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Legacy systems and the vast amount of valuable data they contain play a critical role in accelerating enterprise AI strategies. With Skytap, you can migrate and run your IBM Power estate as-is in Azure to ensure legacy data is easily accessible and included in your cloud-based AI initiatives.

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Accelerate Enterprise AI Strategies with Legacy Systems, Data & the Cloud

Include legacy data in AI strategies

Include legacy data in AI strategies

AI strategies depend on data and vast amounts of it can be found in legacy systems. Failure to include your legacy data can prevent you from fully monetizing your AI investments and keeping it siloed on-premises can hinder progression.

Migrate legacy data to the cloud

Migrate legacy data to the cloud

Migrating legacy systems to the cloud reduces latency and ensures this critical data is easily accessible and included in your AI initiatives. Shifting to a consumption-based model frees up valuable financial resources that can be re-directed at AI strategies that increase your bottom line.

Innovate faster and gain competitive advantage

Innovate faster and gain competitive advantage

Once legacy systems are migrated to the cloud, take advantage of the cloud’s agility and efficiency so your talented IT team can spend more time innovating and implementing AI strategies even faster. Resulting AI-driven insights and capabilities can quickly be converted into competitive advantage.

Artificial Intelligence Customer Use CaseIndustrial conglomerate transforms with use of legacy data and AI

A multi-billion dollar industrial conglomerate decided to partner with Microsoft Azure, OpenAI and Skytap to further its AI strategies. Its goal was to synthesize the data captured in its ERP systems located in its regional offices to improve business efficiency and effectiveness by running holistic analytics on sales trends, buying behaviors, technical decisions and customer experience. It migrated all of its legacy ERP systems running IBM i or AIX on Power hardware to Azure leveraging Skytap.

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Once its ERP systems were running in Azure, it became very easy to feed all the data into a data lake for analytics and vector databases for training AI models. It chose to use OpenAI LLM with private refinements leveraging its context and data to make it useful while protecting its data privacy. It used a private instance of ChatGPT to create an internal natural language interface to provide GenAI assistance to its business and technical teams. These approaches helped it increase its competitiveness, drove double-digit growth in an otherwise flat industry, and established the company as a thought leader in its industry.

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