Technical Debt Management Practices

Technical debt isn’t just an IT problem—it’s an enterprise-wide drag on transformation and evolution ⛔. And a show-stopper for AI multi-agent systems. Left unchecked, it erodes business agility, locks innovation behind constraints, and amplifies risk across architectures. But technical debt is more than one thing, it plays out across all the four architecture domains: Business, Application, Data, and Technology Architectures: 🔹 Business Debt: Misaligned capabilities, redundant processes, and legacy constraints slow down strategic execution. Scaling AI, automation, or new business models? Good luck if you’re trapped in outdated operating models. 🔹 Application Debt: Spaghetti integrations, monolithic structures, and brittle workflows create friction for change. Every new initiative turns into a costly workaround instead of an accelerant. 🔹 Data Architecture: Inconsistent, duplicated, and poorly governed data corrupts decision intelligence. AI and analytics investments won’t drive value if they rely on unreliable, siloed, or inaccessible data. 🔹 Technology Architecture: Legacy infrastructure, technical sprawl, and fragmented ecosystems increase operational risk and limit scalability. The shift to cloud, AI, and modern platforms gets bogged down by outdated dependencies. 💡 Transformation isn’t just about adopting new technology—it’s about managing and eliminating technical debt. 🔹 Tackle it proactively with architectural guardrails, modernisation roadmaps, and incremental refactoring. 🔹 Quantify the cost—how much is technical debt limiting business innovation, AI adoption, or operational resilience? 🔹 Embed technical debt management into governance frameworks to ensure it doesn’t accumulate unchecked. 🚀 Organisations that treat technical debt as a strategic risk—not just an IT burden—will be the ones that evolve faster, innovate smarter, and scale sustainably. How does your organisation approach technical debt? Let’s discuss. 👇 #EnterpriseArchitecture #TechnicalDebt #AI #BusinessArchitecture #ApplicationArchitecture #DataArchitecture

Technical debt killed my startup. Not market fit. Not funding. Not competition. Technical debt. We spent 18 months taking shortcuts to "move fast and break things." By month 19, we couldn't move at all. Every feature took 3x longer than estimated. Every fix broke two other things. Every sprint became a firefighting exercise. Our best engineer quit with this exit note: "I'm tired of putting band-aids on broken bones." The rewrite took 8 months. We ran out of money in 6. Now when someone pressures me to skip proper testing or rush architecture decisions, I show them that obituary. I've learned: You can't debug your way out of fundamental design problems. Build it right the first time, or build it twice. Change my mind: When is technical debt actually worth it? Editing to add: I have lots more lessons learned from both successes and failures during decades of building and advising, DM me or find my newsletter here: https://jordanambra.com

You can’t see it on the balance sheet. But your company’s carrying it everywhere. Every outdated library you’re afraid to update. Every integration duct-taped together. Every sprint derailed by “unexpected” rework. That invisible load? It’s 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐝𝐞𝐛𝐭. And it’s costing the world $𝟑 𝐭𝐫𝐢𝐥𝐥𝐢𝐨𝐧 in lost productivity, delayed releases, and developer burnout., according to Stripe (Source: https://lnkd.in/eXYy8u3M) Gartner says it can slow progress by 𝐮𝐩 𝐭𝐨 𝟓𝟎%, yet only 17% of companies can make a strong business case to tackle it. (Source: https://lnkd.in/e4SmbzuX) If you could do one thing differently starting tomorrow? 𝐒𝐭𝐚𝐫𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐝𝐞𝐛𝐭 𝐥𝐢𝐤𝐞 𝐫𝐞𝐚𝐥 𝐝𝐞𝐛𝐭. Step one: 𝐋𝐨𝐠 𝐢𝐭. Build a technical debt register. Every time a developer hacks a workaround, delays an update, or marks something “we’ll fix later,” record it. Include: • A short description of the issue. • The system or component it affects. • Estimated time lost per month (hours). • The number of people impacted. • The risk level (low/medium/high). Step two: 𝐏𝐮𝐭 𝐚 𝐩𝐫𝐢𝐜𝐞 𝐨𝐧 𝐢𝐭. Take the total hours wasted per month and multiply by your average loaded engineering cost (salary + overhead). That’s your “interest payment”: what you’re paying to maintain the mess instead of fixing it. Step three: 𝐓𝐫𝐚𝐜𝐤 𝐭𝐡𝐞 𝐝𝐫𝐚𝐠. Look at metrics like: • % of sprint time spent on rework or maintenance. • % of projects delayed due to legacy constraints. • Time-to-deploy compared to a “clean” project. Now you’ve got something powerful: a 𝐝𝐞𝐛𝐭 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝. When you show a CFO that modernizing one system could free 200 engineer-hours a month, you’re no longer making a technical argument. You’re making a financial one. Because once you can see the weight, it’s a lot harder to justify carrying it. ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

🎧 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝗯𝘁 𝗗𝗮𝘆𝘀: 𝗦𝗶𝗻𝗴 𝗮 𝗧𝘂𝗻𝗲 𝗟𝗶𝗸𝗲 𝗦𝗽𝗼𝘁𝗶𝗳𝘆 Every team hits the same wall: too much tech debt, not enough time. But Spotify found a rhythm. They introduced quarterly Architecture Debt Days—dedicated time to fix what slows you down. The result? ✅ 𝟯𝟱% fewer critical incidents ✅ 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗲𝗱 innovation speed ✅ 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 team satisfaction Architecture debt doesn’t vanish on its own. It compounds. Here’s how to turn cleanup into a culture: 𝟭 | Schedule It—And Stick to It Don’t wait for a system crash to act. Make debt work part of the rhythm. ✔ Block out recurring Architecture Debt Days ✔ Focus on cleanup, not new features ✔ Treat it as essential, not optional 🎯 𝗧𝗿𝗲𝗮𝘁 𝗶𝘁 𝗹𝗶𝗸𝗲 𝗽𝗿𝗲𝘃𝗲𝗻𝘁𝗮𝘁𝗶𝘃𝗲 𝗰𝗮𝗿𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝟮 | Empower Teams to Prioritize Top-down lists rarely match the day-to-day pain. ✔ Let teams choose what to tackle ✔ Encourage refactoring, deprecation, cleanup ✔ Focus on what slows delivery or adds risk 🎯 𝗧𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝗰𝗹𝗼𝘀𝗲𝘀𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲 𝗸𝗻𝗼𝘄 𝘁𝗵𝗲 𝗯𝗹𝗼𝗰𝗸𝗲𝗿𝘀 𝟯 | Track the Business Impact Cleaning up code isn’t sexy—but the outcomes are. ✔ Track reduction in incidents and recovery time ✔ Measure developer velocity improvements ✔ Monitor satisfaction and morale 🎯 𝗧𝗲𝗰𝗵 𝗱𝗲𝗯𝘁 𝗶𝘀 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗿𝗶𝘀𝗸—𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘁 𝗮𝘀 𝘀𝘂𝗰𝗵 𝗧𝗵𝗲 𝗕𝗶𝗴 𝗜𝗱𝗲𝗮: Debt isn’t the problem. Ignoring it is. If innovation is the engine, then debt reduction is the tune-up. Make time for both. 🧠 How do you manage architecture debt where you are? Let’s discuss 👇 — ➕ Follow Kevin Donovan 🔔 👍 Like | ♻️ Repost | 💬 Comment 🚀 Join 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐬’ 𝐇𝐮𝐛 - Join our newsletter and connect with a community that understands. Enhance your skills, meet peers, and advance your career! Subscribe 👉 https://lnkd.in/dgmQqfu2

𝗧𝗲𝗰𝗵 𝗗𝗲𝗯𝘁 𝗜𝘀𝗻’𝘁 𝗮 𝗦𝗶𝗻, 𝗜𝘁’𝘀 𝗮 𝗦𝗶𝗴𝗻𝗮𝗹 Kevin Goldsmith brilliantly reframes technical debt, not as a failure, but as a natural, often necessary outcome of delivering value. Instead of chasing "clean code" at all costs, he urges us to ask: - Why does this debt exist? - What does it reveal about our systems, teams, and choices? 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 from his article: 𝗧𝗲𝗰𝗵 𝗗𝗲𝗯𝘁 = 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗧𝗿𝗮𝗱𝗲-𝗢𝗳𝗳 It’s not bad engineering, it’s real-world prioritization under pressure. 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Different causes (from quick fixes to systemic issues) demand different responses. 4 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗧𝗲𝗰𝗵 𝗗𝗲𝗯𝘁: - Pragmatic: Intentional and strategic. - Required: Blocking business outcomes, must fix. - Incidental: Old but stable, can often be left alone. - Symptomatic: A red flag for organizational dysfunction. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗣𝗶𝘁𝗳𝗮𝗹𝗹𝘀: - Treating all debt as urgent. - Overengineering to avoid future debt while ignoring today’s needs. 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: - Categorize pain points. - Revisit deliberate debt. - Link technical concerns to business impact. - Address root causes, not just the symptoms. 𝗙𝗶𝗻𝗮𝗹 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆: - “𝘛𝘦𝘤𝘩 𝘥𝘦𝘣𝘵 𝘪𝘴 𝘤𝘰𝘥𝘦 𝘵𝘦𝘭𝘭𝘪𝘯𝘨 𝘺𝘰𝘶 𝘢 𝘱𝘦𝘰𝘱𝘭𝘦 𝘴𝘵𝘰𝘳𝘺.” - Kevin Goldsmith - Tech debt isn’t a defect. It’s data. - It reveals where your systems, priorities, or leadership need attention. Let’s stop chasing perfection and start reading the signal. #TechDebt #EngineeringLeadership #SoftwareDevelopment #ProductManagement #DevEx

Confronting Technical Debt: The Strategic Imperative for Banking IT Over the last 30 years, I have seen banking software where only 2% of the code is still active. A resulting mantra in many banks is: "never touch a running system." However, the accelerating increase in regulatory requirements and innovation forces banks to change faster and faster. As a consequence, technical debt in banking IT is a ticking time bomb that threatens the stability and future of the entire industry. Decades of neglect, quick fixes, and prioritization of short-term gains have left banks saddled with creaking legacy systems that are ill-equipped to handle the demands of the digital age. This accumulated debt is not just an IT problem; it's a strategic risk that can lead to catastrophic consequences. Security breaches, system outages, and missed opportunities for innovation are just some of the dangers that lurk beneath the surface. Banks must confront this debt head-on, investing in modernization and adopting agile practices to build a technology infrastructure that is resilient, secure, and adaptable. Failure to do so will leave them vulnerable in an increasingly competitive and technologically driven landscape, risking irrelevance and ultimately, extinction. In this labyrinthine world of banking IT, I see software refactoring as the unsung hero battling the looming specter of technical debt. It's a meticulous process of restructuring existing code, not to add new features, but to enhance its internal structure and maintainability. Refactoring breathes new life into aging systems, making them more adaptable to evolving business needs and regulatory requirements. It's a proactive measure, akin to regular maintenance of a complex machine, that prevents the accumulation of technical debt and its associated risks. By improving code readability, reducing complexity, and eliminating redundancies, refactoring enhances the efficiency and reliability of banking software. My belief and recommendation: A bank should spend 20% of its software development budget on refactoring and should prefer vendor software with a transparent and relevant refactoring approach. It's an investment in the future, ensuring that the technology infrastructure remains robust, secure, and capable of supporting innovation. In the long run, refactoring is not just a technical necessity but a strategic imperative for banks aiming to thrive in the digital age. #banking #IT #digital #technicaldebt #refactoring #SundayThoughts

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

4 Salesforce technical debt to work on in 2025 (Give your org some TLC) Technical debt creation is inevitable. But like any debt, there comes a time you need to pay it back. If you don't, you'll pay "interest”. It becomes higher as you wait longer. The sooner you catch problems, the cheaper it is to fix them At some point, it becomes critical: → The development team gets slowed down → It causes org crashes →Your ability to use Salesforce features becomes limited. Don't wait until that point. Identify them and work on them now. Here are a few aspects you should stop ignoring: 1. Stop ignoring unused fields and objects. → More fields, more headache when troubleshooting. → You might be hitting the limits. How to eliminate: ✅ Run a field and object usage report using Field Trip (AppExchange). ✅ Identify unused ones and archive or delete them. ✅ Keep only what adds value to reporting and automation. 2. Stop relying on outdated automation. → Workflow Rules and Process Builder are deprecated at the end of the year. → Those old Visualforce pages?... Time to say goodbye → They are inefficient and resource-consuming How to eliminate: ✅ Audit all workflows, process builders VFP, and maybe even triggers (Flows/Apex). ✅ Migrate legacy automation to Flow/Apex for better performance (Split to Before/After Save) ✅ Consolidate redundant processes, remove irrelevant archival logic. 3. Stop neglecting data quality. → Don't say "We’ll clean it up later”... Later is now. → To get the most out of AI, this is a must. How to eliminate: ✅ Implement required fields, visibility filters, and validation rules to prevent bad data. ✅ Schedule regular deduplication and data enrichment. ✅ Monitor with reports crucial data. 4. Stop hoarding old reports and dashboards. → More reports don't mean more insights. → Too much clutter hides the valuable items. How to eliminate: ✅ Identify reports with zero recent views (LastViewedDate, LastRunDate fields). ✅ Consolidate duplicates and outdated dashboards. ✅ Move reports to folders Clean up your Salesforce org now. Future you will thank you. Which of these is the biggest issue in your org right now? --- Found this helpful? Like 👍 | Comment ✍ | Repost ♻️

"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 has multiplied the interest rate on the debt you’ve been carrying- your process debt, your data debt, and your technical debt.” It's a statement I've made and perspective I've shared with leaders on many occasions. Here, I've gone ahead and written it out as a full article, complete with symptom spotters and a debt estimator. The article explains how this debt is getting rapidly more costly as AI amplifies not only the risks you are currently exposed to, but also the future opportunities you will stumble trying to seize. What are process, data, and technical debt - and how does AI multiply the interest rate? Process Debt: When steps and handoffs are undocumented or inconsistent, work relies on tribal knowledge. With AI, copilots and agents need predictable flows—ambiguity quickly turns into errors, escalations, and customer friction. Data Debt: Messy, duplicative, or stale records and mismatched definitions create confusion. With AI, bad data becomes confident but misleading answers, leading to poor decisions and eroded trust. Technical Debt: Brittle integrations, legacy parts, and shaky access paths may “mostly work” but remain fragile. With AI, weak links fail loudly—small upstream changes trigger outages, and without strong monitoring, problems surface in public. The article concludes with three likely mistakes leaders will need to avoid (you can't buy your way out of this kind of debt) and four steps leaders can take to begin servicing the debt so they can capture opportunities. Enjoy and share (please) with leaders and change agents who are feeling the burden of the debt and could use language and a path to move forward! TLDR: AI raises the “interest rate” on your process, data, and technical debt, turning once tolerable inefficiencies into visible, compounding, costly risk and unseized opportunity. Undocumented or inconsistent processes make copilots and agents fail at the exceptions humans once handled. Messy, mismatched data gets confidently amplified into wrong answers. Brittle stacks turn minor changes into public outages. Leaders should resist buying AI as a bandage, skipping straight to agents, or neglecting governance and monitoring. Instead, they need to make the debt visible, prioritize a few high-impact fixes (document one process, clean one dataset, stabilize one integration), and build routine debt service into sprints and budgets. Tie each payoff to improved AI outcomes so the technology compounds value rather than risk. With each new AI advancement, the debt gets more costly. #AI #AIDebt #AILeadership