EncodeAgent AI Digest #5
Gary Zhang
Construct exceptional SaaS & AI products and businesses | AI Advocate | Entrepreneur | Business/Technical Advisor | Startup Mentor | Investor
In every issue of our digest, we carefully curate a trio of articles that we believe hold considerable importance for professionals involved in product development or business management, especially those leveraging the revolutionary potential of artificial intelligence, with an emphasis on generative AI technologies.
Your Organization Isn’t Designed to Work with GenAI
The article discusses the challenges organizations face when integrating generative AI (GenAI) into their operations, emphasizing that GenAI should not be viewed as traditional automation but as an assistive agent that enhances human capabilities over time. It argues that to fully harness GenAI’s potential, companies must reimagine their business processes rather than simply integrating the technology into existing ones. The authors propose a new paradigm called “Designing for Dialogue,” which treats GenAI as a dynamic coworker, sharing responsibilities with humans based on context and competence. This approach involves task analysis, interaction protocols, and feedback loops to create a symbiotic relationship between humans and AI, allowing for continuous improvement and efficiency gains.
The article critiques traditional business process reengineering (BPR) methods, which are not suited for GenAI due to their rigid task assignments and fixed processes. Instead, GenAI requires a more flexible and iterative approach, with a dynamic interaction between humans and machines. The authors illustrate the effectiveness of Designing for Dialogue with the example of Jerry, a company that significantly improved its customer service by allowing AI to lead the process, with human intervention at strategic points.
To implement GenAI successfully, organizations should identify high-value processes, perform task analysis, design interaction protocols and feedback mechanisms, train teams, and continuously evaluate and adjust the integration. The article concludes that organizations adept at Designing for Dialogue will gain a competitive advantage and be able to innovate new products and services in an AI-powered age.
领英推荐
How Companies Are Starting to Use Generative AI to Improve Their Businesses
In 2023, CIOs and CTOs are increasingly deploying sophisticated AI systems to enhance productivity and efficiency within their organizations. In an interview with The Wall Street Journal, Sesh Iyer of Boston Consulting Group, Sathish Muthukrishnan of Ally Financial, and Fletcher Previn of Cisco discussed their use of generative AI. Previn highlighted its application in improving help desk efficiency and employee productivity, while Muthukrishnan emphasized its role in summarizing customer-care calls, leading to time savings and allowing associates to focus more on customers. Iyer mentioned a productivity lift of 10–20% in day-to-day work and even higher in critical functions like software development and customer service. The cost of implementing these AI systems varies, with Ally Financial spending about half a million dollars on platform build and $20,000 monthly thereafter, experiencing significant productivity gains. Previn pointed out the importance of strategic investment in AI to avoid missing out on crucial technology advancements. Iyer noted that a $50 million investment in generative AI is indicative of a company’s serious commitment, with such companies aiming for substantial productivity improvements and revenue growth. This investment threshold is not absolute but depends on a company’s ambition and includes both direct and indirect costs, such as upskilling employees and transforming functions.
3 Ways Predictive AI Delivers More Value Than Generative AI
The article discusses the debate between focusing on generative AI, which creates content like writing and images, and predictive AI, which enhances operations like marketing and fraud detection. It argues that the question of which AI to prioritize is misguided and suggests that companies should instead identify key problems and determine how AI can solve them. Generative AI is noted for performing tasks typically done by humans, while predictive AI is lauded for streamlining large-scale, systematic processes and often delivering greater improvements to enterprise efficiencies. Predictive AI is advantageous because it often yields higher returns, can operate autonomously, and requires lighter-weight models compared to the resource-intensive models of generative AI. Despite the allure of generative AI’s novelty, the article emphasizes that predictive AI should not be overlooked, as it still holds significant untapped potential and is projected to be a larger market. Both types of AI share core machine learning principles and should not be seen as mutually exclusive but rather as complementary technologies that can be leveraged according to the specific needs of an organization.