Data analytics

The difficult part starts when an agent stops describing the business and starts changing it Enterprise AI has become very good at telling us what it thinks should happen next. It can investigate an incident, review a supplier, classify a service request, prepare an employee onboarding plan or recommend a configuration change. In many cases

Microsoft Copilot is becoming more than a productivity assistant. That is the part many businesses are watching closely. The first wave was about helping users write, summarise, search and move a bit faster. Useful, yes. But not transformational on its own. The more interesting shift is now happening around agents. Can an agent check an

Enterprise AI is crossing an important boundary. Until recently, most enterprise AI systems were primarily asked to read information and produce an answer. They could summarise a contract, explain a report, find customer information or draft a response. Agentic AI changes that relationship. An agent can retrieve information from several systems, reason across it, choose

When Enterprise Data Platforms Become Too Complex Enterprise data platforms usually start with a sensible goal: Connect the data Make it trustworthy Make it useful The problem is that, over time, the platform itself can become part of the complexity. More services are added. More specialist skills are needed. More workloads become dependent on one

Entity Resolution at Scale: Merge Duplicates as Data Moves Enterprise data rarely arrives clean. The same customer, supplier or product can exist across multiple systems under different names, IDs or formats. For reporting, this creates inconsistency. For AI and automation, it creates unreliable context. Entity resolution has traditionally been handled through batch processing. But when