The AI Corner

Join host Mike Bayly and Nic Cox, principal data engineer at Sharesies, for a look inside a company putting AI to work across its teams.
Nic shares what getting data ready for AI actually involves, why he built a monitoring tool in 48 hours, and how an agent now investigates overnight data failures before he wakes up. They discuss the Sharesies “brain”, how staff are taking on work outside their traditional roles, and the questions this raises about oversight, junior talent and costs.
In this episode we discuss:
Getting business data ready for AI.
Building custom tools instead of buying more software.
Giving agents work while keeping human approval.
Capturing team knowledge for people and agents.
Training junior engineers when AI does more.
Managing experimentation without unexpected bills.
NIC’S LINKEDIN:https://www.linkedin.com/in/nic-cox-data-engineering/
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Join host Mike Bayly and guest Andrew Wierda, founder of Auckland-based AI startup Nylon and former EY tax lawyer, to explore how a Kiwi company is transforming tax and legal research. Backed by Blackbird, Nylon is now used by more than 2,000 firms across New Zealand, Australia and the UK, including two of the Big Four.
Andrew shares what sets Nylon apart from tools like ChatGPT and Claude, why accuracy and trust are critical in professional services, and how the company combines frontier and open-source AI models to deliver reliable research.
He also explores why building great AI products goes beyond the underlying models, how AI is changing the skills businesses hire for, and his vision for the future of trusted advisors.
In this episode we discuss:
Andrew's journey from EY tax lawyer to AI startup founder

How Nylon grew to more than 2,000 firms across three countries

Raising investment from Blackbird and scaling internationally

Why specialised AI can outperform general-purpose models in legal and tax research

Building trust through accuracy, citations and confidence scores

How Nylon complements tools like ChatGPT, Claude and Harvey

Protecting sensitive client data in AI-powered professional services

Combining open-source models with frontier AI models

Why the AI harness and verification layer matter as much as the model itself

The challenges of benchmarking AI accuracy and preventing regressions

Why training proprietary AI models may not make sense for startups just yet

Building defensible AI businesses as foundational models continue to improve

Hiring for problem-solving, pattern recognition and learning agility

Why AI will fundamentally change how businesses hire and the skills they look for

The future of accountants, lawyers and trusted advisors in an AI-powered world

Nylon's plans for international expansion and deeper product integrations

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Join host Mike Bayly and guest Natalie Kim, founder of Inflection Group and former VP Legal and AI Transformation Lead at Omnidian, for a practical look at what it actually takes to drive AI adoption across an organisation. After 18 years in the US working across companies including Apple, Cisco and Alphabet-backed Intrinsic, Natalie has returned to New Zealand with a unique perspective spanning law, technology and enterprise AI transformation.
Natalie shares how she led an organisation-wide AI rollout from the legal seat, why simply giving people AI tools and training rarely changes how work gets done, and the four pillars she believes need to move together: culture, process, governance and technology.
You’ll hear why AI transformation needs to be cross-functional, why enthusiastic internal champions still need the right mandate and support, and how organisations can move beyond individual AI use towards redesigned team workflows. Natalie also explains why lawyers have an opportunity to become the “judgment architects” of AI-enabled organisations, helping determine where human oversight matters and what can safely be automated.
In this episode we discuss:
• Natalie’s journey from New Zealand to Silicon Valley and back again
• Leading enterprise AI transformation from the legal function
• The four pillars of AI adoption: culture, process, governance and technology
• Why rolling out AI tools and training alone isn’t enough
• Creating guardrails that encourage safe experimentation
• Moving from individual AI use to team-wide workflows
• Why AI transformation needs cross-functional leadership
• Giving internal AI champions the right mandate and support
• Measuring AI adoption, efficiency and business impact
• The similarities between AI adoption in New Zealand and the US
• Why lawyers are well positioned to become AI “judgment architects”
• Deciding where human judgement should remain in AI-enabled workflows
• Natalie’s work with Anthropic and Claude for Legal
NATALIE’S LINKEDIN:
https://www.linkedin.com/in/nekim1/
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Join host Mike Bayly and guest Jackson Lee from Air New Zealand for an inside look at how one of New Zealand’s most recognised businesses is approaching AI engineering. Jackson shares how Air New Zealand embeds AI engineers directly into business teams, why engineers are spending less time writing code and more time reviewing, architecting and applying judgement, and how the airline thinks about getting AI safely into production.
You’ll hear why strong software engineering fundamentals still matter in an age of coding agents, how Air New Zealand uses eval-driven development and domain experts to test AI systems, and why communication, influence and business understanding are becoming increasingly important skills for the next generation of AI engineers.
In this episode we discuss:
• Air New Zealand’s approach to AI adoption
• Embedding AI engineers directly into business teams
• Moving beyond AI training towards redesigning how work gets done
• Governance across security, privacy, legal and digital integrity
• How coding agents are changing software engineering
• Why engineers are shifting from writing code to reviewing and architecting
• What AI means for junior engineers and the traditional apprenticeship model
• Why software engineering fundamentals still matter
• Eval-driven development for production AI systems
• Building golden datasets with business domain experts
• How Air New Zealand assesses and hires AI engineers
• Why communication and influence now matter alongside technical ability
JACKSON’S LINKEDIN:
https://www.linkedin.com/in/jackson-lee-372338a3/
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Join host Mike Bayly and guest Carin Hercock, Country Manager NZ at Ipsos, to explore what the June AI Monitor study of 32 countries and 1,000 New Zealanders reveals about a country that keeps learning more about AI and keeps getting less excited about it. You'll hear why 65% of Kiwis report nervousness against only 40% excitement, why just 21% trust AI outputs enough to skip double-checking them compared with a 31% global average, and why only 35% believe the benefits to society outweigh the environmental cost, one of the lowest results measured anywhere. Plus her view on the Anglosphere cluster that puts New Zealand alongside Canada, Britain and Australia and far from the more enthusiastic markets in Asia and Latin America, the data sovereignty trade-off nobody has connected to the data centre debate, and why government and employers now carry the job of framing AI as something other than a payroll reduction exercise.
In this episode we discuss:
• 00:00 – Intro & guest bio
• 00:45 – How the AI Monitor is run: 32 countries, 1,000 Kiwis, 18 plus
• 01:30 – 65% nervous, 40% excited, 72% claim they understand AI
• 02:00 – "Sceptically optimistic": why understanding rises as excitement falls
• 02:30 – Power used for good or ill, and the time spent checking outputs
• 03:30 – Saving time at work but expecting a worse job market
• 04:00 – Consultancies dropping graduates and government headcount messaging
• 05:00 – The growth-opportunity case the headlines miss
• 06:00 – Framing AI as a leadership failure rather than a technology problem
• 06:53 – Reskilling tasks versus losing whole jobs
• 07:30 – The Anglosphere cluster: NZ, Canada, US, Britain, Australia
• 08:00 – Economic growth potential and ageing populations as explanations
• 09:00 – Leapfrogging: why less legacy infrastructure means faster adoption
• 10:00 – NZ plateaued while Canada, Britain and the US went backwards
• 11:00 – Anthropic's Economic Index: NZ ranked fifth of 121 countries
• 12:00 – Super users snowballing while others feel left behind
• 13:00 – Only 21% trust AI enough to skip double-checking outputs
• 14:00 – Testing and tricking the model, and AI plus human intelligence
• 15:30 – Legacy distrust from earlier models, and knowledge decay worries
• 16:30 – 35% say benefits outweigh environmental cost, against 49% globally
• 17:30 – Data centres, energy draw, and protecting the tourism economy
• 18:30 – The data sovereignty trade-off nobody has joined up
• 19:30 – Only 39% trust companies to protect their data
• 20:30 – Where Kiwis accept AI: research and restaurant orders versus hiring and health
• 22:00 – Disclosure: 85% want AI use declared up front
• 22:30 – The biggest surprise: three flat years against soaring AI valuations
• 24:00 – "We trained the team and gave them the tools, but nothing changed"
• 25:00 – Why individual training alone will not shift an organisation
• 26:30 – Trench or trend: where sentiment goes from here
• 28:00 – Where to find the report and get in touch
• 28:45 – Outro & next steps
CARIN'S LINKEDIN: https://nz.linkedin.com/in/carin-hercock-20118313
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