A recent Capital One survey found that 87% of business leaders believe their organization has a modern data ecosystem capable of building and deploying artificial intelligence (AI) solutions at scale but only 35% believe they have the strong data culture needed to ensure AI’s success.[1]
AI is unquestionably the most important new source of competitive advantage, but lack of familiarity and questions about effective AI applications continue to stifle many initiatives.
The time to fix these problems isn’t after time and money have been wasted on projects without clear strategic objectives. A disciplined approach to modernizing and becoming AI-ready starts with achieving executive alignment on AI applications and developing realistic assessments of an organization’s business and technical maturity.
In engagements with hundreds of clients, Ingram Micro has developed a framework that identifies the best approach to modernizing infrastructure and applications in the cloud and the most promising use cases for machine learning and analytics. Service providers can use this methodology to help their customers develop a structured path to modernization and AI that ensures long-term success.
7 steps to success
The seven-step process begins with awareness and education, in which an assessment of partner skills and customer needs identifies gaps that need to be filled.
Potential AI use cases are then explored, based on customer-defined problems. In the third stage, partners work with customers to define proofs of concept and adjust the scope according to the customer’s infrastructure.
An architecture is then developed around one or more vendors selected by the project team. System benchmarks are conducted to optimize the use of software and hardware. Scale-out options are defined to ensure the necessary capacity is in place.
After the AI project is live, the team collects feedback, continuously optimizes the environment, and defines enhancement priorities.
Modernization of Hadoop platform with AWS
B3, a leading financial infrastructure company based in Brazil, used the Ingram Micro methodology to migrate its Hadoop platform to Amazon Web Services (AWS) while implementing best-of-breed security and compliance practices. Its on-premises environment incurred high maintenance costs, licensing fees, and scalability issues, with data storage needs and processing demands doubling every two years.
The company partnered with Ingram Micro to facilitate migrating to and modernizing in the AWS cloud. Ingram Micro optimized the AWS infrastructure with end-to-end encryption, adopted Amazon’s EMR managed cluster platform for big data workloads, and Amazon Redshift and Dremio for fast data access. A sandbox environment enabled B3’s data scientists to experiment with AI and machine learning (ML) experimentation.
Benefits of the year-long project included cost predictability, enhanced security, automated updates, expert support, intuitive platforms, rapid deployment, and scalability. With AWS as an extension of its data center, B3 now leverages big data for advanced storage, visualization, and processing, ensuring long-term efficiency and support for business growth.
Automating document review
Similarly, Eduzz, an online education platform, partnered with Ingram Micro to automate a manually intensive process of reviewing more than 100,000 text files each month for compliance with company content standards. Ingram Micro developed a fully serverless, highly scalable AI-powered moderation system with low operational costs.
The solution leveraged AWS Lambda, Step Functions, Simple Queue Service, and EventBridge for processing, while Rekognition, Textract, and Comprehend handled content analysis. Deployed in a virtual private cloud for security, the automation significantly reduced manual workload while enhancing content compliance.
Technology solutions for powering your business | Ingram Micro
[1] AI readiness survey: Are companies ready for AI adoption? Capital One, November 21, 2024