A new case study highlights how HSP Gruppe, a tax advisory business, is building usable AI capability rather than running experiments that never make it into day to day work. The focus is on practical productivity, better work quality, and freeing capacity to support client service.
What the firm implemented
The company built its AI capability around ChatGPT Enterprise, with an emphasis on using the tool to support tax advisory tasks and client work. The reported goals are to increase productivity, strengthen the quality of delivered work, and generate additional capacity for the team.
How to apply this approach in your business
Use a clear operational purpose. Identify where AI will reduce effort or improve consistency, then connect it to real workflows that your advisors, analysts, or customer facing teams already run.
For business leaders looking to adopt AI in operations, the main takeaway is the workflow mindset. Instead of positioning AI as a standalone assistant, the case describes capability building aimed at day to day advisory and client service. That means measuring outcomes that matter for ROI, such as time saved per task, consistency of outputs, and whether additional capacity can be redirected to higher value client work.
What to do next if you are evaluating AI for client work
- Map your most time consuming customer and internal tasks, then shortlist those where improved drafting, summarisation, or task support could reduce manual effort
- Pilot with a specific outcome in mind, for example faster turnaround or more consistent work product, then expand only if quality holds
- Train teams on how to use AI output safely, including when to verify facts and when human review remains required
- Track operational metrics during the rollout, productivity per task and work quality indicators, so you can link adoption to measurable impact
If you are planning an AI rollout, this example supports a straightforward operational path. Start with enterprise grade access, build around concrete advisory and client workflows, and focus on productivity, quality, and capacity rather than novelty.