AI & Analytics

Generative AI’s edge: Where business value meets technical feasibility

Nov. 7, 2023 | Analyst Report | 2-minute read

Generative AI’s edge: Where business value meets technical feasibility

Forrester Research correctly identifies that success with generative AI is found at the intersection of business value and technical feasibility. Curious about how to find this intersection for your specific needs? Here are some real-life examples we’re working on to inspire your approach:

  • Obtaining better and deeper insights. Organizations can more quickly extract deeper and more accurate insights from their structured and unstructured data. In healthcare, for example, generative AI agents equipped with natural language processing capabilities can sift through vast volumes of medical records or clinical trial notes to yield crucial information. AI also can scan electronic health records to identify patient data anomalies, patterns and trends to improve diagnostic accuracy and produce personalized treatment plans for better patient outcomes. It also can be used to aid clinical decision-making by predicting how individual patients will respond to a given treatment based on their genetics, biomarkers and treatment history.
  • Ensuring regulatory and quality standards compliance. Across every facet of their operations, organizations can use generative AI for real-time monitoring and automated reporting. Companies keep humans in the loop, but AI can collect required data and evidence and draft responses to formal inquiries. Customer service interactions can be monitored and analyzed in real time to ensure they meet regulatory requirements and quality standards for customer experiences.
  • Creating personalized sales and marketing content. Generative AI allows teams to move beyond generic, one-size-fits-all sales and marketing approaches to create campaigns that reflect customers’ unique interests and needs. Engagement becomes more meaningful, and teams can experiment with what types of content meet changing preferences. By analyzing large data sets, AI develops insights into customer behaviors, preferences and engagement patterns that are used to craft tailored messages and product recommendations that resonate.

Getting generative AI right is a journey, not a quantum leap

For more on finding your sweet spot and other best practices for success with AI, we’d like to share this complimentary Forrester Research-authored white paper, “The Executive’s AI Primer.” It offers invaluable insights for leaders working to build a foundational knowledge of AI and ensure their decision-making is grounded in smart resource allocation and knowing where the pitfalls might be.

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