Your AI Project Has Been "90% Done" for 6 Months
I help companies break through AI project paralysis and ship in 3-6 weeks—not prototypes, not demos, but real products with real users getting real value.
3-6 Weeks
From chaos to shipped product
100% Ship Rate
Code in production, not stuck in 'testing'
70% Less Waste
No more endless experiments going nowhere
Discover Your Path
Your team is brilliant. The technology works. But somehow, you're still stuck in:
Endless experiments that "just need one more week"
Requirements that shift every sprint
Demos that wow execs but never reach users
Data scientists and engineers speaking different languages
"We need more data" becoming the excuse for never launching
The VP asking "when will this actually ship?" in every meeting
You're not alone—and it's not your fault.
Most companies think they need "an AI strategy."
What they actually need is one of three things—and we figure out which one in the first conversation.
We built AI but nobody's using it
Adoption Strategy Framework
This is you if:
- You have a working prototype or beta
- Usage is < 20% of what you expected
- People say "it's interesting" but don't actually use it
- You're not sure if it's a product problem or change management problem
What We Do:
- Diagnose resistance
- Design gradual rollout
- Measure real behavior change
Outcome:
90-day adoption playbook
Timeline:
8-12 weeks
We want to build AI but don't know what
V1 Scoping Framework
This is you if:
- You have data but don't know what to do with it
- Every AI idea feels "too big" or "too small"
- You're paralyzed by choice—too many possibilities
- You need to prove AI value before bigger investments
What We Do:
- Find the ONE question your data can answer
- Design the transition
- Set realistic metrics
Outcome:
Scoped V1 feature with launch strategy
Timeline:
4-6 weeks
We're thinking about AI
AI Readiness Assessment
This is you if:
- You're hearing "we need AI" but don't know where to start
- You're worried your data isn't "good enough"
- You want to avoid expensive AI mistakes
- You need a reality check before committing resources
What We Do:
- Audit your data
- Evaluate user readiness
- Identify where you can actually win
Outcome:
Clear 'yes, here's where' or 'no, fix these 3 things first'
Timeline:
2-4 weeks
Most consultants sell you "AI transformation."
I solve the actual problem you have right now.
Here's what I don't do:
200-page strategy decks you'll never implement
"Roadmaps" with no accountability for shipping
Workshops that end with "homework" for your team
Vendor pitches disguised as consulting
Here's what I do:
Ship working code in 3-6 weeks
Work alongside your team, not above them
Make decisions fast, cut scope ruthlessly
Leave with something in production, not a PowerPoint
My only success metric: Did we ship?
Anthony Ludwig
The AI PM Dude
I help companies ship AI products that actually work—no hype, just results.
I Build AI Products That Actually Work
For over a decade, I've been in the trenches building data-centric and AI-powered products across enterprise organizations and SaaS platforms.
I've led the full stack: managing cross-functional teams of 32+ people, overseeing $5M annual budgets, and shipping products from concept to scale. My work spans AI-enhanced search aggregating data across multiple repositories, machine learning platforms serving Fortune 50 organizations, and unified product ecosystems driving 100% usage growth.
The technical side: I've architected scalable data pipelines, integrated disparate systems, migrated legacy platforms to modern microservices, and built governance frameworks for regulated environments.
The leadership side: I've managed the budgets, negotiated vendor contracts, collaborated with data science teams, and sat with users to understand what they actually need.
What makes me different: I sit at the intersection of technical execution and business strategy. I speak both languages—engineers trust my technical judgment, and executives trust me to deliver on commitments.
Most AI projects fail not because of bad technology, but because of unclear use cases, poor data foundations, or teams trying to build everything at once. I help companies avoid these pitfalls and ship iteratively.
I also founded Product Manager Hub, a growing community platform where I share frameworks and best practices for AI product management—staying connected to what's working across the industry.
My approach is simple: Start with clarity, build the right foundation, ship the MVP, and iterate based on real usage. No hype, no endless strategy decks—just shipping products that work.
The Real Cost of Waiting
Your team's time:
If 3 engineers + 1 PM are spending 50% of their time on a project that's not shipping...
That's $75K/month
in fully loaded costs going nowhere.
Your opportunity cost:
Every quarter you delay = competitors shipping, customers expecting more, and your technical debt compounding.
Industry data backs this up:
87%
of AI projects fail to reach production
(MIT Sloan Management Review)
$1.3M
average cost of failed AI initiatives
(Deloitte AI Institute)
18
months average time to first AI deployment
(McKinsey Global Institute)
73%
of companies report AI project delays
(Gartner Research)
The good news?
You can ship in 6 weeks instead of 6 months.
Ready to Ship Instead of Stall?
Just want to learn more?
Download our free guide: "5 Reasons AI Projects Stall (And How to Fix Them)" to understand common pitfalls.
No contract lock-in
Every engagement has a defined end date
Money-back guarantee
If we don't ship code in agreed timeline
First call is free
No pitch, just honest assessment