Hi friends,
Happy September!
A few weeks ago I wrapped up Stanford’s Digital Transformation program focused on AI. It was expensive but worth every penny.
I’m grateful I carved out the time to catch up, step back, learn, and think.
Leaders from Google, Coca‑Cola, the Bank of Nigeria, and peers from Abu Dhabi, Saudi Arabia, Ghana, Australia, New Zealand, and Germany all brought real stories of what’s working and what’s hard.
The common thread was clear:
AI is moving fast, and thoughtful founders can turn it into practical wins.
Below are my (technical) takeaways, a playbook you can run this year, and a worksheet to help you choose a smart first use case.
I’m so grateful for my 25 years running a tech company, so while this information really stretched my brain, I can always simplify it in a one-on-one if you’d like!
What AI really is, in plain English
Artificial intelligence is a set of methods that let computers perform tasks that usually require human intelligence, like understanding language, recognizing patterns, learning from data, making predictions, and making decisions under constraints.
Think of it in layers:
- Data feeds the system.
- Models learn from that data.
- Applications wrap the model so people can actually use it in their work.
- You can create an AI product into results and revenue.
Python (a programming language) came up a lot in class because it’s the most common way teams prototype AI.
It’s great for quick experiments, API calls to large models, and turning rough ideas into working demos you can test with customers or your team.
One standout lesson I’m bringing to you
Go one layer deeper than the vendor.
Don’t just buy an “AI-in-a-box” wrapper and hope it fits.
Appoint someone on your team to ask hard questions one or two layers below the glossy demo:
- What protocol and interfaces does it use, and can we switch later without a rewrite?
- How will it handle output formats we rely on, like PDFs or structured CSVs?
- What’s the security model, and how would we replace this tool in six months if pricing or quality changes?
This small habit protects you from lock‑in and future‑proofs your investment.
It came through loud and clear at Stanford: wrappers add convenience, but you still need in‑house fluency to evaluate them and to react when the underlying tech shifts.
Related tips we discussed:
- A lightweight hack week that pairs data folks with operators can surface high‑value use cases quickly.
- Most companies sit on a lot of “dark data” they never use. Start there, and you’ll often find fast savings.
A simple step‑by‑step playbook you can run in 30 days
Step 1: Pick one sharp, low‑risk workflow.
Choose a task that’s frequent, well‑defined, and painful enough to matter, like answering repeat support questions or drafting first‑pass proposals.
Step 2: Define a clear win.
Example: “Cut response time by 40% while keeping quality at or above today’s baseline.”
Step 3: Inventory the real data.
List what you already have, where it lives, who owns it, and what’s off‑limits. Expect gaps and quality issues. Tackle the highest‑impact fixes first.
Step 4: Start with out‑of‑the‑box models.
Modern models are strong without fine‑tuning.
Don’t overcomplicate.
Prove value first, then refine.
Step 5: Keep your answers grounded in your own knowledge.
If the use case involves company facts, consider retrieval over your documents so the system cites your source of truth. (This is the common “RAG” pattern.) Retrival Augmented Pattern)
Step 6: Put a human in the loop.
Route outputs to the right reviewer. Decide what’s auto‑approve, what needs a quick glance, and what always needs full review. This is how you get speed and safety together.
Step 7: Ask one layer‑deeper questions of every vendor.
Output formats, switching costs, security model, and how they’ll adapt as model internals change.
Build an internal “trigger list” of low‑level shifts that would cause you to revisit your plan.
Step 8: Run a 10‑day pilot.
Pick 5–10 users, capture baseline metrics, and compare side‑by‑side.
Keep a simple scorecard: quality, time saved, errors caught, user satisfaction.
Step 9: Plan the rollout like a product launch.
Create a short playbook, quick videos, and office hours.
Celebrate wins publicly.
Keep opt‑out paths and a rollback plan.
Step 10: Lock in governance from day one.
Decide what’s logged, how long you retain it, who can review prompts and outputs, and how you handle PII and vendor changes.
Quick Python ideas you can try
- Batch drafting: Pull a spreadsheet of FAQs, generate clean first‑pass answers, and log confidence scores for review.
- Doc answering: Point a simple script at your policies or proposals and answer natural‑language questions with citations.
- Quality checks: Auto‑flag tone, missing fields, or risky phrases in outbound emails before they go out.
Want help applying this to your business?
If you’d like to pick a use case, review your data, or design a fast pilot, I’m opening a few discovery call slots.
Book here: https://calendly.com/Kalika
Wishing you all the very best always,
Kalika
