What startups can learn from how large companies use generative AI

Did you know 96% of AI/ML unicorns run on AWS? They use generative AI to enhance customer experiences, increase productivity, and optimize operations. As large companies adopt generative AI on a significant scale, they uncover innovative strategies and insights that can benefit startups like yours.

With the generative AI market projected to exceed $66 billion by the end of 2024, this technology offers vast potential for startups to innovate and grow. A recent McKinsey Global Survey on AI found that 65% of organizations now regularly use generative AI—nearly double the adoption rate from the previous survey.

For startups, tapping into generative AI with AWS opens up new possibilities for scalability, efficiency, and customer satisfaction.

Introduction to generative AI in business

What is generative AI?

Generative AI, or GenAI for short, is artificial intelligence (AI) that can create new content and ideas. It uses machine learning (ML) algorithms to generate original content like images, text, music, or synthetic data based on the data it has been trained on. Unlike traditional AI, generative AI does not depend on explicit sets of rules and can adapt to a diverse set of tasks.

Benefits of early adoption by large companies 

Generative AI adoption among large companies reveals several advantages that drive innovation and efficiency:

• Accelerates research: It explores complex data, identifies trends, and offers new insights—enabling faster problem-solving and innovation.

• Enhances customer experience: Generative AI models can respond naturally to customer interactions, enabling personalized and efficient customer workflows.

• Optimizes business processes: AI-powered tools streamline processes across departments, from engineering and marketing to sales and finance.

• Boosts employee productivity: It supports team members by automating tasks like content generation, data analysis, and more, freeing time for strategic work.

How large companies are leveraging generative AI

Generative AI is transforming how large corporations operate, and AWS has been a critical partner in enabling this transformation. Here’s a closer look at how top companies are using generative AI for specific business goals:

Exscientia: Reimagining drug discovery

By leveraging AWS's generative AI capabilities, Exscientia accelerates the traditionally lengthy and costly process of identifying viable drug candidates. Generative AI models analyze vast chemical datasets to predict molecular properties, design novel compounds, and simulate their potential efficacy.

AWS provides the computational power and scalability required for Exscientia’s complex workloads, enabling them to optimize compound design at unprecedented speeds. For example, using generative AI on AWS, Exscientia can evaluate thousands of compounds in a fraction of the time it would take using traditional methods. This precision allows them to prioritize only the most promising candidates, reducing the time to market for critical treatments.

By partnering with AWS, Exscientia is not only saving time and costs but also advancing medical research and improving patient outcomes globally.

Read more about Exscientia’s AWS-powered journey.

Crypto.com: Real-time sentiment analysis

Crypto.com, a leading cryptocurrency platform, uses AWS’s generative AI solutions, including Anthropic Claude 3 large language models on Amazon Bedrock for sentiment analysis and application development, as well as Amazon SageMaker to fine-tune its custom models.

This capability is essential in the volatile world of cryptocurrency, where market sentiment can shift dramatically in minutes. By analyzing millions of unstructured data points from social media posts, news articles, and other online content, Crypto.com offers insights into how the market perceives specific cryptocurrencies and trends.

With AWS, Crypto.com can process these vast datasets in seconds, delivering sentiment analysis results in less than 1 second. This enables users to make informed decisions based on up-to-the-minute data. Generative AI on AWS is key in transforming raw data into actionable insights, providing Crypto.com with a competitive edge in the fintech industry.

Additionally, AWS’s scalable infrastructure ensures that Crypto.com can handle spikes in data volume during peak trading periods, maintaining consistent performance and reliability for its users.

Learn more about Crypto.com generative AI implementation.

Box: Intelligent content cloud with generative AI

Box, a content management and collaboration leader, is empowering its users to extract deep insights from their data with AWS generative AI technologies. By integrating the Amazon Q Business Connector, Box allows customers to analyze unstructured content—such as documents, images, and videos—to uncover trends, patterns, and actionable insights that were previously hidden.

For example, a business using Box can utilize generative AI to analyze customer feedback stored as documents, identifying common themes and actionable suggestions. Similarly, teams can process large libraries of videos or presentations, extracting highlights or generating summaries for easier consumption.

This integration is particularly valuable for startups managing vast amounts of content, as it transforms data into a strategic resource for innovation and growth.

By leveraging AWS generative AI, Box helps organizations unlock the full value of their content, improving efficiency and driving more intelligent business strategies.

Discover more about Box generative AI journey.

Lessons for startups

As large companies continue to innovate with generative AI, startups can draw important lessons from their experiences. Here’s how emerging businesses can effectively adopt these practices to accelerate growth:

Identifying opportunities 

For startups, the key to generative AI success is identifying areas where AI can create the most value. Instead of deploying AI across every department, startups should focus on high-impact areas like customer support, marketing, or personalized product recommendations.

Analyzing customer pain points or workflow inefficiencies is a great way to uncover opportunities. For example, suppose your startup struggles with resource-intensive customer interactions. In that case, generative AI tools like Amazon Bedrock can enable you to integrate foundational models that streamline personalization and enhance automation at scale.

Additionally, Amazon Q, an AI-powered assistant, helps startups improve operational efficiency by enabling deeper insights, faster decision-making, and easier access to critical information. This tool effectively supports teams by generating concise summaries or recommendations based on internal data.

For startups focused on personalization, Amazon Personalize incorporates generative AI features, such as creating recommendations with themes from its Content Generator. This allows businesses to build dynamic systems that adapt to user preferences in real-time, creating more meaningful customer experiences. Learn more about Amazon Personalize’s generative AI capabilities.

Building a GenAI strategy

An effective AI strategy is essential for realizing the potential of generative AI. Startups should consider the following steps:

• Define clear goals that align with business objectives. Your goals might include automating repetitive tasks, improving customer satisfaction, or reducing operational costs.
• Understand technical requirements: Generative AI often requires specific technical resources, including large datasets, high computing power, and model training expertise. Leveraging AWS tools like Amazon SageMaker helps startups develop, train, and deploy models efficiently without building infrastructure from scratch.
• Understand technical requirements: Generative AI often requires specific technical resources, including large datasets, high computing power, and model training expertise. Leveraging AWS tools like Amazon SageMaker helps startups develop, train, and deploy models efficiently without building infrastructure from scratch.
• Secure and compliant implementation: For sensitive industries like finance or healthcare, ensure your AI solutions meet regulatory standards. AWS offers compliance-focused resources and tools for secure data management, making it easier to adopt AI safely.

Overcoming generative AI challenges: Tools and resources for startups

Startups face unique challenges when implementing generative AI, from data constraints to technical complexities. Here are some common challenges and solutions:

Data quality and volume

AI models require large amounts of high-quality data to perform accurately. For startups with limited data, AWS Data Exchange offers access to third-party datasets that can complement in-house data and improve model training. Alternatively, startups can use synthetic data generation techniques to simulate real-world conditions.

Technical skill gaps

Generative AI requires machine learning and data science expertise, which can be difficult for early-stage startups to access. AWS offers a range of educational resources, including courses, tutorials, and online communities, to help startups upskill their teams and gain the necessary knowledge to manage AI models effectively.

Cost of AI resources

Training and deploying models can be resource-intensive. AWS’s cost-management tools, like AWS Budgets and AWS Cost Explorer, allow startups to monitor spending and optimize costs by identifying areas for efficiency improvements. Additionally, AWS Free Tier services allow testing basic functionality without initial costs.

Future Outlook and Strategic Planning for Startups

As we have learned, Generative AI holds significant long-term benefits that can help your startup innovate and scale sustainably. Working with AWS’s dedicated network of startup partners gives you access to proven best practices and expert guidance to help you thrive.

Book your call now and start on your unique startup journey with AWS Startups.