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The Expansive Power of Generative AI
What is the difference between generative AI and predictive AI (traditional AI)?
Augmented by human interaction, predictive AI focuses on pattern recognition, forecasting outcomes, or making decisions based on historical data; it excels at tasks like classification, recommendations, and automation of rule-based processes.
The benefits of generative AI include helping humans create seemingly new content—text, images, code—based on what it has learned from data. The most powerful generative AI algorithms are built on top of foundation models that are trained on a vast quantity of unlabeled data to identify underlying patterns for a wide range of tasks. While significant generative AI customization still requires human expertise, adopting a generative model for a specific task can be accomplished with relatively low quantities of data or examples through APIs or by prompt engineering.
The benefits of generative AI include helping humans create seemingly new content—text, images, code—based on what it has learned from data. The most powerful generative AI algorithms are built on top of foundation models that are trained on a vast quantity of unlabeled data to identify underlying patterns for a wide range of tasks. While significant generative AI customization still requires human expertise, adopting a generative model for a specific task can be accomplished with relatively low quantities of data or examples through APIs or by prompt engineering.
What is the difference between generative AI and AI agents?
Generative AI powers the underlying capability to create content and understand complex instructions.AI agents go a step further—they use generative models to pursue goals; to reason, plan, and execute tasks across systems. While generative AI might write a report, an agent can interpret a business objective, gather the right data, write the report, and send it to the right people. Think of generative AI as an engine and AI agents as goal-oriented (digital) coworkers powered by that engine to act across systems, both guided and supervised by humans.
How is generative AI beneficial for businesses?
The biggest benefits of generative AI show up when GenAI initiatives are tied to core business functions, not just experiments. Enterprises benefit most when they move beyond isolated pilots and use GenAI to transform foundational workflows linked to strategic priorities. GenAI turns manual processes into fast, data-driven cycles, and is already unlocking tangible productivity gains across areas like software development, customer service (for example, smarter contact centers), marketing content, and R&D.
What is the biggest challenge facing organizations that want to implement generative AI?
The biggest challenge to GenAI ROI is not the tech—it’s the people and the process. This type of change management is often overlooked, but top-performing organizations follow the 10-20-70 principle: they dedicate 10% of their efforts to algorithms; 20% to data and technology; and 70% to people, processes, and cultural transformation. Successful GenAI adoption requires redesigning how work gets done, redefining roles, upskilling teams, and fostering collaboration between humans and AI. Without this shift, even the best transformation efforts will fail to achieve their goals. Business leaders who invest in training and change management position their organizations to effectively accelerate generative AI adoption.
What are the risks of generative AI?
Generative AI systems are democratizing AI capabilities that were previously inaccessible due to the lack of training data and required computing power. While wider GenAI adoption is a good thing, scaling generative AI become problematic when organizations don’t have an appropriateresponsible AI (RAI) framework in place from day one of deployment. As users experiment with these systems, generative AI risks need to be addressed:
- Unknown Capabilities. Large GenAI systems have exhibited a massive capability overhang—skills and dangers that are not planned for in the development phase and are generally unknown and unexpected even to the developers. This can pose a serious threat if the right guardrails are not in place to effectively manage unexpected usage.
- Bias and Toxicity. Outputs from GenAI will be as biased as the data it is trained on. Many popular language models today are trained on the wilds of the internet, where there is plenty of bias—along with toxic language and ideas.
- Data Leakage. Many companies have quickly put policies in place to forbid employees from entering sensitive information into GenAI models, fearing that it could get incorporated into the AI model and re-emerge in public.
- Hallucination. GenAI systems can make arguments that sound extremely convincing but are 100% wrong. Developers refer to this as “hallucination,” a potential outcome that limits the reliability of the answers coming from AI models.
- Lack of Transparency. GenAI models currently provide no attribution for the facts underlying the content they generate, which makes it impossible to verify the correctness of generated claims—further increasing the danger posed by AI-model hallucinations
What challenges does generative AI face with respect to data?
Companies with clean, well-governed, and accessible data can move faster and start scaling generative AI solutions sooner, training models more effectively and generating more accurate insights. But organizations lagging in data maturity or missing that layer of foundational data often find themselves stuck in the pilot stage, unable to scale their GenAI initiatives. A modern, maturetech and data platform is necessary to pave the way for widespread generative AI adoption and avoid generative AI challenges.
How can business leaders get started with generative AI today?
Start strategic, prioritizing a small number of high-value pilots, aligned directly with core business objectives, that balance short-term momentum with long-term transformation. For example:
- Product leaders can use GenAI for summarizing research and drafting product documents.
- Ops executives might develop an agent to streamline a bottlenecked internal process.
- Engineers can use GenAI tools to accelerate development, automate testing, and manage CI/CD pipelines.
Deploy, Reshape, Invent
Boost performance, transform core functions, and innovate at top speed. Part of a broader approach to AI and GenAI, our DRI strategy helps drive substantial strategic value. Learn about these three interconnected plays from three BCG experts.
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Featured Expert:Julie Bedard
Your people need to get excited about how the latest tech can help them succeed. Our Deploy play gets employees using the technology as soon as possible—a critical first step towards realizing value from GenAI.
Your people need to get excited about how the latest tech can help them succeed. Our Deploy play gets employees using the technology as soon as possible—a critical first step towards realizing value from GenAI.
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Featured Expert:David Martin
End-to-end AI transformation is within reach—but if you think too small, you’ll come up short. Our Reshape play reimagines entire functions to deliver the cost savings and greater ROI that AI makes possible.
End-to-end AI transformation is within reach—but if you think too small, you’ll come up short. Our Reshape play reimagines entire functions to deliver the cost savings and greater ROI that AI makes possible.
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Featured Expert: Beth Viner
With AI, innovation can move at a pace you’ve never seen before, bringing new products and businesses to your organization. Our Invent play is a chance to find growth opportunities that you may never have found otherwise.
With AI, innovation can move at a pace you’ve never seen before, bringing new products and businesses to your organization. Our Invent play is a chance to find growth opportunities that you may never have found otherwise.
Our Client Impact with Generative AI
Client Impact
Discover how PHOENIX group is using AI to streamline supply chains, cut costs, and free employees to focus where human judgment matters most.
BMW Group is partnering with BCG to optimize efficiency and innovate workflows with GenAI leveraging Auto AI by BCG X. The solution was developed in collaboration with BCG X, AWS, and BCG Platinion. The streamlined approach to day-to-day operations is creating exciting new opportunities for growth.
Working with BCG, Commvault has improved time to closure of projects while handling customer data responsibly and solving problems. Here's how.
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New York Life partnered with BCG to see how GenAI, coupled with transformational governance, could deliver exceptional experiences as well as efficiencies across the enterprise—furthering the company’s growth and advancement as an industry leader.
Our Generative AI Products
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GenAI Evaluator by BCG X
Our comprehensive solution ensures GenAI systems are proficient, safe, and secure—allowing you to accelerate your strategic AI transformation with confidence.
GenAI Evaluator by BCG X
Deep Customer Engagement AI by BCG X
As part of Deep Customer Engagement AI by BCG X’s end-to-end customer transformation, our GenAI accelerators help clients revolutionize their customer service operations.
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Conversational Commerce by BCG X
Conversational Commerce by BCG X
Data Intelligence AI by BCG X
Data Intelligence AI by BCG X transforms data into actionable insights by combining internal and third-party datasets to optimize supply chains, enhance customer experiences, and more.
Merch AI by BCG X
Merch AI by BCG X helps decode one of retailers’ fundamental challenges: how to offer customers the right products in the right stores at the right cost.
The Future of Science Is AI-Powered
Join the BCG X AI Science Institute—where cutting-edge AI research, academic rigor, and real-world business impact converge.
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Meet Our Generative AI Experts
BCG’s generative AI experts have deep experience in AI technology, neural networks, generative models, the benefits of generative AI, and more. Here are some of our experts in generative AI.
