BlogArticles: 195
Our latest articles on business systems, automation and implementation.
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Most failed IT projects fail because of how business and developers work together, not because of the technology. This is the four-stage process we use on CRM and ERP projects.
Most CRM and ERP projects fail because nobody defined the business goal first. Here is the approach we use to tie technology to strategy and measure the return.
How companies split work between people and AI in sales, support, project management and HR, a five-step plan for introducing it, and the mistakes that cost the most. Companies that do this well report productivity gains of 25 to 40%.
A salesperson is on a call, needs the order history, and the CRM takes 30 seconds to load. This article walks through 8 technical causes of a slow CRM, from hosting to missing monitoring, with fixes we have used in projects for B2B, e-commerce, manufacturing and service companies.
Quote requests buried in the inbox, no record of what was said to a client, sales and delivery teams telling different stories. That is daily life in many service firms. A CRM fixes it, and Salesforce data suggests it can lift sales by 29% and customer satisfaction by 47%.
Plenty of companies start digitising too early and buy systems they don't need yet. Others wait too long and drown in operational chaos. Here are 10 concrete signs that it is time to look at a custom CRM, ERP, automation or AI.
Companies spend tens or even hundreds of thousands on CRM, automation and AI, and still don't see the results. Gartner reports that 55% of digital transformation projects fail to deliver the expected return. More often than not, the tool is not the problem. The missing strategy before the rollout is.
A $40,000 CRM that only 40% of the sales team uses is a design problem, not a technology problem. We show how UI/UX affects measurable KPIs in business systems and how to calculate the return on a better interface.
Industry figures often put failed CRM and ERP projects at up to 60%, and budget overruns at 70%. Here is how developers, DevOps, testers and UX designers working as one team lower that risk, with examples from real projects.
Some processes are safe to hand to AI, and some fall apart without a person. This article shows where the line usually runs, with examples from different industries and a way to count whether automation pays off.
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