Operationalising AI for Business Teams
AI is moving fast out of engineering and into finance, marketing, people ops and customer-facing teams. In this KiwiSaaS webinar, David Clearwater recaps the sharpest ideas from a half-day Auckland workshop with 20+ leaders, focused on what it really takes to operationalise AI across a SaaS business.
Drawing on examples from DataTorque, Serko, AutoHive, Sharesies, Cogo and others, the session looks beyond the tools themselves and into the leadership patterns, team behaviours and practical systems that help AI adoption stick.
Key themes:
What’s actually working across SaaS teams?
We explore how AI is already being used across customer support, sales, product, finance and operations. At Sharesies, automation has helped customer support people move into higher-value subject matter expert roles as simpler work becomes easier to handle. At Cogo, AI agents are supporting a model where one salesperson can manage more market activity with overnight research, recommendations and analysis. The shift is not just about doing the same work faster. It is changing what teams can attempt, how roles evolve and how quickly companies can test new ideas.
Why tools alone are not enough
A key lesson from DataTorque’s AI transition was that making tools available did not automatically create behaviour change. The team saw pockets of enthusiasm, some resistance and plenty of old habits. What helped was creating space for people to learn, making experimentation feel fun, supporting curious internal champions, pairing people up, and using hackathons to build confidence.
Serko’s experience showed that AI quickly exposes unclear or poorly designed business processes. If a team cannot clearly describe what goes in, what happens, what comes out, and how success is measured, it becomes much harder to automate that work well.
AutoHive’s lesson was about trust and testing. JD Trask’s advice, as summarised in the webinar, was that business teams need their own version of ‘fitness tests’ — clear ways to know whether an AI-assisted output is good enough. Just like software teams test code, business teams need to test workflows, outputs and decisions.
Building momentum across the organisation
David emphasised that AI adoption is a leadership and culture challenge as much as a technology one. Teams need permission to experiment, but they also need guardrails around data, security, review and production use.
The strongest organisations are not trying to control every AI experiment from the centre. Instead, they are creating a ‘digestion system’ for AI ideas: a way for people to test things, get support, validate quality, involve product or engineering where needed, and turn the best ideas into useful internal tools or workflows.
Practical first moves for leaders
Leaders are encouraged to start with real work, not abstract AI strategy. Look for painful processes, repetitive tasks, old roadmap ideas, or work that has always felt too slow or too expensive to attempt. David’s own advice was simple: spend regular time using AI on your own work, refine the process, and build trust through repeated use. Once people have their own ‘oh wow’ moment — where AI helps them make something genuinely useful — adoption starts to feel much more real.
Key takeaways:
Start with business pain. Find the slow, frustrating or repetitive work that teams already want to improve.
Create space for curiosity. The fastest adopters are often not the most technical people, but the ones willing to experiment and learn.
Build capability, not just access. Tool rollout needs to be supported with coaching, peer learning, hack days and internal champions.
Make processes clear before automating them. AI works best when the team understands the workflow, inputs, outputs and measures of success.
Create trust through testing. Define what ‘good’ looks like, review AI outputs, and keep improving the loop.
Give internal AI work a pathway. Make someone responsible for helping useful experiments move safely from idea to production.
Watch the full discussion below to explore how KiwiSaaS companies are embedding AI across their organisations — and what leaders can do next to bring the whole business along.