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AI discussions have been mostly around labour productivity. Organisations have begun to track metrics around increased outputs, decreased costs, shorter timeframes for decision-making and greater efficiency or automation. These days, dashboards are mostly created with productivity in mind. Such measures matter. Productivity, competitiveness and effectiveness are constant pressures on organisations. But maybe there is one more thing to be considered. As technology handles more

One of the things that has always fascinated me as a certified Business Analyst is how many concepts originally related to process analysis are popping up in talks about AI workflows. Much has changed in the technology. The need for understanding how the work creates value has not changed. Years ago, companies used to invest a lot of resources into process analysis before implementing any new technology. Business analysts would spend a lot of time studying processes, activiti

This recognition from Thinkers360 reflects contribution in a space that is evolving quickly. Generative AI is now widely accessible. It can produce text, code, and answers at speed. That layer is already understood. The shift is happening beyond that. Generative AI is moving from output to influence. It is starting to shape how decisions are explored, tested, and understood before action. This changes the role of AI. It is no longer only about generating content.It is about s

Goldman has recently said of Fed signals that such a shift can be easily missed if the focus remains on tools, and their data only trains through October 2023. In the last couple of years, AI has enhanced its way of providing output. This, too, has already altered the way we now implement work as systems are capable of quickly generating text, code and answers. That layer remains useful. The direction now goes further. AI is now beginning to simulate situations and understand

Recent research suggests a distinct tension. About one quarter of communications leaders plan to allocate more than 10% of their budget for AI, but 88% feel unprepared to lead an AI transformation (Boston Consulting Group, 2026; Dubner, 2026). This is not a contradiction. It is a signal. Expenditure is advancing more rapidly than the capability of leadership. And that gap now is becoming operational. This is not a technology gap. This is a gap in the leadership system. When w

A. Technology and Automation 1 . Recycling technologies today combine process automation, industrial digitalisation, and sustainability-focused engineering. How do these technological areas work together to improve efficiency in recycling plants? Efficiency comes from combining machines with smart software. Process automation (PLC/SCADA) controls physical operations, while digital systems convert sensor data into useful information. Sustainability engineering improves energy