Home / Toolkit / Why Executives Should Build
WHY EXECUTIVES SHOULD BUILD.
The case for executives who pick up the tools their teams use. Plus the patterns that make it stick: an AI Chief of Staff, calendar delegation, a personal knowledge base, disposable software, and how to drive org-wide AI adoption.
Written by Yannic Desch · Last updated June 2026
Why Executives Should Build
There's a moment I've seen repeatedly in PE portfolio companies — and lived myself. An executive sits through an AI demo, nods, and leaves with a vague sense that something important is happening. Then they go back to their calendar and do nothing different.
The gap between understanding AI in principle and understanding AI in practice is not bridged by better presentations. It's bridged by building something — anything — yourself. Even once. Even badly.
Understanding replaces skepticism
When executives build with AI themselves, abstract becomes concrete. They learn what AI does well — rapid synthesis, pattern recognition, drafting, automation — and where it struggles — complex multi-step reasoning, knowing when to stop, context it hasn't been given. That calibrated understanding makes for better decisions than either blind enthusiasm or reflexive skepticism.
Credibility through demonstration
"Show me your prototype" carries weight when the person saying it has built one. Mandates from executives who have never touched the tools create compliance, not momentum. Mandates from executives who have shipped something, even a rough internal tool, create a different kind of permission.
The P&L case
In PE-backed environments, AI adoption is not a culture question — it's a value creation question. Executives who understand what's buildable can prioritize use cases that move EBITDA. Executives who don't understand it will over-invest in the wrong places and under-invest in the right ones.
The AI Chief of Staff
One of the highest-leverage things an executive can build is a personal AI assistant configured around how they actually work. Not a general-purpose chatbot. A Chief of Staff that knows your portfolio, your communication style, your recurring decisions.
This isn't about replacing judgment. It's about reducing the friction on the repetitive 40% of executive work so that judgment gets applied to the 60% that needs it.
What an AI Chief of Staff can do
Analyze the agenda, surface relevant context from your knowledge base, draft talking points.
Review your week and flag which meetings you could skip, delegate, or compress to async.
Sort and prioritize incoming messages. Draft responses to the predictable ones.
Direct feedback on memos, decks, or decisions — without the social dynamics of human feedback.
Compile and summarize from across your knowledge base before you walk into a conversation.
Building for an audience of one
When you build for yourself, you can hyper-customize. Your AI Chief of Staff knows your preferences, your portfolio companies, your meeting patterns. That level of personalization is impossible in general-purpose tools. And iteration has no roadmap to negotiate — if something doesn't work, change it that afternoon.
Calendar Delegation
Calendar management is one of the highest-leverage use cases for executive AI. Time compounds — small inefficiencies repeated 5 times a week become hours lost.
Meeting Audit
For each meeting, your AI reviews the agenda and suggests: whether your presence is actually required, who could attend instead, whether the outcome could be reached asynchronously, and what decision or context is actually needed from you.
Delegation Messages
The AI doesn't just flag meetings to skip — it drafts the delegation message. "Hey [person], I won't make this one, but [delegate] can cover. Here's what they need to know..." Reduces the friction of delegation from five minutes to thirty seconds.
Focus Time Protection
Beyond auditing existing meetings: identifying which recurring meetings could go biweekly, where you need protected blocks for deep work, and which time patterns are consistently your most productive.
Personal Knowledge Base
Markdown files are the best personal knowledge base for AI-augmented work. Simple, portable, and accessible to any model you use. The more context you give an AI about how you work, the more useful it becomes.
What to store
Portfolio context
Company overviews, key metrics, strategic priorities
Decision logs
What was decided, why, what alternatives were considered
Communication style
How you write, what you expect, your defaults
Templates
Common formats: board updates, feedback, memos
Research
Industry notes, competitor analysis, market data
Team context
Who does what, key relationships, ongoing initiatives
Why Markdown works
Text-based and universal. Works with any AI tool, can be versioned in git, and doesn't lock you into any platform. Your knowledge base becomes portable context that improves every AI interaction — whether you're in Claude, Cursor, or a custom tool you built last week.
Disposable Software
One of the most liberating mindset shifts in AI-native work: personal software can be ephemeral. Build a dashboard for Q4 portfolio review, use it for six weeks, throw it away. Software becomes as accessible — and as disposable — as documents.
Build it. Use it. Discard it.
Not every tool needs to live forever. A quick prototype for a board meeting, a one-off analysis dashboard, a temporary tracker for a PMI sprint. Creates value without creating tech debt. Archive when done, delete when stale.
Imperfection is a feature
Personal tools don't need to handle edge cases they'll never see. They don't need polished UIs. They don't need to scale past one user. That freedom is what makes rapid building possible. A working rough tool beats a perfect tool that doesn't exist.
Software as a medium for thinking
You make a spreadsheet for one analysis, then never open it again. Interactive tools get the same treatment. An AI-powered brief for a management presentation, a scenario model for an investment decision, a chatbot that knows your portfolio. Software as temporary thinking, not permanent infrastructure.
Driving Org Adoption
Effective AI adoption requires both top-down mandate and bottom-up enthusiasm. In PE environments, this is not optional — AI readiness is increasingly a value creation lever and an exit factor.
Top-Down: Setting Clear Expectations
"Show me your prototype" — a clear expectation that drives adoption faster than any training program
Visible usage: when leadership uses AI tools in meetings and memos, it signals how work gets done here
Resource allocation: dedicated time, licensed tools, and protected exploration time show organizational commitment
Bottom-Up: Nurturing Grassroots Enthusiasm
Builder Days: dedicated time for hands-on exploration, not training decks
Recognition: prizes, showcases, and celebration of creative AI use — make the early adopters visible
Community: Slack channels, office hours, peer networks that make it easy to share and ask
Champions: identify enthusiastic early adopters and give them air cover to go further
The Flywheel Effect
When leadership demonstrates value through their own AI use, and teams discover value through hands-on experience, a flywheel emerges. Success stories spread. Skeptics get curious. The organization's collective capability compounds. This is how teams become AI-native: not through mandates alone, but through a combination of clear expectations and genuine, experience-driven enthusiasm.
Getting Started
Ready to begin? Five concrete steps. In order.
Identify your friction
What eats your time? Meeting prep, email triage, research synthesis, status updates. Pick one where automation would land immediately. Don't try to solve everything.
Start your knowledge base
A folder of markdown files. Your role, the companies you manage, your communication preferences, recurring decisions. The foundation for any tool you build later.
Build something small
Use Cursor or Claude Code. Don't aim for polish. A meeting prep assistant, a portfolio briefing tool, a decision memo generator. Learning matters more than finishing.
Show your team
Not as a product. As an example. Firsthand experience persuades better than any slide deck. The most powerful thing a leader can say is "here's what I built last week."
Create space for others
Dedicate time for hands-on AI exploration. Set clear expectations from the top. The combination of permission and urgency is what separates organizations that transform from those that run pilots forever.
The most important step?
Start building. The understanding you gain from hands-on experience will transform how you think about AI, how you lead your team, and ultimately how your organization operates. The tools matter less than the act of creation itself.