How to adopt AI in business without vendor lock-in: IBM's advice
IBM says the era of AI as an experiment is ending: for large companies, models are becoming part of core infrastructure. The main risk is no longer just the quality of a model's answers but its opacity. Opacity drives up API costs, complicates integrations, slows work with sensitive data and deepens dependence on vendors. IBM sees the way out in strict governance and a more open approach to models.
AI-processed from AI News; edited by Hamidun News
When adopting AI in business, the main risk lies not in the capabilities of individual models but in how they are built into corporate infrastructure, IBM says.
Why does AI governance matter when adopting AI in a company?
Governance provides strict control and auditing of embedded models, protects the company's margin and reduces dependence on a single vendor.
What does adopting AI in business require?
Not only powerful models, but also a strict system of governance, control and auditing, once AI becomes the base layer for development, security and automation.
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Key Questions Answered
Why does AI governance matter when adopting AI in a company?
Governance provides strict control and auditing of embedded models, protects the company's margin and reduces dependence on a single vendor.
What does adopting AI in business require?
Not only powerful models, but also a strict system of governance, control and auditing, once AI becomes the base layer for development, security and automation.
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