ERP Software Solutions

I've been using Clean Architecture for 6+ years. Here’s why I think it’s amazing. 👇 The biggest pain in enterprise systems? A lack of structure. Every project reinvents the wheel. Every team builds layers differently. And knowledge doesn’t transfer between systems. But there’s a proven way to fix this. It’s called Clean Architecture. It’s not about how many projects you create. It’s not about fancy patterns. ✅ It’s about the direction of dependencies. Inner layers (domain, app) define abstractions. Outer layers (infra, presentation) implement those abstractions. Never the other way around. That’s it. That’s the rule. You can package this as: - Layers (domain, app, infra, web) - Vertical slices (grouped per feature) - Components (layers + vertical slices) They all work — if you follow the rule. What are the benefits? - Modular code - Clear separation of concerns - Easy-to-test business logic - Faster onboarding - Loosely coupled components Clean Architecture has helped me ship excellent products. And I’ll keep using it because it works. Want to simplify your development process? Grab my free Clean Architecture template here: https://lnkd.in/eDgfyWKB

𝐀𝐫𝐞 𝐲𝐨𝐮𝐫 𝐜𝐨𝐫𝐞 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐫𝐮𝐥𝐲 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐨𝐫 𝐣𝐮𝐬𝐭 𝐬𝐩𝐞𝐞𝐝𝐢𝐧𝐠 𝐮𝐩 𝐨𝐥𝐝 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬? I spent my early years in SAP consulting, watching ERP transform businesses. Back then, ERP was all about process control. Now, AI is driving a whole new level of intelligence. 1️⃣ 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: Imagine ERP that sees problems before they happen and forecasts demand in real time. 2️⃣ 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐌𝐚𝐤𝐢𝐧𝐠: Data silos vanish. Finance, operations, and supply chain teams all make decisions together, faster. 3️⃣ 𝐀𝐈-𝐃𝐫𝐢𝐯𝐞𝐧 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: Systems adjust on the fly, reducing errors and freeing up people to innovate. ⚠️ 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 However, integrating AI into ERP is rarely smooth. I’ve seen integration struggles arise - often because different teams (business, IT, data, and AI) work in silos. Misaligned priorities and fragmented data foundations slow progress and dilute impact. 🛠️ 𝟑 𝐀𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐋𝐞𝐚𝐝𝐞𝐫𝐬 1️⃣ 𝐑𝐞𝐭𝐡𝐢𝐧𝐤 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: Bring ERP, AI, and business teams together under a unified vision. Integration isn’t just technical, it’s organizational. 2️⃣ 𝐆𝐨 𝐁𝐞𝐲𝐨𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Challenge your teams to think bigger. Use AI not only to automate but to predict, optimize, and innovate across the business. 3️⃣ 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐞𝐧 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐂𝐨𝐫𝐞: AI can only be as smart as the data it processes. Clean, integrated, and real-time data must be a top priority. 𝐖𝐡𝐲 𝐈𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 In a world of constant disruption, businesses that modernize their core systems with AI don’t just run 𝐟𝐚𝐬𝐭𝐞𝐫 - they run 𝐬𝐦𝐚𝐫𝐭𝐞𝐫. They turn data into decisions, complexity into clarity, and innovation into competitive advantage. At Deloitte, we’re helping organizations redesign their ERP foundations with 𝐀𝐈-𝐟𝐢𝐫𝐬𝐭 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬, ensuring systems are intelligent, connected, and future-ready. 𝐒𝐨, 𝐰𝐡𝐚𝐭 𝐢𝐬 𝐲𝐨𝐮𝐫 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐄𝐑𝐏 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰? 𝐋𝐞𝐭 𝐦𝐞 𝐤𝐧𝐨𝐰 𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬. #Deloitte #AI #ERP #SAP #IntelligentCore #Innovation #DigitalTransformation #Leadership #CoreModernization #TechnologyTrends 𝑇𝑟𝑎𝑛𝑠𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝑡ℎ𝑟𝑜𝑢𝑔ℎ 𝑙𝑖𝑔ℎ𝑡𝑒𝑛𝑖𝑛𝑔 𝑢𝑝 𝑡ℎ𝑒 𝑐𝑜𝑟𝑒. 𝐶𝑟𝑒𝑑𝑖𝑡𝑠 𝑡𝑜 𝑗𝑝𝑔𝑜𝑛𝑐𝑎𝑙𝑣𝑒𝑠𝑎𝑟𝑡. 𝐹𝑜𝑢𝑛𝑑 𝑎𝑡 𝑑𝑜𝑒𝑠𝑎𝑟𝑡𝑤𝑜𝑟𝑘.

Before I touch any ERP, I ask the CFO just 3 questions. If they can’t answer them fast… the system’s already leaking. I’ve done this across 100+ audits in UAE, KSA, and Oman. 𝐀𝐧𝐝 𝐭𝐡𝐞𝐬𝐞 3 𝐧𝐮𝐦𝐛𝐞𝐫𝐬 𝐭𝐞𝐥𝐥 𝐦𝐞 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐄𝐑𝐏 𝐡𝐞𝐚𝐥𝐭𝐡 𝐭𝐡𝐚𝐧 𝐚𝐧𝐲 𝐟𝐥𝐚𝐬𝐡𝐲 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝. (1) Invoice Approval Cycle Time. If this is over 72 hours… I don’t need to see anything else. You’ve got a workflow problem. Either your approvers don’t trust the data, or the system isn’t pushing the right context at the right time. (2) Actual vs. Committed Spend Deviation. I once saw a $120M oil & gas group miss plan by $19M. Why? Their procurement commitments were in Excel. The ERP was blind. No wonder budgets were “accidentally” overrun every month. (3) % of Manual Journal Entries. This one’s a CFO killer. If more than 10% of entries are manual, you’re inviting human error, fraud, and reconciliation hell. One client had 5 people just fixing entries every week. You don’t need a full audit to know if your ERP is broken. Just ask your team: “What’s our invoice approval time?” “How far off are we from committed spend?” “How many JEs are still manual?” The truth usually hurts. But it also saves millions. ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 so others can learn.

Choosing the right database is critical for application performance, scalability, and efficiency. Whether you're building transactional systems, AI-powered applications, or real-time analytics, understanding database types is essential.  SQL (Relational) Databases   SQL databases are structured, ACID-compliant, and designed for data integrity. They use tables, relationships, and indexing to ensure high-performance query execution.  ✅ Key Features:   ✔ ACID Transactions (Atomicity, Consistency, Isolation, Durability)   ✔ Structured Query Language (SQL) for data manipulation   ✔ Strong Relationship & Referential Integrity   ✔ Optimized Indexing & Security Features  🛠 Popular SQL Databases: MySQL, PostgreSQL, Oracle, Microsoft SQL Server  Best For: Financial applications, CRM systems, and structured data that require strict consistency and relationships.  📌 NoSQL (Non-Relational) Databases   NoSQL databases are designed for horizontal scaling, high availability, and flexibility. They store semi-structured or unstructured data and optimize performance for various workloads.  🔹 Columnar Databases → Best for Big Data & Analytical Queries   ✔ Stores data in columns instead of rows for faster aggregations   ✔ Great for OLAP workloads   ✔ Examples: Apache Cassandra, DataStax  🔹 Graph Databases → Best for Relationship-Intensive Data   ✔ Stores data as nodes & edges for complex relationships   ✔ Used in fraud detection, social networks, recommendation engines   ✔ Examples: Neo4j, AWS Neptune  🔹 Key-Value Databases → Best for Fast Lookups & Caching   ✔ Uses a key-value pair structure for ultra-fast reads/writes   ✔ Ideal for caching layers & session storage   ✔ Examples: Redis, DynamoDB  🔹 Document Databases → Best for Flexible, Semi-Structured Data   ✔ Schema-less design, great for JSON & hierarchical data   ✔ Used in content management systems & e-commerce   ✔ Examples: MongoDB, Couchbase  🔹 Time-Series Databases → Best for Real-Time Monitoring & Metrics   ✔ Optimized for time-stamped data storage & retrieval   ✔ Used in IoT, system monitoring, & financial analytics   ✔ Examples: InfluxDB, Prometheus  🔹 Spatial Databases → Best for Geospatial & GIS Data   ✔ Supports location-based queries & geospatial indexing   ✔ Used in navigation, logistics, and mapping   ✔ Examples: Snowflake, Oracle  🔹 NewSQL Databases → Bringing SQL & NoSQL Together   ✔ Provides SQL capabilities with NoSQL scalability   ✔ Used in distributed applications with transactional needs   ✔ Examples: CockroachDB, VoltDB  Which Database Should You Use?   ✅ Use SQL when you need structured data, strong consistency, and well-defined relationships.   ✅ Use NoSQL when you need scalability, flexible schemas, and optimized performance for specific data models.   ✅ Use Hybrid (NewSQL) when you need the best of both worlds—scalability and ACID compliance.  What’s your go-to database choice, and why?   

We’re teaching AI to understand the language of business. Together with Stanford University, our SAP research team has developed a new approach for understanding enterprise data: the Relational Transformer (RT) – a foundation model that learns directly from multi-table, key-linked business data, not just text. The paper (currently under review) explores how RT can read relational databases like humans reason over spreadsheets. The model learns to predict masked cells during pretraining, and once trained, it can be prompted to solve new predictive business tasks, like payment dates, delivery delays, or upsell opportunities for customers, without any task-specific retraining. Why this is important? 💡 ➡️ RT achieves strong zero-shot accuracy on unseen datasets, coming close to fully supervised models – but with a fraction of the compute needed for prompting LLMs. ➡️ Fine-tuning RT is 10-100× more efficient than traditional baselines – making it practical for real-world enterprise data. ➡️ Its relational attention explicitly models rows, columns, and key links – the real structure of business data – rather than forcing it into plain text.   Huge thanks to our collaborators from Stanford University! We’re looking forward to the next steps in the review and publication process with you. This represents a major step toward AI that truly understands how businesses operate.   👉 Read the paper: https://lnkd.in/d4i83Dhc   Rishabh Ranjan Valter Hudovernik Mark Žnidar Charilaos Kanatsoulis Roshan Reddy Upendra, PhD Mahmoud(Reza) Mohammadi Joe Meyer Tom Palczewski Carlos Guestrin Jure Leskovec Johannes Hoffart Markus Kohler Yaad Oren Zhuohan (Mark) Li

Reflecting on my conversation with CNBC’s Carolin Roth at the World Economic Forum, I was struck by how quickly the conversation around enterprise software has evolved. There is a lot of attention on what AI can do. We focused instead on what actually drives value: disciplined execution. I believe AI only creates lasting advantages when it is built into real workflows, connected to proprietary data, and scaled responsibly within operating businesses. What feels increasingly clear is that this is not about replacing software. It is about advancing it. We see the next phase as the rise of Agentic Enterprise Solutions, trusted systems that, in our view, move from supporting work to carrying it out and can deliver more predictable and accountable results. As AI moves into regulated and mission-critical environments, context and trust become essential. We believe the platforms that pair deep domain expertise and embedded workflows with the ability to innovate quickly are positioned to lead. At Vista Equity Partners, we see this as part of software’s long trajectory of growth. The agentic era is here. As with every major technology shift, the winners will be defined by how well they execute. https://bit.ly/4tjKSiO

ERP Projects Fail for Many Reasons. Ignoring Integrations is the Fastest Way to Doom One. Too often, ERP projects run over budget, take too long and fail to deliver. The culprit? Overlooked integrations. I see this mistake all the time. Companies focus on ERP functionality but forget that no system operates in isolation. Data flows, third-party systems, and automations must be planned from day one—not as an afterthought. That’s why I put together a no-nonsense whitepaper on how to make ERP integrations work instead of becoming a hidden pitfall. 5 Practical takeaways from the whitepaper: 1. Define all data flows at project kickoff – Document dependencies between systems early. Surprises later = delays & cost overruns. 2. Master data first, transactions second – Sync customers, vendors, and products first. If your master data is broken, transactions will fail. 3. Set a realistic integration timeline – Sync integration tasks with ERP rollout. If integrations are late, the entire project stalls. 4. Test with real data, not fake records – Your ERP test system should mirror production. Otherwise, the first real transaction is your actual test. 5. Make integrations visible – Use visual mapping tools to align teams, avoid assumptions, and ensure all critical systems stay connected. Get the full whitepaper here: https://lnkd.in/dfNHA9nN ERP success is not just about the ERP—it’s about how well everything connects. Integrations First. Always. #ERP #Automation #iPaaS #PMO #ProjectManagement

It starts with one missing value, one duplicate row… and suddenly your entire system can’t be trusted. Because data issues don’t fail loudly. They compound silently. Here’s what keeps pipelines reliable 👇 - Null value checks Missing fields in key columns can quietly break logic and downstream outputs. - Duplicate checks Repeated records distort metrics, models, and business decisions. - Primary key validation Every record must be unique, or nothing stays consistent. - Referential integrity Broken relationships between tables lead to incorrect joins and insights. - Data type & format validation Wrong formats or types cause subtle but costly errors. - Range & outlier checks Values outside expected limits often signal deeper issues. - Freshness & volume checks Unexpected delays or spikes usually point to upstream failures. - Schema change detection Even small structural changes can break entire pipelines. - Distribution drift checks Data patterns shifting over time can silently degrade models. - Business rule validation If domain logic breaks, the output becomes unreliable. - Aggregation & historical checks Totals and trends must stay consistent across layers and over time. Data quality issues don’t crash systems. They corrupt them. What’s the one check your pipeline is missing right now? Follow Sumit Gupta for more such insights!!

Enterprise AI is moving beyond systems of record toward systems of outcomes. The first generation of enterprise AI focused on isolated agents and copilots: • Answer a question • Generate content • Execute a task • Make a recommendation Useful? Absolutely. But enterprise operations do not break down because employees cannot find buttons faster. They break down because execution across the business is fragmented. Take sales order execution. A single customer issue can require coordination across: • inventory • fulfillment • pricing • finance • customer service • logistics • revenue operations Most organizations already have the operational data. What they do not have is a system that continuously understands the operational context, coordinates decisions, prioritizes issues, and helps move work forward across the enterprise. That is where #Fusion_Agentic_Applications represent a very different architecture pattern. The Sales Order Command Center is not just AI sitting beside an application. It is an agentic operational system built from teams of specialized AI agents collaborating continuously, sharing context, evaluating operational signals, and helping drive outcomes 24/7. The system continuously evaluates: • fulfillment risks • inventory shortages • pricing discrepancies • customer priority • margin exposure • workflow bottlenecks • operational exceptions Then helps coordinate execution through: • prioritized operational workflows • recommended next best actions • guided resolution paths • cross-functional coordination • transactional actions directly inside Fusion Applications The important shift is this: The application is no longer passive software waiting for human instructions. The application becomes an active participant in enterprise operations. Sometimes guiding the human. Sometimes executing parts of the process directly. Always helping move the business forward. This is not just “AI added to ERP.” This is enterprise software being re-architected around agentic execution. That is the shift from systems of record to systems of outcomes. 👇 Check out the demo below https://lnkd.in/gpk3TP2r #Fusion_Agentic_Applications #AI #EnterpriseAI #SystemsOfOutcomes #AgenticAI

Broadcom's extreme makeover of VMware—killing perpetual licenses, consolidating product lines, and pushing hardball subscription-based pricing—has prompted a mass exodus of enterprise users. Worst move: sending cease-and-desist letters to long-time VMware customers who have let their support contracts lapse, threatening to sue them for simply installing patches or updates. Legally, Broadcom is on solid ground. But in terms of market trust and customer experience, it is destroying two decades of goodwill. Toshiba and MSIG, with 16+ years of dedication and thousands of virtual machines, are switching away from VMware because of 3x–10x cost hikes, unbundling rigidity, and removal of technical support for core-only scenarios. This change is more than a vendor disappointment—it's the start of a structural alignment of the virtualization marketplace. Alternatives such as Nutanix, Proxmox, Red Hat, and even homegrown KVM stacks are gaining traction. Open-source and HCI products are adapting so rapidly, Broadcom's strategy may inadvertently accelerate decentralization of virtualization the same way hyperscalers have done to legacy data centers. For IT executives, this represents a disruption and an opportunity. No longer can they trade off enterprise agility for vendor lock-in. Next-generation infrastructure needs to be open, cost-controllable, and vendor-agnostic in its approach—particularly in a period where software-defined everything is taking on business-critical roles. #Virtualization #Broadcom #VMware #Nutanix #OpenSource #ITInfrastructure #CIO #DigitalTransformation #CloudStrategy #Kubernetes