How to Scale the AI ladder: Watch these enterprises
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 survey 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:
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.
Get started today with the corresponding set of IBM solutions and expertise by visiting ibm.com/data-ai.
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.
Learn from iKure: IBM’s Data Science and AI Elite team used IBM Cloud Private for Data to develop and deploy a predictive model for cardiac care for Indian health tech startup iKure using multiple AWS data sources. Watch this video to see how ICP for Data helped iKure to get up and running quickly without needing to recode if they have to deploy or embed their application elsewhere.
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.
Learn from Capitalogix: This Dallas-based next-generation hedge fund relies on data science to outpace the market. With IBM Integrated Analytics System, its embedded data science tools and cloud connectivity, they are turning real-time data into real-time insights via machine learning and artificial intelligence. Capitalogix can now inform their business strategy by accessing and collecting data of all types – from stock-ticker transactional data, to unstructured social media sentiment data and even satellite imagery.
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.
Learn from Sonoma County: Sonoma County had developed an integrated, multi-disciplinary team to focus on the most vulnerable individuals and administer holistic services to help them get back on their feet. Sensitive client data existed separately across all these departments. It needed to be unified and made accessible to all team members. Using IBM Master Data Management, Sonoma County’s integrated multi-disciplinary teams can glean insights developed from the full picture of citizens’ information. With this level of access and visibility, duplicated efforts could be eliminated, and client needs can be met in intricately layered ways to lead to better outcomes.
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.
Learn from Experian: The IBM Data Science Elite team had a simple mission: apply AI to learn what Experian has done over the years building corporate hierarchies and then apply that to the full universe of companies that they traditionally couldn’t evaluate. The goal was to increase the number of corporate hierarchies and increase the frequency of corporate hierarchy matching. AI and machine learning are now helping Experian solve a problem building and maintaining business families and corporate linkages with a potential 500 percent increase in coverage and 80 percent reduction in cost.
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.
Learn from Deutsche Bahn: See how this German railway company leverages the speed, power, and performance of IBM Planning Analytics to unite its global enterprise and gain deeper insights, ensuring that the most accurate data is being used to create critical plans and forecasts that drive their business forward.
Follow IBM clients throughout their journey to AI in our new client gallery and learn who were among the first to confidently put AI to work in their industry.
Accelerate your journey to AI with a prescriptive approach. Visit ibm.com/data-ai to learn about how IBM’s ladder to AI helps you modernize, collect, organize, analyze and infuse all your data.