Welcome!
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By a show of hands, who likes earning money?
What You Are Getting
With this cast, you are going to get real hands-on value from every one of them.
Fer Patel
Molly Mahoney
Austin Armstrong
Nipun Syal
Jennifer "AI Tech Boss" Eastmond
Kaitlyn Portillo
Ron Graziano
Mario Aldayuz
Nuno Tavares
Jeremy Rice
Christina Molaison
Anthony Mallett
My Promise
I will help you answer two major questions about your AI/automations.
How do I MAP the money?
How do I KEEP the money?
Meet the speaker
Cody Landry
- Founder, Requisite Designs
- AI-Powered RevOps Strategist
- Cybersecurity/Compliance Specialist
- 2x Founder and 2x SaaS Creator
- Founding Board Member of AI-focused non-profit, IntegrateAI
Cody Landry is an AI-Powered RevOps Strategist, cybersecurity leader, and 2x SaaS founder and a founding board member of IntegrateAI, a nonprofit dedicated to preparing veterans and transitioning service members for careers in AI. He speaks on RevOps, AI adoption, cybersecurity/compliance, automation, and the systems that connect revenue strategy with operational execution. Cody helps organizations modernize how revenue systems are built, connected, and scaled through AI, automation, and operational infrastructure.
Over the past 17 years, he has worked across DoD, Fortune 150, and SMB environments, designing, implementing, securing, and auditing enterprise platforms built from the ground up that support more than $1 billion in business operations annually. Cody has individually helped grow multiple companies from 7 to 8 figures annually.
What does it mean to Map the Money?
Mapping the money means that before you build anything, you consider every element that can create value for your business or your client: time, money, speed, or scale. You also weigh every element that can create risk: security, compliance, dependency, exposure.
Everything between here and the tool is that exercise, worked by hand, so the number the tool gives you later is one you can defend in the room with the client.
Why foundation comes before pricing, and not the other way round
The instinct is to price first, especially standing in front of a client who has already asked what this costs. Naming a fee before you have named the failure modes is how a value based number turns into a promise you cannot keep.
Skip foundation and you can still produce a fee. You cannot defend it. A number that assumes the AI can be audited, reversed and controlled is priced on a system you never actually checked, and the first incident that proves one of those assumptions wrong turns the fee into evidence against you rather than for you.
A fee with foundation behind it is different. You can say what was checked, what a gap would have meant, and why the number holds up when a client asks a hard question about it in the room. So the order is: name what could go wrong and confirm it will not, then price what is left once that is settled.
There is a second reason, and it is the commercial one. Foundation is the part a client can watch you run in real time, in the scoping call, at no cost to either of you, and it is what tells you how big and how risky the build actually is before you commit to a number for it.
Which one does this move?
Every system this firm builds gets evaluated on the same three tests. Pick the one this idea is actually about.
Which of these does your solution solve for?
Money
It creates or protects revenue directly: a lead that would have been lost, a deal that closes because it moved faster, a customer who would have churned.
Time
It removes hours of manual work a person is doing today: retyping, reconciling, chasing, checking. Countable in hours, not dollars, until somebody says what those hours go to.
Speed
It makes something happen faster than a person could do it, not less of it: a quote out same day instead of Thursday, a response inside a minute instead of a day.
Scale
It helps you scale faster: handle more customers, more locations or more volume without adding headcount at the same rate you would need to today.
“Everything needs a name in sales.”
What would the AI solution be named based on what it does?
Quantitative or Qualitative?
AI makes this harder to eyeball than plain automation did. A script either ran or it did not; an AI system can be right most of the time, wrong in ways nobody reviewed, and still feel like progress. Some of what it does shows up as a clean number on day one. Some of it is real value that never turns into one. Both are legitimate reasons to build with AI. Neither is a reason to skip the math.
Quantitative
The AI produces a number you can put on it directly.
- An AI agent that qualifies and routes inbound leads before a rep ever sees them
- AI-drafted replies that cut first response time from a day to under a minute
- A support ticket that never gets created because an AI answer already solved it
- Chasing a 10x productivity number nobody can trace back to a task the AI touches
- An AI wrapper on a task that already ran fine in two minutes and never broke
Qualitative
Real value the AI creates, hard to price directly.
- An inbox an AI keeps triaged instead of one more thing somebody has to keep up with
- A client who feels like the AI caught something a tired human would have missed
- Keeping the one person who actually knows how the business runs happier because their job got easier
- An AI note-taker for meetings nobody rewatches or acts on
- Replacing a two-minute manual check with an AI step nobody can explain when it is wrong
Additional Questions
Hours a week it would touch
Across everyone involved, not per person. Three people at four hours each is twelve.
Coverage
How much of those hours the system actually absorbs once review, exceptions and the cases it hands back are counted.
Fully loaded hourly cost
Wage times roughly 1.3 for a US employee. If your finance team has the real multiplier, use theirs.
Software this depends on to run
Zapier, Resend, Supabase, any other subscription this build leans on, added up as a one time estimate of what standing it up costs beyond your own hours.
Monthly running cost
Licences, API usage, and somebody fixing it when the system on the other end changes underneath it.
Extra profit per month, if any
Only if this brings in business you would not have gotten otherwise, not for time it saves on business you already have. Use your profit on that extra business, not the full sale price. Leave it at zero if you are not sure.
What would those hours go to instead?
This is the question that decides whether the number is real. It is required.
95% of enterprise generative AI pilots never scale and show zero measurable P&L impact.
How do you become the 5% that do?
Source: MIT Project NANDA, "The GenAI Divide: State of AI in Business 2025" (July 2025), 300+ deployments and 150+ executive interviews.
Compliance
A regulation does not care how small your build is. It cares what data touched it and who it touched.
The questions ahead in this session map straight to the regimes that actually get enforced against small AI builds: ADA and WCAG accessibility, FERPA for student records, PCI DSS for card data, Sarbanes-Oxley for public-company financial reporting, HIPAA for health information, CCPA and CPRA for California residents, COPPA for anyone under thirteen, PIPEDA for Canadian customers, and GDPR for anyone in the EU or UK.
None of that means do not build. It means know which of those apply before you ship, not after a demand letter arrives.
Security
Every one of these is a pitfall I have watched a real build hit, not a hypothetical.
Row-Level Security left off, or never tested against a second tenant, lets customer A read customer B’s rows through the API even when the UI never shows it to them.
No password manager discipline. A shared spreadsheet of client credentials, or one password reused everywhere, is not a policy, it is a matter of time.
API keys committed to the repo in plain text. .gitignore does not undo a commit already pushed, and "the AI put it there" is not a defense once that repo is public.
Local-only tools versus a secure VPS session. Know which one your build actually needs. Treating every build the same way governs a low-stakes prototype and a client’s production data identically, which is wrong in both directions.
Every build needs an admin panel: log reviews, error handling, user management, global feature flag controls to shut it off fast, and a 50,000 foot view of API usage and cost.
Even Austin gets how important it is. 😉
Map Your Own Build
Run the numbers on your own idea, end to end: what it is worth, what it costs, and what it exposes you to.
QUESTION 1 OF 38
Who is this report for?
Your name, or the name of the business this is for. Optional, used only for the cover page of your PDF.
Where That Leaves You
Map the Money
Name the value lever before you build: Time, Money, Speed, or Scale.
Foundation Before Pricing
Audit, security, and compliance settled before a fee is defensible.
Manage the Risk
Know which regulations actually apply before you ship, not after a demand letter.
Run Your Own Numbers
The tool above gives you a floor you can defend in the room, not a guess.
Two Problems AI Still Has Not Solved
Ideas and building are far easier now than they used to be. Two of the hardest problems in software are exactly where they always were.
Integration
If it does not work with what they already run, none of the rest of this matters.
Adoption
If the people who are supposed to use it never do, none of the rest of this matters either.
Either one, and you are looking at churn, or a refund.
Book a Free 30-Minute Call
Questions?
Thank You!
A special thank you to Fer Patel and Kaitlyn Portillo for everything they have done to make this possible.