What is AI-assisted decision making?

It combines operational evidence, AI and quantitative models to help people compare options and consequences. Accountable humans decide; systems can then route approved actions and record outcomes.

What does critical operations mean?

Operations where a poor decision carries a material cost: manufacturing, logistics, agriculture, supply chain, utilities and mining. It describes the consequence of a decision, not a legal critical-infrastructure classification.

How do forecasting, simulation and optimisation differ?

Forecasting estimates what is likely to happen. Simulation explores what could happen under different assumptions. Optimisation compares feasible options against defined objectives and constraints. All depend on assumptions and should expose uncertainty.

What is decision intelligence?

Decision intelligence connects data, models, decision processes and feedback. Beyond Data applies these ideas through AI-assisted decision making for critical operations, with human accountability and traceable evidence.

beyond data

Case Studies

The decisions behind the outcomes.

Explore the decision before, the intelligence added, what changed for the people making the call, and the outcomes reported by our clients.

Impact Spotlight

Partner: Tier-2 Financial Services Operator

“The prediction engine has fundamentally changed how we operate our business.”

Managing Director

Brisbane

Decision before: Spreadsheet analytics were used to prioritise sales targets.
Intelligence added: A prediction engine identified leads most likely to convert.
Decision after: Human effort was reallocated to those leads, with managers freed to train their teams.

Reported outcomes

Human effort re-allocated to leads most likely to convert — 40% YoY revenue increase
Staff morale boost from a more effective, less grinding sales process
95% reduction in time spent on spreadsheet analytics to prioritise targets
Managers freed up to train their teams more deeply

Solutions this case study proves

Sales Prioritisation EngineRead about this solution

Partner: National Mining Operator

“We had a bunch of reports, but nothing really proactive, and nothing connected. Now we just ask anything we need to know, and bang — the right answer just… appears.”

Chief Operating Officer

Queensland

Decision before: Reports were disconnected and did not give the operator a proactive view.
Intelligence added: Connected evidence made asset ownership costs and maintenance needs visible.
Decision after: Asset purchasing and spare-parts decisions could draw on maintenance needs and total cost of ownership.

Reported outcomes

15% reduction in overstocked spare parts
Risk of incorrect asset purchasing decisions significantly mitigated with clear total cost of ownership
Cashflow forecast reliability boosted with visibility of true maintenance needs beyond OEM schedules

Solutions this case study proves

Predictive Maintenance & Asset IntelligenceRead about this solution

Partner: Due North

“It's incredible… we're looking at up to 30% reduction in wasted click spend for our customers.”

Ben Hirons

Founder & CEO, Melbourne

Decision before: Irrelevant search terms and declining campaign profitability consumed advertising spend.
Intelligence added: AI monitoring surfaced campaign and keyword insights to ad managers.
Decision after: Ad managers could identify wasted spend and intervene when profitability trended down.

Reported outcomes

AI monitoring of Google Ad campaigns and keywords, surfacing insights directly to ad managers
Automatic identification of up to 30% of wasted ad spend on irrelevant search terms
Early detection of ads trending down in profitability — intervention before the loss lands

Solutions this case study proves

Marketing Spend OptimisationRead about this solution

Partner: Multi-site Retailer

“Our operational leaders always had unique analytical questions because they're always pushing the boundaries of what's possible. Now they can get fast, accurate answers in minutes.”

Chief Financial Officer

Brisbane

Decision before: Operational leaders had analytical questions spanning multiple parts of the business.
Intelligence added: An intelligence layer connected operational, sales, financial and payroll data across 29 sites.
Decision after: Non-technical leaders could ask questions and identify contracts eligible for cancellation and revenue opportunities.

Reported outcomes

Whole-of-business intelligence layer connecting operational, sales, financial and payroll data across 29 sites
AI-assisted analytics so non-technical leaders can ask natural questions and get accurate answers in minutes
Identified ongoing costly contracts eligible for cancellation and revenue improvement opportunities — without needing an analyst

Solutions this case study proves

Ask Your BusinessRead about this solution

01 / 04

Start with one consequential decision.

Book a workshop

Cookie preferences

We use essential cookies to run the site. With your permission, we also use analytics cookies to understand what content helps visitors make better data and AI decisions. Read our privacy policy.