By Subbu Iyer & Siddharth Patel Human Engineering represents the pinnacle of Engineering Excellence, centered around Intelligence or Making Meaning. Human Cognition works when its five senses are sharp and add patterns to make meaning with meta data gathered through the power of Learning / Observability. For more than a century, significant efforts have been made to replicate and reproduce human intelligence at scale, popularized as Artificial and Generative Intelligence. The three S’ on which Human Engineering creates Intelligence is Sensing, Seeking and Seeing. This when aggregated to a community results in Invention, Innovation and Improvisation.
It should not be too difficult from here to recognize that prioritizing Personalization over Industrialization is the key to successfully implementing AI and GI at scale. "From System 1 to System 2: A Survey of Reasoning Large Language Models" ( https://bit.ly/43e91wJ is a compelling survey paper that explores the practicality of AI development, emphasizing that reasoning is fundamental to this progress. While the Survey effectively maps and documents the evolution of AI and highlights the current technological gaps, it overlooks three critical aspects that keep us on the periphery rather than at the core of such advancements in Intelligent Technology Adoption.
Exhibit 1: Tracing the development of AI from 1961 - 2024 referenced from the aforementioned Research Survey It is not too late to shift the focus of Industrialization to Personalization in making Artificial and Generative Intelligence cross the chasm of experimentation to implementation. 2024’s AI experiments have yielded a wealth of insights. Now it's time to turn those insights into tangible results writes Simon Kahn in his Think with Google December 2024 article “From AI experimentation to implementation: 3 learnings for 2025” (bit.ly/4kkKbRT). In India, the Android marketing team ran a pilot to speed up creative production, leveraging Pencil Pro to create over 100 highly contextual video ads in near real time from a longer livestream video.
As a result of the pilot, the team was able to save over 200 hours on creative production time and approximately 70% in cost. However, the pilot also provided valuable lessons about the limitations of AI and the importance of human-AI collaboration.
Intelligence is Personal
One can never grow tired of the phrase "Intelligence is Personal" due to the intricate design of life and the principles of Human Engineering. From Human Intelligence emerges the creation of products and services that sustain life, with design being the crucial first step. While it's always been acknowledged that context, rather than condition, would shape intelligence, not everyone has embraced the idea of being a Force for Good. As a result, designs that were originally aligned with the essence of life became skewed by the conditions of industrialization. This shift occurred primarily because the mantra of success focused on extracting more than creating value.
However, we are at a Strategic Inflection Point (SIP) on the Planet when there is very little left to extract and the only real option is to create. Exhibit 2: Technological Advances enable Contextual over Conditional Reasoning now The aforementioned survey paper has taken a comprehensive approach to understanding the evolution of the AI/GI development, particularly highlighting the Conditional Reasoning Approach embraced by early industry leaders. From OpenAI's o1/o3 and DeepSeek's R1 to everything in between have accomplished expert-level performance in fields such as mathematics and coding at a task level but have not aggregated to a Process Level Application.
Adopting their generic models will not provide industries with the benefits they desire from their own AI/GI implementation. The Large Language Models (LLMs) utilized in your enterprises must be contextual. Instead of strengthening their core models, Industry must focus on their enterprise models (LLM’s) and Agentic Framework. Industry should not wait for these commercial software entities to solve their complexity. Innovation is about Simplifying one’s legacy processes, modernizing technology and disrupting the industry with new models that respond to the relevance of the emerging future.
AI is transforming every organization around the world and represents an unprecedented opportunity to solve complex problems, drive growth, create efficiencies, and open up new business opportunities. This is particularly true for startups, who are moving very quickly to address new market opportunities with AI. Thomas Kurian; CEO, Google Cloud
Three Immutable Principles for the Successful Adoption of AI / GI
Despite significant investments made in AI / GI experimentation with perhaps a misguided perspective, the following three essential principles should guide the transition from Experimentation to Implementation that is centric to the Digital Transformation of the enterprise: Unimodality to Multimodality: All four data types (Text, Video, Audio, and Graphics) must be prepared for real-time computing enabling the Continuum of data from Creation to Consumption. Task to Process Automation: Automation should extend beyond the enterprise level to encompass the entire ecosystem that allow roles to meaningfully participate.
Universal to Personal Digital Agent: This involves a tangible shift from Search to Contextualization that drive Productive decision and actions in real time. The advancements in Digital Technology with AI / GI leading them is an opportunity for Innovation before Operational Optimization. The June 2024 BCG article Innovation Needs a Reboot (https://on.bcg.com/4idwK4q) reveals Innovation readiness since 2022, has plummeted, with just 3% of companies in the ready zone today. BCG Innovation Research 2024 confirms that 83% respondents reported that their