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 large volumes of data. It is powered by very large machine learning models, often called foundation models (FMs). When these models are focused on text, they are typically large language models (LLMs).

Earlier generations of AI and machine learning were mainly about mapping inputs to simple outputs—for example, predicting a number or classifying an image as “cat” or “not cat.” Generative AI goes further by mapping complex inputs to complex outputs, such as:

• Summarizing long documents and extracting key insights
• Drafting content, code, or reports from natural language prompts
• Answering questions based on large collections of documents

Business leaders should pay attention now because adoption is accelerating and the economic impact is expected to be significant:

• 27% of professionals in large US organizations have already used generative AI for work-related tasks (Fishbowl survey of ~4,500 professionals).
• Gartner estimates that by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated.
• Goldman Sachs projects generative AI could increase global GDP by up to 7%—around $7 trillion—over the next decade.
• The global generative AI market is forecast to grow at a 34.2% CAGR.

In practical terms, generative AI can help your organization:

• Reimagine customer experiences with virtual assistants and conversational search
• Boost employee productivity through code generation, document drafting, and summarization
• Optimize processes such as contact center analytics, personalization, and knowledge management

Because the technology is broadly accessible and moving quickly, it is becoming a strategic imperative for leaders to understand where it fits in their business and how to use it responsibly.

Organizations are using generative AI to enhance productivity and create value across many functions. Common capability areas include:

• Code generation: AI coding companions (such as Amazon CodeWhisperer) can improve developer productivity—Amazon observed a 57% productivity improvement in an internal challenge.
• Contact center analytics: Automatically summarize customer calls and extract insights to improve service quality and training.
• Personalization: Generate tailored recommendations and content based on customer behavior and preferences.
• Virtual assistants: Provide human-like responses to customer and employee queries, improving self-service and reducing wait times.
• Design and creativity: Generate ideas, prototypes, and design variations for products, content, and campaigns.
• Conversational search: Let users query all your corporate information in natural language and get synthesized answers.
• Content generation: Draft marketing copy, reports, knowledge articles, and more.

Industry-specific examples include:

• Healthcare & Life Sciences: Accelerating drug discovery, improving clinical documentation, summarizing research, and supporting personalized medicine.
• Financial Services: Drafting investment research and loan documents, analyzing market sentiment, and automating creation of structured data products from unstructured sources.
• Automotive & Manufacturing: Optimizing part designs, enhancing in-vehicle experiences, improving testing and maintenance, and increasing factory equipment effectiveness.

To get started, a practical approach is:

1. Clarify the problems: Work backwards from your customer or internal user. Identify pain points such as slow document workflows, high support costs, or underused data.
2. Make data your differentiator: Treat your proprietary data as a strategic asset. Customize or fine-tune foundation models with your own data (for example, past reports, interaction logs, or design files) to create experiences that reflect your brand, tone, and domain expertise.
3. Start with focused pilots: Choose a few high-impact, low-risk use cases—like internal knowledge search, report summarization, or code assistance—and run controlled experiments.
4. Plan for scale: As you see value, think about the infrastructure and governance you’ll need to run models cost-effectively and securely at scale.

The key is to combine curiosity about the technology with a disciplined focus on measurable business outcomes—such as reduced cycle times, higher customer satisfaction, or improved employee productivity.

Generative AI introduces new opportunities, but it also brings specific risks that leaders need to manage thoughtfully. Because foundation models are large and open-ended, they raise questions around:

• Accuracy and hallucinations: Models can generate confident but incorrect or misleading content.
• Fairness and bias: It can be difficult even to define fairness for large language models. For example, how should a model assign pronouns when referring to different professions, or handle gendered references in specific contexts like sports leagues?
• Toxicity and safety: Outputs may include harmful, offensive, or inappropriate content if not properly constrained.
• Intellectual property (IP): Organizations need clarity on how training data is used and how generated content relates to copyrights and licensing.
• Privacy and data use: When customizing or fine-tuning models, you must know where your data goes and ensure that private or customer data is not used to train public models without consent.

From a business perspective, leaders should look for solutions and partners that provide:

• Security, scalability, and privacy by design: Controls for data access, encryption, logging, and monitoring built into the platform.
• Clear data boundaries: Assurance that your proprietary data and customer information remain private and are not commingled with public training data.
• Cost-efficient, performant infrastructure: The ability to train and run models at scale without overspending, while meeting performance requirements.
• Responsible AI tooling and practices: Ways to measure and mitigate issues like bias, toxicity, and hallucinations across the model lifecycle.

The broader ecosystem—including academia, industry, and government—is actively collaborating on new approaches to keep generative AI responsible, private, and secure. Inside your organization, it is important to:

• Set clear policies for acceptable use and data handling
• Educate executives and teams on both capabilities and limitations
• Address people, processes, skills, and culture alongside the technology

By treating governance and responsible AI as core design requirements—not afterthoughts—you can adopt generative AI in a way that supports innovation while managing risk.