Expert-led webinars

Procurement is where the business case for AI is easiest to quantify. Every invoice that passes through a manual process has a measurable cost: time to register, time to match, time to approve, time to pay. The maturity model we use in this session maps the journey from that starting point through managed systems to full end-to-end AP automation, and is honest about what each stage requires to function reliably. The live demonstrations cover two practical workflows. The first uses Copilot Studio integrated with Business Central to process incoming invoices through OCR scanning and automatic registration. The second goes further: a WhatsApp-based agent that receives an invoice, extracts the relevant data, creates the vendor record if it does not exist, and registers the document -- without a human touching the system. Both are production-ready patterns, not prototypes. The misconception we address directly in this session is that AI implementation requires perfect data and complete system alignment before you can start. It does not. What it requires is a clearly scoped workflow, a reliable data source for that workflow, and defined rules for exceptions. Start there, prove the value, then expand. The session closes with an overview of the one-day AI Readiness Workshop, which helps procurement and finance teams build their agentic backlog and execution plan from a standing start.

Field service is one of the clearest examples of where the gap between early movers and late adopters will compound fastest. A technician who can query, create, and update work orders through a voice interface while standing in front of a machine is not a marginal improvement over one carrying a clipboard -- it is a fundamentally different operating model. This session maps the progression from manual, paper-based operations through managed systems to full automation, and shows what each stage actually requires to function reliably. The demonstrations cover building work order management systems on Microsoft Power Platform, creating AI agents that interact with operational data through natural language, and implementing voice-enabled interfaces that work in the conditions field technicians actually operate in. The distinction between Copilot and true autonomous agents matters here more than in most departments. A Copilot assists the technician. An agent acts on the work order independently, within defined boundaries, without waiting for a human to initiate each step. Getting that boundary right is an architectural decision, not a configuration choice. The broader point is about compounding. Organisations that build the foundational systems now -- structured work order data, integrated scheduling, documented exception handling -- will compound their learning and value creation over the next three years. Those that wait for a complete solution before starting will find the gap has widened considerably by the time they begin.

The mistake most customer service leaders make is waiting for perfect data, a complete knowledge base, and a fully integrated ticketing system before touching AI. The reality is that you can start benefiting from agentic technology before the foundations are complete -- as long as you understand what the agent can and cannot do at each stage of readiness. This session maps the progression from manual customer service to fully AI-operated processes, and is honest about what each stage requires. The foundational elements are not optional: a structured ticketing system, a maintained knowledge base, and clear escalation rules. Without these, an agent cannot resolve a ticket consistently. With them, the same agent handles the majority of routine queries without human intervention. We also address the misconceptions that slow organisations down: that AI implementation is straightforward and fast, or conversely that everything must be perfect before you start. Neither is true. The right question is not "are we ready" but "what are we ready for right now, and what do we build next." The session includes a live demonstration of AI automating service processes at different levels of readiness, giving teams a realistic picture of what is achievable in the near term and what requires investment first.