Hamish Blank

It has been a challenging, rewarding and incredibly intense 11 months since we kicked off the Enterprise Contact Centre program. As of yesterday, after an enormous effort from our teams and our partners at AWS, ANZ’s new customer contact centre platform is live in our biggest market, Australia. Over the past few years, much of my focus has been on learning and applying AI at enterprise, from building Ensayo accelerate software delivery, to ZorroAI, taking AI to bankers and real business workflows. Then, 11 months ago, my focus shifted. Not away from AI, but towards something I increasingly believe is essential to making AI useful at enterprise scale: the foundation on which it operates. Everyone would agree we are still at the beginning of the AI journey. Models, agentic capabilities and the broader ecosystem will continue to evolve rapidly. And particularly in financial services, the challenge is much bigger than choosing the best model. AI needs an environment where customer conversations, data, banker knowledge, workflows and systems can be end to end connected safely and reliably, supported by strong engineering, security, resilience and governance. That is why, looking back, I still believe my decision last year to join this program and lead its technical implementation was the right battle to fight. A contact centre is becoming much more than a call centre. What we have built is not simply about replacing one contact-centre technology with another. It creates a foundation from which we can progressively rethink how customers interact with the bank. Over time, AI can increasingly understand customer intent, bring the right knowledge into conversations, assist bankers, automate workflows across banking systems and ultimately resolve more customer needs directly. The important question therefore becomes less: “Which AI model should we use?” and more: “How do we build an enterprise platform that can continuously take advantage of the AI capabilities of tomorrow?” Getting here required an extraordinary effort across engineering, architecture, operations, security, risk, business teams and our technology partners. I am incredibly proud of the team and grateful for the partnership and commitment from AWS. Yesterday wasn’t the finish line. We will continue landing new contact-centre capabilities across our International Institutional businesses, New Zealand Retail and for also for our employees support. And for me, this milestone also marks another transition. My gear is shifting back towards AI. Ensayo taught me about applying AI to engineering. ZorroAI extended that into bankers. Building customer contact centre has added another piece: creating the enterprise foundation through which AI can ultimately reach customers, bankers at scale! The platform is live. Now the AI journey continues.

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ASIC report 779 isn't just a governance problem. It's a data and interpretation problem. Return objectives sit in PDS documents. Performance data sits in platform feeds. Comprehensive benchmark data is cost-prohibitive for most to access. And the qualitative research context that sits behind a rating, is captured in a ratings report or held in analyst notes and institutional memory. Nobody has joined these things up...yet. A significant portion of PDS objectives we've reviewed don't include a measurable timeframe. A benchmark might be named without a fixed measurement window - "over the longer term," for example, doesn't translate directly into a monitoring rule. And even where timeframes exist, what counts as persistent underperformance? Bridging that gap requires research judgment, not just data. As part of FE fundinfo Group, Zenith Investment Partners sits at an unusual intersection as an investment research house, portfolio manager and a data business all in one. Our investment research capability means we can advocate for investment managers to establish executable objectives, and where that's not possible, determine an appropriate analytical standard to apply. Our data infrastructure makes it monitorable at scale. And our experience building solutions across the Australian financial services industry means we understand what the output needs to look like for advisers, licensees and platforms. We're building a reporting solution for our managed accounts clients to do exactly this, and we suspect the same solution is needed across advice APLs and platform governance processes more broadly. I would love to connect if you're working through this currently or would be interested in a conversation about what we're building. #ManagedAccounts #Report779 #InvestmentResearch

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