An AI readiness assessment for a mining or heavy industrial engineering firm reviews eight areas: data quality and governance, knowledge management, integration and automation, technology infrastructure, strategic alignment, analytics capability, workforce AI competency and cybersecurity. At Skyo Digital, a Perth-based engineering-led AI advisory, the assessment typically runs four to six weeks and ends with a readiness report, a prioritised roadmap and the AI use cases worth funding first.
This article is written for operations leaders, engineering managers and technical leads in Australian mining, oil and gas and heavy industrial businesses who are being asked what their AI plan is and want to know where they actually stand before committing budget. It sets out what a good assessment covers, how it runs, what it produces and how to judge whether a provider understands an operating environment.
Why do mining and heavy industrial firms need a different kind of AI readiness assessment?
A heavy industrial business has to assess its operational technology and engineering knowledge alongside its corporate IT, because that is where most of the valuable data sits. Maintenance histories, historian and SCADA data, inspection records, design documents and management of change records are spread across systems that were never designed to talk to each other, and much of the critical knowledge sits with experienced engineers rather than in any system at all.
Generic AI readiness checklists tend to focus on software licences, cloud platforms and office productivity tools. Those matter, but in a mine, processing plant or gas facility the harder questions are about data provenance, safety-critical decisions, remote sites with limited connectivity, contractor-heavy workforces and regulatory obligations. An assessment that skips these questions will produce a roadmap that looks sensible in a boardroom and stalls the first time it meets a site.
What are the eight areas an AI readiness assessment covers?
Each area is scored against current practice and against what the business needs for the AI use cases it cares about. The descriptions below show what each area means in practice for a mining or heavy industrial operation.
1. Data quality and governance
This area covers the ownership, accuracy, completeness and access rules for key data. On a mine or plant, it tests whether work order histories, failure codes and historian tags can be trusted enough to train or ground an AI tool.
2. Knowledge management
This area looks at how engineering knowledge is captured, stored and found. It asks whether lessons learned, design basis documents and operating procedures are searchable, or whether they live in personal drives and the memories of senior engineers.
3. Integration and automation
This area measures how well systems connect and how much work is still manual. A typical finding is that maintenance, production and engineering systems hold related data but share none of it without spreadsheets.
4. Technology infrastructure
This area reviews platforms, site connectivity, security controls and approved AI tools. It confirms whether the cloud tenancy, site networks and tools such as Microsoft Copilot or Claude are ready for wider use.
5. Strategic alignment
This area checks whether AI ambitions link to business objectives and budgets. Strong use cases tie directly to throughput, equipment availability, safety or cost targets that leaders already track.
6. Data management and analytics
This area covers reporting, analytics skills and how decisions get made with data. A common gap is engineers spending days assembling monthly reports by hand before any analysis can start.
7. Workforce AI competency
This area gauges skills, confidence and appetite for AI across site, engineering and leadership roles. Teams need to know what AI can safely do and where human judgement must stay in control.
8. Cybersecurity posture
This area examines controls for AI tools, data leakage and operational technology. AI access has to respect the boundary between corporate IT and operational systems.
The data and integration areas usually decide how fast a heavy industrial business can move, so they deserve the most time. Skyo Digital publishes a five layer AI readiness framework that explains why data governance and integration sit underneath every successful AI use case.
How does the assessment run?
A well-run assessment follows a clear sequence, and each step produces something the client can review before the next one starts.
Agree the objectives, scope, sites and the business outcomes the assessment needs to inform.
Interview stakeholders across leadership, operations, engineering, maintenance, IT and operational technology.
Review real systems and real data samples, such as work order records, historian exports and engineering document libraries.
Identify and size candidate AI use cases, ranked by value and by how quickly each can be proven.
Score each of the eight areas and identify the gaps that block the highest-value use cases.
Present the findings, roadmap and recommendations to the leadership team.
The data review step is where heavy industrial assessments most often differ from corporate ones. Looking at actual records, rather than relying on interviews alone, is the only reliable way to tell whether a predictive maintenance or reporting automation use case is realistic in the next six months or needs foundation work first.
What do you get at the end of an AI readiness assessment?
The output is a set of practical documents that a leadership team can fund and an engineering team can act on. A Skyo Digital AI readiness assessment delivers:
A tailored "How to be AI-ready" report covering all eight areas.
Recommendations for advancing AI across the business.
AI strategy guidance linked to business objectives.
An AI readiness roadmap showing what to do first and what depends on what.
A stakeholder engagement summary capturing what leaders and teams said.
A useful test of any assessment is whether the roadmap names owners, dates and the first two or three use cases to prove. A report that only describes maturity levels leaves the hardest decisions with the client.
How long does it take and what does it cost?
A typical AI readiness assessment takes four to six weeks, depending on the number of sites, business units and systems in scope. A single-site operation with a focused question sits at the shorter end, while a multi-site business with separate operational technology environments sits at the longer end.
For a small team with a focused domain, such as a single engineering team, a Skyo Digital AI readiness review costs around A$7,000 to A$9,000. Work with larger teams and across a whole organisation generally lands around A$15,000 to A$20,000, depending on the size and complexity of the organisation.
The main cost drivers are the number of stakeholders to interview, the number of systems and data sets to review and whether the client wants use case sizing included. We confirm the scope and price after an initial conversation, so the client knows the cost before work starts.
Which frameworks should an AI readiness assessment align with?
An assessment should align with the frameworks your board, regulators and customers already recognise, so its recommendations are easy to defend. The most relevant references for Australian heavy industry are:
The Australian Government's Guidance for AI Adoption, which sets out six essential practices covering accountability, impact assessment, risk management, transparency, testing and human control.
The NIST AI Risk Management Framework, which is widely used internationally and now includes work on a profile for AI in critical infrastructure.
ISO/IEC 42001, the international standard for AI management systems, which the Australian guidance is broadly aligned with.
Using these as the scoring backbone means the assessment doubles as a starting point for AI governance, rather than becoming a separate exercise later.
What this looks like in practice
Our work with Santos shows why the foundations matter before AI. Santos' engineering function was receiving technical queries, operational events, management of change requests and assurance reviews from multiple assets, with no clear view of demand against capacity. Skyo Digital built a lightweight task capture process and two dashboards, one for team leads and one for management, which gave daily and weekly visibility of work in progress and shifted prioritisation from intuition to data. With that foundation in place, the next phase uses AI to capture and categorise engineering requests from emails. You can read the full Santos case study.
The same principle applies to every heavy industrial AI roadmap we build. The use cases with the fastest return sit on data and processes that are already captured consistently, and the assessment's job is to find them.
How Skyo Digital can help
Skyo Digital runs AI readiness assessments for mining, oil and gas and engineering businesses from our Perth office, with leadership that has spent around twenty years in operating and advisory roles across energy and resources. If you are unsure whether you need an AI readiness assessment or a broader digital maturity assessment, we can help you decide in a first conversation. You can find more about our work across Australia.
Frequently asked questions
Is an AI readiness assessment the same as a digital maturity assessment?
They overlap but answer different questions. An AI readiness assessment asks whether the business can adopt AI safely and profitably, and focuses on data, knowledge, integration and AI skills. A digital maturity assessment looks more broadly at leadership, governance, technology, reporting and change readiness, and includes benchmarking and a 90-day action plan. Businesses early in their digital journey often start with maturity, while those under pressure to act on AI start with readiness.
Do we need clean data before we start using AI?
You need trustworthy data for the specific use case, not perfect data across the whole business. An assessment identifies which data sets are already good enough for early use cases, such as reporting automation from consistent engineering records, and which need foundation work before they can support use cases like predictive maintenance. Starting with use cases that suit the data you already have builds momentum while the foundations improve.
Can the assessment include operational technology such as SCADA and historians?
Yes, and for mining and heavy industrial businesses it should. The assessment reviews how operational data is captured, stored, governed and accessed, and whether it can be connected safely to analytics and AI tools without weakening the boundary between operational and corporate networks. Operational technology often holds the highest-value data for AI, so leaving it out produces an incomplete picture.
Who should be involved from our side?
A sponsor from the leadership team, plus representatives from operations, engineering, maintenance, IT, operational technology and cybersecurity. Including people who do the day-to-day work is important, because they know where data is unreliable and where time is lost. Most stakeholders need one interview of about an hour, so the time commitment for the business is modest.
If you would like to understand how ready your business is for AI, email info@skyodigital.com or book an introduction with the Skyo Digital team.
