Generative AI refers to a class of algorithms that can create new content and ideas—such as text, images, code, audio, and video—based on patterns learned from very large datasets. Earlier machine learning models typically mapped simple inputs to simple outputs. For example: - Traditional ML: takes numeric inputs (like transaction amounts) and predicts a numeric output (like fraud risk). - Classic deep learning: takes complex inputs (like images or video) and produces relatively simple outputs (for example, whether an image contains a cat). Generative AI, by contrast, works with complex inputs and produces complex outputs. It can, for example: - Summarize long documents and extract key insights - Generate code from natural language prompts - Draft marketing copy, emails, or reports - Answer questions based on large collections of documents This capability is powered by very large machine learning models called foundation models (FMs). A specific type of FM, the large language model (LLM), can perform a wide range of text-based tasks across domains: writing code, solving math problems, engaging in dialogue, and analyzing documents. A key difference for businesses is that these foundation models can be customized with your own proprietary data. That allows you to: - Embed your brand’s tone and style into generated content - Build recommendation engines tailored to your customers (for example, a grocery chain using shopper preference data) - Auto-generate internal reports that reflect your specific formats and data sources (for example, a financial firm’s daily activity reports) In short, generative AI lets organizations move from using AI mainly for prediction and classification to using it to generate new, context-aware content and insights that are specific to their business.

Organizations are using generative AI to reshape how work gets done across functions and industries. Some of the most common capabilities and use cases include: 1. Productivity and software development - Code generation: AI coding companions such as Amazon CodeWhisperer can improve developer productivity by up to 57% (based on an Amazon productivity challenge). - Documentation and boilerplate: Automatically generate tests, comments, and standard code patterns. 2. Customer experience and support - Virtual assistants and chatbots: Provide more natural, human-like responses to customer queries. - Contact center analytics: Summarize and extract insights from customer calls to improve service quality and training. 3. Personalization and marketing - Personalized recommendations: Use customer behavior and preference data to generate more relevant product suggestions. - Content generation: Create marketing copy, outbound messages, and tailored content at scale. Gartner estimates that by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated. 4. Knowledge management and search - Conversational search: Let employees query corporate information in natural language and get synthesized answers. - Summarization and extraction: Turn long documents, reports, or transcripts into concise summaries and key points. 5. Industry-specific use cases - Healthcare and life sciences: Accelerate drug discovery by predicting protein structures, generating novel amino acid sequences, and identifying docking sites; improve clinical workflows by auto-generating chart notes and summarizing research. - Financial services: Draft investment research, loan documentation, and regulatory communications; analyze market sentiment from social media, news, and financial data. - Automotive and manufacturing: Optimize part and material design, create new in-vehicle experiences with virtual assistants, and improve maintenance by generating suggestions from historical repair and equipment data. - Education: Summarize research documents, lecture transcripts, and class notes to make information easier to browse and search. From a macroeconomic perspective, research from Goldman Sachs suggests generative AI could increase global GDP by as much as 7%—roughly $7 trillion—over the next 10 years. That projection reflects both consumer use and, importantly, productivity and output gains inside organizations. For business leaders, the takeaway is that generative AI is not just about chatbots. It can help reimagine customer experiences, boost employee productivity, ignite creativity, and optimize processes across the value chain.

A practical approach to getting started with generative AI combines strategy, technology choices, and responsible AI practices. 1. Start with learning and exploration - Build a basic understanding of what generative AI is and what problems it can realistically solve. - Don’t delegate all learning to IT—business leaders should understand the opportunities and constraints as well. - Encourage teams to experiment with low-risk use cases to build familiarity. 2. Work backwards from business problems - Identify high-value opportunities: improving supply chain efficiency, enhancing customer service, reducing manual document work, or creating new services. - Focus on problems where better content, faster insight, or more personalized experiences would make a measurable difference. - Avoid “technology first” pilots; instead, define the business outcome and then design the generative AI solution. 3. Choose the right foundation models and infrastructure When evaluating models and platforms, look for: - Simple, secure ways to build and scale generative AI applications, with security and privacy built in. - Cost-efficient, high-performance infrastructure to train or fine-tune models and run inference at scale. - Generative AI–powered applications that can plug into existing workflows to transform how work gets done. - Strong support for using your data as a differentiator—customization and fine-tuning with your proprietary data. 4. Treat your data as a strategic asset - Use your proprietary data to customize foundation models so outputs reflect your brand, domain, and customers. - Examples include tailoring recommendation engines with your customer behavior data or training models on your historical reports and documents. 5. Address responsible AI, security, and privacy from the start Generative AI raises specific risks and questions: - Accuracy and hallucinations: Models can generate plausible but incorrect information. - Fairness and bias: Even defining fairness can be complex (for example, how pronouns are assigned in different professional contexts). - Toxicity and IP: Outputs may include harmful content or raise intellectual property concerns. - Privacy and data use: You need clarity on where your data is stored, how it is used, and whether it is used to train public models. To manage these risks: - Establish guidelines for acceptable use, review processes, and human oversight. - Ensure that security, scalability, and privacy are built into your chosen platform and architecture. - Confirm that customer and proprietary data remain private and are not used to train public models without explicit control. - Stay engaged with evolving best practices from industry, academia, and regulators. 6. Move early, but iterate - Most initiatives take time to gain traction, so it helps to start now with focused pilots. - Use early experiments to refine your strategy, understand costs, and identify where generative AI delivers the most value. By combining thoughtful experimentation with clear business goals and strong governance, organizations can begin to realize the business value of generative AI while managing risk in a structured way.