CONCLUSION
Solving the biggest
artificial intelligence
challenges
Most organizations have made investments and progress
in their AI journeys and are exploring the possibilities of
generative AI. But many find themselves hitting stopgaps
along the way, worried that costs and complexities will grow
too high as they progress.
Throughout this eBook, we explored the steps to forge ahead
and realize the full power of generative AI. To recap, let's
look at the biggest challenges we identified along the way,
along with a brief recommendation of how your organization
can solve them.
Challenge Solution
Discouraging
failures
Developing a fault-tolerant culture
Siloed,
unprocessed data
Creating a modern data strategy that
includes data lakes
Finding the right
business problems
Building blended teams that include both
technical and domain experts
The AI skills gap Adopting new organizational models,
processes, and team management
philosophies
Sustainably scaling
beyond pilot
projects
Leveraging end-to-end tools like Amazon
Bedrock and SageMaker to build and scale
generative AI applications
Measuring the
results
Forgoing traditional ROI metrics in favor
of agility, competitive advantage, and risk
tolerance using the value tree model
To learn more about how you can overcome
obstacles and accelerate your AI journey, visit
the AWS AI Resource Hub.
To learn more about how generative AI can
boost productivity, build differentiated
experiences, and innovate faster for every
businesses, visit the AWS Generative
AI Homepage.
Get started ›
2024, Amazon Web Services, Inc. or its affiliates. All rights reserved.
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