Using AI to revolutionise your QBR prep - how CS can save a day per week

For those of us in CS roles, there can be a certain sense of dread when quarterly business review season approaches. Endless hours spent trawling spreadsheets, formatting slides, chasing usage statistics and trying to surface meaningful insights from scattered data. If you're anything like me, formatting slides alone can be half a day's work!

What if you could reduce that prep time by nearly 80 per cent, while delivering more personalised, strategic QBRs?

That is exactly where AI comes into play, and can having meaningful impact almost straight away.

Step 1: audit your current QBR process

Before deploying any AI tool, map out your current process in detail. For a typical manual QBR, your workflow might resemble this:

That adds up to a saving of around 23 hours per QBR. Repeat that across your portfolio and the return on investment becomes clear.

But the first step is not about jumping straight to AI, its about taking time to observe and understand your current process. Ask questions such as:

• Where do most bottlenecks occur?
• Which data sources prove most time-consuming to consolidate?
• Which insights feel superficial and which drive real action?

This diagnostic phase ensures that your AI implementation is laser-focused on real problems rather than hype and random analysis that serves no purpose.

Step 2: choose the right AI tools for QBR impact

Once you understand your process, it is time to evaluate tools based on impact, not just feature lists. Here is a comparison of some leading platforms that deliver measurable value (obviously you are likely to be limited by the solutions your company has in place, but the majority of CS tools now have some level of AI capability):

Your priority should be platforms that integrate seamlessly with your CRM, analytics and support tools via API. Siloed, standalone or “shiny toy” solutions often end up collecting dust, so integration-readiness is a crucial deciding factor. You can also of course use the AI insights from your CS system and then further build out those insights using consumer analysis tools such as Perplexity, Claude, or ChatGPT.

Step 3: define what success looks like

This should be obvious to us as CS professionals, however once tools are deployed, use a metrics-first approach to measure success. Consider tracking:

• hours saved per QBR cycle
• depth and personalisation of insights
• levels of client engagement and feedback
• business impact measured by renewals, upsells and NPS movement

You can then use this data to build business cases if you need to for further investment into AI solutions or enablement. Money talks, as does efficiency and margin improvement.

Step 4: watch out for common pitfalls

AI adoption is not without its challenges, be mindful of these risks when you're putting together QBR materials, as your clients WILL notice:

Poor data quality If your underlying data is inaccurate or inconsistent, your AI outputs will be too, garbage in, garbage out.

Low adoption Tools do not use themselves. You need to foster trust, offer training/enablement and highlight positive use cases to ensure team uptake.

Integration headaches Avoid solutions that operate in black-box modes. Prioritise API-friendly platforms that work within your existing tech stack. Ensure that your IT team or whoever is responsible for integrations is involved early on, as getting their buy in and resources will be essential.

The best way to overcome these issues is to start small: automate one part of your QBR process first, gather feedback, iterate and then expand.

Step 5: get started with these practical tips

To begin your AI for QBR journey, follow these steps:

1. launch a pilot—select one account and run a fully AI-augmented QBR.
2. appoint an AI champion—someone who tests the tool, develops best practices and evangelises its use.
3. create reusable templates—automate repetitive slides and narrative text.
4. track your metrics—closely monitor hours saved, insight quality and client feedback.
5. preserve the human element—AI should support your strategic thinking, not replace it.

These steps build confidence, uncover tangible wins and fuel momentum for broader adoption.

AI will not run your QBRs for you, and if it does, your QBRs will cease being valuable to your clients. But it can take care of the heavy lifting so you can focus on what really matters, strategic conversations, deeper insights and delivering value that drives impact.