Theme 6: Putting a SUV Motor in a Ferrari
Legacy infrastructure can sabotage even the most ambitious AI initiatives. In 2024, your data and systems need to keep up with your AI ambitions.
Many companies are trying to bolt advanced AI onto outdated legacy systems, creating significant friction and limited results. When AI runs on insufficient infrastructure, it’s like putting a SUV engine in a Ferrari—neither performs as expected, and the potential is wasted. For AI to truly deliver, organizations must invest in modernizing their data architecture, integrating legacy systems, and ensuring clean, scalable data.
Putting a SUV Motor in a Ferrari challenges executives to confront the hidden costs of legacy systems.
• 63% of companies say legacy systems are a major barrier to AI adoption (Deloitte). Without modern infrastructure, AI initiatives are likely to fail.
• MIT Sloan reports that 56% of executives cite data quality as their biggest AI challenge. Without clean data, even the best AI models can’t deliver accurate insights.
• JPMorgan Chase invested heavily in modernizing its data infrastructure, building a centralized data lake and adopting advanced data integration practices. By doing so, the bank created a single source of truth across its various business units, enabling seamless data flow and reducing redundancy. This overhaul allowed JPMorgan Chase to leverage AI at scale for a range of applications, from fraud detection to personalized customer services.
In 2025, it’s time to upgrade or get left behind. Are you investing in the foundational infrastructure that AI requires, or are you hoping for magic from legacy systems that can’t keep up?
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