Project Two: People make change - Part 2
How Personal AI Adoption is Redefining Work and Life. The AI generated audio and text on the same theme as Part 1
This is part 2 of my post on the first major theme of Project Two - People make change. In this post the podcast and the article generated by Notebook LM on this topic. See part 1 for my version of the same material or subscribe to receive all Project Two updates in future.
The common narrative surrounding Artificial Intelligence often focuses on centralized, top-down transformation—massive corporate projects dictated by IT departments or C-suite strategy. However, the true revolution in how we work and live is fundamentally bottom-up, driven by the personal choices of individuals seeking leverage and efficiency. The history of technology adoption, from the iPhone to WhatsApp, shows that people will always bypass restrictive or inconvenient corporate systems to use the tools that bring them immediate value. This dynamic is already reshaping professional life, making personal utility the ultimate driver of organizational change.
This article explores five key themes illustrating how individual adoption of AI is progressing, examining the sheer speed of change, the barriers facing corporate mandates, the overwhelming focus on personal leverage and growth, and the nascent future of AI-driven collaboration and competition.
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1. The Quiet Revolution: Widespread Adoption in Daily Life
Change from AI is already underway because virtually every professional interacting with the technology reports incorporating it into their daily workflow or recognizing its use by colleagues. Drawing from widespread inputs across multiple professions, overwhelming qualitative evidence suggests that usage approaches 90% or more of professionals, ranging from frequent, daily use to cautious exploration, contrasting starkly with the small number of absolute avoiders.
This adoption isn’t limited to trivial tasks; individuals are often using AI for things that truly matter to them, fundamentally enhancing their capability to navigate complex professional and personal challenges.
High-Leverage Personal Use Cases:
Professionally, AI acts as a collaboration partner, often described as having an on-demand “raft of experts”. Core capabilities leveraged daily include generating content for communications, analyzing data, refining job specifications, summarizing long or technical documents, drafting legal agreements, and supporting technical work like coding. One individual drastically reduced the time required to create new training slide decks from days to hours, and elapsed time from ten days to about one, using ChatGPT Enterprise and Microsoft CoPilot. Another reduced the time needed to develop contingent fee models from 48 hours to closer to one hour.
For entrepreneurs and founders, this usage is existential. As a solo founder bootstrapping a business, AI tools (such as LLMs and AI agents) are used extensively as virtual co-workers to maximize leverage and overall output. One founder successfully built a software solution in two months at minimal cost, a task that previously would have taken four to six months and cost tens of thousands of pounds.
AI in the Home and Health:
AI is integrated into personal life for managing fitness (planning workouts, finding high-protein recipes), planning travel itineraries, and even creative projects like designing fashion collections, generating unique digital artwork, or writing illustrated poems for children about family pets.
In critical personal situations, AI offers unexpected support. One respondent used ChatGPT to troubleshoot a malfunctioning car charger when the electrician couldn’t find the necessary QR code, with the AI successfully identifying the pre-QR code unit and suggesting a resolution. Another individual used AI to generate a three-day travel itinerary tailored for their family’s interests. In terms of health, individuals are using AI for researching medical symptoms before consulting a GP to ensure a proper conversation takes place, or to research experimental treatments for severe illnesses. Similarly, AI is used to create bespoke exercise and meal plans based on specific goals.
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2. The Barrier of the Familiar: Bypassing Corporate Systems
Users who are already finding value from AI using accessible consumer tools (like ChatGPT, Claude, and Perplexity) will be exceptionally hard to persuade to adopt new, often clunky, corporate tools. Their preference for the tools they know, which often provide immediate utility with a superior user experience, creates a significant barrier to centralized AI implementation.
The Enduring Challenge of User Adoption:
This resistance is historical. The struggle against employees bringing personal devices like iPhones into the workplace (and bypassing IT security controls) is cited as a parallel; people used the consumer tools they liked regardless of corporate policy. Today, the same pattern is seen:
Mindset and Preference: Knowledge workers are accustomed to cobbling together solutions that work for them. For many, AI adoption is happening on a standalone basis, outside corporate networks, for personal benefit. The user interface (UI) of consumer AI tools like chat interfaces and Microsoft’s Copilot bundling makes AI highly accessible at the ground level.
Trust and Convenience: Many employees use AI more than they are comfortable admitting, suspecting that corporate efforts to restrict access are often driven by fear. Users often rely on general models like ChatGPT for immediate results or research, often bypassing Google search entirely.
Security Concerns as a Corporate Block: While companies worry about data leakage, individuals themselves also exhibit caution, particularly regarding privacy and sharing proprietary client information. Firms are advised to use AI output as “guidance” and sense-check the results before committing. Due to these valid concerns, some companies restrict public LLMs entirely and focus only on embedded tools like Copilot for Office 365.
The fact that these individual tools are powerful and easy to use creates friction for organizations attempting to implement top-down AI strategies, often leading to slow uptake and limited scalable solutions.
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3. The Bias Towards Growth: Efficiency Over Cost Cutting
The individual adoption of AI shows a strong bias towards positive use cases such as improving capability, growing revenue, and other forms of business development, rather than a focus on explicit, organization-wide cost cutting. When AI is used by individuals, it is primarily seen as a tool for leverage and personal efficiency, enabling them to do their job better and move faster.
Personal Efficiency: The Immediate Payoff (Leverage):
The most consistently cited benefit of AI is personal time saving and getting started with challenging tasks. Examples include:
Writing and Drafting: AI is excellent for polishing tone, tightening structure, adapting content for different audiences, creating first drafts of training materials or reports, and reducing the time spent drafting proposals or cumbersome emails. One executive noted annual performance feedback was completed in five minutes per person instead of hours previously.
Content Generation: AI is used to structure raw ideas into white papers and blog posts, develop LinkedIn strategies and content, and generate short-form content for social media quickly. One individual found that transcribing and synthesizing employee interviews and writing the report took only four hours, down from 12.
Coding Acceleration: Software engineers report easily achieving a 5 to 10x gain by using AI coding tools (like Cursor or GitHub Copilot) to write code in languages or frameworks they haven’t learned, or by generating simple code snippets for troubleshooting.
Strategic Use Cases (Growth and Business Development):
Users leverage AI for high-impact activities that directly support growth:
Market and Sales Intelligence: AI is used to find new investors and potential acquirers not listed in internal databases, aid in market and competitor research, and generate detailed sales leads based on specific customer profile criteria. One B2B example demonstrated the transformative capability of finding 100 global companies matching an ideal customer profile, effectively enabling a mid-market UK company to act like a global multinational.
Complex Problem Solving: AI acts as a sparring partner to challenge thinking and structure arguments when navigating complex problems. For corporate users, AI helps in rapidly generating documents for regulatory submissions or commercial terms for Statements of Work (SOWs).
Limited Focus on Cost Cutting:
While personal efficiency is pervasive, there are limited examples of corporate-mandated cost cutting driven by AI adoption, especially in regulated environments. In audit, for instance, manual checks still need to be done, which “massively reduce the impact” of AI, meaning the cost savings aren’t always realized or passed on.
The professional consensus suggests that selling AI based on “saving time” or “cost reduction” is often ineffective because cost reduction is notoriously difficult to sell in large organizations. Instead, the more successful framing focuses on leverage: getting people to do more with the time they have and performing their job better. AI is viewed as an extension of human efforts, augmenting expertise rather than replacing it outright.
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4. The Next Frontier: New Forms of Communication, Collaboration, and Competition
Individual use of AI is poised to develop into new forms of collaboration, communication, and competition, driven by the individual’s comfort level and demand for greater automation and specialized capabilities. The next stage of development centers on transitioning from individual “co-pilots” to autonomous, interconnected agents.
Evolution of Collaboration and Communication:
The use of AI is changing the fundamental nature of communication and professional collaboration:
AI as the “Foreman”: Professionals are learning to treat the Large Language Model (LLM) not merely as a worker performing a task (a “bricklayer”), but as a “foreman” used to structure and define the task itself. This shift changes the human role from “doer” to “editor and orchestrator” of modular workflows.
Synthetic Collaboration: Individuals use AI to prepare for high-stakes negotiations by setting up various personas to strengthen arguments and gauge likely reactions. Some plan to experiment with giving AI psychological descriptions of real individuals they are negotiating with to tailor arguments.
Seamless Documentation: Tools are emerging that enable “living documentation” by allowing users to interrupt conversations to dictate notes or system updates to the AI, which documents the process as they build it. AI meeting summarization tools (like Copilot in Teams or Granola) are already considered “unbelievable help,” ensuring all attendees can participate fully while accurate notes and action items are captured.
Specialized Agents: While many users haven’t yet found the right use case for autonomous agents, viewing them as too experimental or risky, the creation of tailored GPTs is a growing trend. These include specialized agents to prep for sales calls, curate leads, aid the research and consumption of academic literature, or structure an entire online course using modular thinking. The development of autonomous AI business teams that run businesses without human intervention, allowing owners to focus on wealth creation, is a goal for some in the AI space.
Reshaping Competition:
The individual-driven adoption of AI is creating a competitive leveling effect, allowing small, high-trust firms and individual entrepreneurs to challenge larger, established players:
Flattened Competitive Advantage: An AI expert working from their kitchen table can now compete with—and win against—a traditional agency. Boutique firms, like a small, high-trust consulting firm, use AI to compete effectively with much larger players by delivering high-quality outputs at speed.
Software Democratization: The ability for individuals to quickly build software solutions means that many more innovative ideas can potentially reach the Minimum Viable Product (MVP) stage. This democratizes innovation and reduces the time and capital traditionally required to launch a product.
Branded Content & Authenticity: While AI can streamline content creation, the ease of generating generic material means consumers are already starting to look for “serendipity” or even “odd spelling mistakes” to reassure them that content is authentic. Consequently, the true competitive edge is shifting toward individuals who use AI as a co-pilot to amplify their unique voice, institutional wisdom, and specific market knowledge. Institutional knowledge—the wisdom gained from prior experiences—remains crucial for applying AI output effectively.
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Conclusion
The core lesson from current AI adoption is that people drive change by seeking out tools that offer immediate, tangible utility, overriding corporate mandates or skepticism. The pervasive use of individual tools like ChatGPT and the overwhelming focus on personal efficiency and revenue growth underscore a fundamental market dynamic: users value leverage and capability augmentation over mandated cost reduction.
This shift, driven from the bottom up, necessitates a change in how organizations approach AI. Rather than forcing new systems, companies must integrate AI as a powerful support act, leveraging the technology to automate processes and provide humans with critical intelligence quickly, allowing them to focus on judgment and wisdom, which remain irreplaceable. The ability of AI to compress time and amplify individual capability is undeniable, but the ultimate success lies not in the technology itself, but in the human professionals who learn to trust and orchestrate its powerful, yet still probabilistic, output.
Thanks for reading
This is great Kenny, really interesting analysis.