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Hire Another Engineer Or Invest In Automation? Rethinking Platform Engineering In The AI Era

Ian Amit, CEO & Co-Founder at Gomboc AI.

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In 2025, platform teams face a critical choice: add headcount or buy/invest in automation and deterministic tooling to scale developer velocity and control costs. With margins tightening and demand for speed escalating, framing this as a cost-efficiency decision is no longer optional—it's strategic.

The Economics Of Headcount Vs. Automation

Hiring an experienced infrastructure engineer costs upward of $150,000 annually, plus overhead. Yet modern platform tooling delivers at-scale automation, managing CI/CD pipelines, auto-scaling clusters and governance-as-code, often doing the work of several engineers while avoiding onboarding delays.

Google Cloud research found that over 55% of global organizations now adopt platform engineering in earnest, not as an experiment but as a mainstream infrastructure backbone. Meanwhile, platform teams have reported scaling operations across regions without growing their team size by leveraging internal developer platforms.

ROI Isn't Just About Savings—It's About Velocity

PwC's 2025 AI predictions highlight that nearly 50% of technology leaders view AI as fully integrated into corporate strategy; incremental gains include 20%-30% productivity improvements, which compound quickly across delivery and operations. The message for platform leaders is clear: Automation buys more than cost savings—it buys runway.

McKinsey's Global Survey similarly emphasizes that organizations tracking gen‑AI KPIs outperform cousins still focusing on proofs-of-concept. Importantly, headcount reductions correlated strongly with bottom-line AI value.

Automation Drives Cost Avoidance, Not Just Cuts

Avoiding hiring can result in millions of dollars in cost savings. For example, ServiceNow projected $100 million in headcount savings in 2025 thanks to internal AI deployments. KPMG's survey shows that 68% of business leaders are justifying AI investments by not hiring rather than just cutting costs.

But it isn't about headcount forecasting alone. Automation enables platform teams to focus on high-impact tasks, enforce policy as code and deliver repeatable pipelines without scaling up personnel.

When Headcount Wins: Context Still Matters

Of course, human judgment remains essential, especially in bespoke systems or one-off platform modules. McKinsey notes that IT/product development functions often still see headcount growth even as AI automates service functions elsewhere.

The question isn't "Automation or people?" but "Which parts of our platform strategy can be automated reliably—and which require strategic investment in human capital?"

Decision Framework: Cost Vs. Customization Vs. Predictability

Option 1: Hire Engineer(s)

Pros: Offers deep understanding of internal systems and provides flexibility for custom development. Cons: High cost, longer onboarding times and limited ability to scale rapidly.

Option 2: Buy Or Build Internal Tooling

Pros: Enables predictable automation, scales faster and reduces operational overhead. Cons: May require upfront effort to build or integrate into existing workflows.

Option 3: Hybrid Approach

Pros: Combines the strengths of both human expertise and automation. Cons: Requires clear governance, well-defined roles and disciplined implementation to avoid overlap or inefficiency.

Five Questions To Ask Before You Act

What repeatable tasks are costing developer or platform time?

Can deterministic tooling (e.G., policy-as-code, platform agents) replace manual work reliably?

Have we measured developer velocity and incident impact before and after automation pilots?

Is the automation platform extensible and explainable enough to support changes long term?

Are we reinvesting labor savings into strategic human roles rather than just reducing headcount?

Final Takeaway

The modern platform decision isn't "more engineers or more AI"; it's targeted investment in automation that multiplies team output, reduces cost at scale and accelerates velocity. Equip platform engineers with tooling that functions like a force multiplier. Use headcount strategically for new problems, not the same problems repeated.

In 2025, organizations that treat platform engineering as a product-led, AI-augmented function—not simply a tribal group of ops-days—will win. The choice to hire or automate isn't binary. It's a design question, and the right balance sets the foundation for long-term scale.

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