Read: August 2023
Inspiration: Came across on Amazon’s bestseller list; interested in learning more perspectives on the impact of AI
Written with the help of ChatGPT, below is a brief summary to understand what is covered in the book.
“Competing in the Age of AI”, published in 2020 by authors and professors Marco Iansiti and Karim R. Lakhani, is a comprehensive analysis of how artificial intelligence (AI) is transforming businesses and industries. The book explores how AI is reshaping the competitive landscape, driving innovation, and redefining business strategies. Iansiti and Lakhani emphasize that successful organizations in the age of AI are those that can harness the power of data and machine learning to create new business models and deliver superior customer experiences. The authors provide numerous case studies, from companies like Amazon, Alibaba, and IBM, to illustrate how AI is being implemented across various sectors, from healthcare to manufacturing. They also address the challenges and ethical considerations associated with AI adoption. Furthermore, the book highlights the importance of fostering a culture of learning and experimentation within organizations to fully leverage the potential of AI. “Competing in the Age of AI” serves as a roadmap for businesses looking to stay competitive in an increasingly data-driven and automated world, emphasizing the need to adapt and innovate to thrive in the AI era.
Direct from my original book log, below are my unedited notes (abbreviations and misspellings included) to show how I take notes as I read.
Last 2 weeks of March 2020 were 2 of the most consequential weeks in digital age, shift in education to digital that was projected to take 5-10 years to go all online, shift in ecommerce and retail (from brick and mortar to online and fulfillment centers), shift in drugs and medicine tech, nimble is required, centralized organization around AI powered system wins—not fragmentation, those who look ahead and plan vs chasing what happens win, political response to covid hurt by “chasing/reactionary” nature of gvt, telemedicine became essential to healthcare in weeks when prior to covid onset was said to be not worthwhile, operating architecture is what matters—AI-centric with structure and agile processes, but also AI does not mean complex—simplicity is an advantage, “runtime” is the environment that shapes the execution of all process—now being controlled by AI and software not humans, more scale and scope possible and connectedness/learning, business model is overarching goal and operating model is how achieved, value creation vs value capture–important to do both with focus on capture, uber creates value via mobility and convenience but value capture is based on operating model (economics, scale, etc), Ant Financial began as escrow system for Alibaba to create trust b/w buyers and sellers as value creation and capture via 0.6% fee then expand as payments platform to wealth mgmt/investing for pocket change and savings, and transactions beyond just alibaba platform, key is an integrated AI based central platform that takes real time data and learns from all consumer touchpoints (also experiments), Ant is like if Facebook had a bank on top and everyone had an account—scale and scope is hard to compare, key features of an “AI factory”: data pipeline (gathers, cleans, process data), algorithm development (predicts future actions/states and drives operating activities), experimentation platform (test algo predictions), software infrastructure (connected internally and externally), Netflix “datafied” TV entertainment, every user interface is unique, data is the driver, 3 types of ML: supervised, unsupervised, reinforcement learning, traditional operating architecture was siloed and multi-unit (Dutch East India Co of 1602 was first modern corporation pioneer as merged 7 rival trading co’s), but siloed structure no longer advantageous in age of data, industrial age continue siloes as specialization dominate in mass production, Bezos 2002 memo propel Amazon to new operating architecture around AI and pivot from siloed development with clear APIs for all units to interact—no longer duplicate code across units, enabled agility via common foundational data inputs for all to manipulate/analyze, AI not need to be human to be effective (weak AI not bad/unproductive), “digital operating model” overtaking “tradition op model”—diff is employees now design and oversee software-automated algo-driven digital organization that actually delivers goods, removed bottlenecks and constraints—scale without human interaction as essential element, Nadella spark Microsoft redesign to cloud/AI company—CEO in 2018 but prior led integration of Azure into company in 2011 when used to be siloed and conflict with traditional product ecosystem, had to redesign Azure to do so, cloud business became one with hardware and other software product of MSFT, then layer AI into operational infra 2014 onward, horizontally connected, with AI businesses must consider network analyses not just industry—look beyond industry silo to see where else a business can touch and how data can create value, leverage network effects, network effects bring learning effects with AI (which did not happen in traditional biz), network clustering matters for barriers to entry—Uber has scale but local clusters so lower barriers, riders in Bos don’t care about drivers in LA—local focus vs global clusters (airbnb has global clusters so harder for new entrants), ride sharing struggling to profits is due to “multihoming”—drivers can use many apps as can riders so have to undercut price, if struggling to capture value then best to think of who your network is and providing complementary services/content to increase engagement and data flow, benefits of traditional scale not insurmountable as get saturated and slow, but scale with AI can be insurmountable given network and learning effects, Nokia’s collapse was tale of siloed failures and lack of single central architecture vs iOS and Android—lost out in 5 years despite being leading and creator before iOS launch 2007, too fragmented to be dyanmic and evolve and integrate apps easily and was too product/hardware focused, msft acquired nokia 2012 for $7bn (10% of peak value), mobile division sold years after for a few hundred mill, with AI the “conglomerate” approach no longer slows down a biz, Amazon and Alibaba can expand across disparate verticals because underlying driving is the tech/AI and analytics/data which can scale efficiently, differentiated data at massive scale wins and grows—parlay data into new networks/nodes