Table of Contents
Sign up for major executives in San Francisco on July 11-12, to listen to how leaders are integrating and optimizing AI investments for good results. Find out Additional
It feels like generative AI is everywhere you go. The explosive launch of advanced chatbots and other generative AI technological know-how, like ChatGPT and some others, has commanded the notice of anyone, from shoppers to small business leaders to the media.
But these chat equipment are just the idea of the iceberg when it arrives to gen AI’s likely impression. The even better benefit of generative AI will appear as companies get started to implement it on behalf of their clients and personnel. There are a wide range of enterprise use cases, from solution design to customer services to provide chain administration and a lot of, lots of much more. New versions, chips and developer providers in the cloud, like those from AWS, are opening the door to widescale adoption throughout each and every marketplace.
>>Follow VentureBeat’s ongoing generative AI protection<<
Understanding the realm of possibility — and the risk — of generative AI is critically important for CIOs who want to start using this technology to gain an advantage for their businesses. The following are my five tips for getting started.
Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.
1. Get your data house in order
Generative AI is here, and it’s poised to have a transformational impact on our world. The potential upsides of leveraging it in your business are too great — and the downsides of being a laggard too many — not to get started now. But the very beginning of this journey is making sure you have the right data foundations for AI/ML. In order to train quality models, you must start with quality, unified data from your business.
For example, Autodesk, a global software company, built a generative design process on AWS to help product designers create thousands of iterations and choose the optimal design. These machine learning models rely on a strong data strategy to user-defined performance characteristics, manufacturing process data, and production volume information.
2. Envision use cases around your own data
Generative AI could be used to develop predictive models for businesses or to automate content creation. For example, companies could generate financial forecasting and scenario planning to make more informed recommendations for capital expenditures and reserves.
Or generative AI might act as an assistant for clinicians to create recommendations for diagnosis, treatment and follow-up care. Philips is doing just that. The health technology company will use Amazon Bedrock to develop image processing capabilities and simplify clinical workflows with voice recognition, all using generative AI.
We’re also seeing AWS customers harness generative AI to optimize product lifecycles, like retail companies looking to more precisely manage inventory placement, out-of-stock issues, deliveries and more — or using generative AI to create, optimize and test store layouts. By identifying these scenarios early and exploring the art of the possible with the data you already have, you can ensure your investment in gen AI is both targeted and strategic.
3. Dive into developer productivity benefits
Generative AI can provide significant benefits for developer productivity. It can be a powerful assistant for repetitive coding tasks like testing and debugging, freeing developers to focus on more complex tasks that require human problem-solving skills. CIOs should work with their development teams to identify areas where generative AI can increase productivity and reduce development time.
4. Take outputs with a grain of salt
Generative AI is only as good as the data it’s trained on, and there’s always the risk of bias or inaccuracies. Sometimes the output is a hallucination, a response that seems plausible but is in fact made up. So guide your developers, engineers and business users to regard gen AI outputs as directional, not prescriptive.
Manage the business expectations about accuracy and consider some of the special challenges surrounding responsible generative AI. These models and systems are still in their early days and there’s no replacement for human wisdom, judgment and curation.
5. Think hard about security, legal and compliance
As with all technology, security and privacy are paramount, and gen AI introduces new considerations, including around IP. CIOs should work closely with their security, compliance and legal teams to identify and mitigate these risks, ensuring that generative AI is deployed in a secure and responsible manner. Further, scope your plans around compliance and regulations and think carefully about who owns the data you’re using.
Generative AI has the potential to be a transformational technology, tackling interesting problems, augmenting human performance and maximizing productivity. Dive in now, experiment with use cases, harness its benefits, and understand the risk, and you’ll be well-positioned to leverage generative AI for your business.
Shaown Nandi is the director of technology, strategic industries at AWS.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.
You might even consider contributing an article of your own!
Read More From DataDecisionMakers