For two decades, Master Data Management (MDM) gave organizations a single source of truth for customers, products, and suppliers so analytics and operations could run on clean, governed data.
When a leading global commodity trading company sought to accelerate its AI transformation, it turned to Artefact to turn potential into measurable performance. The goal was to make generative AI a daily productivity tool across departments while ensuring robust governance, compliance, and lasting cultural change.
Every decade or so, the way companies organize themselves quietly changes – not through grand announcements, but through a series of accumulated decisions that suddenly reveal a new pattern. We saw it with digital transformation in the 2000s, when data and connectivity became the fabric of the modern enterprise. Then came the agile revolution, which redefined how teams collaborate, iterate, and deliver value. Each shift didn’t just alter tools or processes—it changed what we believed possible about work itself.
At Artefact, we have designed a proven methodology to help corporations move from exploration to sustainable impact. This article explains the journey, the challenges, and the pragmatic steps to success.
How can organizations move from experimentation to true scale with generative and agentic AI? Over the past two years, many companies have launched promising pilots only to hit a wall when trying to operationalize them. The difficulty isn’t accessing technology anymore. Today, the challenge lies in scaling AI responsibly and effectively across the organization. To do that, companies must look beyond individual use cases and focus instead on holistic transformation. There are four critical dimensions that determine success: Processes, People, Platform, and Position: the 4Ps of Agentics.
Many industries sell into highly fragmented ecosystems composed of thousands of small, independent outlets spread across cities and regions - whether it is a global beverage company supplying high-end F&B venues or a local bank selling EDC terminals to micro-SMEs in an emerging market.
In our previous articles, we challenged conventional ROI models for AI and introduced a multi-layered, context-driven framework for measuring AI’s unique value.
Assortment optimization is a critical process in retail that involves curating the ideal mix of products to meet consumer demand while taking into account the many logistics constraints involved. The retailers need to make sure that they offer the right products, in the right quantities, at the right time. By leveraging data and consumer insights, retailers can make informed decisions on which items to stock, how to manage inventory, and what products to prioritize based on customer preferences, seasonal trends, and sales patterns.
In today’s data driven marketing landscape, Customer Data Platforms (CDPs) have become an essential component of a successful strategy. A CDP is a centralised system that collects, cleanses, and unifies customer data from various sources to build comprehensive customer profiles.
