Premise
Generative AI is a fantastic accelerant for software development velocity and product delivery. But it’s not magic. VPs and Engineering Directors are under pressure to deliver the same roadmap with dramatically less headcount. GenAI helps, but only when deployed alongside strong governance, automation, and experienced oversight.
Without these, GenAI becomes less of a force multiplier and more of a liability generator.
Below are several shapes that we see emerging as worksites inject generative AI into their SDLC.
GenAI and Near/Offshore – If You’re Still Heavily Using Offshore, You’re Likely Overspending and Overcomplicating
If your organization already relies on near/offshore, you hopefully have a mature governance model: established CI/CD practices, automated testing, code reviews, and observability for applications and the software systems that they enable. That’s great — you’re better prepared than most to absorb GenAI output.
However, if you’re still using offshore teams primarily for code generation, you are overpaying and adding unnecessary communication, coordination, and time zone overhead.
Junior-level near/offshore developers, which make up the bulk of such teams — are now effectively replaced by GenAI tools that are:
- Orders of magnitude cheaper
- Faster to iterate
- Never asleep or waiting for direction
- Not impacted by org churn or communication overhead (big teams of humans)
Recommendation: Transition from offshore code factories to lean, senior staff led GenAI-enabled teams, that stay close to Product Owners and the business.
If You Lack Governance Today, GenAI Will Wreck Your SDLC
Organizations not accustomed to managing high-output environments (like offshore) and who lack strong testing, review, and release controls are at high risk.
Giving GenAI tools to teams without strengthening your:
- Automated test coverage
- Deployment validation
- Observability and alerting
- Code review practices
…is a recipe for:
- Massive tech debt accumulation
- Inconsistent software quality
- Fragile deployments
Recommendation: Before scaling GenAI in your teams, modernize your SDLC. Codify testing practices, automate deployment gates, and establish reliable change monitoring. In fact, you can use GenAI to streamline this process, but you still need an experienced hand to make sure you’ve installed the seatbelts correctly.
Don’t Hand GenAI to Juniors Without Oversight – You’ll Break Your Product and Lose Your Org’s Technical Memory
Junior devs often lack the experience to understand:
- What should be written vs. handed to Gen AI, or not written at all
- How it fits into the systemic architecture
- Whether GenAI code is performant, secure, or maintainable
Giving GenAI tools to underqualified staff leads to:
- Fragile subsystems
- Unreviewed complexity
- Organizational amnesia (nobody understands the code but the LLM)
- Total dependency on LLMs to maintain or enhance your product
Result: Your engineering org can no longer reason about or control the codebase without asking AI. You’ve essentially outsourced your core competency.
Recommendation: Ensure that only experienced engineers are tasked with reviewing and integrating GenAI output. Use juniors to assist, but not to guide these activities.
The Right Model: Senior Engineers + GenAI + Strong SDLC = Scalable Velocity
The ideal GenAI integration looks like this:
- Retain or hire experienced engineers — people who can already write the code, but choose not to, because it’s more efficient to guide GenAI to do it.
- Ensure DevOps is mature: CI/CD, rollbacks, feature flags, and infrastructure-as-code.
- Invest in Testing: unit, integration, performance, security.
- Prioritize Observability: logs, metrics, traces, and anomaly detection.
- Build a workflow where GenAI handles the volume of work (especially well-established boilerplate tasks), and senior staff handle vision, validation, and refinement.
Outcome: High-quality velocity, lower cost, and lower risk (you improved your SDLC right?). This produces an engineering team that is enabled, not replaced by AI.
Final Word: GenAI Is Not a Staffing Model, it’s an (Existing) Capability Multiplier
Treat GenAI as you would an extremely fast, extremely junior developer. It needs direction, boundaries, and review — but it can do amazing things under the right leadership (that your org intentionally installs).
The organizations that win with GenAI won’t be the ones who first inject it into all workstreams. They’ll be the ones who combine AI, governance, and experience into a work structure that scales up what the org already knows how to do.