At Andymus Consulting, we continue to expand our specialist capability to better support clients operating complex, asset‑intensive systems. We’re pleased to welcome Abdulla Toma to the Andymus Consulting specialist team as a Principal Contract Consultant. Abdulla strengthens our advanced engineering analysis and technical assurance offering.

Abdulla brings over two decades of experience delivering high‑value mechanical and structural engineering analysis across the energy, infrastructure, mining, rail and offshore sectors. This reflects our ongoing commitment to providing rigorous, independent, and practical decision support for clients managing risk, life extension and performance optimisation challenges.

Strengthening Our Finite Element Analysis (FEA) Capability

A key area of focus in Abdulla’s work with Andymus Consulting is advanced Finite Element Analysis (FEA) – supporting clients where conventional hand calculations or simplified models are no longer sufficient.

His experience spans a wide range of analytical and assurance activities, including:

• Static and dynamic structural analysis
• Thermal and thermo‑mechanical behaviour
• Fitness‑for‑service and life extension assessments
• Independent technical assurance for complex assets
• FEED and detailed design support

This capability enables Andymus Consulting to support evidence‑based engineering decisions where safety, reliability and capital efficiency are critical – particularly for ageing or highly utilised assets.

Practical, Independent Engineering Judgement

What differentiates Abdulla’s approach is the combination of advanced numerical analysis with sound engineering judgement. His work focuses not just on generating results, but on ensuring those results are interpretable, defensible, and aligned with real‑world operating conditions.

This aligns strongly with Andymus Consulting’s philosophy:

analysis should support better decisions – not just more complex models.

Through Abdulla’s involvement, we are enhancing our ability to support:

• Asset integrity and risk management decisions
• Life extension and continued operation strategies
• Design verification and optimisation
• Independent peer review and assurance

Expanding Specialist Support for Clients

Abdulla joins Andymus Consulting as part of our broader specialist partner model. This enabls us to scale deep technical expertise alongside our core strengths in:

• Engineering strategy and advisory
• Simulation and digital engineering
• Techno‑economic analysis
• Systems thinking and decision support

This model allows clients to access the right depth of expertise at the right time, without sacrificing independence or commercial pragmatism.

Looking Ahead

Over the coming months, we’ll be sharing more content on how we apply FEA and advanced engineering analysis in practical client contexts – including examples, decision frameworks, and guidance on when deeper analysis delivers real value.

We’re excited to have Abdulla as part of the Andymus Consulting specialist team and look forward to the value this expanded capability will bring to our clients.

Interested in learning more?

If you’re dealing with asset integrity challenges, life extension decisions, or complex engineering risks, get in touch to discuss how Andymus Consulting can support your next decision.

The post Welcoming Abdulla Toma to the Andymus Consulting Specialist Team appeared first on Andymus Consulting.

If you’re feeling pressure to “do something with AI”, you’re not alone. But here’s the uncomfortable truth: many organisations don’t need more AI – they need better workflows.

In conversations across mining, oil & gas, infrastructure, and SMEs, I keep seeing the same pattern: teams try to bolt a chatbot onto messy processes, then wonder why it doesn’t stick. The result is often more noise than value – a classic “shiny tool” trap.

A better approach is to start with automation – and then use AI selectively for the parts that genuinely benefit from it. That’s where platforms like n8n, Zapier, and Make earn their keep: they let you turn your business process into a reliable, auditable workflow, and then “drop in” AI for narrow tasks where it adds leverage.

This is also the practical foundation of agentic AI: not a free-roaming bot, but an orchestrated system that can plan, execute, and adapt inside guardrails using tools and approvals.

Please reach out and contact Andymus Consulting to discuss any of your business process automation requirements.

The core problem with a “straight AI” approach

AI is excellent at working with unstructured information: messy text, email threads, PDFs, photos, natural language requests, and fuzzy classification problems.

But AI has two realities that matter in operational environments:

1. It’s grounded in data it has seen (or been given). Traditional ML (and a lot of AI use) depends on historical examples, and you should be cautious about extrapolating outside those limits.
2. It’s not inherently a process. A prompt can generate an answer, but it doesn’t guarantee the right people were notified, the right system was updated, or the right control was applied.

So if your goal is repeatable outcomes – invoices issued, documents filed, approvals recorded, customer records updated, dashboards refreshed – then you need a workflow engine first.

Why automation platforms often beat “AI-first”

Automation platforms give you what organisations actually need day-to-day:

1) Reliability and repeatability

A workflow is the same tomorrow as it is today. You can standardise how work moves through the business, and only vary where it makes sense.

2) Narrow scope and tighter risk control

Instead of giving an AI model broad access to data and decision-making, you can confine AI to specific steps:

• extract key fields from a document,
• draft a first-pass email,
• categorise incoming requests,
• summarise a meeting transcript,
• detect duplicates or anomalies.

Everything else remains deterministic: create the record, route approvals, store files, update systems.

3) Better governance: constraints, verification, approvals

Good workflows can include:

• constraint checks,
• verification steps,
• audit trails,
• role-based access,
• and human-in-the-loop approvals for higher-risk actions.

That matters even more when you’re working in regulated or safety-critical environments, or when IP and customer confidentiality are key concerns.

4) Faster time-to-value

You don’t need a full AI program to start. You can automate a process in days, and then iterate.

Where n8n, Zapier, and Make fit (and how they differ)

All three platforms connect systems together (apps, databases, email, forms, CRMs, accounting platforms, websites) and let you build workflows that trigger events and take actions.

The difference is in control, flexibility, and usability:

n8n (control + flexibility)

n8n is often the best fit when you want:

• greater flexibility,
• deeper customisation,
• and the option to host it yourself – which can matter when you want to reduce data movement to third-party platforms.

In practical terms, n8n tends to suit teams who want a “workflow backbone” they can extend.

Zapier (speed + simplicity)

Zapier is designed to be simple and fast to adopt. It’s very approachable for business users and is great when:

• workflows are straightforward,
• you want quick wins,
• and you’re working primarily with mainstream SaaS tools.

Make (visual building + moderate complexity)

Make is often a sweet spot between simple and powerful, with a very visual way to build scenarios. Like Zapier, it’s generally user-friendly and suits teams who want more complexity without dropping into heavy custom build work.

A practical rule of thumb:
If the workflow is simple and speed matters, start with Zapier/Make. If you need deeper control, complex logic, or hosting flexibility, n8n becomes attractive.

It’s also worth noting that Microsoft Power Automate, AWS Step Functions, and Google Workflows play similar roles inside their respective ecosystems.
The same principle applies in each case: define the workflow rails first, then apply AI selectively where it adds real leverage.

A concrete example: automation first, AI where it helps

Here’s a real-world style workflow many membership organisations and SMEs recognise:

1. A new member (or customer) completes a web form
2. The workflow creates/updates their record on the website
3. An approval step happens (if required)
4. An invoice is issued in Xero
5. After payment, the member profile is made visible
6. Logos and documents are automatically filed into SharePoint
7. Social media images are generated from a template (optionally Canva-based)
8. Posts are scheduled (e.g., via Buffer)
9. Attendance/events data is linked back for reporting and analysis

Notice what’s going on: none of that requires an AI model to make the process work. Automation alone delivers value.

Now add AI selectively:

• Draft the “welcome email” in your tone (still reviewed before sending)
• Extract key fields from a submitted PDF
• Categorise the enquiry type and route it correctly
• Summarise a weekly membership update for stakeholders

That’s the sweet spot: workflow-led, AI-enhanced.

So where does “Agentic AI” actually fit?

Agentic approaches are often described as systems that can plan, execute, and adapt to accomplish objectives using tools.
In other words: agentic AI isn’t just generating content – it’s driving multi-step work.

But the best agentic implementations don’t remove automation – they depend on it.

Think of it like this:

• Automation platforms are the rails: triggers, actions, integrations, logs, approvals.
• AI is the reasoning layer: interpreting unstructured input, deciding between options, generating drafts, summarising, classifying.
• Agentic AI is the conductor: it chooses which tools to use, and when – but it still needs the rails and guardrails.

The operational necessities remain the same:

• track constraints,
• verify against requirements,
• protect IP (RBAC, encryption, audit logging),
• keep humans in the loop where it matters,
• and monitor drift/quality over time.

In short: agentic AI without workflow discipline becomes unpredictable.
Agentic AI with workflow discipline becomes a scalable capability.

Decision guide: should this be AI, automation, or both?

Use this quick filter:

When to choose automation-first:

• The steps are known and repeatable
• You need auditability, approvals, or traceability
• The outcome must be consistent
• You’re integrating systems (finance, CRM, website, documents)
• Errors are costly

Choose AI-first when:

• The input is unstructured (emails, PDFs, notes, chat logs)
• You need interpretation, classification, summarisation, drafting
• The output is advisory or a first-pass (not final action)

Select automation + AI when:

• You want AI to interpret/decide, but the workflow to execute
• You need constrained AI actions (specific tools, limited data scope)
• You want scalable “agent-like” behaviour with approvals and logging

A practical way to start (without boiling the ocean)

At Andymus Consulting we typically begin by focusing on business value first, then designing the right approach – which may include automation, AI/ML, and agentic patterns depending on the objective.

A low-risk, high-value starting path is:

1. Pick one process with clear pain (time, rework, bottlenecks)
2. Map the workflow end-to-end (inputs → decisions → outputs)
3. Automate the rails (systems integration, approvals, filing, notifications)
4. Insert AI carefully where it reduces effort or improves quality
5. Add controls (human approvals, logging, role-based access)

That gives you momentum – and a platform for agentic AI later, rather than trying to jump straight there.

Closing thought: “AI” isn’t the strategy – outcomes are

AI can be powerful. But most organisations don’t need AI everywhere. They need:

• fewer manual steps,
• better flow of information,
• controlled decision points,
• and predictable delivery.

Automation platforms like n8n, Zapier, and Make help you build that foundation. Then AI becomes what it should be: a targeted accelerator, not a vague promise.

So what are you waiting for? What if your competitors do this first?

If you’d like help identifying the best “automation-first + AI-where-it-matters” opportunities in your business, we can run a short technology adoption assessment and map the quickest paths to measurable value.

Please contact Andymus Consulting to discuss how we can assist you to automate your processes.

The post AI vs Automation: Why Workflows Beat “Just Add AI” (and how Agentic AI fits) appeared first on Andymus Consulting.

In an increasingly uncertain operating environment, organisations can no longer rely on static assumptions or “average case” planning. Whether managing supply chains, energy systems, logistics networks or industrial operations, leaders need to understand how systems behave under stress, how resilient they are to disruption, and what the financial consequences of different choices really are.

This is where advanced simulation, combined with techno‑economic analysis, becomes a powerful strategic capability.

At Andymus Consulting, we use simulation to help organisations move beyond intuition and spreadsheets – enabling evidence‑based decisions that explicitly link operational behaviour to commercial outcomes. Please Contact Andymus Consulting to discuss how we may be able to assist with your needs in this area.

Why Simulation Is Essential for System Resilience

Many operational and investment decisions are still supported by deterministic models or point‑in‑time financial analysis. While useful at a high level, these approaches often fail to capture:

• Variability in demand, supply, and processing rates
• Interdependencies between assets, infrastructure, and logistics
• Bottlenecks that only emerge under specific conditions
• The true impact of disruption, geopolitical risk, or renewable variability

The result is often systems that look robust in planning, but prove fragile in reality.

Simulation allows organisations to explore how systems perform over time, across thousands of scenarios – before capital is committed or operating models are locked in.

Discrete Event Simulation for Complex Operational Systems

A core capability at Andymus Consulting is Discrete Event Simulation (DES).

DES models a system as a sequence of events – such as arrivals, processing, failures, repairs, storage, blending, and dispatch – allowing complex operations to be represented realistically. This approach is particularly valuable where timing, queues, utilisation and constraints matter.

We have applied DES across a wide range of asset‑intensive and networked systems.

Managing Geopolitical Risk in Energy Supply Chains

Several years ago, we performed simulation work examining crude oil pipeline movements designed to avoid reliance on the Strait of Hormuz, in response to geopolitical risk associated with potential disruption to shipping through the region.

The simulation explored alternative pipeline routing, capacity constraints, throughput variability and utilisation under different disruption scenarios. Importantly, the work linked operational outcomes to economic exposure and system resilience, allowing decision‑makers to understand the value of diversification and redundancy.

Those pipelines have since been constructed and are now actively used – reinforcing the importance of forward‑looking, risk‑informed system modelling.

Hydrogen Value Chain Modelling Under Renewable Variability

In the energy transition space, Andymus Consulting has developed end‑to‑end hydrogen value chain simulations to understand optimal system sizing and configuration.

A key challenge in hydrogen production is the variability of renewable energy sources, particularly wind and solar. This variability directly affects:

• Electrolyser utilisation
• Storage requirements
• Capital efficiency
• Unit cost of hydrogen production

Using discrete event simulation, we modelled the full value chain – from renewable generation through to hydrogen production, storage and offtake – capturing the dynamic interaction between energy availability and process utilisation.

By linking the simulation outputs to techno‑economic analysis, we were able to quantify how different design choices impacted both resilience and economics, supporting more informed investment and policy decisions.

Mining and Minerals Logistics Simulation at Scale

In mining and minerals processing, material transport can extend for hundreds of kilometres and involve multiple transport modes – including trucks, conveyors, rail and shipping – often combined with blending strategies to achieve target grades.

Simulation has been used to examine systems where:

• Multiple transport modes interact
• Bottlenecks shift depending on operating conditions
• Blending strategies affect both throughput and product value
• High‑value products must be prioritised or segmented

DES enables these systems to be analysed holistically, revealing constraints and trade‑offs that are not visible when each component is considered in isolation.

When combined with techno‑economic analysis, organisations can assess not just what works operationally, but what creates the most value, and where investment delivers the greatest return.

Linking Simulation to Techno‑Economic Analysis

Operational insight alone is not enough. The real value comes when simulation outputs are directly connected to financial outcomes.

At Andymus Consulting, we integrate simulation with techno‑economic analysis to examine:

• Capital expenditure versus system resilience trade‑offs
• Cost of congestion, downtime or under‑utilisation
• Sensitivity of project economics to variability and disruption
• Return on investment for redundancy, storage, or capacity expansion

This approach moves organisations away from single‑point business cases toward risk‑aware, scenario‑based decision‑making.

Designing Resilient Systems Without Over‑Engineering

Resilience is not simply about adding more capacity. In many systems, small, well‑targeted changes deliver disproportionate benefits.

Simulation allows organisations to identify:

• Where resilience investments matter most
• Where additional spend delivers diminishing returns
• How systems degrade before failure occurs
• Which risks are best mitigated, transferred, or accepted

This supports deliberate system design – rather than reactive fixes after problems emerge.

Simulation‑Based Decision Making for Executives and Boards

Modern industrial and infrastructure systems are complex by nature. Simulation allows that complexity to be embraced, rather than simplified away – while still providing clear, decision‑ready insight for executives and boards.

At Andymus Consulting, we specialise in translating complex engineering, logistics and energy systems into models that support confident strategic decisions, aligned with commercial reality. Please contact us to discuss any requirements that you would like to discuss with us.

The post Using Simulation to Understand System Resilience, Capability and Financial Impact appeared first on Andymus Consulting.

“Responsible AI” isn’t a slogan in 2026 – it’s a concrete set of governance, risk, privacy, security, transparency, and oversight practices you can stand up and audit.

The good news: you don’t have to invent them. Mature, globally recognised standards now exist – ISO/IEC 42001 (AI management system), NIST AI RMF (risk management), ETSI TS 104 223 (baseline security for AI), ISO/IEC 23894 (AI risk), C2PA (content provenance). These standards align with national rules such as the EU AI Act and public‑sector policies in the US, Australia, Singapore, Japan and others.

Why this matters now.

Between 2025 and 2027, the EU AI Act phases in obligations for General Purpose AI (GPAI) and high‑risk systems

the US has operationalised the NIST AI RMF and a Generative AI Profile

Australia has signalled mandatory guardrails for high‑risk AI and uplifted public‑sector AI governance and privacy

Singapore finalised a Model AI Governance Framework for GenAI

Japan published AI Guidelines for Business

Canada is steering with a Voluntary GenAI Code pending future law

This post distils requirements you can implement—and cites the standards and rules behind them. If you would like assistance in this area, please contact Andymus Consulting.

1) Governance & Accountability (make it formal, make it auditable)

• Stand up an AI Management System (AIMS): Use ISO/IEC 42001:2023 to define policy, roles, objectives, competence, documentation, and continual improvement across the AI lifecycle—think “ISO 27001, but for AI.”
• Run risk management as a programme: The NIST AI RMF 1.0 (Govern–Map–Measure–Manage) is the global de‑facto operating model; it’s backed by a Playbook and Resource Center for implementation.
• Name accountable leadership: For public agencies (and suppliers to them), OMB M‑24‑10 requires a Chief AI Officer, inventories and governance controls for rights/safety‑impacting uses—this is a strong pattern for private‑sector role design too.

2) Risk Management (documented, measurable, repeatable)

• Adopt an AI‑specific risk process: ISO/IEC 23894:2023 adapts ISO 31000 to AI (context, identification, analysis, treatment, monitoring, review) and is often paired with NIST AI RMF. [iso.org], [nist.gov]
• Align with regulatory risk tiers: The EU AI Act requires risk management, data governance, documentation, logging, human oversight, robustness/cybersecurity and post‑market monitoring for high‑risk systems. Map your controls now to smooth conformity assessments as timelines bite (2025–2027). [whitecase.com], [europarl.europa.eu]

3) Data Governance & Data Quality (lawful, fit‑for‑purpose, traceable)

• Engineer for data quality: The ISO/IEC 5259 series sets measures, governance and process frameworks for data quality in analytics/ML—use it to operationalise “good data in.” [iso.org]
• Respect privacy realities in training: Australia’s OAIC guidance warns that publicly available data is not automatically lawful to scrape for training, especially where sensitive information may be present. Build consent/notice or alternative lawful bases into your data plans. [oaic.gov.au]
• Meet high‑risk data obligations: The EU AI Act expects appropriate data governance for training/validation/testing—document sources, relevance, representativeness and mitigations. [whitecase.com]

4) Transparency & Documentation (internal traceability + external clarity)

• Adopt a transparency taxonomy: ISO/IEC 12792 (transparency taxonomy for AI systems, in publication cycle) and ISO/IEC 5338 (AI system lifecycle processes) help structure disclosures and artefacts. [iso.org]
• Produce system‑level technical documentation for high‑risk AI (EU AI Act) and keep logs—this underpins conformity and post‑market duties. [whitecase.com]
• Publish inventories and use‑case statements where required (e.g., US OMB M‑24‑10 mandates public inventories/transparency statements for federal use). [whitehouse.gov]

5) Human Oversight, Competence & Literacy

• Design for meaningful human control: Embed oversight mechanisms and intervention points—explicitly required for high‑risk AI under the EU AI Act and addressed in ISO/IEC 42001 organisational controls. [whitecase.com], [iso.org]
• Upskill users and staff: Jurisdictions are codifying AI literacy (e.g., EU application phases include literacy expectations) and Singapore’s MGF‑GenAI frames stakeholder competence as part of accountability/testing dimensions. [europarl.europa.eu], [imda.gov.sg]

6) Security & Robustness (treat AI like critical software—then go further)

• Baseline security for AI: Implement ETSI TS 104 223—a lifecycle set of 13 core principles → 72 provisions covering development, deployment, operation and maintenance, tuned to AI‑specific threats (poisoning, prompt/indirect injection, model theft). [etsi.org], [iot-now.com]
• Integrate with risk & testing: The NIST AI RMF and Generative AI Profile emphasise threat modeling, vulnerability management, red‑teaming and secure operations—use them alongside ETSI to get both management and technical depth. [nist.gov]

7) Privacy & Rights (go beyond baseline compliance)

• Plan for ADM transparency & remedies: Australia’s Privacy reforms (2024) introduced new enforcement tools and automated decision‑making transparency obligations (phased commencement), with a statutory tort commencing in 2025—align notices and review pathways. [ashurst.com], [corrs.com.au]
• Bake privacy into training & inference: Follow OAIC’s GenAI guidance for lawful basis, sensitive data handling and accuracy testing; mirror principles in NIST AI RMF and ISO/IEC 23894 risk treatments. [oaic.gov.au], [nist.gov], [iso.org]

8) Model Evaluation, Red‑Teaming & Safety Testing

• Standardise evaluations: The NIST Generative AI Profile sets out concrete actions for safety evals, misuse testing and monitoring—make this a gate before deployment and a control during operations. [nist.gov]
• Use third‑party or sandbox testing: Singapore’s Model AI Governance Framework (GenAI) calls for Testing & Assurance and Incident Reporting as core dimensions—practical patterns for evals and playbooks. [imda.gov.sg]
• Meet high‑risk testing expectations: The EU AI Act anticipates suitable testing and documentation as part of conformity assessment for high‑risk systems. [whitecase.com]

9) Content Provenance & Labeling (especially for GenAI outputs)

• Attach Content Credentials: Adopt C2PA (v2.x) so images/video/audio carry cryptographically verifiable provenance and edit history—this is fast becoming the interoperability norm across tooling and platforms. [spec.c2pa.org]
• Be aware of labeling laws: China’s Deep Synthesis Provisions mandate visible labels for AI‑generated/edited content (face/voice/immersive scenes) and other controls—useful as a strict reference even if you don’t operate in China. [loc.gov]

10) Lifecycle Monitoring, Incidents & Post‑Market

• Operate post‑market monitoring: The EU AI Act requires providers of high‑risk systems to operate post‑market plans and cooperate with market surveillance authorities. [europarl.europa.eu]
• Establish incident reporting: Singapore’s MGF‑GenAI includes Incident Reporting as a core dimension—use it as the blueprint for triage, notification and corrective action. [imda.gov.sg]

11) Public‑Sector Procurement & Use (what governments are asking for)

• Australia (Commonwealth): The Policy for the responsible use of AI in government v2.0 (Dec 2025) mandates accountability, transparency statements, risk‑based use‑case assessments, registers and training for all agencies—vendors should align their solutions and documentation to these controls. [digital.gov.au]
• United States (Federal): OMB M‑24‑10 requires CAIOs, use‑case inventories and elevated controls for rights/safety‑impacting AI—expect procurement to reference these. [whitehouse.gov]

12) A pragmatic control set you can deploy this quarter

• AI Policy & AIMS → ISO/IEC 42001 (scope, roles, training, supplier controls, continual improvement). [iso.org]
• AI Risk Register & Treatment Plans → NIST AI RMF + ISO/IEC 23894 (risk identification, measurement, treatment, monitoring). [nist.gov], [iso.org]
• Data Management & Quality Plan → ISO/IEC 5259 (measures and governance for ML data fitness). [iso.org]
• Model Card + Evaluation Report → NIST Generative AI Profile (evals/red‑teaming/results/sign‑offs). [nist.gov]
• Security Design & Operations Plan → ETSI TS 104 223 (threats, controls across dev/deploy/operate). [etsi.org]
• Transparency Statement & Use‑Case Inventory → EU AI Act documentation norms; OMB M‑24‑10 for public‑sector parity. [whitecase.com], [whitehouse.gov]
• Incident Playbook → MGF‑GenAI (Singapore) incident reporting + EU post‑market monitoring. [imda.gov.sg], [europarl.europa.eu]
• Content Credentials integration → C2PA for verifiable provenance on AI‑generated media. [spec.c2pa.org]

Quick references (selected)

• ISO Standards
  • ISO/IEC 42001 (AIMS);
  • ISO/IEC 23894 (AI risk);
  • ISO/IEC 5259 (data quality);
  • ISO/IEC SC 42 portfolio. [iso.org], [iso.org], [iso.org]
• NIST AI RMF 1.0; NIST Generative AI Profile (NIST‑AI‑600‑1); NIST AIRC Playbook. [nist.gov]
• ETSI TS 104 223 (2025) baseline cyber security requirements for AI. [etsi.org]
• EU AI Act – law + phased implementation 2025–2027. [whitecase.com], [europarl.europa.eu]
• OMB M‑24‑10 (US Federal AI governance). [whitehouse.gov]
• Australia:
  • Guardrails/existing policy;
  • AI in Government Policy v2.0;
  • privacy reforms (2024–25). [industry.gov.au], [digital.gov.au], [ashurst.com]
• Singapore
  • MGF‑GenAI;
  • AI Verify Foundation. [imda.gov.sg], [aiverifyfo…ndation.sg]
• Japan AI Guidelines for Business v1.0. [meti.go.jp]
• Canada Voluntary Code for Advanced GenAI. [ised-isde.canada.ca]
• C2PA (Content Credentials). [spec.c2pa.org]

The post Responsible AI Requirements in 2026: What You Must Put in Place appeared first on Andymus Consulting.

Australia is pivoting to a risk‑based approach to AI in 2026 – tightening guardrails where harm could be serious or irreversible while keeping low‑risk innovation unimpeded. In late 2024 the Commonwealth released a Proposals Paper to define “high‑risk AI” using principles and to mandate lifecycle guardrails focused on testing, transparency and accountability, with consultation closing 4 October 2024. A Senate Committee later recommended dedicated, economy‑wide legislation for high‑risk uses, backed by a principles‑based definition and a non‑exhaustive list (explicitly including general‑purpose AI).

This post distils what to treat as high‑risk AI in Australia right now, and sets it against the European Union (EU), United Kingdom (UK), United States (US), Canada, and Singapore so you can align your governance and assurance to international expectations.

At Andymus Consulting we recognised that understanding the risk, and particular high-risk AI activities is important in developing trust with your clients and other stakeholders. Fee free to contact us to discuss any assistance you may need in this area.

Why this matters now

• The Government’s January 2024 interim response committed to guardrails for AI in legitimate but high‑risk settings, prioritising ex‑ante prevention via testing, transparency and accountability. [industry.gov.au]
• The September 2024 Proposals Paper sketches how to define high‑risk AI and apply 10 guardrails across the supply chain—either via sectoral amendments, a framework law, or a cross‑economy AI Act.
• The Nov 2024 Senate report recommends a dedicated Act, a principles‑based high‑risk definition backed by an illustrative list of uses, and explicit coverage of GPAI.

The Australian definition: what to treat as high‑risk AI

Australia’s direction is use‑based: an AI system is high‑risk when its intended or foreseeable use could materially affect safety, human/worker rights, access to essential services, health outcomes, public benefits, or critical infrastructure, including general‑purpose AI embedded in those settings.

Common high‑risk settings to flag in your portfolio:

• Safety‑critical & critical infrastructure (e.g., energy, water, transport control, core digital infrastructure)
• Decisions with legal or livelihood impacts in health, education, employment/HR, credit/insurance, eligibility for public benefits and essential services
• Biometrics & identity (remote identification, categorisation, emotion recognition), where misuse can harm rights or safety.
• GPAI/frontier models used in or adapted for high‑risk contexts, or supplied to downstream deployers for those contexts.
• Public‑sector deployments rated high/critical residual risk by the NSW AI Assessment Framework—these must be escalated to the AI Review Committee.
• Any AI that processes, infers or generates personal information at scale, especially where outputs can cause non‑trivial or irreversible harm if wrong (e.g., hallucinations, misidentification)

What the guardrails will likely require: documented risk & impact assessment, pre‑deployment testing and in‑life monitoring, meaningful human oversight, and clear accountability across the developer → deployer chain.

What’s already actionable in Australia

• National framework for AI assurance in government (June 2024)
  implements the Australian AI Ethics Principles in government deployments; a practical reference for governance, transparency and accountability even outside the public sector.
• NSW AI Assessment Framework (2024)
  mandatory for NSW Government, with structured risk self‑assessment and escalation of high‑risk systems to the AI Review Committee; a strong process model if you need a concrete definition‑by‑assurance.
• OAIC guidance (Oct 2024 / updated Jan 2025)
  requires PIAs for high‑privacy‑risk uses, cautions against entering personal/sensitive data into public GenAI, and stresses transparency where AI outputs affect individuals.
• APRA lens (May 2024)
  no AI‑specific prudential rulebook (for now); entities must manage AI risks under technology‑neutral standards (e.g., CPS 234, CPS 230) with human accountability and robust oversight. [insurancenews.com.au], [brokerdaily.au]

Global benchmarks for “high‑risk AI”

European Union — legal list of high‑risk

The EU AI Act classifies high‑risk via two routes:

1. AI that is a safety component of regulated products, and
2. AI used in Annex III contexts—biometrics, critical infrastructure, education, employment/HR, essential services & benefits, law enforcement, migration/asylum, justice & democratic processes.

These systems face stringent obligations (risk management, data governance, logging, transparency, human oversight, robustness, post‑market monitoring). [eur-lex.europa.eu], [bundesnetzagentur.de]

United Kingdom — context‑based, regulator‑led

The UK applies five cross‑cutting AI principles (safety, transparency, fairness, accountability, contestability) via sector regulators rather than a single statute, with a central function supporting risk assessment; government is exploring binding requirements for highly capable GPAI. [gov.uk], [cdp.cooley.com]

United States (Federal use) — rights‑/safety‑impacting

OMB M‑24‑10 compels agencies to identify rights‑impacting and safety‑impacting AI uses and implement minimum practices (or stop using them), creating a clear high‑risk category for government AI. In industry, the NIST AI Risk Management Framework (plus a Generative AI Profile, 2024) is the de‑facto baseline for assessing and mitigating high‑risk AI. [whitehouse.gov], [crowell.com] [nist.gov], [data.aclum.org]

Canada — high‑impact (status update)

Canada’s proposed AIDA would have regulated high‑impact systems (akin to high‑risk) in areas like employment, essential services, biometrics and credit‑type determinations, but Bill C‑27 died on the Order Paper on 6 Jan 2025 following prorogation; any federal regime will need re‑introduction. [fasken.com], [gowlingwlg.com]

Singapore — model governance and GenAI focus

Singapore favours standards and testing over statutes: the Model AI Governance Framework and the 2024 Generative AI Framework detail practical controls—accountability, testing/assurance, content provenance, incident reporting—that organisations scale in higher‑risk contexts; IMDA maintains a crosswalk to NIST for interoperability. [imda.gov.sg], [aiverifyfo…ndation.sg]

Side‑by‑side: comparing high‑risk across jurisdictions

Build once, comply many: standards that travel

• ISO/IEC 42001:2023 (AI Management System) — the world’s first AIMS standard; operationalises governance (policy/roles), AI risk & impact assessment, lifecycle controls and continual improvement. Certification helps evidence readiness for higher‑risk deployments.
• ISO/IEC 23894:2023 (AI Risk Management) — lifecycle guidance to identify, analyse and treat AI‑specific risks; complements ISO 42001 and maps well to NIST AI RMF.
• NIST AI RMF (2023) + Generative AI Profile (2024) — widely adopted US framework; a strong reference model for categorising and mitigating high‑risk characteristics.

A practical action plan for Australian organisations

1. Triage your use‑cases
   Classify any AI that touches critical infrastructure, safety, or material rights/outcomes (health, HR, credit, benefits, education, justice) as high‑risk by default and plan for stronger assurance. [Safe and responsible AI in Australia: Proposals paper for introducing mandatory guardrails for AI in high-risk settings]
2. Adopt assurance‑first governance
   Institute AI risk & impact assessments, pre‑deployment testing, drift monitoring, and human‑in‑the‑loop oversight across high‑risk systems; align your management system to ISO/IEC 42001 and your risk controls to ISO/IEC 23894 / NIST AI RMF. [iso.org], [iso.org], [nist.gov]
3. Privacy by design for AI
   When AI collects, infers or generates personal information, conduct PIAs, minimise data, and maintain clear user notifications; avoid entering personal/sensitive data into public GenAI. [oaic.gov.au]
4. Prepare for export markets
   If you sell into the EU, assume Annex III where applicable and build EU‑grade documentation and testing now to de‑risk CE‑style obligations. [eur-lex.europa.eu], [bundesnetzagentur.de]
5. Leverage government exemplars
   Use the National AI assurance framework and NSW AIAF as templates for escalation triggers, documentation, and independent review pathways. [finance.gov.au], [digital.nsw.gov.au]

Closing thought

Australia’s line of march is clear: focus guardrails where the stakes are high and keep room for low‑risk innovation. If you treat the contexts above as high‑risk now and align your program to ISO 42001 / ISO 23894 / NIST, you’ll be ready for the Australian regime and interoperable with the EU, UK and US expectations when they knock on your door. [industry.gov.au] [iso.org], [iso.org], [nist.gov]

At Andymus Consulting we are able to assist with your needs in this area. Please contact us to discuss your requirements.

References & further reading

• Australia:
  • Proposals Paper – Mandatory Guardrails for High‑Risk AI (Sept 2024);
  • Interim Response (Jan 2024);
  • Senate Final Report (Nov 2024). [consultati…tga.gov.au], [industry.gov.au], [aph.gov.au]
• Government practice:
  • National framework for AI assurance in government (June 2024);
  • NSW AI Assessment Framework (2024). [finance.gov.au], [digital.nsw.gov.au]
• Privacy:
  • OAIC guidance on AI (Oct 2024 / Jan 2025 update). [oaic.gov.au]
• Financial services:
  • APRA remarks on AI risk (May 2024). [insurancenews.com.au], [brokerdaily.au]
• EU:
  • AI Act (Regulation (EU) 2024/1689)
  • Annex III. [eur-lex.europa.eu], [bundesnetzagentur.de]
• UK:
  • DSIT guidance for regulators (Feb 2024);
  • GPAI binding requirements exploration. [gov.uk], [cdp.cooley.com]
• US:
  • OMB M‑24‑10;
  • NIST AI RMF + GenAI Profile. [whitehouse.gov], [nist.gov], [data.aclum.org]
• Canada:
  • AIDA status (Bill C‑27 died Jan 2025). [gowlingwlg.com]
• Singapore:
  • Model AI Governance Framework for Generative AI (2024) and ecosystem materials. [imda.gov.sg], [aiverifyfo…ndation.sg]

The post What counts as “high‑risk AI” in Australia in 2026 appeared first on Andymus Consulting.

Artificial intelligence (AI) standards and regulation has shifted from ideas to implementation. The EU AI Act is phasing in across 2025–2027. The US is operationalizing the NIST AI Risk Management Framework and a Generative AI Profile. Australia is moving toward mandatory “guardrails” for high‑risk AI while uplifting privacy and public‑sector controls. Singapore, Japan, China, Canada and the UK have each advanced distinct – but increasingly interoperable approaches including international standards.

This post curates what’s binding vs. voluntary, timelines, key standards, and the practical controls organisations should adopt now. If you would like advice or support for this Andymus Consulting can assist.

Why it matters.…

Whether you build foundation models or deploy AI in finance, health, critical infrastructure or the public sector, you’ll need a common controls language that works across jurisdictions: think NIST AI RMF for risk, ISO/IEC 42001 for an AI management system, EU AI Act risk tiers for market access, and content provenance for AI‑generated media. The good news: there’s now enough convergence to act decisively.

Global baselines shaping national rules

OECD AI Principles (2019; updated 2024) are the first intergovernmental standards for trustworthy, human‑centric AI (inclusion, human rights, transparency, robustness, accountability) and underpin many national frameworks.

UNESCO Recommendation on the Ethics of AI (2021) provides global normative guidance with actionable “Policy Action Areas” (data governance, environment, education, gender) adopted by all 193 member states.

Council of Europe Framework Convention on AI (2024) is the first legally binding treaty on AI, human rights, democracy and rule of law—open for signature beyond Europe and already attracting global signatories.

G7 Hiroshima AI Process (2023‑24) issued International Guiding Principles and a Developer Code of Conduct for advanced models—non‑binding but now a reference point for governments and industry.

European Union — the EU AI Act (Regulation (EU) 2024/1689)

What it is. The first comprehensive, horizontal AI law, in force since 1 August 2024, with staged application through 2025–2027 covering prohibitions, GPAI duties, governance, penalties, and high‑risk system obligations. [whitecase.com], [europarl.europa.eu]

Key dates (high level).

• 2 Feb 2025: Prohibitions & AI literacy start.
• 2 Aug 2025: GPAI/model rules, governance and penalties apply; notified bodies operational.
• 2 Aug 2026: General application of most provisions.
• 2027: Broad high‑risk requirements bite fully (with some transitions). [artificial…enceact.eu], [schoenherr.eu]

Why standards matter. Under Article 40, conforming to harmonised standards gives a legal presumption of conformity, so watch CEN/CENELEC and ISO/IEC deliverables mapped to EU requirements. [europarl.europa.eu]

What to do now. Perform risk tiering (prohibited / high‑risk / limited / minimal), build a risk, data & quality management system aligned to ISO/IEC 42001 (AIMS) and ISO/IEC 23894 (AI risk), and prepare technical documentation and post‑market monitoring. [iso.org], [iso.org]

United States — NIST‑led governance + federal policy

• Executive Order 14110 (Oct 2023) directed a whole‑of‑government approach to safe, secure, trustworthy AI with assignments to NIST, DOE, DHS and others (e.g., testing, critical infrastructure, biosecurity, civil rights). [bidenwhite…chives.gov], [federalregister.gov]
• NIST AI Risk Management Framework (AI RMF 1.0, 2023) is the de facto national baseline (Govern–Map–Measure–Manage), supported by an AI Resource Center and Playbook. [nist.gov]
• NIST Generative AI Profile (NIST‑AI‑600‑1, July 2024) adds concrete measures for GenAI (e.g., evals/red‑teaming, misuse mitigation, data controls). [nist.gov]
• OMB M‑24‑10 (Mar 2024) mandated Chief AI Officers, public inventories, and risk controls for rights‑ and safety‑impacting federal AI; subsequent 2025 memoranda under a new administration adjusted the approach to accelerate adoption while retaining core safeguards. [whitehouse.gov], [jdsupra.com]

Takeaway. For US‑market operations and global alignment, implement NIST AI RMF and the GenAI Profile as your operating control set, then crosswalk to ISO/IEC and local laws. [nist.gov]

Australia — guardrails, privacy uplift, and public‑sector policy

• Government interim response (Jan 17, 2024) signalled mandatory guardrails for high‑risk AI (testing, transparency, accountability) and launched a Voluntary AI Safety Standard as an immediate uplift; proposals consulted through late 2024. [industry.gov.au], [minterellison.com]
• Policy for the responsible use of AI in government v2.0 (effective Dec 15, 2025) requires accountable officials, transparency statements, risk‑based use‑case assessments, registers, and staff training across non‑corporate Commonwealth entities. [digital.gov.au]
• Privacy reforms (Privacy and Other Legislation Amendment Act 2024) introduced stronger enforcement, automated decision‑making transparency obligations, and a statutory tort for serious invasions of privacy (commenced 2025). [ashurst.com], [corrs.com.au]
• Regulator coordination (DP‑REG) continues with working papers on LLMs and multimodal foundation models, pointing to competition/consumer, online safety and privacy risks—helpful signals for enterprise risk assessments. [accc.gov.au], [acma.gov.au]
• OAIC GenAI guidance (Oct 2024) clarifies that publicly available data isn’t automatically fair game for training; treat sensitive information with heightened consent and risk controls. [oaic.gov.au]

Takeaway. Expect mandatory guardrails in high‑risk contexts; uplift your privacy, ADM transparency, and safety testing practices now to get ahead. [minterellison.com]

United Kingdom — principles‑based and regulator‑led

The UK’s “pro‑innovation” approach empowers existing regulators to apply five cross‑cutting principles (safety, transparency/explainability, fairness, accountability & governance, contestability & redress) rather than introducing an immediate horizontal AI law, with the AI Safety Institute deepening evaluations of frontier systems. [questions-…liament.uk], [kpmg.com]

Canada — federal law paused; governance via code(s) and instruments

After the Artificial Intelligence and Data Act (AIDA) within Bill C‑27 died on the order paper in Jan 2025, Canada relies on a Voluntary Code of Conduct for advanced GenAI and sector/treasury instruments while policymakers consider next steps. [mcinnescooper.com], [ised-isde.canada.ca]

Singapore — practical governance + AI assurance

• Model AI Governance Framework (GenAI) (final May 30, 2024) sets nine dimensions: accountability, data, trusted development & deployment, incident reporting, testing/assurance, security, content provenance, safety/alignment R\&D, AI for public good; developed by IMDA and AI Verify Foundation with NIST cross‑walks. [imda.gov.sg], [imda.gov.sg]

AI Verify Foundation provides open‑source evaluation tooling and a global assurance sandbox—useful if you need practical test assets for model/app assessments.

Japan — business‑ready guidance and G7 leadership

• AI Guidelines for Business v1.0 (Apr 19, 2024) (METI & MIC) consolidate earlier guidance and set role‑based expectations for developers, providers, and users with principles for safety, transparency, privacy, fairness, and accountability. [meti.go.jp]
• Japan also led the G7 Hiroshima AI Process, shaping Guiding Principles and the Developer Code of Conduct for advanced AI systems. [japan.go.jp]

China — algorithmic governance + deep synthesis + generative AI rules

• Algorithmic Recommendation Provisions (effective Mar 1, 2022) mandate disclosure, opt‑out, protections for minors/elderly, anti‑manipulation and labeling obligations for algorithmic content.
• Deep Synthesis (Deepfake) Provisions (effective Jan 10, 2023) require visible labeling of synthetic content, security assessments for sensitive features (e.g., facial/voice editing), and content controls.
• Interim Measures for Generative AI Services (effective Aug 15, 2023) set duties for public‑facing GenAI services, including security assessments for services with “public opinion/social mobilization” attributes and algorithm filings.
• AI Standardization Guidelines (2024 Edition) outline a plan to build a comprehensive national AI standards system by 2026 across seven domains including safety/governance (50+ new standards targeted). [wap.miit.gov.cn], [cspress.cn]

The standards stack you can implement now

NIST AI RMF 1.0 + Generative AI Profile — lifecycle risk management and GenAI‑specific controls (evals, misuse mitigation, monitoring). Ideal as an operating control set across jurisdictions. [nist.gov]

ISO/IEC 42001 (AI Management System) — the management‑system backbone for policies, roles, documented processes and continual improvement (think “ISO 27001 for AI”). Pair with ISO/IEC 23894 for risk.

ETSI TC SAI — TS 104 223 (2025) — baseline cyber security requirements for AI models/systems across the lifecycle (13 principles → 72 provisions), plus supporting reports on traceability, testing, mitigations, data supply chain. Great for secure‑by‑design programmes.

C2PA (Content Credentials v2.x) — open standard for cryptographically verifiable provenance of images/video/audio; increasingly adopted by tools and platforms to identify AI‑generated or manipulated media.

Contact Andymus Consulting to discuss your requirements for implementing AI standards and governance.

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In the world of engineering design and analysis, professionals often face a critical decision: should we rely on established methods, or is it time to innovate and start from scratch? This question has been on my mind recently, and I’d like to share some reflections from my years in the field.

The Comfort of Familiar Systems

There’s a certain comfort in sticking with what we know. Established engineering design systems, despite being fragmented and sometimes cumbersome, offer a sense of certainty. Their processes and integrations are familiar, and the handover of data to clients or operators follows a well-trodden path. This “tried and trusted” approach is often seen as the safe bet.

The Challenge of Change

However, disruptive and breakthrough innovations rarely fit neatly into existing molds. Take, for example, the steel and base metals industry. Traditional pyrometallurgical (high-temperature smelting) processes have dominated for decades, and there’s a massive global infrastructure built around them. Yet, new electrochemical (low-temperature) processes are being piloted and scaled, promising significant benefits. The challenge? Convincing industry players to move away from what’s familiar and invest in something new.

Weighing the Options: Evolution or Revolution?

So, should you stick with what you know or design something from the ground up? The answer isn’t black and white. It’s essential to:

• Define your goals: What are you trying to achieve? What adds value or makes economic sense?
• Identify challenges and opportunities: Understanding the limitations of traditional approaches can highlight where innovation is needed.
• Assess the benefits of new processes: What could a new approach unlock for your business or industry?
• Motivate change: Change is hard, and risk aversion is natural. Business owners need clear incentives and a compelling case to take the leap.

Final Thoughts Ultimately, the decision to stick with the tried and tested or to innovate should be driven by a clear understanding of your objectives and the potential value of change. By thoughtfully weighing the risks and rewards, you can position yourself—and your organization—to make informed, forward-thinking choices.

The post Reinvent or Rely? The Engineer’s Dilemma Between Old and New appeared first on Andymus Consulting.

As an undergraduate engineering student many years ago, I recall being taught about fundamental concepts through the use of diagrams and charts. Following on from my post last week, I felt it important to dig into the way we use images and other graphical tools to communicate.

As a PhD student in London, I was combining a range of physics in computational fluid dynamics (CFD) modelling. It is a powerful tool based on the flow of fluids – liquids and gases. The visualisation tools allow the flow fields and other parameters such as temperature and composition to be examined to gain insight into what is happening.

Looking at hydraulic flows like a spillway, or a channel, the surface velocity is very much different to what is underneath.

Also this looks so realistic, but is actually an AI generated image!

Fast forward past going back to working on site for BHP at Olympic Dam copper smelter in the middle of Australia and then working in consulting I came across the terms of “Colourful Fluid Dynamics” and “Colour for Directors”. These colloquial sayings in the CFD fraternity were termed to indicate that due to the pretty pictures anyone would believe the results – without understanding what validation and other good practices have been applied. Check out this blog on Siemens Simcentre for some more background https://blogs.sw.siemens.com/simcenter/Colorful-Fluid-Dynamics-Say-it-again-I-dare-you/

What these sayings demonstrate is that people are generally more comfortable with graphical representations and understand if we can provide a picture or even a flow chart to illustrate a point. As experts in a particular field, it is so easy to jump to the final answer without taking our clients on the journey and explaining how we got there. Think about describing your process or result in words and then think about how a graphic or image would work.

Using a flowchart to demonstrate what a calculation or a business process consists of is generally more powerful than describing in text.

In a lot of the simulation work I have been involved with over many years, clients find the use of diagrams, visuals and graphs helpful in understanding the trends in models and results. A lot of clients can better associate their understanding and experiences with what they are seeing to build their confidence in the results.

In a lot of occasions full testing and verification (and comparison to the limited measurements of existing plant/equipment/flows) is not practical, costly and time consuming – this is the value of simulation. As with all technology it is not fool proof, and getting feedback from peers and clients is important quality assurance process which is assisted by the graphical visualisation that can be created.

Turbulence is a classic flow phenomena that in physical testing in labs uses smoke and lasers to measure. In CFD we can show the variations in flow due to turbulent eddies.

In closing it is always important to remember what the key question that is being asked – this is helpful as in some instances capturing the right data to generate a visual takes some planning and forethought. I have re-run many simualtions when not thinking about this…. as it is not always possible to save all the data from models.

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Professionals with strong technical backgrounds often encounter feedback indicating that clients find presented results, reports, or modelling and simulation work difficult to understand, frequently likening these methods to a “black box”. This is becoming more prevalent due to the application of Machine Learning (ML) and Artificial Intelligence (AI) approaches used by data scientists and more generally. One of the key challenges in such assignments is becoming deeply involved in technical detail and inadvertently assuming clients possess similar knowledge.

Communicating both the methodology and subsequent results through effective storytelling is essential to ensure alignment with client objectives and expectations. Bridging the gap between interpreting technical outcomes and meeting clients at their level of understanding is crucial, especially as many may not share a technical background. Below are important considerations to bear in mind when conducting technical work while maintaining meaningful engagement with clients:

Acquisition Phase:

• Ensure client requirements are thoroughly defined.
• Clarify the value expected from the assignment’s outcomes.
• Confirm that project boundaries and information comprehensively address client needs.
• Identify other stakeholders who may have an interest in the work; consider workshops to facilitate engagement and alignment.
• Evaluate whether initial solutions introduce bias and assess any overlooked elements.

Planning:

• Avoid immediate problem-solving; even a brief planning session can help identify key steps and benefit from peer or client review.
• Incorporate sufficient granularity into plans to facilitate demonstrating measurable progress.
• Establish clear billing segments for larger tasks to support business cash flow, which sometimes may be overlooked in larger organizations by technical professionals.

Progress:

• Regularly communicate updates to clients and managers to build and maintain confidence in your approach and capabilities.
• Set achievable intermediate objectives to show ongoing progress, as clients may equate a lack of information with a lack of advancement.

Outcomes:

• Validate details and conduct reviews to mitigate errors in deliverables.
• Articulate results in terms relevant to the client.
  • Provide insights by comparing findings to industry benchmarks, standards, and norms.
  • Remain adaptable to changes over the course of assignments, discussing scope changes transparently with clients to add value.
• Assess how evolving project parameters may affect the overall value delivered.
  • Adapt the level of detail according to organizational hierarchy:
  • Executive-level audiences typically require concise summaries (two to three pages).
• Technical professionals, such as reliability engineers, often need comprehensive reports referencing applicable codes and standards.

Engineers, scientists, and related professionals must remain attuned to the sources of value perceived by clients. Effectively conveying technical information tailored to the audience is critical; otherwise, work may be misunderstood as a black box, prompting scrutiny regarding its validity and relevance.

The post Analysing the Black Box – The Significance of Storytelling for Engineers and Technical Professionals appeared first on Andymus Consulting.