For many organisations, Generative AI began with a chatbot. It was a useful introduction, but it barely hints at the technology's business potential. The greater value appears when AI is built into the way work actually gets done.
Five areas worth attention
Knowledge management. Policies, contracts and project history often sit across disconnected folders. Generative AI can answer questions from that material in plain language and point to the original document.
Document processing. Teams can summarise tenders, compare contract clauses or extract key terms from supplier agreements in minutes, leaving people to review the judgement calls.
Business intelligence. A manager can ask, "Why did margin fall in this region?" and receive a clear explanation alongside the dashboard, without waiting for a new report.
Customer experience. Service teams can receive a case summary and a suggested response drawn from approved content, improving both speed and consistency.
Operational productivity. Drafting reports, preparing meeting notes and creating first versions of procedures can all take less effort.
People are already there
Microsoft's 2026 Work Trend Index analysed more than 100,000 Microsoft 365 Copilot conversations and found that 49% involved cognitive work such as analysis and problem-solving. Among the 20,000 AI users it surveyed, 66% said AI had let them spend more time on high-value work.
Yet individual gains do not automatically become business results. Gartner predicted in 2024 that at least 30% of Generative AI projects would be abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs or unclear business value.
Making it stick
Start with a specific business problem and a baseline you can measure. Ground the AI in governed company data. Decide where human review is mandatory. Most importantly, redesign the process around the tool, so the time saved is put to good use.