AI
TRANSFORMATION
Solve Small
and
Think Like
a Founder
Don't fall for the big talk
Editor’s note: Our Co-Founder has developed this perspective about AI transformation after hearing countless talks about the so-called AI revolution. We think it’s such a good break from the conventional approach to AI adoption that we organized a summit around it.
Artificial Intelligence is sold to the C-suite as transformation at scale—a revolution in business, a redefinition of the workforce, a paradigm shift. Every AI keynote, whitepaper, and corporate summit emphasizes “epic transformation,” the kind that reshapes industries overnight.
But here’s the truth: AI transformation rarely happens in a single leap. Instead, it evolves through a series of incremental, often messy, small-scale shifts. And it’s in those smaller moves—often overlooked in corporate case studies—where AI’s true impact is being felt. This is the Solve Small approach: focusing on targeted, bottom-up AI interventions that remove inefficiencies while preserving the human touch where it matters most.
This is where AI transformation mirrors the way great founders run their companies. Conventional business wisdom says that scaling an organization requires distributing decision-making, adding layers of management, and diffusing control. Yet, the most effective founder-led companies—like Apple, Airbnb, Shopify, and Nvidia—reject this model. Instead, they remain deeply involved in the details that matter, ensuring that speed, adaptability, and clarity drive their organization forward. AI transformation requires the same approach: high-touch, iterative, and deeply embedded within the business.
Toby Daniels
Founder, ON_Discourse, former Chief Innovation Officer, Adweek, Founder and Chair, Social Media Week
The Problem With Epic AI
The way we talk about AI inside boardrooms is broken. The discourse is full of sweeping, cinematic narratives—AI will “reinvent how we work,” “unlock human potential,” and “create limitless efficiency.” Yet, this kind of hype obscures the real work required to integrate AI successfully.
Consider the C-suite executive who leaves an AI conference with visions of radical automation, only to return to an organization struggling with basic data hygiene. Or the startup founder promising a fully autonomous AI-powered workflow, only to realize that employees don’t trust AI-generated insights. The gap between expectation and execution is vast because the AI discourse favors spectacle over substance.
This is why AI transformation should be approached like a founder running their company—not through bureaucratic committees and abstract strategies, but through direct involvement, rapid iteration, and a relentless focus on solving small, meaningful problems.
Thinking Like a Founder: AI Transformation in Small, Impactful Steps
The best founder-led companies thrive because they embrace hands-on decision-making and fast, iterative improvements. AI adoption should follow a similar model. Here’s what that looks like in practice:
1.
Solve Small: Incremental Change That Compounds Over Time
Great founders don’t overhaul their entire organization overnight; they make continuous, strategic adjustments. AI transformation should follow the same principle. The most effective AI-driven businesses treat AI like compounding interest—small investments that build on each other:
- A sales team starts with AI-assisted meeting transcriptions, then layers in automated CRM updates, and later integrates predictive sales forecasting.
- A manufacturing plant implements AI for maintenance logs, extends it to predictive downtime prevention, and eventually integrates it into supply chain optimization.
Like a founder iterating on product development, AI transformation isn’t about flipping a switch—it’s about stacking small improvements until they create something larger than the sum of their parts.
2.
AI as a Bottom-Up, Ground-Level Initiative
The best ideas don’t always come from leadership—they emerge from people closest to the work. Founder-led organizations like Nvidia empower employees at every level to share insights directly with leadership. AI adoption should work the same way:
- A call center rep starts using ChatGPT to summarize support tickets before management even considers AI integration.
- A junior designer leverages AI-generated layouts to speed up work, improving both quality and output.
- A coder uses AI-assisted debugging not because leadership mandated it, but because it’s simply faster and more efficient.
AI initiatives should mirror the Solve Small model—where leadership listens, learns, and scales what works, rather than imposing AI from the top down.
3.
AI as a Fast, Iterative Process
Founder-led companies don’t rely on long planning cycles. Airbnb’s Brian Chesky eliminated unnecessary layers of management and engaged directly with product teams to make faster, better decisions. AI transformation should follow the same principle:
- A legal team pilots AI contract review with one clause at a time rather than automating the entire process at once.
- A retail company A/B tests AI-generated product descriptions for a subset of SKUs before rolling it out across the catalog.
- A logistics firm implements AI-driven route optimization for a single delivery region before expanding nationwide.
Successful AI adoption moves at the pace of iteration, not perfection.
4.
AI as Local, Not Just Enterprise-Wide
Not every AI innovation needs massive cloud infrastructure. The most impactful AI-driven improvements happen at the local level—on an individual’s laptop, phone, or department-specific system:
- A doctor using AI for voice-to-text medical notes on their own device, rather than a hospital-wide AI integration.
- A journalist using AI summarization locally for research without relying on centralized editorial AI mandates.
- A salesperson using an AI-powered meeting assistant that operates on their phone, rather than waiting for IT to implement a corporate-wide AI tool.
5.
AI as Specific, Not Broad
Great founders don’t try to do everything at once. They focus on solving one problem exceptionally well before expanding. AI transformation should be approached the same way:
- AI for one type of document scanning (e.g., invoices) works better than trying to automate all document types at once.
- AI in one language model per department (e.g., legal vs. marketing) avoids generic, diluted results.
- AI that refines a single metric (e.g., reducing customer service handle time) often outperforms AI designed to “optimize” an entire workflow.
6.
AI as an Invisible, Seamless Part of Work
Founder-led companies prioritize clarity—teams work best when they know exactly what to focus on. AI should operate the same way: it should be so seamlessly integrated that it disappears into the workflow.
- AI-powered email filters reduce spam and prioritize important messages.
- AI-driven search ranking surfaces better results without users thinking about it.
- AI-enhanced writing suggestions feel like part of the workflow, not a separate tool.
The Future of AI Is Solve Small—And Founder-Driven
Big AI transformation stories will always dominate headlines, but in reality, the organizations that win will be the ones that think like great founders—staying hands-on, moving fast, and solving small, again and again, until the transformation is undeniable.
If business leaders want to “go big” on AI, they should start by solving small—and staying directly involved every step of the way. This is why AI transformation should be approached like a founder running their company—not through bureaucratic committees and abstract strategies, but through direct involvement, rapid iteration, and a relentless focus on solving small, meaningful problems.
Interested in attending the Summit? Learn more and request an invitation here.