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AI is weaponizing tech debt.
I’ve written earlier about why it’s so hard to get tech debt prioritized and what you can do about it. Leaders want revenue. Features generate revenue. So, tech debt keeps growing in the dark.
Now, AI is making this worse. AI may enable us to build apps faster. But rather than hiding tech debt, it magnifies it.
The reason is simple: Models learn from whatever data and systems you have and then make those problems bigger and faster.
Data debt → model debt. Messy data and old code turn into bias, bad answers, and unsafe suggestions.
Feature chaos. Random one-off reports and hidden dashboards make it hard to repeat results or retrain models.
Evaluation gaps. If you don’t have clear “right answers,” your great demo can fall apart in the real world.
Change blindness. People, rules, and behaviors change, but your model won’t notice unless you watch for it.
Fragile infrastructure. Weak pipelines break easily and make it seem like the model is wrong when the system is the real problem.
Governance holes. Poor tracking and weak data-privacy controls can turn small mistakes into big audit issues.
This means the playbook to deal with tech debt needs to change.
So, I asked my friend Matt Moore, product executive with extensive experience guiding innovation using emerging technologies, to give us a clear path forward.
In today’s essay, he teaches us how AI reshapes tech debt, how fast the risk grows, and what you must do to keep your products and teams safe, steady, and effective.
What You’ll Learn Today
Why AI accelerates tech debt and exposes weak foundations.
The hidden risks behind dirty data, aging models, and tool sprawl.
How to build an AI roadmap that protects speed, stability, and scale.
The operating model, metrics, and governance that keep AI healthy.
A practical 1-year plan to grow AI features without chaos.
AI is Supercharging Tech Debt. You Need To Keep Up.
AI is fast. It writes, predicts, answers, and summarizes. It feels like magic.
But magic is dangerous.
Fast AI without discipline is a silent killer of products:
Three months after a shiny demo, the same feature can collapse.
Models break.
Data misbehaves.
Glue code mutates into a haunted attic.
The irony with AI is, the faster you move, the worse the crash if you ignore the basics.
A House of Cards
Think of your product as a house. AI is the flashy new room with skylights and heated floors. Looks amazing.
But if you ignore the foundation, plumbing, and wiring, small problems hide behind the walls.
During the open house, everything looks perfect. Then the first storm hits, and the ceiling leaks.
AI features behave the same way. They dazzle in demos. But without fundamentals, they crumble in the real world.
Your job isn’t just to ship AI features. You need to manage the engine that makes them work.
That engine is built on data, code hygiene, reliability, and governance. Ignore it, and AI becomes a compounding liability.
Tech debt has always been the invisible tax on speed. And AI accelerates it:
Dirty Data → Garbage in. Garbage out. Confident wrong answers in production.
Aging Models → Models get outdated. Language changes, rules change, customers change. Skip retraining, and the engine stalls.
Glue Code Chaos → Random notebooks, APIs, scripts. Fine for demos. Disaster in production.
Tool Sprawl → Every team picks every vendor/library. Chaos grows exponentially.
Superficial Testing → One-off checks won’t survive the real world. You need ongoing monitoring, scoring, and validation.
Every one of these is a hidden liability waiting to blow up. Shortcut today = crisis tomorrow.
Most Roadmap Make Things Worse
Traditional roadmaps focus on features and deadlines. They ignore the necessary routine work that keeps AI alive:
Retraining models.
Cleaning datasets.
Building robust test cases.
Monitoring production health.
This work is labeled “maintenance.”
Maintenance sounds optional.
Optional becomes “never.”
Pretty soon, you’re firefighting every weekend.
Roadmaps also mis-order work: shiny features first, foundations later. Like putting a hot tub on the roof before checking the beams. Looks fun. Collapses under gravity.
Risk is invisible on feature-focused roadmaps. Ignore it, and decisions become random. The loudest person wins. Usually not the right one.
A Smarter Way to Plan
A good AI roadmap builds strength, not just speed. Protect time for health, not just delivery.
1. Reserve time for reliability.
Model lifecycle, data quality, evaluation assets, platform hardening. No exceptions.
2. Assign ownership.
Version your golden examples, prompts, scoring rules. Treat them like products.
3. Lay a runway.
Critical data contracts, basic model registry, safe secrets handling. Think of it as helmets and seatbelts.
4. Cut sprawl.
Limit vendors and libraries. Fewer moving parts = fewer disasters.
5. Plan exists
Some models will fail or get too expensive. Have fallback paths: smaller models, cached answers, rule-based flows.
Shift the Operating Model
A roadmap is a promise. The operating model is how you deliver.
PMs → Own outcomes and rules.
Data & Platform → Own pipelines and infrastructure.
Every model and evaluation asset → Has an owner.
Then, raise the bar for “done”:
Evaluation coverage
Golden examples
Production monitoring
This isn’t about adding bureaucracy. It’s about adding brakes. Brakes make speed possible. Brakes let you go faster because you can handle sharp turns. Miss these, and speed kills.
Next, lock down meaningful model changes:
Track prompt tweaks and training updates.
Quick impact notes.
Rollback plans.
Finally, align risk with growth. Put model health, drift alerts, incidents, cost trends right next to revenue and adoption.
This way, decisions get sharper and loud voices don’t dominate.
The Metrics That Actually Matter
Stick to a simple, balanced dashboard.
Effectiveness: Are tasks done right?
Reliability: Drift, safety issues, incidents.
Efficiency: Cost per resolved task, token usage, latency.
Equity & Compliance: Fairness, privacy.
Set targets, show trends, and put your money behind what actually moves these numbers.
Demos impress. Metrics sustain.
Budget Without Starving Key Work
AI budgets tend to blend tools, people, and usage fees. And if you only fund new features, reliability and cost control get left behind.
Instead, split your AI spend into three buckets:
New customer value
Reliability & risk
Cost optimization
Protect all three. When the pressure’s on, people will want to raid the risk and cost buckets. Don’t let them. You’re safeguarding the product, your customers, and your own sanity.
Governance That Moves Fast
Good governance is a green light with cameras: allows free flow, flags bad behavior.
Short rules and clean records.
One simple intake form: purpose, data, evaluation, rollback.
Quick approval of low-risk changes.
Cross-team check for high-risk changes.
Quarterly review of outcomes and risks.
Document your decisions. No drama. Just discipline.
Common Traps to Avoid
Pilot sprawl → “Innovation” that’s really tiny campsites with no plumbing.
Evaluation theater → Looks good. Doesn’t prove quality.
Shadow platforms → Wobbly ladders to nowhere.
Throwaway prompts → Break traceability.
“We’ll clean it later” → Later never comes.
Do the work now. It’s cheaper, safer, and faster in the long run.
A 1-Year Roadmap to First-Class AI
Q1: Lay the Foundation
Pick 2 clear use cases.
Setup a basic model registry and simple evaluation harness.
Build a few golden examples for each case.
Lock in the most important data contracts.
Save real capacity for reliability and risk.
Start building the right habits from the get-go.
Q2: Consolidate
Narrow providers and libraries
Add versioning and rollback for prompts/models
Share a lightweight prompt and evaluation library
Ship first production features with observability
You’ll feel slower for a bit, but you’ll pick up speed for the rest of the year.
Q3: Industrialize
Automate retraining
Release gates
Drift, bias, cost alerts
Downgrade paths for graceful system failure
Metrics-driven reviews
When the dashboard does the talking, the loudest voice goes quiet.
Q4: Optimize & Expand
Tune for cost per resolved task and latency.
Expand evaluation assets to cover new groups and edge cases.
Tie governance to audits.
Publish reliability and safety notes to customers.
Transparency builds trust.
By year-end, your AI features are first-class citizens. Debt may exist, but it’s visible, bounded, manageable.
You can launch boldly without holding your breath.
Team Roles & Responsibilities
PMs → Stewards of data & models.
Designers → Shape evaluation & review flows.
Engineers → Lifecycle management, not patchwork.
Leaders → Governance to learn fast, limit risk.
Everyone knows who they are and what they’re supposed to do.
Your Key Takeaways
AI accelerates tech debt if ignored.
Protect reliability and risk as fiercely as revenue.
Treat evaluation assets as first-class products.
Build dashboards that measure what matters, not what looks good.
Discipline beats speed. Brakes let you move faster.
This isn’t religion. It’s honesty. Accept it → plan better → win.
Closing Thought
AI magnifies your product’s strengths – and its weaknesses. Every shortcut compounds. Every blind spot grows faster.
Your product leadership is the difference between a compounding asset and a compounding liability. The result is a roadmap that delivers value today and scales tomorrow.
With a few steady habits in planning and governance, you can ship bold AI features and keep them healthy over time.
Your future self will thank you.
That’s it for this week.
Have a joyful week, and, if you can, make it joyful for someone else too.
cheers,
shardul
Here are 4 ways I can help you today:
Strategy Design Workshop: Transform scattered priorities into clear, actionable direction. I’ll facilitate your team through a customized workshop to align stakeholders and create strategies that actually get executed instead of forgotten. Book a call.
Product Management Audit: Get a clear picture of what’s working and what’s holding your team back. Through a systematic analysis, I’ll evaluate your strategy, processes, roles, metrics, and culture. You’ll walk away a practical set of findings and actionable recommendations to strengthen your product organization. Book a call.
Corporate Training: Elevate your entire product organization. I’ll teach your team how to think and act strategically, craft outcome-driven roadmaps, and dramatically improve how they deliver measurable results that matter to your business. Book a call.
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Shardul Mehta
I ❤️ product managers.