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In this episode of Lead With Data, Rina Gami sits down with Clare Kitching, a senior data leader who works closely with organisations navigating complex data and AI decisions. The conversation explores why many data and AI initiatives stall and not because of tooling gaps.
The conversation unpacks how data teams are navigating generative AI, agentic AI and increasing pressure to move quickly, often without the shared language, context and alignment required to succeed. Clare shares what she is seeing first-hand across organisations grappling with executive expectations, shadow AI, governance concerns and the widening distance between technical teams and business leaders.
This episode focuses on the growing importance of translators in data teams' people who can bridge strategy, technology and value and what leaders should be paying attention to before scaling AI.
In this episode, we cover:
- Why tools alone do not solve AI and data challenges
- The shift from traditional data initiatives to generative AI and agentic-based AI
- The critical role of senior executive sponsorship in AI success
- How translators help close the gap between technical teams and the business
- Practical ways organisations can start or scale AI responsibly
- Evolving roles in data teams, including analytics translators and AI governance specialists
- Common challenges such as shadow AI, data quality and organisational buy-in
- Indicators that signal true readiness to scale AI
- Why value-led AI initiatives matter more than experimentation
Timestamps
00:00 – AI as a high-return capability in modern data teams
01:32 – Claire’s career journey and move into consulting
03:16 – What has changed in data and AI over the last five years
04:00 – Where organisations are really at in their AI journeys
05:24 – Pressure to move quickly and what gets missed
06:03 – Executive sponsorship and its impact on outcomes
07:30 – Data team structures and emerging gaps
08:53 – Organisational barriers to AI adoption
10:38 – Shadow AI and responsible use
12:34 – Initial assessment points when working with organisations
14:47 – Asking the right questions to align AI to value
17:11 – Why communication is now a core data capability
19:09 – Telling the data story in a way the business understands
22:18 – Structuring data teams for sustainable AI outcomes
25:26 – Evolving roles across data, engineering and governance
28:11 – Everyday AI, generative AI and agent AI explained
32:21 – Leadership priorities when advancing AI initiatives
33:49 – Signs an organisation is ready to scale AI responsibly
35:11 – Maintaining momentum without creating risk
36:08 – Hiring for AI capability beyond technical skills
38:25 – When and why to involve external expertise
41:17 – The value of an external perspective
44:48 – Avoiding stagnation in AI programs
45:27 – Communication and storytelling as critical capabilities
46:16 – Leadership alignment and organisational readiness
46:44 – Final reflections and next steps
Resources & Links- Experian Data Studio – supporting stronger data governance and trust
- Claire Kitching Linkedin Website - cambiq.com.au
