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10 Best Practices for Scaling Generative AI
"Through 2025, at least 30% of Gen AI projects will be abandoned after proof of concept (POC) due to poor data quality, inadequate risk controls, escalating costs, or unclear business value."
While Generative AI (GenAI) has the potential to transform businesses across industries and its benefits are widely recognized, lack of clarity and concerns over poor data quality, shortage of skilled talent, and inadequate AI governance and risk controls remain big hurdles.
Many technology/AI leaders struggle to align GenAI strategies with business goals. Organizations are responding in varied ways, from outright bans to isolated experiments. A lack of understanding of best practices is slowing large-scale pilots and deployments.
The rapidly evolving GenAI ecosystem complicates technology choices. Constant changes make supplier selection challenging and long-term plans difficult to sustain.
So, what can you do to overcome these concerns?
This Gartner report presents key strategies and best practices to successfully adopt and scale GenAI projects at your enterprise.
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We feel you must read this report to learn more about:
Framework for prioritizing GenAI use cases
Investing in data and AI literacy
Instilling AI Engineering practices that enable seamless scaling
Build vs Buy Decision framework
Importance of responsible AI and best AI governance practices
