Customization versus Standardization

I was on a call last week with a Top 50 accounting firm. They've built custom Excel templates for every major client. Hundreds of them. Each one slightly different. Each one maintained by someone who "just knows how it works." And now they're trying to scale. Here's the problem: What started as client service, "we'll customize our process for you", has become their scaling ceiling. Training new staff becomes nearly impossible when every client engagement follows different logic. Automation projects stall because there's no standard process to automate. Senior staff can't take vacation because they're the only ones who understand Client X's template. The irony is that most of those customizations aren't solving unique problems. They're solving the same problem in 200 slightly different ways. What I'm seeing from firms that break through this: They audit their processes and ask: "Is this variation actually creating client value, or is it just creating complexity?" They standardize the 80% that's truly the same across clients. They reserve customization for the 20% that actually matters. One partner told me recently: "We realized we were confusing flexibility with lack of discipline. Once we standardized our K-1 intake process, we could actually serve clients faster." Customization has a cost. And sometimes that cost is your ability to grow. Where has customization become a constraint in your firm's operations.

I once inherited a WMS project where the previous consultant had been let go 3 weeks before UAT. When I opened the configuration, I found 47 custom modifications. Forty-seven. Some of them contradicted each other. One was a workaround for another workaround. Three of them nobody on the team could explain — the person who requested them had left the company. The client had spent 8 months and $1.1M getting to that point. Here's what I told them: "We need to strip this back to standard and rebuild only what your operation actually needs." They didn't want to hear it. That meant admitting the last 8 months were partially wasted. But we did it. Reduced 47 customizations to 11. Saved 6 weeks of testing. Cut ongoing maintenance costs by half. The lesson: every customization you add is a decision you'll have to defend, maintain, and pay for — forever. Before you customize, ask one question: can we change the process instead of the system? 9 times out of 10, the answer is yes.

Being a data leader isn’t about choosing right vs. wrong — it’s about navigating right vs. right, timing of these decisions, and most importantly when to change direction. Here are 10 forks in the road that almost every data leader eventually faces: 1️⃣ Governance vs. Innovation Tight policies protect the business and meet regulatory needs — but can slow experimentation. Looser controls speed up AI/analytics innovation — but increase risk exposure. 2️⃣ Centralization vs. Decentralization Centralizing teams, tools, and standards builds consistency and efficiency. Decentralizing puts data skills closer to the business — but can lead to silos and duplication. 3️⃣ Short-term ROI vs. Long-term Foundations Quick wins prove the value of data fast and earn executive buy-in. Long-term platform investments lay the groundwork for scalable, sustainable value — but take years to show results. 4️⃣ Build vs. Buy Building in-house gives flexibility and control over your roadmap. Buying off-the-shelf delivers speed — but risks vendor lock-in and limited customization. 5️⃣ Control vs. Empowerment Strict oversight ensures data quality, security, and compliance. Empowering teams with self-service access accelerates decision-making — but can lead to “data chaos” without guardrails. 6️⃣ Technology-first vs. Culture-first A cutting-edge stack enables advanced analytics capabilities right away. A culture-first approach ensures people have the skills and mindset to use the tech — avoiding “shiny object” underuse. 7️⃣ Data as a Service vs. Data as a Product Treating data as a service maximizes accessibility and reuse. Treating it as a product ensures ownership, accountability, and clear delivery standards — but adds overhead. 8️⃣ Standardization vs. Local Optimization Enterprise-wide standards create one version of truth. Local optimization tailors metrics and models to each business unit’s needs — at the cost of comparability. 9️⃣ Data Quantity vs. Data Quality Collecting everything prevents blind spots — but can create noise and higher storage/processing costs. Focusing on quality ensures trust and usability — but risks missing unexpected insights. 🔟 AI as Differentiator vs. AI as Efficiency Play Using AI to launch new products and revenue streams can redefine the business. Using AI to automate and cut costs improves margins — but may miss the bigger strategic opportunity. Every choice has trade-offs. The real art of data leadership is knowing which hill to climb now — and when to change direction. What’s the toughest data leader crossroads you’ve seen? #DataLeadership #DataStrategy #Analytics #AI #DigitalTransformation #ChiefDataOfficer #DataDriven

The hardest part of D365 implementation isn't choosing between standard and custom processes - it's knowing when to choose which one. Had a fascinating conversation with a retail CEO yesterday that perfectly illustrated this challenge. Their team was fiercely debating whether to adapt their unique inventory management process to D365's standard functionality. Looking at their process, we discovered something interesting. While their core allocation logic was a genuine competitive advantage, the surrounding workflows were actually creating unnecessary complexity. The standard D365 processes were, surprisingly, more efficient. Here's what years of implentation have taught me: The key isn't defending all your unique processes - it's identifying which ones truly drive competitive advantage. We've found a simple framework that helps: - If the process directly impacts customer value or competitive advantage - protect it - If it's mainly internal efficiency - lean toward standard functionality - If it's regulatory or compliance-related - standard processes usually reduce risk One manufacturing client recently saved £300K by adopting standard processes for 80% of their operations while heavily customizing the 20% that genuinely differentiated them. Remember: Sometimes your unique process is pure gold. Other times it's just different. The art lies in knowing the difference. Leading a D365 implementation? How are you distinguishing between processes that need protection and those that need standardization? #D365FO #DigitalTransformation #BusinessStrategy

𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: 𝗡𝗼 𝗢𝗻𝗲-𝗦𝗶𝘇𝗲-𝗙𝗶𝘁𝘀-𝗔𝗹𝗹 When leadership teams talk about “digital transformation,” they often jump straight to the answer: one ERP, a big custom build, or a collection of best-in-class tools. The reality is what this graphic shows: there are four legitimate strategies, each with real trade‑offs. 1️⃣ Single ERP system ✅ Integrated processes and a single source of truth ⚠️ Expensive to implement and risky if it fails Great when you want maximum standardisation and are willing to accept heavy change management and long timelines. 2️⃣ Custom development ✅ Perfect fit to your processes and potential competitive advantage ⚠️ Expensive and hard to maintain over time Powerful when digital capabilities are your product or core differentiator—but dangerous if you don’t invest in strong engineering, architecture, and product management. 3️⃣ Best of breed / composable systems ✅ Better functional fit and agility in each domain ⚠️ Integration issues and a fragmented, short‑sighted digital strategy if not governed Works well for fast‑moving teams that need flexibility, but only when there is a clear integration and data strategy across the stack. 4️⃣ Software platforms ✅ “Best of all worlds” feel and the ability to leverage existing tech ⚠️ Still hard to maintain and prone to fragmentation as platforms multiply Attractive when you want to move quickly on top of tools you already own—but they still need strong architectural guardrails. The key point: none of these is “right” in isolation. 💪 Good digital leaders: ▶️ Start from business strategy and differentiation, not tools. ▶️ Make explicit trade‑offs: cost vs. speed, fit vs. standardisation, innovation vs. stability. ▶️ Treat architecture as a roadmap, not a one‑time decision—you can move between boxes as the organisation matures. When you look at this graphic, where does your organisation sit today—and where should it be in 3–5 years? #DigitalStrategy #DigitalTransformation #ERP #EnterpriseArchitecture #SoftwareDevelopment #SaaS #CIO #CTO #TechLeadership #BusinessTransformation

Find the Sweet Spot: Customization vs. Configuration Too much sugar hurts; too little tastes flat. Same with ERP. Over-customized ERPs give you a short-term buzz… then an upgrade toothache. Under-customized systems feel “healthy” but no one drinks them—adoption fizzles. The goal with Acumatica: hit the sweet spot—configure first, customize where it truly differentiates. How I frame it with clients: Start with configuration: workflows, approval maps, business events, generic inquiries, dashboards, side panels. Prove value fast: pilot one value stream and track cycle time, first-time-right, margin leakage. Customize with intent: xRP/event-driven extensions only when it drives competitive edge or compliance. Stay upgrade-safe: keep core clean, document a “Customization Manifest” (owner, KPI, test, rollback). Integrate at the edges: REST/OData, iPaaS, Power BI—sweeten without dumping code into the core. Question for you: where’s your current mix—too sweet (over-customized), too bitter (under-customized), or just right? #Acumatica #ERP #Manufacturing #Operations #DigitalTransformation #CIO #CFO #BPM #ChangeManagement

Getting lost in a maze of customer configurations? There’s a way out. A seasoned PM recently asked me how to escape the ‘configuration trap.’ They’d joined a company where every customer gets a custom onboarding, which was leading to a maintenance nightmare. How do you communicate this challenge to leadership and navigate away from the trap? Here's what I suggest: Start by clarifying your company’s goals. If scalability is the aim, then heavy custom development work should be kept to a minimum. It's crucial to align your product to serve multiple customers efficiently. This is where the concept of building once and selling many times becomes key. But ask yourself: did customers buy your product because of all that configurability? If so, is that the business you want to build? Configurations aren’t inherently bad. The key is who handles them. Take a page from Salesforce’s playbook: they've built a business on configurability by outsourcing complex tasks to partners. Not only did this reduce internal burden, it also created a new revenue stream. You can do the same: ✅ Standardize where it makes sense ✅ Empower customers to self-configure ✅ Or create a services/partner model to handle the rest Remember: businesses will always ask for tailored solutions. Your job is to find the balance, determine who your best customers are and focus on their common needs. Start the conversation with leadership by identifying your best-fit customers and understanding if you’re chasing too many different use cases. If sales are driving customization by targeting everyone, it might be time to refine your market focus. Then, put on your business hat. Discuss differentiation, revenue impacts, and cost. Break it down in terms they understand and tie everything to how it affects the bottom line. By approaching this operationally and linking decisions to financial outcomes, you’ll help leadership see the bigger picture and ensure growth aligns with strategic goals. If you have questions or want to share your experiences, head over to dearmelissa(dot)com. Your challenges could be the topic of our next discussion. Let's continue untangling these complexities together. PS: This and all past Dear Melissa episodes are available on the "Product Thinking by Melissa Perri" Youtube channel!

📱 The ‘Apple vs. Android’ Moment in ERP: SAP’s Bold Bet on Public Cloud For years, ERP customers have had one main priority—flexibility. Customization was the gold standard. The more tailored your ERP, the better. But SAP is making a bold bet—that the future of ERP is not about customization, but about speed, agility, and continuous innovation. 💡 This is shaping up to be the ‘Apple vs. Android’ moment in ERP. 🔹 SAP S/4HANA Public Cloud (Apple-like) – Standardized, continuously updated, and highly optimized, but with limited customization. 🔹 SAP S/4HANA Private Cloud (Android-like) – More flexible, deeper customization, but also more complex and responsibility-heavy. How Do They Compare? Feature SAP S/4HANA Public Cloud SAP S/4HANA Private Cloud Customization Limited – Designed for best practices High – Tailored to unique business needs Upgrades Automatic, always up-to-date Customer-managed, upgrade cycles required Implementation Speed Faster – Pre-configured business processes Slower – More design and configuration needed Total Cost of Ownership Lower – No infrastructure or major upgrades required Higher – More maintenance, customization costs Flexibility Standardized processes, less variation Greater control over configurations and extensions Scalability Built for rapid scaling with SAP innovation Scalable but requires IT effort to maintain AI & Automation Embedded AI-driven automation, analytics, and best practices AI and automation possible but requires additional effort 🚀 SAP is betting big on Public Cloud—not just as a cost-saving measure, but as a way to future-proof businesses. Customers who embrace standardization and automation will gain agility, resilience, and continuous innovation. But for those with deep industry-specific needs, Private Cloud remains a strong option—offering the best of SAP S/4HANA with customization flexibility. The Big Question for Businesses: 💡 Do you prioritize agility, automation, and continuous innovation (Public Cloud)? 💡 Or do you require deep flexibility and control (Private Cloud)? At ASAR Digital, we help businesses navigate this decision and implement SAP the right way. What’s the best ERP model for your business? Let’s discuss. 👇

AI’s most generous gift may not be productivity. It may be mass customisation at near-zero marginal cost. For years, we’ve been pushed toward one-size-fits-all… in workflows, products, entertainment - even taste itself. There’s a reason for that: variation is expensive. So companies engineered out difference and scaled ‘average’. Efficient? Yes. But the hidden cost to the consumer has been huge. Standardisation has eroded the things we really care about: Interesting independent shops gave way to clone high streets. Flexible ways of working were replaced by rigid workflows. Original films lost ground to endless safe sequels and recycled formulas. As markets globalised, the penalty grew. The more diverse your customer-base becomes, the more awkward it is to serve everyone with the same default version. Now that starts to change. AI is taking the cost of variation down dramatically. Soon, it will be possible to generate bespoke versions of almost any digital asset in near real time. You won’t have to accept the standard version simply because it was the only economical one to make. You can already see the early signs in social media: videos created in seconds from prompts. Now play that forward… Instead of spending an hour negotiating with your partner over what movie to watch this evening, you ask your streaming platform to create a film in real time; tuned to both your tastes; and it starts faster than it used to take to drive to Blockbuster. Then go further down the rabbit hole… You see a T-shirt you like. ‘Change the logo. Darker colour. My custom fit.’ Done. The design is created instantly, sent to a robotised factory, made that day, shipped tomorrow. The marginal cost of customised production starts trending toward zero. And once that happens, some big questions follow. Do brands still matter in the same way? What happens when everyone can create their own version? At what point do you stop being a customer and start becoming a brand yourself? This is a defining moment for the world. Many of the compromises we’ve accepted at the alter of standardisation will be swept away in the coming decade. When the average is no longer economically necessary, what happens to everything built around it? (Now here's a short clip about Back to the Future)