As the CEO of GitHub, Thomas Dohmke, recently stated on “The MAD Podcast with Matt Turck,” depending solely on automated agents could lead to inefficiencies, and it's oftentimes easier to correct AI-based code than try to explain in plain English how to get the AI to do the work itself. Yes, AI is incredibly capable, but it's not a Vending Machine.

The Great Misunderstanding

When ChatGPT burst onto the scene, followed by a parade of increasingly sophisticated AI tools, the narrative quickly became one of replacement. "AI will automate away jobs," the headlines proclaimed. "The machines are coming for your career." But this fundamentally misunderstands the relationship between human expertise and artificial intelligence. Yes, jobs are going to be replaced in droves, and companies like Salesforce are already announcing they will no longer hire developers this year, but GenAI isn't a silver bullet, and experienced developers still have quite a bit to offer (and likely will, on into the future).

Consider how Holmes and Watson operated. Watson wasn't a lesser version of Holmes—he was a different kind of intelligence altogether. He documented cases, offered medical expertise, provided a sounding board for theories, and occasionally spotted details Holmes missed. Most importantly, he never replaced Holmes; he amplified him.

Today's AI tools operate in remarkably similar ways. They're exceptional at certain tasks such as pattern recognition, data processing, natural language generation, and rapid information retrieval. However, like Watson, they lack the intuitive leaps, contextual understanding, and creative problem-solving that define genuine expertise (leaving AGI out of this for the moment), as well as the creativity and intuitive problem-solving capabilities that come from years of experience in a technical field.

The Modern Baker Street Partnership

In software engineering, this Watson-like role manifests in countless ways. AI can generate boilerplate code faster than any human, suggest function implementations, and catch syntax errors. But it's the engineer who understands the system architecture, knows which edge cases matter, and makes the crucial decisions about trade-offs between performance and maintainability.

I've been working a lot with senior developers and seeing how they use AI with tools like Cline to build complicated micro-service-based systems. The AI would suggest code snippets, completed routine functions, and even propose test cases. Every suggestion, though, required the developer's judgment because there are cases where the solution won't scale, or it could violate a security protocol, or perhaps the code needs to handle async operations differently. Given that it can only program on what it knows, and developers like me aren't going to explain EVERYTHING the AI needs to consider, there will be a process in which Vibe Coding puts AI as the assistant but isn't going to simply go off and build everything.

The AI is much like Watson, then to Holmes - capable, helpful, even occasionally brilliant, but always requiring the detective's discernment.

The Deduction Engine Paradox

Holmes famously claimed to use pure deduction, but a fan of the characters knows his greatest insights came from combining logical reasoning with intuition, experience, and keen observation of human nature. He could deduce a man's profession from his hands, but understanding why that man might commit murder required something more.

Similarly, AI excels at pattern matching and logical operations but struggles with the "why" behind the "what." An AI can identify that your application's response time degrades every Tuesday at 3 PM, but it takes a human to realize that's when the marketing team runs their weekly bulk email campaign on the same infrastructure.

This limitation isn't a flaw, but a feature. By handling routine deductive work, AI frees us to focus on higher-order thinking that defines expertise, which is where vibe coding excels today. Handling the parts that are wrapped around grunt work, commoditized coding, or procedural work, but leaving deeper context-rich thinking, creativity, and broader awareness of where the project will live in production to the engineer.

The Art of Asking the Right Questions

Watson's most valuable contribution often came not from his answers but from his questions. His queries helped Holmes clarify his thinking, explore alternative theories, and occasionally stumble upon the crucial insight.

Modern AI tools excel in this Watsonian role. They can help you explore possibilities you hadn't considered, challenge assumptions, and provide alternative perspectives. But like Watson, they need Holmes to direct the investigation.

Think of a data scientist using large language models not to find answers but to refine the questions, asking something like "I'll explain my problem to the AI and ask it to suggest what additional information might help solve it. It's like having a tireless research assistant who's read everything but needs direction on what matters."

It's a librarian who never gets tired of all the questions, available any hour of the day. It's an intelligent assistant that can act as a sounding board for your inquiry. And it's the helpful programmer who can handle the parts of your software you don't want to waste your time on. It can also be the chief of staff to whatever domain expertise you're looking to get support on, which may have nothing at all to do with technology.

The Trust Factor

Holmes trusted Watson implicitly, but he also knew Watson's limitations. He wouldn't send Watson alone to confront Moriarty or expect him to crack a complex cipher without help. This calibrated trust is precisely how we should approach AI.

Today, you can trust AI to:

• Process large volumes of information quickly

• Identify patterns in data

• Generate first drafts and templates

• Suggest solutions based on documented patterns

• Handle routine, well-defined tasks

However, for now, don't trust AI to:

• Make critical business decisions

• Understand nuanced context or company politics

• Take responsibility for outcomes

• Innovate beyond its training data

• Replace human judgment in complex situations

AI is a moving target evolving quickly, so this article may not have a long shelf life :) But it's safe to say, as AI becomes more helpful, more people will have more time to do the things they are most capable of doing (and likely the work they enjoy the most, but lack the time to do).

The Documentation Revolution

One of Watson's primary roles was as a chronicler of Holmes' adventures. He transformed complex investigations into understandable narratives. Today's AI excels at this same function, turning messy codebases into clear documentation, complex analyses into executive summaries, and technical specifications into readable prose.

But just as Watson's chronicles required Holmes to provide the actual detective work, AI-generated documentation needs human expertise to ensure accuracy, relevance, and appropriate detail. The AI can write the story, but only after the human solves the case.

"Hallucinations," for example, are often spoken about as a tragic flaw of AI, but if you approach the tool with the right coaching up front (rules, roles, and "pre-prompts") and guide it as it goes, you'll be amazed at the insights you can uncover. LLMs know practically everything but require the right approach to maximize their value and get the insights and support you need executed effectively and it's easier and easier to feed your agent first hand information and knowledge you yourself have on hand, to further hone in on what insights and direction are going to be most valuable to you.

The most successful Holmes stories weren't those where Watson was absent but those where the partnership functioned at its peak. Watson's medical knowledge complemented Holmes' detective skills. His military background provided practical experience. His humanity balanced Holmes' cold logic.

The Future of Co-Working with AI

As AI capabilities expand, the Watson role will become more sophisticated. Future AI assistants might anticipate our needs better, handle more complex subtasks, and provide increasingly valuable insights. But the fundamental relationship remains: AI is a capable assistant to human expertise.

This isn't a consolation prize or a temporary state before AI "takes over." It's the optimal configuration. The combination of human creativity, judgment, and accountability with AI's speed, consistency, and processing power creates something greater than either could achieve alone.

Yes, AI is going to be a massive disruptor for current jobs, as people predict AI taking 50-100% of all white collar jobs globally in the years to come, but new jobs will be created at the same time, which will rely on people gaining domain expertise in an area and leveraging AI to maximize the services and capabilities that individual can provide. AI won't replace your HVAC system or build your house (we will see what happens with the AI-powered robot revolution), so blue-collar jobs will undoubtedly grow in popularity in the coming years as our economic situation shifts.

However, AI today isn't a silver bullet (nor will it likely be any time soon), as I mentioned earlier, and understanding where it is and where it's headed means gaining a sense of where you fit into the picture, with the opportunity to leverage it immediately. Sure, there are Waymo taxis that drive themselves through downtown San Francisco, but there are still race car drivers who can handle tight corners and fast speeds, as well as semi-truck drivers who can maneuver large trailers through difficult roadways and restrictive alleys. Cars will evolve, but the types of individuals behind the wheel will become more specialized and operate in ways AI won't be able to for some time.

Embracing Your Inner Holmes

The professionals thriving in the AI age right now aren't those trying to compete with machines at machine tasks. They're the ones leveraging AI to become better versions of themselves—more productive, more insightful, more creative. The common saying goes that it won't be AI that replaces you, but someone like you using AI.

Today, great use cases for AI include:

• Eliminating drudgework and focusing on high-value problems

• Exploring more solutions in less time

• Validating ideas against vast knowledge bases

• Communicating complex ideas more clearly

• Learning new domains more quickly

• Improving at your chess game

But never forget that they're not Holmes in this story; you are. You set the direction, make the connections, take the responsibility, and ultimately solve the case.

The Game Continues

The tech industry loves its disruption narratives, but the AI revolution looks more like an evolution, one that will change how we work and what we work on, not whether we work at all. Artificial Sherlocks are not replacing us, but rather displacing us in the coming years while we discover new work, roles, and jobs that perhaps don't exist yet. We're being given artificial Watsons to help us solve cases faster, in ways we didn't think possible before, and use this newfound assistant to evolve your own career into a niche you're best suited for (and enjoy doing the most!).

And that's genuinely exciting. While Holmes was brilliant alone, he was legendary, with Watson by his side. The same potential exists for every developer, analyst, engineer, and technical professional willing to embrace AI as their partner rather than fear it as their replacement.

The game is indeed afoot. But we're not competing against AI; we're investigating the future together. And like Holmes and Watson walking into the London fog to solve another case, the real adventure is just beginning. Elementary, perhaps, my dear Watson (I had to fit that in there somewhere), but sometimes, the simplest truths are the most powerful. AI is our Watson. We remain Holmes. And together, we're capable of solving problems neither could tackle alone.

Resources to Start Your AI Partnership

Ready to lean in more with AI? Here are some links to start, or go deeper.

1. Ethan Mollick's "Co-Intelligence" - https://www.oneusefulthing.org/

Wharton professor who tests AI on real business problems daily. His posts show exactly how to use AI for strategy, writing, analysis, and decision-making. Start with his "Prompting Guide for Work," as it's the clearest explanation of how to get AI to actually help rather than frustrate.

2. NotebookLM by Google - https://notebooklm.google.com/

Upload your documents, reports, or research papers and have an AI that understands YOUR specific context. Like giving Watson your case files before starting an investigation. It can answer questions, find connections, and even create study guides from your materials.

3. Ezra Klein's AI Episodes - https://www.nytimes.com/column/ezra-klein-podcast

Thoughtful conversations with AI researchers and critics about what this technology actually means for work and society. His episode "A Skeptical Take on the AI Revolution" with Gary Marcus provides essential balance, while his interviews with Anthropic researchers explain why AI works best as an assistant, not an autonomous agent.

4. Perplexity Pro - https://www.perplexity.ai/pro

AI search that shows its sources and reasoning. Unlike regular search, you can ask follow-up questions like "but what about..." and it maintains context. Essential for research, fact-checking, and exploring complex topics. The free version is limited; Pro ($20/month) is worth it for serious work.

5. "AI for Everyone" by Andrew Ng - https://www.coursera.org/learn/ai-for-everyone

Four-week course designed for non-technical professionals. Covers what AI can and cannot do, how to identify AI opportunities in your work, and how to work with AI teams. No coding required, just practical understanding.

6. Claude Projects - https://www.anthropic.com/news/claude-pro-projects

Create specialized AI assistants that know your business context. Upload your style guide, past proposals, or industry reports. Your AI "Watson" then understands your specific domain when helping with analysis, writing, or strategy.

7. Matt Webb's "Uses This" AI Posts - https://interconnected.org/home/tagged/ai

Designer and strategist documenting unusual ways to use AI as a thinking partner. Recent examples: using AI to explore "what-if" scenarios for product launches, creating alternative perspectives for stuck problems, and turning messy meeting notes into actionable plans.

8. Khan Academy's Khanmigo - https://www.khanmigo.ai/

See AI tutoring in action. Even if you're not studying, watching how Khanmigo guides rather than gives answers demonstrates the Watson approach perfectly. It asks clarifying questions, provides hints, and helps you reach conclusions yourself.

9. "The AI Companion Strategy" Podcast - https://hbr.org/podcast/2024/01/the-ai-companion-strategy

Harvard Business Review's series on AI as a colleague, not a replacement. Features case studies from consulting, healthcare, and finance showing how professionals amplify their expertise with AI. Episode 3 on "AI as Thought Partner" directly parallels the Holmes-Watson dynamic.

10. Gamma - https://gamma.app/

Transform rough ideas into polished presentations. You provide the expertise and key points; AI handles design and flow. Perfect example of Watson handling the documentation while you focus on the investigation. Used by consultants and executives who need professional outputs fast.

Start with one tool that matches your biggest daily friction. The best AI partnership begins when Watson helps with something that currently slows down your best work, or helps clear out the things getting in the way of the work you really want to be doing.

Without getting into complex strategic frameworks, this write-up covers some examples of who's doing what with AI in farming today and how to get started with "Prompt Engineering" as a simple entry point to get more out of AI and understand how this could work at scale.

Submerged in Data, But Thirsty For Insights

Agricultural businesses generate mountains of data daily—from soil sensors, weather stations, satellite imagery, and equipment telemetry. Yet most farms remain data-rich but insight-poor, drowning in numbers while starving for actionable intelligence. Generative AI offers a practical solution: a digital farm advisor available 24/7 that transforms overwhelming data into clear, profitable decisions.

The agricultural AI market is experiencing explosive growth, expanding from $1.7 billion in 2023 to a projected $4.7 billion by 2028¹. Early adopters report 15-25% operational cost reductions and yield improvements up to 30%². Despite these promising returns, adoption remains modest, with only 3% growth since 2022³, largely because agribusiness executives lack clear guidance on practical implementation. This guide provides that roadmap, offering non-technical leaders a comprehensive guide to leveraging AI for immediate farm improvements.

Why farms need AI now more than ever

Modern farming operations face an unprecedented data challenge. A typical 1,000-acre farm generates approximately 4.5 million data points annually from various sources, yet farmers spend less than 2% of their time analyzing this information⁴. This gap between data collection and actionable insights costs the industry billions in missed optimization opportunities.

Think about it: Weather stations record temperature, humidity, rainfall, and wind speed every few minutes. Soil sensors measure moisture, pH, and nutrient levels across multiple field zones. Equipment generates telemetry on fuel consumption, operating efficiency, and maintenance needs. Satellite imagery provides weekly updates on crop health and growth patterns. Market data streams include commodity prices, input costs, and demand forecasts. Each source operates in isolation, creating information silos that prevent holistic decision-making.

Here's the thing: Agriculture has always been a data-driven industry. Remember the Farmers' Almanac? But today's complexity requires AI assistance to process information at scale. Every farming operation has data that tells a story, and those stories can drive awareness and insights while delivering measurable business goals and improving core operations.

The consequences hit the bottom line hard. Farmers I talk to report spending hours each week trying to synthesize data from multiple sources, often resorting to gut feelings when they are overwhelmed. This reactive approach incurs costs: USDA studies show that data-driven farms achieve 15% higher profitability than those relying on traditional decision-making methods⁵.

Your new end-to-end farm consultant

Generative AI works like a sophisticated pattern recognition system that understands agricultural context and provides tailored advice. Unlike traditional software that requires specific inputs and produces predetermined outputs, Gen-AI interprets natural language questions and synthesizes relevant information from its training on millions of agricultural documents, research papers, and practical farming experiences.

The technology operates through three straightforward mechanisms. First, natural language processing lets farmers ask questions in plain English: "What's causing yellow spots on my corn leaves?" Second, contextual understanding enables the AI to consider multiple factors simultaneously, including weather patterns, soil conditions, crop stage, and regional pest pressures. Third, synthesis capability combines diverse information sources to provide comprehensive recommendations rather than isolated data points.

Here's the philosophy that matters: AI isn't replacing farmers or trampling on hundreds of years of domain knowledge. It's making decision support easier and decision information faster. Think of it as an enhancement tool rather than a replacement for agricultural expertise.

Real farms are seeing real results. Farmers are using AI tools to interpret soil test results and create variable-rate fertilizer prescriptions, reducing input costs while maintaining yields. Agricultural managers employ AI to analyze weather patterns and disease models, preventing potential losses from crop diseases⁶.

The 24/7 availability changes the game during critical decision windows. When storms threaten harvest timing, equipment breaks during planting season, or pest outbreaks require immediate action, Gen-AI provides instant guidance based on best practices and current conditions. This accessibility gives small farms access to advisory capabilities previously available only to large operations with dedicated agronomists⁷.

When global events hit your local farm

Modern agriculture operates within an interconnected global system where distant events create immediate local consequences. The 2022 Ukraine conflict disrupted fertilizer supplies, causing prices to spike 300% and forcing farmers worldwide to recalibrate nutrient management strategies⁸. COVID-19 supply chain disruptions left farmers unable to obtain critical equipment parts during harvest season. Climate volatility brings unexpected weather patterns that invalidate historical farming calendars.

Gen-AI excels at processing these complex, interconnected factors to provide localized recommendations. When asked about fertilizer alternatives during the 2022 shortage, AI systems can suggest specific cover crop combinations, organic amendments, and precision application techniques tailored to individual farm conditions. This adaptive intelligence helps farmers navigate uncertainty by providing multiple scenario analyses and contingency plans.

The ripple effect extends way beyond immediate operations. Market volatility requires constant recalibration of planting decisions, storage strategies, and sales timing. Labor shortages demand automation solutions and workforce optimization. Regulatory changes necessitate rapid adaptation of practices and documentation. Gen-AI serves as an early warning system, alerting farmers to emerging trends and suggesting proactive adjustments.

Case studies show this in action. When new environmental regulations affect growers, AI systems help identify compliant practices that maintain profitability while meeting requirements⁹. The technology also helps farms build resilience against future disruptions by analyzing patterns across multiple crisis events, identifying vulnerability points, and suggesting diversification strategies¹⁰.

The secret sauce: Prompt engineering

Here's where the rubber meets the road. Effective prompt engineering, which is basically the art of asking AI the right questions, determines the quality of agricultural insights you'll receive¹¹. Specificity drives accuracy: vague questions yield generic answers, while detailed prompts generate actionable recommendations tailored to your exact situation.

The fundamental prompt structure follows a simple formula:

Context + Specific Question + Desired Output Format + Constraints.

For example: "I'm growing hard red winter wheat in central Kansas with clay loam soil and 22 inches of annual rainfall. My wheat is showing purple coloration on the lower leaves at Feekes stage 5. What nutrient deficiencies could cause this, and what's the most cost-effective treatment considering current fertilizer prices?"

Context elements that improve response quality include:

• Geographic location (state, county, or region)

• Specific crop variety and growth stage

• Soil type and recent test results

• Current and forecasted weather conditions

• Pest and disease pressure in the area

• Available equipment and labor resources

• Budget constraints or organic certification requirements

• Previous management practices and their outcomes¹²

Advanced techniques multiply AI effectiveness. The decomposition method breaks complex problems into manageable components. Instead of asking "How do I improve my farm's profitability?" try breaking it down: "What are my highest cost inputs?" then "How can I reduce fertilizer costs without sacrificing yield?" then "What precision application technologies offer the best ROI for a 2,000-acre operation?"

Common mistakes I see farmers make:

• Providing insufficient context about their specific operation

• Asking multiple unrelated questions in one prompt

• Failing to specify practical constraints like budget or equipment

• Accepting the first response without requesting clarification¹³ or responding in a way as to further shape the response from the agent (conversation over time vs a one-shot and you’re done approach)

How AI actually processes your farming questions

Understanding the mechanics helps you get better results. When you submit a prompt, the AI first parses your natural language into concepts it understands. It identifies key agricultural terms, recognizes relationships between factors (crop-soil-weather interactions), and determines the type of response needed.

The AI then accesses its training on agricultural documents, connecting your specific situation to relevant research and best practices. This isn't simple keyword matching because the system understands that "yellow corn leaves" might indicate nitrogen deficiency, sulfur deficiency, or disease pressure depending on context.

For a pest management question, the AI considers multiple factors simultaneously, including crop growth stage and vulnerability, regional pest populations and lifecycle timing, weather conditions affecting pest development, economic thresholds for treatment decisions, available control options and their trade-offs, and resistance management requirements.

The system then synthesizes information into practical recommendations, prioritizing actionable advice over theoretical knowledge. Effective agricultural AI responses encompass specific actions with precise timing and rates, cost-benefit considerations, risk factors and mitigation strategies, alternative approaches tailored to different scenarios, and sources of uncertainty that necessitate local validation.

Breaking down complex problems

The decomposition methodology transforms overwhelming agricultural problems into manageable, AI-friendly components. This approach facilitates decision support and provides information more quickly, enabling farmers to tackle multifaceted challenges systematically.

Take drought management for beef cattle operations. Rather than asking "How do I manage my cattle during drought?" break it into focused components:

First, address immediate animal needs: "Calculate daily water requirements for 150 head of 1,200-pound cows in 95°F temperatures." Then evaluate feed resources: "Compare cost and nutrition of drought-stressed pasture supplementation vs. purchased hay for maintaining body condition score 5." Next, consider economic factors: "Analyze cash flow impact of early weaning vs. supplemental feeding through September." Finally, plan long-term strategies: "Develop destocking criteria based on pasture recovery projections and market conditions."

This methodology transforms AI from a simple answer machine into a problem-solving partner, guiding users through complex decisions with systematic precision.

As you go, you can also add your own data and background to better shape the knowledge the agent has going into answering your question, which leverages technologies such as RAG and requires an investment in data and AI infrastructure to facilitate this.

Real success stories from real farms

Let's look at what's working out there. The Saagu Baagu Initiative in India introduced AI-powered advisory services to chili and cotton farmers in Telangana state¹⁴. Farmers received personalized recommendations via smartphone apps, covering planting dates, variety selection, pest management timing, and market price optimization. The results? Farmer income doubled from $400 to $800 per acre per crop cycle, yields increased 21% through optimized input timing, unit prices improved 8%, and pesticide use decreased 9%. The program expanded to 500,000 farmers by 2023.

Blue River Technology's See & Spray system shows how computer vision can revolutionize weed control¹⁵. The system identifies weeds in real-time and applies herbicide only where needed, achieving 90% reduction in herbicide use while maintaining control effectiveness. John Deere's $305 million acquisition validated the technology's value.

Advanced crop monitoring systems demonstrate significant improvements through AI-powered disease detection¹⁶. Operations report substantial yield increases through early disease intervention, water savings via precision irrigation scheduling, and reduction in scouting labor costs. These systems can detect disease infections days before visual symptoms appear.

Common pitfalls and how to avoid them

Let's be honest about the challenges, as farms have had a “Bad Data” problem for a while now

ScoutLabs research identifies fragmented data systems, lack of standardization, and limited technical expertise as major barriers to AI adoption in agriculture⁴. The USDA reports that while farms generate massive datasets, they struggle to process this information effectively due to a lack of integrated platforms⁵.

Technical skill gaps remain a real issue. Extension services report that farmers need hands-on training to effectively use AI tools¹⁷. The Extension Foundation's AI training series provides structured education that significantly improves adoption rates¹⁸.

Cost concerns are legitimate. The agricultural AI market analysis shows the sector growing rapidly, but individual farm investment requirements vary widely¹. McKinsey reports that despite proven ROI, farmers remain cautious about technology investments due to volatile commodity markets³.

Integration with existing systems can be tricky. AIFARMS research at the University of Illinois documents the complexity of connecting AI insights with farm machinery and existing workflows¹⁹. Their findings show that starting with standalone applications before attempting full integration improves success rates.

Trust takes time to build. Syngenta's implementation experience shows that farmers require transparent AI decision-making processes and the ability to validate recommendations against traditional methods⁶. The World Economic Forum's analysis emphasizes the importance of demonstrable results in building farmer confidence⁷.

Data privacy concerns are real and valid. The USDA's inventory of agricultural AI use cases reveals widespread concern about data security and ownership when using cloud-based services²⁰. Choose platforms with clear data ownership policies.

Your 90-day implementation roadmap

Here's a practical path to get started:

Days 1-30: Foundation building

• Week 1: Create free accounts on ChatGPT, Claude, and Microsoft Copilot. Test simple agricultural questions.

• Week 2: Gather your operational data (soil tests, yield history, input costs, problem areas) and leverage that data as part of your AI inquiry.

• Week 3: Practice prompt engineering using the templates provided. Compare responses across platforms.

• Week 4: Share the concept with your team. Demonstrate practical examples. Identify early adopters.

Days 31-60: Focused implementation

• Week 5-6: Launch a pilot in one high-impact area (pest management, irrigation scheduling, or market analysis), working with someone trained in building comprehensive AI solutions.

• Week 7-8: Compare AI recommendations with traditional decisions. Document accuracy and value added, based on the “Minimal Viable Product” produced.

Days 61-90: Scaling up

• Week 9-10: Add second and third use cases based on pilot success. Create role-specific prompt templates, continuing the work with your AI expert to go beyond ChatGPT to build actual AI-based applications.

• Week 11-12: Integrate AI consultations into regular decision-making. Plan resource allocation for continued development.

Track these metrics as you go: time saved on research and analysis, accuracy of AI recommendations, cost savings from optimized decisions, team adoption rates, and new opportunities identified.

Resources to get you started

AI Platforms:

• ChatGPT²¹: Free tier available, $20/month for advanced features

• Claude²²: Free tier with generous limits, $20/month Pro version

• Microsoft Copilot²³: Free with a Microsoft account

Agricultural-Specific Tools:

• Farmonaut²⁴: Satellite-based monitoring with AI advisory

• FlyPix AI²⁵: Drone-based platform reducing data collection costs by 90%

• AIFARMS CropWizard¹⁹: Interactive Q&A service for U.S. agricultural professionals

Educational Resources:

• University of Illinois²⁶: Master's in Digital Agriculture with AI focus

• Extension Foundation¹⁸: AI training series for agricultural professionals

• USDA AI Inventory²⁰: Comprehensive database of agricultural AI applications

• McKinsey Agriculture Practice²: Reports on AI value creation in farming

Funding Opportunities:

• USDA-ARS AI Innovation Fund²⁷: Up to $100K for research projects

• SBIR/STTR Programs²⁸: Small business AI development grants

• AGCO Foundation²⁹: Youth-led agricultural innovation funding

Quick Prompt Templates:

• Crop Management: "Analyze my [crop] showing [symptoms] in [location] with [soil type] and [recent weather]. Recommend immediate actions and long-term solutions considering [constraints]."

• Livestock: "Develop feeding strategy for [animal type] considering current [feed prices] and [production goals]. Include cost analysis and nutrition balance."

• Market Intelligence: "Compare profitability of [crop options] for [acreage] in [region] considering [risk tolerance] and [resource constraints]."

Time to act

Agricultural AI represents the most significant advancement in farm management since GPS-guided equipment. The technology exists, proves profitable, and becomes more accessible daily. The question isn't whether to adopt AI, but how quickly you can capture its benefits.

Start small with free tools and focused applications. Build confidence through incremental successes. Scale strategically based on proven returns. Most importantly, begin today—every day delayed represents missed opportunities for optimization and profit.

The future belongs to farms that successfully blend traditional agricultural wisdom with modern AI capabilities. By following this practical guide, agribusiness executives can lead their operations into a more profitable, sustainable, and resilient future. The digital transformation of agriculture isn't coming—it's here, accessible, and waiting for your leadership.

References

1. MarketsandMarkets. (2024). "Artificial Intelligence in Agriculture Market worth $4.7 billion in 2028." https://www.prnewswire.com/news-releases/artificial-intelligence-in-agriculture-market-worth-4-7-billion-in-2028---exclusive-report-by-marketsandmarkets-301759328.html

2. McKinsey & Company. (2024). "From bytes to bushels: How gen AI can shape the future of agriculture." https://www.mckinsey.com/industries/agriculture/our-insights/from-bytes-to-bushels-how-gen-ai-can-shape-the-future-of-agriculture

3. McKinsey & Company. (2024). "Voice of the global farmer 2024: Farmer survey." https://www.mckinsey.com/industries/agriculture/our-insights/global-farmer-insights-2024

4. ScoutLabs. (2024). "Challenges in AI Adoption for Agriculture and Easy Pest Management Solutions." https://scoutlabs.ag/ai-challenges-agriculture-pest-management/

5. USDA. (2024). "Feed the world: How the USDA is using data and AI to address a critical need." https://news.microsoft.com/source/features/sustainability/feed-the-world-how-the-usda-is-using-data-and-ai-to-address-a-critical-need/

6. Syngenta. (2024). "Syngenta and AI: Pioneering Sustainable Agriculture for the Future." https://www.syngenta.com/agriculture/agricultural-technology/artificial-intelligence

7. World Economic Forum. (2025). "Delivering regenerative agriculture through digitalization and AI." https://www.weforum.org/stories/2025/01/delivering-regenerative-agriculture-through-digitalization-and-ai/

8. IMARC Group. (2024). "AI in Agriculture Market Size, Trends And Forecast, 2033." https://www.imarcgroup.com/ai-in-agriculture-market

9. Intellias. (2024). "AI in Agriculture and Farming: Revolutionizing Crop Growth." https://intellias.com/artificial-intelligence-in-agriculture/

10. ScienceDirect. (2024). "Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications." https://www.sciencedirect.com/science/article/pii/S2772375524000881

11. Grammarly. (2024). "How to Create Effective AI Prompts (With Examples)." https://www.grammarly.com/blog/ai/generative-ai-prompts/

12. Prompt Engineering Guide. (2024). "Examples of Prompts." https://www.promptingguide.ai/introduction/examples

13. IndiaAI. (2025). "AI in agriculture in 2025: Transforming Indian farms for a sustainable future." https://indiaai.gov.in/article/ai-in-agriculture-in-2025-transforming-indian-farms-for-a-sustainable-future

14. World Economic Forum. (2024). "Farmers in India are using AI for agriculture – here's how they could inspire the world." https://www.weforum.org/stories/2024/01/how-indias-ai-agriculture-boom-could-inspire-the-world/

15. BasicAI. (2024). "7 Applications of AI in Agriculture." https://www.basic.ai/blog-post/7-applications-of-ai-in-agriculture

16. FlyPix. (2024). "Top Precision Farming Software, Tools & AI Solutions." https://flypix.ai/blog/precision-farming-software-tools-ai/

17. NC State Extension. (2024). "Extension Agents Explore AI for Agriculture." https://cals.ncsu.edu/news/extension-agents-explore-ai-for-agriculture/

18. Extension Foundation. (2024). "New Web Page Highlights AI Resources and Insights." https://extension.org/2024/10/24/new-web-page-highlights-ai-resources-and-insights/

19. AIFARMS. https://aifarms.illinois.edu/

20. USDA. (2024). "Inventory of USDA Artificial Intelligence Use Cases." https://www.usda.gov/about-usda/reports-and-data/data/usda-open-data-catalog/inventory-usda-artificial-intelligence-use-cases

21. ChatGPT. https://chat.openai.com

22. Claude. https://claude.ai

23. Microsoft Copilot.

    https://copilot.microsoft.com

24. Farmonaut. (2024). "Revolutionizing Agriculture: How Farmonaut's AI-Powered Precision Farming Tools Boost Crop Yields." https://farmonaut.com/precision-farming/revolutionizing-agriculture-how-farmonauts-ai-powered-precision-farming-tools-boost-crop-yields-and-soil-health/

25. FlyPix AI. https://flypix.ai/blog/precision-farming-software-tools-ai/

26. NCSA Illinois. (2024). "Transforming Agriculture with AI." https://www.ncsa.illinois.edu/transforming-agriculture-with-ai/

27. USDA SCINet. (2025). "ARS AI Innovation Fund (FY25)." https://scinet.usda.gov/opportunities/ai-innovation/

28. USDA NIFA. (2024). "Funding Opportunities." https://www.nifa.usda.gov/grants/funding-opportunities

29. AGCO Foundation. (2024). "Grant Program 2024." https://www2.fundsforngos.org/latest-funds-for-ngos/agco-agriculture-foundations-grant-program-2024/

30. FAO E-Agriculture. (2024). "Science and Innovation Forum 2024: Digital Agriculture Changemakers in Action." https://www.fao.org/e-agriculture/news/science-and-innovation-forum-2024-digital-agriculture-changemakers-action

Additional Educational Resources

• University of Florida IFAS Extension:

  • "Understanding Artificial Intelligence: What It Is and How It Is Used in Agriculture" https://edis.ifas.ufl.edu/publication/AE589

  • "Introduction to Artificial Intelligence in Agriculture" https://edis.ifas.ufl.edu/publication/AE605

AI Platform Comparisons

• DataStudios. (2025). "Microsoft Copilot vs. ChatGPT vs. Claude vs. Gemini: 2025 Full-Spectrum Comparison." https://www.datastudios.org/post/microsoft-copilot-vs-chatgpt-vs-claude-vs-gemini-2025-full-spectrum-comparison-and-performance-r

• DEV Community. (2024). "ChatGPT vs Microsoft Copilot vs Claude AI: A Detailed Comparison." https://dev.to/abhinowww/chatgpt-vs-microsoft-copilot-vs-claude-ai-a-detailed-comparison-of-ai-tools-for-2024-f3o

• Microsoft. (2024). "Frequently asked questions about Microsoft 365 Copilot Chat." https://learn.microsoft.com/en-us/copilot/faq

1. Think Like a Cyborg

We already rely on digital memory (Google), shared consciousness (Slack), and extended cognition (YouTube tutorials). AI just takes this further.

Becoming AI Native means understanding:

- You don’t have to hold all the information—you just need to know how to ask.

- You can turn first drafts, decisions, and insights into AI-collaborated artifacts in seconds.

- You’re never really working alone again—you’re pair-programming your life with a very fast, very smart assistant.

- It’s less about replacing your brain and more about rewiring your brain’s relationship to work.

2. Prompts Are the New Literacy

In the AI era, your ability to ask great questions, guide responses, and refine outputs is how you begin to leverage AI effectively. Think of GenAI, LLMs, etc as incredibly smart workers – smarter than you – but ones that have a hard time communicating in a way you’re used to perhaps, with your team or co-workers. You can’t write a prompt and say “Build me something like Salesforce but for Pet Companies” and expect a good outcome.

Yes, AI, as we are referring to it here, is becoming more and more capable as time goes on, but the need to write a good prompt and get what you need AI to do for you is also evolving. Today, you don’t need to have a model with 10 agents and an orchestrator all working together in a super complex configuration, because MCP now allows a baseline AI agent to use tools much like an employee of a company can have a role but leverage tools to do the actual work – tools you yourself don’t need to build – and this is only going to increase in capability and potential. (Learn more about what MCP is here).

To become fluent then, and work towards being an AI native, you need to start with understanding how to maximize your use of AI:

- Write, then rewrite your prompts using AI to help. Start by asking your favorite AI tool to take a question you have and write a good prompt to then copy and paste back into the AI tool. Did you get a good answer? No? Let your AI session know that, and ask why it didn’t give you what you thought you wanted. Use AI to write what you know, leverage it to improve, and iterate until you get a handle of what makes a good prompt. Once you get that answer, build on your success to evolve the answer, and leverage AI to help you get better as you go. Don’t assume you need to know everything or anything, outside of what’s in your head, to start and grow your ability to write a good prompt, leveraging that very same AI session to do it.

- Treat prompt history like version control—save the good ones, build on them, share them. Look for communities of domain experts like you leveraging AI, what prompts are they writing? What are they using AI for? Having a community is key to getting cultural immersion with AI in your given field.

- Learn to layer context, constraints, and tone to turn raw AI into tailored outputs. What surprises new people about AI is just how capable it is to understand you as you go, and allows you to be as expressive and wordy as possible, with the ability to learn you and your approach, style, and tone as you go. You don’t like one part of the response? Tell the AI session that – give it an example of what you’d like to see, and make sure that’s incorporated going forward. A given AI session won’t remember that context in future conversations unless you want it to, though the residual memory capabilities different AI companies have enabled is growing by the day too, between sessions (as something you can opt into).

- AI fluency isn’t technical—it’s conversational precision. Much like Excel taught us to use functions like VLookup, LLMs are teaching us to compose questions and responses in a way unique to AI. AI has a language and a way of working, and the most powerful practitioners learn it and leverage it. Suppose you work at Amazon and want to be successful. In that case, you learn to become a technical writer no matter where you work – the same is true to leverage AI, you need to work on how to communicate more than you need to worry about being technical or understanding everything under the hood. A good driver isn’t necessarily a good mechanic with a car, right? They say in writing that the best authors make bad editors, so don’t worry about not being technical here – just conversational in a way that maximizes the output of your agents.

3. Tool Up Intentionally

The AI Native doesn't jump on every shiny new AI app—they build a personal AI stack:

- A chat assistant for writing, brainstorming, and decision-making (ChatGPT, Claude, etc.)

- A visual assistant for designing, prototyping, and explaining (Midjourney, Runway, etc.)

- A workflow agent for automating your daily grind (Zapier+AI, AutoGPT, personal RAG setups)

AI will take many forms, as companies building tools release AI to operate in a specific way for a specific set of use cases. Don’t expect ChatGPT to do everything for you, as the public-facing “Chat” style AI tools have as many limitations as they have capabilities. Leveraging AI means using the API and tapping right into the backend, which is then leveraged to perform an ever-increasing array of tasks. If you’re not comfortable leveraging an API and using AI to build something yourself, just get your “toolbelt” together to know how to use AI where, based on what you need done.

It’s less about being everywhere and more about choosing high-leverage tools that extend what you already do well.

4. Reclaim Time, Reinvest in Depth

AI can do shallow work for you—drafts, summaries, cold outreach. Great. But don’t use that time just to answer more emails. Think of the things you can do as a pyramid of many layers, with the things at the bottom of the pyramid being the boring grunt work you need to get done and the top level of the pyramid is the purest expression of your ability, something you truly enjoy, and something you and only you can do best. Start using AI for what’s at the bottom, and work your way up. It’ll never hit the top, but it can go pretty far up the pyramid freeing you up for giving you more time for the work that is most meaningful and useful for you to be doing.

The AI Native reinvests their freed-up hours into:

- Learning deeply (AI can teach you anything—fast and point you to resources to extend your knowledge out further on topics relevant to you).

- Thinking strategically (zooming out, not just iterating faster, and getting “meta” in your thinking).

- Creating boldly (ideas that used to take teams now take one person with the right loop, leveraging AI to do the work of folks you may not have access to).

- Rethinking how you think of the world you live in, from your sector, the economy, business structures, etc., and using your newfound AI capabilities to challenge your own biases and blind spots. An AI session makes for a great debate partner that never loses interest in the conversation.

The future belongs to those who let AI handle the bottom 40 %+ of the work that clogs their day, so they can spend the rest of the time doing what only they can do and enjoy doing.

I love to write, but I struggle with writing everything from scratch, writing everything from memory, or writing things in the right way. How much of this was written by AI? How much was written by me? Hopefully you can’t tell ;) But I can tell you a lot got done much faster because of my use of AI, and hopefully, you’re benefiting from this work as a result.

5. Live in Feedback Loops

AI Natives don’t settle for one-and-done outputs. They:

- Ask follow-ups from the AI-generated responses in the same session.

- Explore variations and modify their questions to look at different answers or approaches.

- Tune over time, to adjust the way you ask and interact with AI to work out any lingering bias or unfamiliarity with how best to leverage AI via prompts.

Everything becomes iterative—your content, your ideas, your decisions. You evolve by conversation, not by committee. And you develop a sense of creative agency that compounds, which is where AI becomes an extension of you, no different than any other digital tool you’ve leveraged over the years – just in a much more profound and powerful way.

Click on the Next Chapter Below To Keep Going

Introduction: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 1: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 2: Use AI (more) effectively as a Startup: How startups begin to leverage AI and avoid turning into a failed science project

Chapter 4: Vibe Coding your first AI project: How you (yes YOU) can build your first AI app, and get more comfortable with what AI can beyond ChatGPT

Chapter 5: Surfing the AI Tsunami: Where We Are, What We've Learned, and What Comes Next

- “I’m not technical enough.”

- “I don’t know where to start.”

- “What is LangChain?”

Let’s fix that by helping you leverage the API in an LLM I spoke about earlier to build your own AI-powered application, with just prompts! No coding experience required.

This chapter is your permission slip, your map, your flashlight — and most importantly, your invitation to build. Welcome to vibe coding.

What Is Vibe Coding?

Vibe coding is building with AI when you don’t know everything, and don’t need to.

It’s learning by doing. Assembling blocks. Letting the AI help you write the parts you don’t know. You're not writing code in the dark — you're collaborating with it. Think of yourself as a director working with a super-competent assistant who just needs instructions.

You don’t start by reading a 600-page machine learning book.

You start by making something you care about.

WOW! SO DO WE STILL NEED DEVELOPERS?: Yes

LONGER ANSWER: A lot is discussed about AI replacing all developers, so I want to be clear on my position here. Yes, AI can be a great resource to write code and even write basic applications like the one we are writing here. However, experienced developers are still required to leverage AI for building complex systems and know how systems and architectures operate. Like I said earlier, you can’t just tell AI to write Salesforce and treat AI like an incapable tool because it doesn’t spit out a complicated app that works out of the box. Yes, AI is mighty and grows more capable by the day, but it’s a capability best leveraged by experienced developers to augment their capability because software, systems, and technology are all very complex and that background is vital to leveraging AI to build effectively in any complex environment.

Using AI to build a simple app, like we are going to demonstrate here, is a great way to dive into what more and more technology experts are embracing for their day-to-day work, and AI does get more capable by the day, so this will change as the technology improves. Right now, though, and likely for some time into the future, technology domain experts across the entire stack will still be needed to help instruct AI on specifically what to do and how to do it. More importantly, AI tools just don’t build everything in a single prompt – it’s a process to build iteratively using vibe coding, and for more complex use cases, it still takes a lot of time to get things built correctly. There’s no denying, though, that as AI becomes more capable, it’ll be able to do more with less, so come back here in a year and I have to update this.

So, now back to vibe coding your first app!

Step 1: Set Up Cursor — Your AI-Powered Coding Assistant

Before you dive into app-building, you’ll want an environment that actually helps you code with AI. Cursor is exactly that, and is a great tool to start using for Vibe Coding.

Cursor is a modified version of Visual Studio Code (an IDE or Software Development tool that developers of all kinds use) that’s supercharged with AI built right in, so you can use AI to build alongside your prompts. It’s great for vibe coders like you - you write partial thoughts, and the AI helps you turn them into real apps, from testing to running.

How to Set It Up

- Go to https://www.cursor.so and download the version for your OS (Mac/Windows/Linux).

- Install and launch it, picking a folder on your computer where you want to store the project folder.

- Create a free Cursor account (you can use GitHub/Google).

- Connect your OpenAI API key:

§ Sign in to https://platform.openai.com/account/api-keys

§ Fill out the form to add your credit card and put $5 in the account, which allows you to use “tokens” (using a single token lets you pay for the LLM to compute a single transaction, with $5 getting you pretty far to start).

- Generate a new key and paste it into Cursor’s settings, to leverage the AI inside of cursor.

- Generate a second key called “TestApp” which you’ll need after following the instructions below.

- Setup your dev environment (lets start with Python), and for this, you can actually ask Cursor in the AI chat interface to set it up for you if you’re not comfortable with that. It’s a conversation with Cursor, it’ll ask questions and if you don’t understand the question, tell Cursor that as it’ll break things down for you. Once it says your Python development environment is set, it’ll likely prompt to set up a virtual environment to begin coding in as well, which is fine to do.

- If you get stuck/lost, just tell Cursor that – it’s a pretty amazing instructor/teacher. I could write an exhaustive step-by-step here, but we are using AI now! So, keeping it vague enough that AI can pick up the slack based on the help you need or questions you have.

You’re ready to start vibing.

Tip: Cursor supports both GPT-4 and Claude, and will integrate with your terminal and GitHub repo too (which is where devs publish their code into the cloud to share with others). This is not a toy to be clear - it’s a production-level AI coding assistant, but it’s easy to leverage to start getting comfortable with AI-based coding and can grow as your interest and ability grow as it’s used by developers of all kinds globally today.

Step 2: Pick a Project That Actually Interests You

Don’t build something generic. Build something you’d use. Here are some quick prompts to unlock ideas:

- “I want to create an app that reads my PDFs and lets me ask questions about them.”

- “I want to automatically summarize YouTube videos I bookmark.”

- “I want a web app that turns my daily notes into a to-do list.”

- “I want to text a bot that recommends guitar tabs based on how I’m feeling.”

Let’s pick one to walk through to demonstrate what a prompt for Cursor might look like (feel free to modify as you see fit):

Project Prompt for Cursor (Modify then Paste this Into Cursor AI box):

Create a simple AI chatbot using Streamlit that allows me to paste a page of notes into a chat window, then push a button titled “Submit” to send that note information to the AI agent to process and prompt to ask the agent to ready my notes and share its observations based on what was submitted in a read-only chat window beneath the screen where my notes were pasted. The application should only have two boxes, one for the text input for pasting text, and the second as a read-only window to post the output from the AI agent built into the application. Along with a button to submit my notes for the agent to ingest and analyze, there is a second button to clear the AI session and remove all the text from both boxes. Please also include a window on the left side of the application where I can post the API key into the application, so that I don’t need to pre-populate a key into a secret or .env file, as that may be too technical for me. Also, please write and execute a test script to validate the functionality of this application before I execute the streamlit session to launch the application. I will use OpenAI for this application, and I want you to use GPT-4o for this project. Please also write the requirements.txt file as well as generate a readme file for this project.

So there’s a lot there, and likely will have to be revised perhaps once Cursor goes and builds a first version of the application.

Step 3: Build the App and Run it!

Once Cursor does its thing from the AI chat interface, you’ll see a bunch of coded files appear on your screen as Cursor writes the code based on the prompt above. Once that’s done, tell Cursor to execute the test script to ensure it wrote what you wanted it to, as it’ll catch it’s own errors that way. Once Cursor is done doing its testing and fixes, ask Cursor to launch the streamlit application on your computer. You should then see it spin up a window that is the application, and allow you to do just what you asked. If something doesn’t work or there’s an error, copy the error, paste it back into the Cursor, and tell it to fix the error. Once that’s done, relaunch until you don’t get an error.

Other Things to Try With Cursor

Once you’ve gotten your app to work, the path forks into infinite roads, and you can have cursor keep working on your code once you’re happy:

- Ask Cursor to add a file upload so others can drop in documents.

- Add llama-index for smarter context parsing.

- Deploy the code to GitHub (once you set up an account and a private “repo”)

- Hook it into Twilio to text your chatbot.

- Use ElevenLabs to make it speak.

Each step you take gives you more confidence. AI becomes less magic and more of a co-pilot. This is a lot for someone just starting out, though, so if you’re lost, you guessed it! Use AI to post questions/screenshots/issues and grow as you tinker.

Why This Chapter Matters So Much

You're not here to memorize TensorFlow syntax (or even know what TensorFlow is)

You're here to learn the rhythm of AI creation — to vibe your way into building, so this is really to help you understand how AI works beyond the ChatGPT interface. This is how the next wave of founders, artists, educators, and farmers are going to work. Not from technical mastery alone, but from curiosity and collaboration with machines and understanding where AI can do what and how.

The world is changing fast. But you’re not behind. You’re right on time.

Resources to Go Deeper

• Cursor IDE

• OpenAI API

• LlamaIndex (formerly GPT Index)

• Buildspace – Projects to learn by building

• FlowGPT – Community of prompt engineers

Final Words

AI isn’t the future—it’s the now. Building with it doesn’t take a CS degree—it takes curiosity and momentum.

So go ahead: spin up Cursor. Dream something weird. Build it fast. Break it often. Let the AI help. Let yourself play. And then share it with the world.

You’re not just watching the future anymore—you’re coding it! This doesn’t mean you need to go any further if development isn’t your thing, but you understand now how this works for folks beyond those publicly facing chat websites.

Click on the Next Chapter Below To Keep Going

Introduction: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 1: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 2: Use AI (more) effectively as a Startup: How startups begin to leverage AI and avoid turning into a failed science project

Chapter 3: Becoming an AI Native: How individuals leverage AI, and become an “AI Native”

Chapter 5: Surfing the AI Tsunami: Where We Are, What We've Learned, and What Comes Next

We’ve traveled across decades of human habit, through historical resistance to change, across generational gaps in technology comfort, and into the fast-moving waters of today’s AI surge. If you’re still with me, then congratulations — you’re already doing what many haven’t: actively adapting.

I hope you have thoughts, questions, emotions, and a moment of deep retrospection thinking about some of what you read here.

This wasn’t just a guide to “using AI.” It was a mindset reset — a call to break free from old paradigms, to shift from passive user to proactive co-creator in the age of intelligent machines. If it makes you uncomfortable, you’re not alone. But don’t stop – keep going – because AI isn’t going to stop and wait for us all to catch up.

Let’s land this plane then (autopilot on, of course) by recapping what really matters.

Key Takeaways: What We Now Know

1. Most Important Takeaway: AI Isn’t Optional. It’s Inevitable.

- Whether you’re a company leader, founder, or individual contributor, AI is already shaping the future of your job, industry, and daily experience. Ignoring it is like ignoring the internet in the 1990s — a decision that won’t age well.

2. Every Level Must Embrace Change

- Companies must become “superflexible” and diving into the strategic framework of Drs. Bahrami and Evans is a good start - organizationally modular, strategically adaptive, and culturally courageous is how you win here.

- Startups must be AI-native — building faster, smarter, and with embedded intelligence in every function.

- Individuals must become AI-fluent — not technical wizards, but articulate thinkers who speak the language of prompts and workflows.

3. Adoption Takes Time — But Not Too Much Time

- There’s always an “adoption buffer.” History shows that humans resist, delay, question, and then accept when conditions are right. But AI isn’t waiting around for social consensus. The pace of adoption is accelerating, and so must our capacity to adapt and make sure we have a clear sense of what AI is, where it’s going, and how to best leverage it.

4. The Real Shift Is Psychological

It’s not just about new tools. It’s about new ways of thinking:

- From linear to exponential.

- From “I do it all” to “I orchestrate it all.”

- From fearing replacement to engineering augmentation so you’re working as an AI native

5. You Don’t Need to Know Everything. You Just Need to Start Engaging and asking questions, no matter your starting point.

The whole concept of “vibe coding” captures this. You don’t wait to be ready. You start messy. You build with the machine. You learn as you go. You’re never building alone.

Where We Could Be Headed

Let’s look forward, not with hype goggles, but with informed optimism.

Near-Term (Next 1–2 years)

- AI agents will become embedded in daily workflows across every sector.

- AI fluency will become a standard job expectation, even outside of tech.

- Most apps will have AI baked in — not as a feature, but as a foundation.

- AI adoption will define competitive advantage — in companies, careers, and countries.

Mid-Term (3–5 years)

- Co-pilots will evolve into full collaborators: systems that don’t just assist but initiate.

- Personalized AI assistants (“myGPTs”) will know your habits, values, and goals.

- Agent ecosystems will replace traditional apps — tasks will be orchestrated, not clicked.

- AI-native orgs will drastically outperform their AI-awkward counterparts.

Long-Term (5–10 years)

- AI may become the infrastructure for how society functions: learning, governing, working.

- Entirely new jobs, industries, and institutions will emerge — shaped around symbiotic intelligence.

- The question won’t be “Is this AI-powered?” but “How does the human fit into this loop?”

- The arrival of AGI (Artificial General Intelligence) will force profound reflection on ethics, control, and purpose — but also unlock possibilities beyond imagination.

You’re Not Drowning — You’re Surfing

The metaphor we started with still holds. AI is a wave — huge, powerful, relentless. But waves don’t drown you if you learn to surf.

This guide isn’t your surfboard – rather, it’s your ability to understand, leverage, and harness AI that will keep you on top of the surf. I hope this is just the start, as it’s incomplete but was designed to be the first word of the first sentence on a new chapter you can begin writing yourself with the help of AI to work alongside you.

You’ve now seen:

- How companies can ride that wave with strategy, and what framework to start using.

- How startups ride it with speed, as they adapt to what’s coming with the “Digital Worker”.

- How individuals ride the wave as an AI native, leveraging it in whatever way matters most for what you do.

- How creators can begin riding that wave by vibe-coding new things into the world, and the capability of AI beyond the chatbot.

If nothing else, I hope this helped you realize you are not behind. You’re not too late, and you’re not required to be technical to leverage this. It’s a mindset shift, not a technical one, to begin leveraging AI effectively.

The future is not being built without you (yet…..)

You can be a part of what’s coming, building this future — one prompt, loop, and decision at a time.

So breathe. Relax. Paddle out. Treat it like working out, you don’t have to be an athlete, but we all need to get out for a walk – it’s going to be as crucial to functioning in business with what’s to come as exercising is for staying healthy. The more you use it, the more “muscle” you grow which only makes it easier as you progress.

The next wave is coming.

And you?

You’re ready for it, so grab a surfboard and start paddling out to it!

Click below for the other chapters

Introduction: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 1: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 2: Use AI (more) effectively as a Startup: How startups begin to leverage AI and avoid turning into a failed science project

Chapter 3: Becoming an AI Native: How individuals leverage AI, and become an “AI Native”

Chapter 4: Vibe Coding your first AI project: How you (yes YOU) can build your first AI app, and get more comfortable with what AI can beyond ChatGPT

1. AI Is the Product (Even When It’s Not the Product)

Sure, maybe your startup isn’t building an AI model from scratch, and you likely aren’t doing any foundational modeling. Doesn’t matter. Every startup now should think like an AI company. That means doing things like:

- Using AI to accelerate product development, from copilot-assisted coding to AI-generated UX mockups.

- Building products that learn and evolve over time using user behavior, not static feature sets.

- Creating AI interfaces that understand your customers, not just serve them.

Whether it’s chat interfaces, recommendation engines, or AI-driven diagnostics, your product should be smarter each week it exists, and rethinking how you build and launch products means having a core “agent” taking on roles and leveraging MCP for toolsets vs assuming you have to build everything yourself. You exist in an ecosystem where AI agents are growing incredibly functional beyond what you have to do as a company yourself.

2. AI-Native Orgs Ship Smarter, Not Just Faster

Most startups pride themselves on moving fast. AI-native startups move intelligently fast. They embed intelligence into:

Hiring: Using AI tools to screen, evaluate, and train talent. It’s not just writing the job description; it’s including AI in the entire hiring loop and understanding how to do that effectively.

Go-to-Market: Leveraging LLMs to create targeted outbound campaigns or iterate messaging dynamically. You don’t need to be the most creative, or most effective at getting your message out – leverage AI to help you develop a plan, use AI to execute the plan, and use AI to analyze the feedback so that AI can do the whole loop, with you in the middle offering feedback/input as it goes.

Customer Support: Using AI agents that triage and solve problems before a human steps in isn’t just a chatbot. People will always like talking to people, sure, but people have also been trained to hate chatbots because of how limited they are and end up frustrating the person. Focus on how AI can scale the people on your customer support team and build smart enough tools to function at scale, so the agents can learn as the company grows, and allow the right answers to solve the right problems, no matter how the questions are asked. From day one, this can reduce overhead and scale knowledge. A five-person team with smart AI loops can punch like a 50-person team because your team is focused on leverage, not labor. It doesn’t mean broad sweeping layoffs either, because there’s always more work than people in a startup – so using human capital more effectively, removing the grunt work, making happier customers 24/7, and allowing people to work in a way that’s best for people means the company grows exponentially and in the right way.

3. Train Your AI Like You Train Your Team

You don’t just hire someone and hope they figure it out. The same goes for your AI stack. Prompt design, fine-tuning, embedding pipelines—these are organizational practices, not just technical ones. Set clear expectations. Define feedback loops. Iterate on prompts like you iterate on pitch decks.

Your AI systems should reflect your company's values, tone, and purpose. That only happens if you treat your AI like a teammate, not a black box, and don’t fear AI for the effect on human capital, but leverage AI to allow the people you have scale with the company and allowing people to be used more effectively for things that people will continue to do best.

4. Build the Org Around the AI Loop

Traditional startups build around product and growth loops. The AI-native startup builds around data-feedback loops:

- What signal are we collecting from users?

- How are we using that signal to train/improve models?

- How are model improvements impacting user outcomes?

This means every part of the org—product, ops, marketing, design—is oriented around understanding, feeding, and benefiting from the AI flywheel. If your startup isn’t collecting, learning, and adapting, you’re just playing startup theater while the real AI-native players lap you, which is where you have to balance how much you’re embracing AI in line with the companies around you. Finding a balance between adoption at the rate of the industry while maintaining a healthy culture is going to be tricky – a lot of folks are afraid of what AI is going to mean for employment, but this is where the real innovation for you and your company is going to come in because AI won’t wait for everyone to figure this out at a comfortable pace.

5. AI Is a Culture / Mindset Shift, Not a Tool

Finally, AI-native startups have a different mindset. They don’t fear replacement—they chase augmentation, just like technology adoption waves have happened previously but this time, happening much quicker. They don’t ask “how do we compete with AI?” or “How do we insulate from AI?” they ask “how do we co-create with it and make it part of our DNA?”.

This means:

- Encouraging team members to experiment with AI tools, even outside their role.

- Hosting internal “prompt jams” instead of just hackathons.

- Valuing AI literacy as much as domain expertise.

- Interviewing for AI adoption and understanding as the primary skill you’re looking for (other than not being a jerk, of course)

MOST IMPORTANTLY (UPPER CASE IMPORTANT): Addressing fear of AI by building a company culture that leverages it, vs stepping timidly into the tool out of a concern for backlash. This is all something we need to get comfortable with, and it can only help your team to understand how to leverage it, embrace it, and enhance everyone’s value because of it. People will still have things to do, and understanding what those things are first means you’ll be a part of helping create what this AI native looks like and how your company and the people that work for you will thrive in that space, even if it looks very different.

Click on the Next Chapter Below To Keep Going

Introduction: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 1: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 3: Becoming an AI Native: How individuals leverage AI, and become an “AI Native”

Chapter 4: Vibe Coding your first AI project: How you (yes YOU) can build your first AI app, and get more comfortable with what AI can beyond ChatGPT

Chapter 5: Surfing the AI Tsunami: Where We Are, What We've Learned, and What Comes Next

This is all based on the research done by Drs. Bahrami (Berkeley) and Evans (Carnegie Mellon) who have done amazing work on applied innovation in corporate America, and have the strategy for the time, do yourself a favor and go buy their book here. Full disclaimer, I applied their research to tech adoption use cases at Boeing and went on to apply their work at 21 Fortune 500 companies during my time at Slalom Consulting, so I know it works.

Let’s dive in – to really embrace AI instead of getting overwhelmed by it, companies must become “Superflexible,” per the research here, a term that captures not just agility or adaptability, but an ability to thrive in an environment defined by constant, accelerating change. This requires a deliberate mindset shift and operational overhaul.

First: What is “Superflexibility?”

If there’s one capability that’s non-negotiable in the AI era, it’s adaptability—not just in tech stacks or business models, but across the entire organizational DNA. Dr. Homa Bahrami and Dr. Stuart Evans, longtime observers of how Silicon Valley stays ahead of the curve, coined a term for this: superflexibility. It’s more than just agility. It’s the ability to not only weather change, but to continuously evolve through it.

In environments where breakthroughs come monthly, not yearly—and where your best AI bet today might look outdated tomorrow—rigid roadmaps and top-down org charts just won’t cut it. Superflexibility means designing your company to pivot fast, learn constantly, and recombine talent, resources, and strategy on the fly, without spinning into chaos. It's the ability to operate with both focus and fluidity—think Amazon launching AWS while still dominating e-commerce, or OpenAI shifting from pure research lab to commercial juggernaut in under 24 months.

Bahrami and Evans outline five core pillars that make superflexibility not just possible, but sustainable:

Maneuvering: Always Running Multiple Plays

Superflexible companies don't bet the farm on a single AI initiative. They think in portfolios—running short-term pilots, long-term moonshots, and defensive plays in parallel. It’s about optionality: having multiple paths forward, especially when the map keeps changing. In AI terms, that might mean testing different LLM providers, experimenting with custom agent workflows, and incubating niche applications in various departments simultaneously. When something hits, the organization already has a foot in the door.

Recalibration: Embracing Constant Iteration

Forget five-year plans carved in stone. Superflexibility demands quarterly strategy checks, where assumptions are challenged and plans are adjusted based on live feedback. Companies treat initiatives like living systems—subject to pruning, tweaking, or restarting. A failed chatbot project might resurface as an internal process automation tool. An AI feature that flopped with one customer segment could find surprising traction elsewhere. Test. Learn. Revise. Repeat.

This is also where revision triggers come in—unexpected developments that force a rethink. Whether it’s a competitor releasing a breakthrough, a regulatory change, or a model behaving in unanticipated ways, superflexible firms treat these not as setbacks, but cues to evolve.

Peer-to-Peer Leadership: Breaking the Chain of Command

In an AI-native company, the best ideas might come from a data scientist, a designer, or a frontline operator—not just the C-suite. Superflexible orgs ditch the old pyramid and operate in networks, not hierarchies. Authority is based on contribution and expertise, not just job title. Leaders set vision and remove roadblocks, but real-time decision-making happens closest to the problem. This speeds up experimentation and helps surface edge insights early, especially when AI is being deployed in unfamiliar areas.

Orgitechting: Modular Teams and Federated Ops

Superflexibility also lives in the architecture of the company. Bahrami and Evans call it “orgitechting”—designing modular, reconfigurable units that can plug in or out as needed. This is how large enterprises can act more like startups: small teams own discrete AI initiatives, and if priorities shift, those teams are remixed rather than rebuilt. Add in external partnerships—like a pilot with an AI vendor or a research lab—and you get a federated structure that’s scalable without being fragile. It’s not about centralizing everything, it’s about coordinating the right nodes at the right time.

Spring Cleaning: Shedding What No Longer Serves

The most overlooked trait of superflexible firms? Letting go of products, processes, even people—when the fit no longer works. This doesn’t mean acting rashly, but rather building in rituals of strategic renewal like an annual audit of AI projects to decide what gets scaled, shelved, or recycled. Like revisiting your AI deployment strategy every time a new foundation model emerges. Even within a winning company, yesterday’s edge can become today’s baggage if you’re not continuously making room for what’s next.

All of this—maneuvering, recalibrating, peer-to-peer leadership, modular architecture, and pruning old bets—adds up to one big idea: treat your company like a living organism, not a machine. In the AI age, the playbook isn’t fixed. It’s in flux. And success goes to those who design for that reality, not by reacting faster, but by building a system that expects change, thrives on it, and is ready to rewrite itself at any moment.

So yes, AI is moving fast. But so can you. Superflexibility is how you surf the wave without wiping out.

So now that you know what it is, how do you start to leverage it?

1. Start by Developing an AI Thesis Aligned with Strategic Flexibility

It starts with asking: What kind of AI-powered company are we becoming? Not every company needs to become OpenAI or DeepMind. But every company does need to think like them—futuring backward from AI’s inevitable trajectory and identifying where they fit in. Are you streamlining operations? Automating customer engagement? Building proprietary AI tools? These choices become your AI thesis—one that should be flexible enough to evolve, but focused enough to prioritize action.

Superflexible companies know that strategy isn't about locking in a 5-year plan anymore—it's about designing a living system that flexes in real-time as the technology changes, as well as checking yourself for ingrained biases based on how you’ve worked and operated to date. This isn’t the same thing with a new tool; this is an entirely new way of doing things.

2. Integrate “AI Muscles” into Core Org Functions

Don’t bolt AI on like a sticker. Instead, train up “AI muscles” within core business units. This might mean:

- Giving your operations team a sandbox to run ML automation pilots.

- Embedding prompt engineering practices within sales and marketing workflows.

- Encouraging finance to explore predictive modeling tools beyond spreadsheets.

These aren’t moonshots—they’re practical, iterative shifts. The key is normalizing AI experimentation as a team habit, not just a technical initiative. AI becomes a lever every team can pull on, not just the domain of engineers.

3. Make Culture and Architecture Modular

Just as GenAI models are pre-trained and fine-tuned, your company must balance stable foundations with customizable overlays. Organizationally, this translates to:

- Cultural Modularity: Let local teams adopt AI tools that work for them, without enforcing one-size-fits-all solutions.

- Architectural Modularity: Build data and system architectures that support plug-and-play AI integration.

Superflexibility means not everything has to be centralized. Centralize values and guardrails, decentralize use cases and tool experimentation.

4. Design for Speed, Not Just Safety

Legacy enterprises often mistake compliance checklists for strategy. But in an AI world, speed becomes the differentiator, and slow adoption can be more dangerous than taking a calculated risk. Superflexible companies make room for:

- Micro-decision loops that empower AI pilots without months of approvals.

- Fast feedback cycles to learn what works, scale what shows promise, and kill what doesn’t.

- Compliance still matters, but it’s embedded into the flow, not a blocker outside it.

5. Keep Humans in the Feedback Loop

AI is not replacing people—it’s reshaping the relationship between humans and decisions. A smart company strategy doesn’t just automate. It augments. Think of AI as the ultimate co-pilot—processing scale, surfacing insights, and taking on tasks that unlock human creativity and leadership.

And here’s the kicker: the best AI systems require human judgment. Training loops, prompt tuning, oversight layers—they all need thoughtful humans at the core. A company’s real edge will come from how it orchestrates this partnership.

There’s a lot more involved in understanding and adopting “Super-flexibility,” but I hope this is enough to get you to want to learn more and begin adopting it as the framework for integrating AI into your company, as no strategic framework out there will work better.

For more insight check out Super-Flexibility in Practice: Insights from a Crisis

Click on the Next Chapter Below To Keep Going

Introduction: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 2: Use AI (more) effectively as a Startup: How startups begin to leverage AI and avoid turning into a failed science project

Chapter 3: Becoming an AI Native: How individuals leverage AI, and become an “AI Native”

Chapter 4: Vibe Coding your first AI project: How you (yes YOU) can build your first AI app, and get more comfortable with what AI can beyond ChatGPT

Chapter 5: Surfing the AI Tsunami: Where We Are, What We've Learned, and What Comes Next

It’s no surprise that AI is just about everywhere right now, with every company, startup founder, and investor trying to understand how best to adopt AI in the midst of rapid technology change and a world that’s also changing rapidly before us. As one investor recently said, “investing now isn’t about keeping AI in mind, but it’s understanding everything now revolves around it”.

It's the chief consideration for any CTO to plot a course for leveraging as part of the broader technology strategy and roadmap, and as the Chief Data Officer for an emerging technology company, it’s likely no different in any tech company right now than it is for us at the moment where it’s how to be an effective corporate customer adopting AI, an effective technology startup building with AI, or a technology investor wondering where the best place to invest in AI would be.

Yet, there are tried-and-true methods for navigating this technology despite there being no precedent before it. Past technology revolutions moved more slowly (the adoption of electricity), changed the way we work more gradually (the internet), or happened in subsequent waves that made adoption much easier to understand and leverage (smartphones).

GenAI Started with a Bang and Turned into a Roar

With GenAI, though, it came out with a massive splash when ChatGPT launched, with the technology now poised to disrupt every industry, household, and country globally within the next ten years, whether people are ready for the change or not. This is just the first wave though, as companies leading AI are working on smarter capabilities that can do more at a rapid pace, leading to technology breakthroughs like “Artificial General Intelligence” or AGI that’ll allow AI to self-improve, building subsequent versions of agents that’ll be more intelligent and powerful than previous versions without limitations on what’s possible for AI to do at that point, as well as the huge potential for robotics as machines are growing in capability and intelligence alongside GenAI.

This means we aren’t just dealing with a computer in our pocket or a way to leverage video to stay in touch with grandma on another coastline; this means getting our heads around the concept of a “Digital Worker” that’ll operate as a proxy for an actual employee. which means rethinking everything from a lifelong career trajectory for an individual today graduating from college to the way companies organize their human capital alongside their AI counterparts.

How We Embrace Change Historically

Though it might cause a range of emotions to surface, we have several rounds of new technology adoption to reference as to how folks do or don’t embrace change as time has gone on.

Today I heard the story of an administrator working in city planning about automated crosswalks. For a long time, people have gotten used to hitting the button at the crosswalk in a busy city to get the walk signal to appear, and automating the process of that isn’t as easy as just removing the button. We’ve all been trained to hit the button, so when no button is there, many people get anxiety or worry that there is no successful request mechanism, so the crosswalk sign might never turn green. What happens in most cities, then, is that signs will get posted with the buttons still there, letting folks know the buttons no longer work, but still allow people to push them if they want to. Eventually, the buttons will go away, but it takes some retraining and a fair amount of time for an “adoption buffer” to take place for people to understand and feel at ease around automated crosswalk signs.

When automated doors first came out, people wouldn’t cross the door's threshold if they couldn’t put their hand on a handle to do so as no one trusted what the door would do if they weren’t touching it. What if it shuts on them? What if they don’t go through fast enough? So handles were placed on automated doors during that “adoption buffer” until folks no longer needed the handle to feel good about the door not closing on them.

Another take on this was one we all might know, and that’s when keyboards for computers were released, companies kept the key configuration in the same pattern as the typewriters, because that’s what people were already used to. The “Qwerty” keyboard was optimized, though, based on the most common key combinations being furthest away from each other to avoid typewriter keys from sticking, which means it was the least optimal way for a digital keyboard to be built. The “Dvorak” keyboard layout was created to fix this problem, but no one adopted it because how we type and how we’ve learned to type, is too deeply ingrained and not enough people wanted to re-learn to type – so we’ve all been typing in the least efficient way since then – kind of like not adopting the metric system perhaps?

The rise of data and data analysis is no different, with several companies holding back how smart they are at knowing their customers, their habits, and all the attributes surrounding those individuals. Amazon, Google, etc know a lot about people, but has had to be careful how much to leverage that so as not to scare too many people off and instead, slowly ease into becoming smarter and more targeted around advertising as people get used to tech companies knowing EVERYTHING about them. Sometimes it’s generational, sometimes it happens within a handful of years. In the end, though, technology becomes more capable, and pushes the boundary within the context of that “adoption buffer,” so there isn’t a wholesale rejection or strong emotional reaction which prompts legal action (Facial Recognition), low adoption/rejection (Google Glass and the “Glassholes”), or outright bans both socially and legally (The Luddite movement & the Frame Breaking Act)

We saw this recently with the strike across the East Coast US ports (2024 US port strike) in protest of automated loading/unloading of ships, in line with port automation taking place globally, where society has to balance the need for efficiency with the needs of the people doing the work today. Not all bans are bad, such as the legal work to keep “Deep Fakes” from mass adoption given the significant damage the technology can do in several domains, but technology tends to move forward sometimes whether we are really ready for it or not – pushing up against our “Adoption Barriers” – and AI in companies will likely be no different with a much bigger potential to disrupt.

AI is inevitable, whether we are ready for it or not, though, and a lot of change is coming, but have no fear – next is how you, no matter who you are, can better embrace it in anticipation of where AI is headed.

How we now embrace/adopt/leverage AI, then (given there’s no going around it)

Ok, that’s a lot so far – but we are still going. This next section is big, though, so we can tackle this in 4 sections, which are linked below as 5 different articles or “Chapters” to break this up a bit

Click on the Next Chapter Below To Keep Going

Chapter 1: A Company Strategy for AI: How companies develop a strategy to leverage AI, and change in a way that maximizes AI effectively

Chapter 2: Use AI (more) effectively as a Startup: How startups begin to leverage AI and avoid turning into a failed science project

Chapter 3: Becoming an AI Native: How individuals leverage AI, and become an “AI Native”

Chapter 4: Vibe Coding your first AI project: How you (yes YOU) can build your first AI app, and get more comfortable with what AI can beyond ChatGPT

Chapter 5: Surfing the AI Tsunami: Where We Are, What We've Learned, and What Comes Next