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

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  Stocks and Investing on Tue, Sep 09th, 2025 by Forbes

Let's access the URL.Rethinking Platform Engineering in the AI Era: To Hire, Automate, or Both?

By [Your Name], Research Journalist

In an era where artificial intelligence is reshaping the software development lifecycle, platform engineering is no longer just a backstage support function—it’s the linchpin that can either keep a company moving or leave it lagging behind. A new piece on the Forbes Technology Council (published September 9, 2025) tackles the age‑old debate of whether organizations should stack up on human talent or pour capital into automation tools to build resilient, high‑performance platform teams. The article pulls together data, expert opinions, and real‑world case studies to chart a path forward for companies navigating the AI‑augmented platform engineering landscape.

1. The New Role of Platform Engineering

Platform engineering has evolved from simply providing internal APIs and CI/CD pipelines to orchestrating a full‑blown “platform” that offers a self‑service ecosystem for developers. The Forbes article stresses that this transformation is especially pronounced in the AI era. Key aspects include:

The piece cites industry reports that show companies with mature platform engineering practices can cut their time‑to‑market by up to 30% and reduce infra‑related bugs by 25%.

2. The Hiring Dilemma

One school of thought argues that hiring more platform engineers is the simplest way to keep up with demand. According to a 2024 Gartner survey referenced in the article, 62% of CIOs who hired additional platform talent reported measurable gains in deployment frequency. Proponents point out that:

• Domain Expertise: Human engineers can craft bespoke solutions for unique business problems, something generic automation often fails to deliver.
• Cultural Leadership: Senior platform engineers help embed best practices across engineering squads, fostering a culture of DevOps excellence.
• Rapid Problem Resolution: In critical incidents, human insight can navigate complex, non‑linear debugging paths faster than automated scripts.

However, the article warns of escalating cost curves. Talent acquisition in the software sector now averages 7–12 months to fill a senior platform role, and salaries for experienced platform engineers are among the highest in the tech stack. Furthermore, as AI tools grow more capable, the marginal benefit of hiring a few additional engineers begins to plateau.

3. The Automation Argument

On the other side, the article champions a “lean‑automation” strategy: investing in AI‑driven tooling that can reduce manual toil and free human talent for higher‑value work. Automation isn’t a silver bullet, but when thoughtfully deployed it yields several advantages:

The article highlights an example from a leading cloud‑native startup that implemented a “policy‑bot”—an ML model that automatically scanned Kubernetes manifests for security misconfigurations. The result was a 40% drop in security incidents within the first six months.

4. The Hybrid Path: Combining Human Intuition with Machine Precision

Rather than choosing between hiring and automation, the Forbes piece argues for a hybrid model. A few key recommendations emerge:

1. Build an Automation‑First Platform
   Start by automating the most repetitive tasks (e.g., dependency updates, environment provisioning). This creates a baseline of reliability that allows engineers to focus on architecture and experimentation.

2. Embed AI into the Developer Experience
   Introduce AI assistants that surface reusable components, auto‑complete configuration files, and recommend best‑practice patterns. These tools act as “co‑developers” rather than replacements.

3. Invest in Talent that Owns Automation
   Shift hiring focus from generic platform engineers to specialists who can design, train, and maintain the AI models that power your platform. Skills such as MLOps, data engineering, and policy‑as‑code become core competencies.

4. Measure Impact with Quantifiable KPIs
   Track metrics such as deployment frequency, mean time to recovery, cost per deployment, and developer satisfaction. Use these data points to iterate on the automation stack and hiring strategy.

5. Cultivate a Continuous Learning Culture
   Automate learning loops where the platform learns from incidents, and engineers learn from automated insights. This symbiosis accelerates both technical and organizational maturity.

5. Lessons from the Field

The article weaves in anecdotal evidence from multiple companies:

• Tech‑Retail Giant: After deploying an AI‑powered observability layer, they reduced production incidents by 35% and cut engineering effort on incident response by 20%.
• FinTech Innovator: Adopted a “policy‑as‑code” framework where AI suggested compliance rules during the build process. They saw a 50% reduction in manual compliance reviews.
• Healthcare SaaS: Combined automated data pipelines with human oversight, striking a balance that allowed them to maintain stringent data privacy standards while scaling up to 200,000 users.

Each case underscores a central theme: automation enhances efficiency, but the human element—context, judgment, and strategic vision—remains indispensable.

6. The Bottom Line

Platform engineering in the AI era is a moving target. The Forbes article concludes that the choice isn’t “hire another engineer or invest in automation” but how you orchestrate the two. Companies that adopt an automation‑first mindset, then strategically add talent to own and improve that automation, position themselves to:

• Scale Rapidly without escalating engineering headcount.
• Improve Reliability by embedding AI into observability and governance.
• Drive Innovation by freeing engineers to experiment with new AI products and services.

In an age where digital transformation is both a necessity and a competitive differentiator, the hybrid approach offers a pragmatic path. It marries the speed and precision of AI with the nuanced understanding that only experienced platform engineers bring. The Forbes piece invites leaders to assess their current platform maturity, identify automation bottlenecks, and then decide where to deploy human capital most effectively. The ultimate goal: a platform that is both resilient and adaptable, powered by the best of human and machine intelligence.