In recent decades digitization has transformed organizations. Now artificial intelligence is completely changing the game again, with truly impressive capabilities. Digital systems have been a great business aid for reporting on and monitoring activities, but AI has the potential to move the business forward in ways never before possible – if your infrastructure is set up for it.
Both predictive and generative AI place enormous demands on digital infrastructure. The question isn’t whether enhanced systems are needed, but how best to design and deploy them. For a successful transformation into AI-driven business, start with a clear sense of how business requirements inform the potential use cases. There is a huge range of AI infrastructure choices and without focus, choosing appropriate solutions is impossible. Once IT understands the use cases driven by business needs, it can prepare and clear the hurdles ahead.
Focusing on Business Demands
The term “IT/Business alignment” has been around for decades. However, when it comes to AI, that alignment must become the guiding principle. Engaging LOB or executive management and having them provide details on how they want to use AI to enhance current operations, and what the top business demands are, is the first step in designing and deploying new AI infrastructure. These are not technical discussions, instead they focus on how the firm’s operational functions and processes will evolve.
This powerful information allows an organization to reduce the broad range of AI’s “art of the possible” into more finite use cases. As these examples show, once the business demands are known and the use cases identified, IT can document specific infrastructure needs to support them. This specificity also reduces the scope of the project, making it possible to deliver results faster.
Retail: Retailers always seek to serve their customers better, and truly personalized, real-time product recommendations are a key business requirement for many firms. AI makes it possible to gather and utilize multiple data sets to find compelling recommendations during the shopping experience. However, supporting truly personalized recommendations requires integration and analysis of past purchase data, customer profiles from similar shoppers, real-time input as the customer moves through the website, insights into new trends from public sites, and data from sales in other channels and physical stores. That’s a huge amount of data.
IT needs to build infrastructure that can store this vast amount of internal and external data, process it, and deliver it “in the moment” to support this specific business need. To get the most out of their AI deployments, retail organizations will need AI-ready storage solutions that provide data and application security from edge to cloud, visibility across the entire IT estate, and easy integration of new or different data into the master data set as business demands evolve.
Financial services: Financial services firms want to provide the fastest possible “time to answer” for loan, credit card, and other service inquiries. Speed, accuracy, and data security are critical to the business. This leads to a very intriguing potential AI use case, using generative AI to automate loan review and approval. The system can intelligently evaluate data from existing bank customer information, draw on public records of assets, external credit reports, publicly available/relevant legal matters, and more. The result is a much more complete picture of the applicant, which enables better loan decision making.
From an infrastructure perspective, where data is stored and managed is a major concern, as much of this data is protected and private. Processing power is equally important, as the data must be evaluated quickly, while it is still “fresh.” For their AI-ready infrastructure, financial organizations demand capable, performant, and dense on-premises storage and compute resources to support integration, data matching, and analysis. Just as important: cloud-based processing power and analytical tools. A hybrid cloud environment integrates internal applications and data with external resources. The AI-ready infrastructure must also provide the highest levels of resiliency and security due to strict compliance regimes.
Modern Infrastructure: Commit and Go
IT/business alignment has always been important, but in the age of AI, it is vital. AI-driven business will require capabilities existing hardware can’t provide. Regardless of industry, organizations must commit to a solution quickly to remain competitive – and that solution must be scalable across the entire IT estate. It’s clear that data must be freed from silos and recombined as needed in new AI apps, but where and how to combine and process this data varies from business to business and must be evaluated on a case-by-case basis.
With innumerable modern storage and compute solutions to choose from, identifying key use-cases can help narrow down the selection. Technology partners can help identify products and services to meet the specific demands of your AI use cases and can offer support and training to reduce the skills gap in your organization. AI-ready infrastructure will go a long way in helping businesses gain the first mover advantage of levering AI within their organization. This targeted approach, the right partner, and the right solution can begin to deliver better outcomes almost immediately.
