Why This Is the Year of Post-Sales AI - and How to Use It Before Your Competitors Do

Right now, AI is the word on everyone’s lips. Most SaaS execs have seen it pitched in the boardroom, promised in a vendor demo, or debated in strategy sessions. But here’s what’s surprising: for all the hype, most companies - especially in Australia and New Zealand - are still just scratching the surface of what AI can actually deliver in post-sales and customer success.

It’s not a question of if AI will impact your post-sales function. The real question is how you’ll use it to drive value before everyone else figures it out.

AI in Post-Sales: Beyond the Hype

Let’s cut through the buzzwords. AI’s biggest impact isn’t coming for your team’s jobs. It’s coming for the old habits and legacy bottlenecks that hold your CS team back.

Here are three ways the best SaaS firms are already putting AI to work right now:

1. Onboarding That Adapts to Every Customer: No more one-size-fits-all training emails. AI can map out exactly what new users need (and when), trigger timely support, and highlight areas where clients are getting stuck - before they even raise a ticket.

2. Predicting Customer Health Before It’s Too Late: Forget “health scores” that lag behind reality. Modern AI surfaces risk factors—like sudden drops in usage or subtle shifts in sentiment - weeks before they show up in a dashboard. That gives your CS team a fighting chance to act before churn is locked in.

3. Smarter Expansion Plays: AI can crunch usage data, identify upsell triggers, and even recommend the right moment (and message) for your team to approach clients about new features or upgrades. It’s like having a strategist in your pocket - at scale.

The ANZ Trust Gap: Why Local Context Matters

If you’re operating in Australia or New Zealand, you know there’s still a healthy dose of scepticism around AI. And honestly, with good reason.

Most AI solutions are built overseas and parachuted in without local nuance. We’ve all seen generative AI make a mess of Aussie slang or misinterpret industry specifics. Then there’s the issue of data privacy - nobody wants to be the headline in the next “AI gone wrong” case study.

If you want your post-sales AI projects to land, here’s what to watch for:

• Make sure your AI can handle local language and context, not just US templates.

• Be upfront about how customer data is used - transparency is now table stakes.

• Put clear governance around your AI tools, especially where sensitive data or decision-making is involved.

Where to Start: A No-Nonsense AI Checklist

You don’t need a huge budget or a dedicated data science team to get going. Here’s how execs are making real progress right now:

1. Start with one process: Pick a high-impact, repeatable area - onboarding, support triage, or renewal risk detection.

2. Map the journey: Document each step in your current process. Where do delays, drop-offs, or manual errors happen?

3. Pilot a simple AI tool: Look for something with a free trial or low entry cost. Focus on real outcomes, not shiny features.

4. Track what changes: Measure time saved, customer feedback, or churn signals. If it works, scale up—if not, move on.

5. Keep your team in the loop: Bring your CS and support leads into every step. The best results come when AI makes their jobs easier, not harder.

The Bottom Line

It’s not too late - most ANZ SaaS firms are just getting started. The real edge isn’t being first to market with AI, but being the first to actually get value from it in post-sales.

So, over to you:

What’s your AI “win” or your biggest frustration so far? Are you seeing results in CS, onboarding, or support - or still figuring out where to begin?

Drop your experience (or your toughest challenge) in the comments. Let’s help each other move past the hype and get to what really works.