Getting Tech Debt On Roadmap

At QCon San Francisco 2023, Ben Hartshorne talked about integrating technical debt resolutions into a roadmap. It is essential to articulate tech-debt value beyond just calling them technical fixes. This TensorBlue analysis is based on reporting and source material from InfoQ (https://www.infoq.com/articles/getting-tech-debt-on-roadmap/).

What Happened

InfoQ Homepage Articles How to Get Tech-Debt on the Roadmap

Align technical debt resolution with business priorities to ensure it's incorporated into the roadmap.

Evaluate the impact of reducing technical debt in terms of business growth and efficiency.

Advocate for the importance of technical projects by demonstrating their business value.

Use data to substantiate decisions and outcomes of addressing technical debt.

Celebrate and communicate the successes of completed projects, highlighting their contribution to surpassing previous business limitations.

Over the years, Honeycomb has experienced growth, steadily attracting more customers and facing the challenges of scaling. As the company navigated through these growing pains, it managed each new hurdle, learning and adapting. This growth journey brought about various side effects, each pushing the company's systems to their limits in unique and unpredictable ways. Despite these challenges, the organization’s infrastructure has remained robust, albeit showing signs of strain as it encounters new boundaries.

During my presentation at QCon San Francisco 2023, I recounted the story of how we navigated the challenge of doubling our business.

Initially, it was imperative to determine how to develop a persuasive business case to ensure technical projects were prioritized in the schedule.

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Implications for Product and Engineering Teams

For TensorBlue readers, the useful question is not just what happened, but how this changes product architecture, engineering priorities, AI delivery, observability, team workflows, or executive decision-making.

• Review whether this changes your AI roadmap, platform architecture, or engineering operating model.
• Identify the specific workflow, reliability, governance, or developer-productivity lesson that applies to your organization.
• Convert the lesson into a small production experiment with measurable quality, latency, cost, adoption, or risk metrics.
• Document source assumptions clearly so teams do not overgeneralize from incomplete public information.

TensorBlue Takeaway

The practical opportunity is to turn this signal into a concrete implementation decision: better AI systems, stronger product instrumentation, more reliable automation, and clearer technical governance. Teams that connect public technology shifts to their own delivery systems will move faster without adding unnecessary complexity.