Published: 05 August 2024
GenAI adoption is impeded by unpredictable technology costs, application customization difficulties and rapid evolution. Product leaders developing services or applications around GenAI must prioritize model and data selection, architecture of deployment, and privacy and flexibility for end-users.
Data management, lengthy qualification timelines and stringent testing requirements add further significant cost beyond compute cost to leveraging ITinfrastructure for generative AI (GenAI)services andapplications.
Seamless deployment of GenAI-based services faces key barriers — rapid model evolution, fast pace of hardware/software refresh and siloed deployments — all leading to customer anxiety.
Lack of hardware/software stack flexibility is a challenge for product developers building customer-specific GenAI solutions with a long product life cycle.
Product leaders developing services and applications around GenAI must:
Manage costs as part of a GenAI value by leveraging best practices for robust architecture, effective change management and efficient model
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Analysts:
Gaurav Gupta, Joseph Unsworth, Alan Priestley, Menglin Cao, Akhil Singh
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