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Why organizational change projects fail and how to prevent implementation disaster

New IT installations often fail. At least that’s the widespread belief surrounding organizational change initiatives today. One frequently cited study from the 1993 book Reengineering the Corporation goes as far as saying that as many as 70% of the organizations that undertake a reengineering effort do not achieve the dramatic results they intended. A more recent McKinsey survey of more than 1,500 executives who had undertaken a significant change effort in the past five years found that only 38% of respondents said “the transformation was ‘completely’ or ‘mostly’ successful at improving performance. After two decades of hearing about high failure rates related to change, it’s unsurprising that business leaders are wary of organizational change projects. Organizational psychologist Nick Tasler explained that these negative biases can create a toxic self-fulfilling prophecy. “When a change project falls a day behind schedule, if leaders and employees believe that successful change is an unlikely outcome, they will regard this momentary setback as the dead canary in the coalmine of their change initiative. (Never mind the fact that three other initiatives are still on time or ahead of schedule),” he wrote in an article for Harvard Business Review. “Suddenly, employees disengage en masse and then the change engine begins to sputter in both perception and reality.” Yes, change is hard, and complex IT implementation projects, particularly ERP installations, can be particularly challenging. But it doesn’t mean they are doomed to failure. So where do you start? How can you choose the right technology for your retail business, and ensure that the implementation project runs as smoothly as possible and you get the most from your investment? Here are some of the main causes for failure in any organizational change initiative, and how can you prevent them from happening: Mistake #1: Failure to plan Issue: An outdated legacy system is impacting business performance, and it needs replacing quickly. In their rush to get the project going, business management jump straight into the implementation without taking the time to develop a well thought-out organizational change management plan. Solution: Don’t be tempted to cut corners in your planning. Analyze your business, decide what should be prioritized, and understand all the different ways the project will impact your routines at every stage of the process. “Companies should start by analyzing their current and future requirements and processes,” says Gunnar Ingimundarson, Chief Consulting Officer at LS Retail. “How many software solutions are they currently using, and what are they used for? Map out the disparate solutions in the stack, alongside their dependencies and interconnections. The next step is to figure out where they can draw the biggest – or quickest – benefits. Is your POS system not generating the information you need on stock levels and product visibility? Or, are there integrations that repeatedly cause problems or break down? Do you experience missing data? Identify the area(s) where a new system would bring immediate value in terms of savings or returns. That’s where you should start, and that should determine your priorities.” Once the priorities are set, break the project down into manageable chunks, from pilot phase to initial implementation to company-wide rollout. Consider when it’s most appropriate to start each phase of the installation so you won’t place unnecessary strain on your business during busy times. Mistake #2: Key stakeholders aren’t onboard, or have unrealistic expectations Issue: Management want the new technology in place quickly and only focus on the end goals. They get frustrated by how long the project is taking and threaten to pull the plug. Or they wonder why the new software isn’t being adopted widely and successfully when they failed to communicate the changes to everybody in the business and get company-wide buy in. Solution: All stakeholders need to be committed to the project’s success right from the beginning, and to clearly understand the project’s scope and goals. “Internal resistance can kill even the best implementation project,” says Eric Miller, Regional Director for the Americas at LS Retail, building on his 13 years of experience in software implementations. “Get the buy-in from all stakeholders from the start, and make sure that the goals, objectives and expected end results of the project are clear and communicated from you to the stakeholders, and from the stakeholders to all the customer parties involved. It never pays off to sell a dream you can’t deliver on.” Bring together personnel from different departments to understand their requirements and what outcomes they hope to achieve from the implementation. Similarly, they need to understand how much time should be devoted to a project like this and ensure project teams are given sufficient time to carry out the work. Set realistic timeframes from the start, and ensure everyone knows exactly what’s required of them. Mistake #3: Unforeseen changes throw the project off track Issue: Even the best prepared projects encounter hurdles along the way, but if unforeseen issues arise and major milestones are missed, it can be tempting to throw in the towel and deem the entire project a failure. Solution: Know that when you’re dealing with a large-scale IT implementation, it’s hard to plan for every eventuality. Be willing to adapt and take a different approach if it ultimately means the project will be a success. “What was deemed to be the best approach initially may need to change – this might even happen after the pilot is completed. I have seen companies that went through multiple pilots before finding the right balance. It’s a learning process, and it’s never over,” says Miller. It’s worth learning everything you can from the pilot implementation. Instead of rushing on to roll out store #2, take a moment to see how the system is working and to identify any issues that you couldn’t have planned for in your testing environment. Success comes to those who take a considered approach. Mistake #4: Picking the wrong technology partner Issue: It may be tempting to go for the cheapest technology provider, but cheapest upfront may not necessarily deliver the long-term business value you hoped for. You quickly realize they can’t help you achieve your outcomes, because they lack drive,

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Omnichannel retail strategy connecting POS, eCommerce, inventory, customer data, and analytics across retail channels.

Omnichannel Retail Strategy: Why Only 5% of Retailers Actually Deliver On It

INTRODUCTION Retailers have been talking about omnichannel for more than a decade. However, only about 5% actually allow a customer to initiate a purchase on one channel and complete it on another. Meanwhile, customers aren’t waiting: The vast majority research and select products online before setting foot in a physical store, and half of in-store shoppers check their phone during the visit to check specifications, compare prices or confirm a decision. The gap between what customers expect and what most retail systems can deliver is where sales are lost. Make all channels reflect the same brand A disjointed brand experience—slick in-store, clunky in-app, and slow on social—quietly erodes trust with each inconsistency. Nespresso is a great example of how to do it right: the same visual language is maintained on their e-commerce site, mobile app, order confirmation emails and even on the physical packaging; Thus, the brand is perceived identical at each touch point, instead of reinventing itself channel by channel. Unify your sales channels, not just your brand image Customers expect basic cross-channel functionality: checking if a specific store is in stock, adding an item they saw in person to their online cart, or making a hassle-free return on an in-store purchase. Most retailers cannot yet reliably offer this, as their systems have been assembled from separate, poorly integrated tools rather than being built as a single platform; This often results in the inability to see real-time inventory across locations, difficulty accepting returns across channels, and the risk of selling items that are no longer available. A unified commerce platform like LS Central solves this at the root: centralized inventory and location visibility allows exchanges and returns to work the same way, regardless of the channel where the original purchase was made. Be transparent about shipping costs and conditions Approximately 70% of online shopping carts are abandoned before completing the order, with unclear or unexpectedly high shipping costs being the most common reason. Retailers who clearly list delivery time, shipping cost, and return conditions next to each product—rather than revealing them at the final checkout step—allow customers to make an informed decision from the start, building the kind of trust that reduces cart abandonment. Show real inventory, not just a product catalog Most customers expect to see product availability online before visiting a store; some retailers, like IKEA, even go so far as to display exact stock quantities at each location. At a minimum, product catalogs should be kept up-to-date across all channels (a unified system allows e-commerce, point-of-sale and back-office to use the same data in real time), include detailed product information to compensate for the inability to physically touch or try it, use high-quality images and videos, and display customer reviews; All of these factors directly influence confidence when purchasing and decision-making. Design thinking about what the customer really needs The retailers who succeed are not those who insist most on selling, but those who solve a real problem. CVS’s pharmacy app is a useful example: it helps customers manage complex medication schedules with reminders and notifies them when a prescription is ready for pickup. The value is not promotional, but functional; Precisely because of this, it generates the type of trust that translates into recurring purchases. Turn collected data into actions, not just reports Retailers collect huge amounts of behavioral data (pages viewed, items abandoned in cart, return patterns, top customer preferences), but collecting it is not the same as using it. GameStop’s loyalty program, with tens of millions of members, is a clear example of how to do it right: Analysis of member data revealed that rewards alone don’t drive engagement, but personalized offers do. This change reportedly allowed email open rates to more than double when GameStop moved to using hyper-segmented messages based on purchase history. Turn collected data into action, not just reports Retailers gather vast amounts of behavioral data—pages visited, items abandoned in carts, return patterns, and the preferences of key customers—but collecting data is not the same as using it. GameStop’s loyalty program, with its tens of millions of members, is a prime example of getting this right: analysis of member data revealed that rewards alone do not drive engagement, whereas personalized offers do. This shift more than doubled email open rates when GameStop switched to using hyper-segmented messages based on purchase history. For most retailers, the obstacle isn’t a lack of data, but rather data fragmentation. Having data scattered across disconnected systems means most companies can only meaningfully analyze a small fraction of the information they collect. A unified commerce platform that consolidates data from all channels into a single location is what truly enables a comprehensive view of the customer. Want to see what a unified omnichannel platform would look like for your retail business? Contact the Trident team to discover the possibilities offered by LS Central.

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Microsoft Azure AIOps dashboard displaying AI-powered monitoring, predictive failure alerts, and real-time infrastructure health.

Advancing Azure service quality with artificial intelligence: AIOps

INTRO Somewhere on Azure’s infrastructure, a disk was predicted to fail — and your workload was migrated off it before that failure ever happened. That’s not a hypothetical. It’s Azure AIOps, Microsoft’s use of AI and machine learning to detect, predict, and fix infrastructure problems before they become customer-facing outages. For a business evaluating cloud reliability, this matters less as an engineering curiosity and more as a straight answer to a practical question: why does Azure keep improving its uptime numbers year over year? Failures Get Predicted, Not Just Detected Traditional infrastructure monitoring tells you something broke. Azure’s hardware failure prediction model tries to catch it before it does — flagging disks, memory, and networking hardware likely to fail, then automatically live-migrating affected virtual machines to healthy nodes. The customer impact of a hardware failure, in the cases this catches, is zero downtime rather than an outage ticket. Faster VM Provisioning, Powered by Prediction Azure’s pre-provisioning system uses historical deployment patterns to predict what VM configurations customers are likely to request — and creates a pool of them in advance. When a matching request comes in, it’s assigned from that pool instead of built from scratch. The practical effect for a business is faster deployment latency, without needing to know any of the prediction modeling happening behind it. Incidents Get Resolved Before They Escalate Azure tracks incident response against three metrics: time to detect, time to engage, and time to mitigate. AI-driven anomaly detection — built to catch not just obvious spikes but slow-building patterns like memory leaks — feeds directly into routing the right engineering team to an issue immediately, and in some cases triggers automated fixes with no human step at all. For a business running production workloads, that translates to shorter, less frequent disruptions. Safe Rollouts Prevent Widespread Impact Microsoft rolls out infrastructure changes constantly, which creates real risk of a bad change spreading before anyone notices. An internal system (code-named Gandalf) analyzes rollout patterns to catch issues that surface hours or days later, flagging suspicious changes before they propagate further. This is part of why platform-wide incidents from routine updates are rare rather than common. What This Actually Means for Your IT Roadmap None of this requires action on your end — it’s infrastructure Microsoft operates on your behalf. What it does mean is that Azure’s reliability improvements aren’t marketing claims; they’re the output of a systematic prediction-and-automation investment, which is a reasonable thing to weigh when comparing cloud providers on uptime and reliability, not just price. Want to know how Azure’s reliability engineering translates to SLAs for your specific workload? Talk to Trident about your cloud infrastructure options. FAQ What is Azure AIOps?Azure AIOps is Microsoft’s use of AI and machine learning to predict, detect, and resolve infrastructure issues on Azure automatically — including hardware failure prediction, faster VM provisioning, and automated incident response. Does Azure AIOps require any setup from customers?No — it operates at Microsoft’s infrastructure level. Customers benefit from improved reliability and uptime without configuring anything themselves. How does Azure predict hardware failures before they happen?Microsoft Research and Azure built models that analyze disk, memory, and networking behavior to flag components likely to fail, then automatically migrate affected virtual machines to healthy hardware.

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Microsoft Dynamics 365 for Manufacturing dashboard displaying production planning, inventory, supply chain management, and quality control.

Manufacturing’s Biggest Inefficiency Isn’t the Plant Floor — It’s the Gap Between Systems

INTRO A machine on the shop floor and a service technician in the field often work off completely different data — one sees production output, the other sees a support ticket, and neither sees the customer’s purchase history. Dynamics 365 for manufacturing exists to close exactly that gap, by ending the divide between CRM and ERP instead of running them as separate systems that happen to sit on the same network. Here are the six shifts that gap-closing actually produces on the ground. 1. Supply Chain Visibility That Goes Beyond a Dashboard Collecting and visualizing supply chain data across every location does more than create a nicer report — it changes how fast a disruption gets caught. Manufacturers using remote monitoring across distributed installations have shortened time-to-market by catching supply issues before they cascade into production delays, rather than discovering them after a shipment is already late. 2. Asset and Production Management, Consolidated Into One View When production oversight and real-time equipment monitoring sit in one system instead of three, manufacturers stop reacting to breakdowns and start resolving issues remotely before they cause downtime. This is the operational basis for near-continuous uptime models in equipment-heavy manufacturing — and it also opens a second revenue line: monitoring and proactive support sold as an ongoing service, not a one-time sale. 3. Customer Engagement Built on Usage Data, Not Guesswork Personalized service at scale requires predictive analytics and self-service options that are actually relevant to what a specific customer does with the product — not a generic contact form. Manufacturers with a connected sales-through-service platform can flag potential equipment issues before a customer notices a problem, while also tailoring offers based on that customer’s real purchasing and usage pattern. 4. Service Centers as a Profit Center, Not a Cost Center Falling costs for IoT sensors and mobile devices have made remote monitoring and proactive maintenance commercially viable additions to standard break/fix support — not just a premium add-on for enterprise accounts. Combining customer records, technician availability, and inventory into a single mobile-accessible system is what lets a service team actually deliver on that model instead of just theorizing it. The Data Advantage: Better Products, Not Just Better Service IoT-connected parts and equipment feed usage data back to engineering — which components fail early, which are over-built, how products actually get used in the field. That feedback loop is what shortens the cycle between a design flaw and a fix, rather than waiting for failure reports to pile up. 5. Technicians Who See the Full Job, Not Just the Ticket A 360-degree view of a customer’s asset and service history changes what a technician can do on-site — they’re working from context, not just a work order. Paired with machine learning that surfaces similar past cases, this turns troubleshooting into pattern-matching against real precedent instead of starting from zero on every call. 6. One System Connecting the Floor to the Front Office Manufacturers that unify production and project management data with CRM stop treating customer service and customer engagement as separate departments working from separate records. The practical result is service and recommendations grounded in what a customer has actually bought and experienced — not assumptions. Why Dynamics 365 Specifically Dynamics 365 for manufacturing removes the artificial line between CRM and ERP, running both on one system with embedded analytics rather than bolting a reporting layer on top of disconnected tools. For a mid-size manufacturer, this matters less as a technology upgrade and more as an operating model change — supply chain, service, and sales working from the same data instead of reconciling three versions of it. Curious what a unified CRM-ERP model would look like on your production floor? Talk to Trident about a Dynamics 365 manufacturing assessment.

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