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Why Enterprise Integrations Become Harder to Manage as Businesses Grow

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  Introduction Most companies don't wake up one morning with an integration problem. It usually starts with a simple request. Sales need the CRM to send customer information to finance. Operations need orders from the ERP. HR adds a new payroll system. Then another application is introduced for reporting, customer support, or a new business unit. Each connection makes sense when it is created. A few years later, IT may be looking after dozens of connections between systems that were never designed to work together. One application update breaks another workflow. A failed data sync goes unnoticed. Someone maintains a spreadsheet because two systems still don't share the same information. The business has grown. Technology has grown with it. The connections between those systems have become a problem. Growth Adds More Systems Than Most Businesses Expect As companies expand, their technology landscape usually expands with them. When a company grows , it may use a CRM for ...

Why Moving to the Cloud Doesn’t Automatically Make Applications Scalable

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Why Moving to the Cloud Doesn't Automatically Make Applications Scalable Introduction   Moving an application to the cloud can solve several infrastructure problems. It can reduce dependence on physical servers, make resources easier to provision, and give engineering teams more flexibility. But there is a common assumption that moving an application to AWS, Azure, or another cloud platform will automatically make it scalable. Press Enter or click to view image in full size It doesn’t. If an application was designed around fixed infrastructure, moving it to the cloud doesn’t suddenly change how the application handles traffic, databases, deployments, or failures. The application may be running in a new environment, but many of its old limitations can still be there. This is where cloud engineering is instant. Migration Doesn’t Change Application Architecture A straightforward cloud migration often involves moving an existing application with relatively few changes. This can be the ...

Why Better AI Models Don't Always Lead to Better Business Outcomes

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  Introduction Every few months, a new AI model emerges that can do what the previous generation could not. It can handle longer documents, answer more complex questions, write better code, or process information faster. Technology executives operate under the assumption that better models lead to better business outcomes. It is not always the case.   An organization might be equipped with such a highly skilled model but still not manage to extract value from it. And it may not even be about the model itself. It may be related to the fact that data is  disperse d among different systems. Th e employees may find it difficult to  use  the model's output. Some critical business processes may still be carried out using Excel and manual approvals. That is how many enterprise AI implementations fail to deliver results. A Better Model Doesn't Fix a Broken Process Consider a customer service team handling a growing number of requests. The team uses a CRM, a billing ...