Generative 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.

Generative AI helps 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.

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.

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.

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.

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 appropriate responsible 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
  

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, mature tech and data platform is necessary to avoid generative AI challenges.

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.

Senior leaders should use GenAI every day, staying current on important developments to make more informed decisions around their GenAI investments, reinforce enterprise-wide initiatives, and  foster a culture of GenAI adoption and innovation. Leaders must also establish clear RAI guidelines,  GenAI governance processes, and oversight mechanisms; balancing speed with responsibility is the key to unlocking GenAI’s potential at scale. Keeping the latest tech on track requires disciplined execution, a clear focus on value, and a workforce ready to adapt.