Managing Technical Debt in Software Modernisation

I was a CTO at a company with a LOT of technical debt. Here's how I handled it. 1. I found someone in the org (non-exec) who cared about the issue and was organized. 2. We created a framework to rank our tech debt & built a common mini "language" to talk about it easily. 3. Next we documented the entire tech ecosystem & applied the framework to categorize it all. 4. We met with business stakeholders like Product & Sales to add their perspective into the ranking. 5. We grouped the tech debt into a) never touch, b) fix ASAP and c) fix incrementally. 6. We calculated the potential ROI on each item to help acquire funding to fix it. (This was difficult). 7. We built a plan for remediation and integrated the plan into the roadmap. 8. We created a tracking / monitoring best practice specifically for the tech debt remediation work. 9. We were pretty hardcore about reporting the ROI up to the CEO on all the tech debt fix work. 10. After a while of doing this tech debt remediation got baked into our organization. What's the big lesson? Anything can be done in an org if its important enough, you focus on it and you work hard to achieve it. Interesting in more content like this? Sign up for my free newsletter at https://buff.ly/4ccyrM0. #TechLeadership #softwaredevelopment #CTO

𝗧𝗵𝗲 𝗦𝗲𝗰𝗿𝗲𝘁 𝗞𝗶𝗹𝗹𝗲𝗿 𝗜𝗻𝘀𝗶𝗱𝗲 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗲𝗯𝗮𝘀𝗲: 𝗛𝗶𝗱𝗱𝗲𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁 Most teams feel technical debt. Very few teams can see it early enough to act. Mike Godfrey shares how loveholidays uses static analysis (CodeScene) as an early-warning system for technical debt, not as vanity metrics. What I like about this: it’s basically cybernetics for software delivery: sensors → signals → feedback → control actions. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 “𝘀𝗲𝗻𝘀𝗼𝗿𝘀” 𝗿𝗲𝘃𝗲𝗮𝗹: - Hotspots: where change and effort concentrate (your real pressure points) - Change coupling: files that keep changing together → hidden dependencies - Knowledge distribution (bus factor): where knowledge is lost or stuck in "islands" - Team–code alignment (Conway’s Law): overlap often signals unclear ownership 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝗴𝗲𝘁𝘀 𝗿𝗲𝗮𝗹: They integrate it into the workflow with branch analysis, so PRs can be flagged/blocked when code health declines. That’s a closed feedback loop, not a dashboard. If you want sustainable speed, this is the game: Make technical debt observable, actionable, and governed by clear guardrails. Especially now in the age of AI. #TechnicalDebt #StaticAnalysis #CodeHealth #PlatformEngineering #DevOps

Your PM just spent 45 minutes in 2026 planning making the case for critical tech debt. They had everything: Last quarter's three production outages. Customer complaints about reports timing out. That security vulnerability that made the CISO lose sleep. Even showed how fixing this would save 200 engineering hours next year. The CEO listens politely. Then: "This sounds important, but can it wait? We really need that enterprise feature to close the xyz deal." Tech debt loses. Again. When you try to trade infrastructure work against revenue features one by one, infrastructure loses every single time. Revenue is tangible now. Technical risk is abstract future. In our recent Supra 2026 planning session, Rich Mironov shared an effective approach to scope tech debt. He doesn't fight feature by feature. He negotiates the entire portfolio upfront. His breakdown: ↳ ~50% on customer-facing features (stuff they ask for by name) ↳ ~35% on "keeping executives out of jail" tech debt (compliance, security, scalability)   ↳ ~10% on executive escalations (the random fires from sales) ↳ ~5-10% on innovation/discovery That second bucket; Rich literally calls it "keeping you from being arrested" work. When executives push back, he gets specific: - "Remember last month when the site went down during the customer conference and you had to apologize on stage?" - "Remember when the board grilled you for 20 minutes about why our main competitor's app loads 10x faster?" - "Want to explain to investors why we had to pause new customer onboarding because our database is melting?" Now they're listening. What makes this work is that you negotiate this percentage once at the start of the year. Not every sprint. Not every quarter. After that, that 40% is sacred. Someone wants to raid it for their pet feature? "We agreed that not getting sued or having the site explode was worth 40% of our capacity. Let's discuss in our next board meeting to ensure everyone is ok with the change." No more justifying individual infrastructure projects. No more death by a thousand "but can't this wait?" conversations. The portfolio is approved. The percentage is locked. I've watched too many product leaders burn out trying to defend every single piece of infrastructure work. Meanwhile, the technical debt compounds until something catastrophic happens, and suddenly it's "why didn't anyone warn us?" + the product leader is on the hook for the consequences. Rich's model flips the whole thing. Instead of begging for permission to keep the lights on, you're protecting executives from explaining to the board why everything caught fire.

"We'll clean Salesforce up later." Those five words cost companies millions. McKinsey found something brutal: 10-20% of every IT budget vanishes into technical debt. Gone! Burned on decisions made two years ago when someone said "just ship it." Here's the math on your Salesforce investment. $500K annual spend? You're throwing away $100K fixing yesterday's shortcuts. Team of 5 developers? One person exists solely to put out fires. That quick hack from Q1? It's consuming 40% of your Q4 budget right now. Stripe studied this. Developers spend 42% of their time wrestling with technical debt instead of building features. Nearly half their capacity locked in maintenance mode. The compound interest destroys budgets: Year 1: Hard-code a value, save 10 minutes Year 2: System breaks, 20 hours to diagnose, $3K in emergency consultant fees Year 3: Complete rebuild required, $25K project cost CAST Software proved technical debt grows at 15-20% annually when ignored. That's worse than credit card interest. The biggest debt creators in Salesforce: -Hard-coded values fail 35% of the time when systems change. -Undocumented automations take 3x longer to fix. -Duplicate processes create conflicting logic 60% of the time. -Bypassed validation rules cost an average $50K in data cleanup. Gartner predicts by this year that organizations will spend 40% more fixing than building. The solution exists. -Track your debt. -Budget 20% of dev time for cleanup. -Document everything obsessively. -Build right once instead of rebuilding twice. Smart organizations manage technical debt like financial debt. They track it, reduce it systematically, and refuse to accumulate more. Everyone else pays 20% interest on three-year-old decisions. Which approach does your team take?

AI can steam-roll codebases, but one neglected Excel macro can still derail the locomotive. That 2013 spreadsheet isn’t “technical debt”—it’s structural debt, woven into approvals, audits, even bonus formulas no one remembers writing. 🚂🗂️ Modernization fails when we treat replacement as a toggle instead of a migration trail. The real task is decoding tribal knowledge, not just porting formulas. Here’s the playbook that turns hidden sheets into fuel rather than wreckage: - Map the dependencies – log every downstream report, email rule, and cron job that touches the file; honor reality before refactor. - Strangle with services – wrap the sheet behind a thin API, then peel features into reproducible notebooks or micro-ETL jobs one slice at a time. - Instrument trust – parallel-run outputs for a full cycle; diff anomalies in a dashboard so skepticism becomes statistics, not politics. - Archive the intent – push calculations—and their business rationale—into version control; future hires inherit context, not guesses. When the last formula ships to production telemetry, AI finally earns the right to optimize, forecast, and automate. Until then, your smartest model will keep slamming into hidden cells named “Sheet1 (2).” ♻️Follow LUNARTECH and SeleneX for frameworks that align capability, context, and career growth.

Tech debt isn't a technology problem. Most companies treat tech debt as an IT issue. That's why 56% say it's blocking new investment. Recent KPMG research surveyed 648 US tech leaders. ·      56% say tech debt prevents new investment ·      50% cite talent gaps as the primary barrier ·      40% experience weekly IT disruptions from legacy systems These look like three separate problems, but it is one failure in capital allocation showing up in three places. Breaking the cycle requires a shift in framing: 1. Connect debt to what it's actually blocking ERP not providing real-time financial visibility is working capital trapped in manual cycles. CRM/CPQ not providing pipeline clarity and win/loss insight impacts forecast accuracy and deal velocity. Data architecture not standardized across systems means analytics teams spend more time reconciling than analyzing. Instead of cataloging technical debt, quantify the strategic drag. 2. Prioritize by what delay actually costs Opportunity cost: What revenue isn't being captured because systems can't scale? Risk cost: What's the exposure when compliance gaps become audit findings? Competitive cost: How much faster are competitors moving without legacy constraints? Instead of the loudest noise, focus on fixing what unlocks enterprise value. 3. Anchor before you propel Before layering AI/ML on top, stabilize the foundation. Core systems. Data architecture. Security baselines. Not because it's leading practice. Because unstable foundations make innovation exponentially more expensive. The real question is capital allocation: Do we invest now to remove what's blocking growth? Or do we fund innovation that our infrastructure can't support? The companies breaking out of the 56% are connecting tech decisions to growth, margin, and competitive position. Tech debt stays debt when it is managed like a technology problem. It becomes a strategy when it is treated like a capital allocation decision. #Leadership #EnterpriseValue #AnchorMoatPropel #TechStrategy

For years, my teams have been shackled by decades of legacy tech debt. The core systems built for scale (like IBM big iron) now present the biggest obstacles to innovation and create a crippling skills gap. But that leads to an exciting opportunity as we are finding that AI-assisted modernization is the key to finally getting unstuck. I was initially skeptical as I had been down a path a decade prior where tech modernization would address what was slowing us down with these legacy systems, but to no avail. At the same time, I had many doubts about how AI on our mission critical platforms would perform given the high risk of errors and hallucinations. So we started with governance, robust process and creating guardrails and that is where the aha moment came. Simply we acknowledged that in order to be successful, we must address the governance gap first to minimize the high risk of breaking things in systems. For banks and credit unions, these are systems where people buy groceries and pay for childcare. AI coding assistants are poised to be game-changers. I would submit we can expect them to help document legacy applications, rewrite old code, and automate testing. This dramatically accelerates the pace of modernization, overcoming the traditional barriers of mainframe skills gaps and complex integration. Like most, I am sure most are facing tens of millions of lines of code? My advice is always not to attempt to boil the ocean. Instead focusing on the highest-value modules first and ensures upgrades occur in a secure sandbox before hitting production. This disciplined approach minimizes risk while maximizing immediate ROI. Legacy tech upgrades often stall or fail initially. Success demands the kind of courageous, disciplined leadership that prioritizes process and risk mitigation before chasing speed. AI is the tool, but governance is the strategy. #CIO #CTO #DigitalTransformation #TechDebt #AIGovernance #Fintech #LegacyModernization #Leadership

Technical debt is the cost of moving fast. When you cut corners to hit deadlines, you’re taking out a loan against your future productivity. Manage it before it owns you. Start by tracking your debt. Document what it is, why it’s there and what it’ll take to fix. Treat it like an actual loan with interest. Some debt won’t hurt you immediately, but the critical high interest stuff needs fixing fast. Maintenance isn’t optional. Set aside time every sprint to clean up. Think of it like brushing your teeth. Be honest about the trade-offs. If you’re shipping fast, tell the stakeholders what it’ll cost to clean up later. Be transparent about the development choices. Design modular systems that are easy to fix or swap out. Untangling spaghetti code is a waste of everyone’s time. Leave clear comments and TODOs where shortcuts are taken. Your future self (and your team) will thank you. Unchecked debt only grows. Revisit old systems regularly. Don’t chase perfection. Good enough and stable beats over-engineered every time.

After years as a Team Lead at Google, I can confidently say: 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗯𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗰𝗼𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗜𝘁’𝘀 𝗮 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗮𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. The problem isn't the code quality; it's the 𝗹𝗶𝗲 that leadership accepts when prioritizing 100% features and 0% maintenance. If you lead a team, stop thinking about debt as 'bad code' and start thinking about it as a 𝗵𝗶𝗱𝗱𝗲𝗻 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝘁𝗮𝘅 on every feature you ship. Here are the three unexpected costs of "quick wins" that eventually crush teams: 𝗧𝗵𝗲 "𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗟𝗼𝗮𝗱" 𝗧𝗮𝘅: Every piece of ignored debt adds complexity. New engineers spend 2x longer onboarding. Existing engineers spend 3x longer debugging. Your velocity looks good on the spreadsheet but is silently being suffocated by mental friction. 𝗧𝗵𝗲 "𝗔𝘁𝘁𝗿𝗶𝘁𝗶𝗼𝗻" 𝗧𝗮𝘅: Your best, most detail-oriented engineers leave first. They leave because they came to solve challenging new problems, not fight the same old mess inherited from a rushed deadline two years ago. Debt is a talent retention killer. 𝗧𝗵𝗲 "𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝘆 𝗢𝗻𝗹𝘆" 𝗧𝗮𝘅: By only tackling debt during an immediate, catastrophic failure (the outage), you guarantee two things: 1) The work is done under maximum stress, increasing risk, and 2) You solidify the negative perception that maintenance work is only necessary when the business is actively losing money. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: As a leader, you must mandate and protect a 20% 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗛𝗲𝗮𝗹𝘁𝗵 𝗕𝘂𝗱𝗴𝗲𝘁 every sprint. If the business won't budget 20% for maintenance, they are implicitly budgeting for 100% of future chaos. #TechnicalDebt #SoftwareEngineering #EngineeringLeadership #ProductManagement