio of technology.

Step 5: Infuse – operationalize AI throughout your business

Many businesses create highly useful AI models but then encounter challenges in operationalizing them to attain broader business value. This rung of the ladder infuses AI to build trust and transparency in model-recommended decisions, decision explainability, bias detection and decision audits. For clients with common use cases, the infuse rung operationalizes those AI use cases with pre-built application services, speeding time to value.

Source:
1 MIT Sloan Survey

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