• What does mean to be data-driven & how does that differ from intelligence?

• Why dashboards and descriptive reports aren’t enough

• Integrating analytics, intelligence and operations into one coherent function.

• Lessons learned from restructuring, building platforms, and changing decision-making culture

• How to structure an intel function

• Where to position it (within investigations? within data?)

• How to get traction and embed with enforcement teams

• Reflections from the ACCC experience trying two different models

• How to set up data and intelligence teams within regulatory agencies

• Including integration with investigation teams, setting up structures, operating models, skill sets needed, and workforce development.

• How do you translate field-based observations into a systemic regulatory approach

• How to bring inspectors, case officers and compliance staff into the design of risk models.

• How can regulators overcome a “captain’s pick” culture and gain trust in model outputs

• Keeping models fresh: building feedback loops, retraining, incorporating qualitative intel.

• Building effective partnerships between the frontline and analytics teams

• What a real regulator data strategy should include (and what to leave out).

• Linking data and analytics directly to regulatory strategy, harms and risk appetite.

• Structuring the operating model: platform, analytics, intelligence and operations.

• Measuring success: from outputs (dashboards) to outcomes (reduced harm / better targeting).

In this fun, interactive session, you’ll meet fellow attendees and speakers in quick two-minute bursts to exchange contacts and lay the    groundwork for deeper connections and collaboration throughout the rest of the event.

• Designing teams that combine analytics, intel and operational understanding.

• Funding, capability, and making the case for investment.

• Exploring the critical capabilities required

• Metrics that show impact including harm reduction and better targeting

• How AI-enabled OSINT technologies can improve intelligence-led investigations in an anti-corruption environment

• The risks associated with the procurement and deployment of AI-enabled OSINT technologies in an anti-corruption environment

• Procurement and governance considerations for the use of AI-enabled OSINT technologies in an anti-corruption environment

Setting up intel function and how it works with your agency?

• How your intel team can get traction, often not well integrated into the enforcement teams

• How data team works with the intel team

New methods for gathering and synthesising data and intelligence

• How do you organise it and get insights out of it?

• How can you do that with a limited budget

• Why are metrics important?

• Lessons for introducing metrics: Building a regulatory KPI from scratch and the behaviour you are trying to change vs the metric you are actually measuring

• What to do when you’ve got established the metric and how to get the best outcomes

• What moves the dial: Identifying drivers of regulatory compliance

• Understanding the elasticity of behaviour of the regulated

• How do you decide where to invest your resources and what drives the metric

• Why VGCCC adopted a risk-based, intelligence-led regulatory model and the transition journey

• Learn how VGCCC restructured people, processes, and technology to transition to an intelligence-led, risk-based regulatory approach.

• Understand the steps taken to close data gaps, create feedback loops, and disseminate actionable intelligence across the organization.

• Shifting people, culture & capability: Lessons from transitioning an existing workforce to risk-responsive ways of working, including capability gaps, cultural resistance, and practical change management OR

• Navigating the cultural challenges of shifting workforce mindset, upskilling staff, and measuring real regulatory impact, beyond compliance metrics

• Hear how the Aged Care Quality and Safety Commission have embedded the use of analytics and intelligence in regulatory decision-making

• Learn from the challenges and opportunities faced by the Commission as they implemented these changes, during a once-in-a-generation reform process

• An overview of the development of the Commission's Regulatory Strategy and operating model, including aligning the use of analytics and intelligence to decisions at the strategic, operational and tactical level

• Understand how analytics and intelligence tools and services have supported the shift from reactive to proactive regulation

• How the ATO leverages its large data repository to identify candidate populations, profile behaviours, and patterns indicative of illegal phoenixing and support frontline enforcement with tactical intelligence.

• Exploring the use of data, analytics, and visualisation to tell the story

• Governing data sharing across the Phoenix Taskforce: navigating legislative constraints, privacy, ethical considerations, and custodianship obligations when supporting state and federal partner agencies.

• How we continue to be future focused to combat illegal phoenix activity.

• Strategic intelligence in practice: Annual environmental scanning to identify sector-wide regulatory risk priorities and inform agency strategy

• Tactical and operational intelligence integration: Tip-off reports and translating intelligence into prioritization, case selection, and regulatory treatment across the organization

• Lessons from ASQA’s maturity journey: What worked, what didn’t, and how other regulators can design an intelligence blueprint at different stages of maturity

• Moving from traditional face-to-face inspections model to using data and intelligence to do compliance work

• Overcoming resistance and the cultural shift required to engage the workforce to adopt these changes

• Reflections and lessons learned from successfully embedding this approach

• What regulators get wrong about hiring?

• How to build capability, and why technical skills matter more than sector experience.

• What is the journey like when building it from scratch?

• How can regulators leverage technology as a tool for improvement vs replacement

• Building a culture of innovation and using AI ethically

• People Process Technology: embedding change in the organisation

• How can we use data effectively to achieve better outcomes

• AI in Regulatory Practice: Real Implementation Case Studies

• Roadblocks to adoption and lessons learned

• How natural language processing was applied to years of inspection and audit reports to identify recurring compliance issues

• How analytics enabled inspectors to move from “gut feel” to data-backed regulatory conversations, improving consistency, confidence, and targeting of regulatory engagement

• Practical lessons for regulators at any data maturity level: What worked, what didn’t, and how agencies with limited analytics capability can start extracting meaningful insights from complaints, claims, and narrative regulatory data

• Moving from tactical, investigation-support intelligence to proactive, ROI-driven compliance targeting

• How regulators decide who to target, where the biggest regulatory return lies

• Translating risk scores and models into frontline action

• Moving beyond operational metrics (inspections, complaints, fines) into outcome-based reporting

• Using data to demonstrate impact (e.g., harm reduction, avoided homelessness, improved consumer protection)

• How to build performance stories that resonate with ministers, media, and the public

• How do we tell our story better?

• How can we navigate enduring threats through preventative action and system level thinking

• What are some of the other indicators we can name and measure over time, where the scale of the problem and threat change as opposed to elimination

• Taking intelligence and data and turning it into targeted

Join your table to reflect on what modern regulation truly requires. Discuss what your agency can start, stop, and scale tomorrow across analytics,  intelligence, governance, infrastructure, strategy, and culture.

• How do we modernise?

• What can your agency do tomorrow

• What we should be doing and what we should stop doing?

• What regulators should stop doing, start doing and scale up now.

• Audience Q&A focused on “what would you do if you were starting again?”

• Each table shares one big shift they expect in the next 3–5 years (AI, ADM, data sharing, workforce, culture).