“Any sufficiently advanced technology is indistinguishable from magic.”
—Arthur C. Clarke
Thomas Edison Center at Menlo Park in Edison, New Jersey, commemorates the birthplace of recorded sound and the world’s first practical incandescent light bulb. On New Year’s Eve, 1879, Thomas Edison used his newly invented incandescent lamp to illuminate Christie Street, where his laboratory was located. This public demonstration conveyed the art of the possible in electricity. It was novel and magical, and people flocked from New York City on trains to see the first street lit by the electric bulb.
But Edison didn’t stop there. He used his electric bulb demonstration to light the way to establishing the first central commercial power plant in the United States on Pearl Street, New York City, in 1882. Soon electricity was commercially produced and distributed, leading to the rapid electrification of homes and towns. This widespread adoption changed how we lived and worked; businesses could remain open late, home appliances improved the division of domestic labor (enabling many women to join the workforce), and factories reorganized production processes to build faster and better. Using electricity to illuminate one street created value in the immediate vicinity, but widespread electrification had far-reaching benefits beyond what was imaginable at the time.
Like electricity, AI (especially generative AI) is the transformative technology of our time. According to Goldman Sachs , AI can potentially lead to a $7 trillion increase in global GDP over the next 10 years.
Illuminate Your First Street with Generative AI
Edison’s demonstration of illuminating his street can teach us a few things as we consider initial use cases of generative AI in our organizations. Your goal with these early use cases is not only to deliver value for an immediate opportunity but also to showcase the potential for broader adoption.
How do you choose early use cases for generative AI? Look for things that are impactful, visible, and relatable. I use all three adjectives intentionally:
- Impactful: Early use cases should solve real business problems (or create new opportunities that matter to your business) and demonstrate the differentiated benefit of using generative AI to solve them.
- Visible: Select use cases with broader visibility in your organization or create a communication plan to generate visibility—the equivalent of people taking a train to see Edison’s light bulb.
- Relatable: Initial use cases shouldn’t be limited to solving one problem. Spark team members’ imaginations and inspire them to think about what they can solve within their domains using generative AI.
There are a few questions that can help when considering which “street” to illuminate first with generative AI:
- If this use case is successful, is the impact meaningful to my business or just a novelty?
- How does generative AI help me solve this problem? Can I demonstrate the differentiated power of generative AI with this use case?
- Will this just be an item in our department’s weekly report, or will this be an item that gets talked about at the water cooler? How will I brand and communicate this so it gets talked about at the water cooler?
- Is this something others in the organization can relate to, or does it require a deep understanding of a specific domain? How will I build a relatable story to explain this to someone who doesn’t understand the specific functional area so they can apply it to their domain?
Our AI Use Case Explorer is a good place to start to get some ideas.
Just as Edison’s first electric streetlight sparked excitement and imagination that led to lasting changes in business, the workforce, and broader society, the power of generative AI can usher in transformational change. In our discussions, many AWS customer executives are excited by the opportunities I describe here to light the way for broader transformation.
Generative AI Lights the Way to a More Inclusive Workforce
In his book Evolving Households: The Imprint of Technology on Life, economist Jeremy Greenwood talks about the significant impact of electricity and labor-saving household appliances on the division of labor in the home, which led to a dramatic increase in women’s workforce participation in the twentieth century. Similarly, generative AI-powered tools like the coding companion Amazon CodeWhisperer can make coding and development accessible to underrepresented groups, people with different learning abilities, employees with limited engineering or technical backgrounds, and communities that lack access to traditional computer science education.
Amazon CodeWhisperer can significantly increase developer productivity. In a productivity challenge we ran, participants were able to complete coding tasks on average 57% faster than those who did not use CodeWhisperer. You are no longer required to master and memorize specific syntax of a highly specialized coding language and can instead focus on business logic and problem-solving. CodeWhisperer is free for individual developers, and I cannot wait to see the doors it opens for a more inclusive workforce.
Generative AI Lights the Way to Wider AI Adoption
The widespread adoption of electric power went beyond Edison’s initial demonstration of simple illumination, powering factories and extending productive work hours by ending reliance on daylight. Generative AI has similarly made the power of AI relatable to many roles within an organization—from the board of directors and fellow C-suite peers to employees.
This is akin to the early days of the cloud when the ability to build products within weeks instead of years or scale up or down almost instantaneously allowed CIOs and CTOs to transform IT departments from order-taking service teams to strategic partners. It helped drive the adoption of cross-functional teams organized around outcomes and DevOps practices.
Many AWS customer executives are using early generative AI use cases to drive a much larger adoption of AI in their organizations. Use cases like document processing, automated metadata extraction, image sorting, and sentiment analysis can potentially deliver significant value immediately. I recommend checking out some of these AI services from AWS to get started right away.
Generative AI Lights the Way to Becoming a More Data-Driven Business
The cloud has enabled us to use the exponential rise in available data to build and train foundation models, which has led to rapid advancement in generative AI. As access to foundation models becomes more widespread, your business’s ability to tune models and inputs using your own data is critical for differentiation.
Just as data leaders have used the power of visualization and dashboards to explain the value of investments in data foundation, generative AI’s ability to extract insights, converse using natural language, contextualize, summarize, and present information in an easily consumable way can help drive the adoption of data-driven mindsets in your business.
Generative AI Lights the Way to Reimagining Your Operating Model
As we have seen in past technological revolutions, a value gap is created when an organization’s pace of change lags behind the pace of technology change. Success with generative AI requires changes to your organization’s operating model.
Use your early use cases to not only build a technical solution but also to learn and experiment with different aspects of your operating model. Examples include how you recruit and organize teams around specific skills and activities; how you might evolve practices around security, privacy, and compliance; skills and capabilities you need to build, such as validations and human-in-the-loop decisions for outputs generated by the models; and how you build responsible AI understanding and practices.
In Edison’s time, electrifying an organization meant going beyond turning on a single lightbulb or powering an isolated machine. Similarly, going beyond a few early generative AI use cases will require integrating the technology across your business processes and applications.
The cloud’s agility, scalability, elasticity, and cost efficiency are instrumental to adopting generative AI at scale. Demonstrating and harvesting the value of early use cases of generative AI to accelerate your cloud transformation and modernization will differentiate those who stop at one or two successful pilots and those who can adopt generative AI at scale.
Edison was not only an inventor but also a builder. At Amazon, we like to think of builders as people who challenge the status quo and reinvent on behalf of their customers. For leaders with a builder’s mindset, there has been no better tool in recent years than generative AI to act as a catalyst for bringing about lasting, transformational change. I cannot wait to hear about the streets you choose to illuminate and how you will light the way for broader transformation with generative AI.
- How Leaders Can Navigate Generative AI
- Amazon CodeWhisperer
- AI Use Case Explorer
- AWS AI services
- How Technology Leaders Can Prepare for Generative AI