CONCLUSION
Solving the biggest
artificial intelligence
challenges
Most software companies have made investments
and progress on 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 business 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.
Visit us ›
2024, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Let's get started
To learn more about how your software company can overcome obstacles
and accelerate your AI journey, visit the AWS AI Resources Hub.
22