What it is

The Better Business Summit 2026 is a national roadshow for mortgage and finance professionals, touring major capitals including Brisbane, Adelaide, Perth and Sydney. The agenda is unapologetically commercial: how to scale from a sole-principal or small shop into a systematised brokerage. A central thread is applied AI—brought into sharp relief by live demonstrations from Bluewire Media marketing and AI coach Adam Franklin—connecting front‑end marketing with back‑office productivity and compliance. With coverage across Broker Daily and The Adviser, the event is positioned as both capability building and competitive benchmarking for the industry.

Why now

Three forces make timing critical. First, competition is heating up across residential, commercial and specialist finance segments, squeezing margins and elevating the importance of differentiated customer experience. Second, digital acquisition remains shaped by platform dynamics: the ACCC reports Google holds nearly 94% share of general search in Australia (as at August 2024), underscoring how discoverability and paid performance are gate‑kept by a small number of channels. Third, governance expectations for AI are rising. Australia’s AI Ethics Principles (2019) already provide a responsible framework, and the Australian Government’s 2024 consultation has sharpened focus on the oversight of general‑purpose AI. The Australian Taxation Office’s own governance work on AI signals how large institutions are formalising controls—an approach brokers will be expected to echo as they scale.

Layer in macro sentiment: while not a direct predictor of housing demand, recent US consumer confidence data shows fewer consumers expect business conditions to worsen (21.8%, down from 25.8%), a modest indicator that decision‑making environments may be stabilising. For brokerages, that points to a window to invest in capability before the next upswing in enquiry volume.

How it works: the AI-enabled brokerage stack

Think in four layers—Acquire, Verify, Advise, Comply—and design for data flow across each:

- Acquire: Performance marketing and SEO still ride on search monopolies; brokers need high‑signal lead capture. Practical tooling includes AI‑assisted creative testing, predictive lead scoring, and conversation intelligence that mines call transcripts for intent and objections. With Google’s dominance, rigorous cost‑per‑acquisition discipline is non‑negotiable.

- Verify: Automate document and income verification using OCR and document AI to classify payslips, bank statements and IDs; use bank transaction categorisation to detect undisclosed liabilities. LLMs fine‑tuned for financial documents can summarise affordability narratives for credit teams.

- Advise: Recommendation engines can map client goals to product shortlists, while AI‑assisted scenario modelling explains trade‑offs (e.g., fixed vs variable, offset utilisation). The broker remains accountable; AI elevates throughput and consistency.

- Comply: Embed policy libraries and AML/CTF checkpoints in workflows. A risk & controls matrix—bias tests, audit logs, human‑in‑the‑loop approvals—aligns with Australia’s AI Ethics Principles and mirrors the governance stance emerging from public agencies like the ATO.

Technically, the imperative is data plumbing: unify CRM, marketing platforms, document systems and aggregator tools via APIs; capture ground‑truth outcomes (approved/declined, settlement time) to train models on what actually predicts conversion and quality.

Who it affects

- Brokers and principals: Productivity leverage and marketing efficiency determine whether you can profitably handle more files per head without service decay.

- Aggregators: Opportunity to standardise AI toolkits, compliance guardrails and data schemas across member firms, improving lender relationships and portfolio quality.

- Lenders: Expect cleaner files, faster time‑to‑yes, and better fact‑finds; reciprocate with quicker SLAs and API‑first interactions.

- Tech vendors: Demand is shifting from point features to integrated outcomes (lead quality, time‑to‑credit, compliance exception rate). Vendors that prove measurable deltas will win.

- Regulators and consumers: Better documentation and explainability raise trust; governance shortcuts will be penalised by both.

Business impact: a practical ROI playbook

Early adopters should model ROI along three vectors:

- Funnel quality: Use lead scoring to prioritise high‑intent prospects and route appropriately skilled brokers. Tie model performance to conversion, churn and net promoter outcomes.

- Cost‑to‑serve: Time studies typically reveal document handling and rework as the biggest drags. Automating classification and checks reduces touchpoints; measure minutes saved per file and redeploy capacity to advice.

- Risk & rework: Policy‑aware prompts and on‑form validations lower missing‑info rates and lender kickbacks. Track exception rates and settlement slippage weekly.

Proof beats promise. Stand up a 12‑week pilot with clear baselines (conversion, turnaround, cost‑per‑lead, exception rate), then expand. A single‑source dashboard and an experimentation cadence (A/B creatives, model thresholds) keep gains compounding.

Market context and competitive landscape

The Summit’s multi‑city format signals a capability race. Trade coverage has highlighted the widening gap between brokers who adapt quickly and those who don’t—a function of AI readiness, changing borrower behaviour and rising competition. Thematically, this is a classic consolidation cycle: firms that standardise processes and harness data create scale economics; others remain trapped in artisanal operations with brittle capacity. Australia’s broader AI landscape still shows a commercialisation gap (noted in 2025 analysis of the ecosystem), meaning brokerage leaders should expect to assemble solutions from a mix of global platforms and local specialists rather than wait for a single local champion.

Customer acquisition remains a choke point. With the ACCC confirming Google’s near‑total search share, organic presence, ratings management and smart paid strategies are table stakes. The broker brand has to perform in a world where gatekeepers own discovery—AI can’t fix weak positioning, but it can multiply well‑positioned propositions.

Implementation reality: governance, data and change

- Governance: Treat the AI Ethics Principles as design criteria—fairness, privacy, transparency—and align with emerging public‑sector governance patterns (e.g., clear risk tiering for general‑purpose AI, human oversight, audit trails).

- Data: Clean in, clean out. Create a canonical client profile; tag outcomes meticulously; negotiate data portability with vendors to avoid lock‑in.

- Build vs buy: Buy for commodity capabilities (OCR, transcription, analytics); build or co‑develop where your process IP creates defensible advantage (lead qualification heuristics, advice workflows).

- People: Upskill brokers on prompt discipline, policy‑aware advice, and tool fluency. Incentives should reward documentation quality and cycle time, not just volumes.

What’s next

Over the next 12 months, expect pilots to move into production with formal model risk management and vendor scorecards. Aggregators will curate shortlists of compliant tools and push for API‑native integrations with lenders. Vendor consolidation is likely as buyers demand outcome‑based pricing and referenceable results. Over 24 months, the AI commercialisation gap should narrow via partnerships between local fintechs and global AI providers, with Australian governance expectations shaping exportable compliance features. For leaders, the strategic posture is clear: codify your operating system, wire it with data, and use AI to compound learning loops. The Summit offers a timely proving ground—what you take back to the office will decide whether you scale, stall or get subsumed.