No clear owner
AI touches operations, technical teams, IT, data, cybersecurity, legal, finance and management. Without an accountable owner, initiatives stall between functions.
Fractional AI Transformation Lead
Move AI from scattered experiments to working mining workflows.
Many mining and engineering organizations know they need to respond to AI. Far fewer have someone internally who can determine what is worth implementing, translate operational problems into practical use cases, coordinate the technical work, establish appropriate controls and keep the program moving.
Senior AI transformation leadership provides ownership and direction without immediately creating a full-time AI leadership position.
Transformation workstream
Business problem
Workflow
AI use case
Pilot
Governed implementation
Measured outcome
Mining & engineering context
Strategy + implementation
Vendor-neutral
Governance-aware
Hands-on delivery
NDA available
The Implementation Gap
Most organizations do not need another AI strategy presentation.
They need someone to own the work.
Employees are experimenting, executives are asking questions, vendors are presenting products and teams are purchasing licences. The harder problem is turning that activity into controlled, useful, measurable change.
Too many tools
Teams face general AI tools, automation platforms and specialist vendors. The question is not which tool is fashionable, but which approach fits the workflow.
Unstructured experimentation
Different employees adopt different tools with inconsistent security and validation, creating risk and making successful workflows difficult to scale.
Pilots never reach operations
A proof of concept may work technically but fail because nobody owns integration, adoption, process redesign, controls or measurement.
Technical teams have no spare capacity
Engineers, geologists, metallurgists and project professionals cannot become transformation managers on top of their full-time work.
Strategy becomes disconnected from delivery
A roadmap has little value if nobody converts it into workflows, tools, procedures and measurable outcomes.
An Accountable AI Owner
Senior AI transformation leadership without creating a full-time internal role.
The Fractional AI Transformation Lead works across leadership, technical teams, IT and implementation partners to keep the organisation's AI transformation program moving.The role provides an accountable owner for the transformation workstream: identifying priorities, coordinating decisions, directing pilots, establishing appropriate governance, monitoring value and ensuring that successful initiatives move beyond experimentation.The engagement is structured around the transformation program and required leadership capacity rather than fixed weekly attendance.
Board / Executive Team
↕
Fractional AI Transformation Lead
↙↓↘
Technical Teams
IT / Data / Security
Vendors / Implementation Partners
This role does not replace the CIO, IT department, engineering team or operational leadership. It creates an accountable bridge between them for AI-related transformation.
From Strategy to Execution
One accountable workstream across the AI lifecycle.
01
Workflow Discovery
The objective is not to find somewhere to use AI. It is to identify operational problems where AI or automation could create material value.
- Repetitive manual tasks
- Document-heavy processes
- Knowledge bottlenecks
- Research-intensive workflows
- Reporting burdens and duplicated work
- Slow handoffs and high-volume review
- Processes dependent on individual expertise
02
Use-Case Prioritization
The organization gets a prioritized pipeline rather than an unstructured list of ideas.
- Business value and hours consumed
- Technical feasibility and data availability
- Data sensitivity and governance risk
- Implementation and integration complexity
- Reliability and adoption requirements
- Cost and measurable return
03
Build-versus-Buy Decisions
Sometimes the correct AI recommendation is not to use AI.
- Existing enterprise software
- Capabilities already in current tools
- Specialist third-party software
- Workflow automation
- Custom development or internal prototype
- Vendor implementation—or no AI at all
04
Pilot Design & Implementation
Where specialist engineering is required, LOMExcel can act as the technical-business owner while internal IT or specialist vendors implement.
- Workflow and functional requirements
- Prototypes and selected automations
- Configuration and vendor coordination
- Testing criteria and validation procedures
- Human review points
- Operating documentation
05
AI Governance & Risk Controls
Governance should enable useful adoption rather than become a policy document nobody uses.
- Confidential and technical information
- Approved tools and access controls
- Human validation and model limitations
- Source verification and record keeping
- External services and vendor risk
- Acceptable use and safety-critical applications
06
Adoption & Change Management
Training is integrated into transformation rather than treated as a generic seminar.
- Role-specific onboarding
- Workflow demonstrations and SOPs
- Prompt and workflow standards
- Internal champions and feedback loops
- Team-specific training
- Management communication and adoption tracking
07
Measurement & Business Case
Evidence supports the decision to scale, modify or stop each initiative.
- Hours saved and cycle time
- Throughput and response time
- Error reduction, quality and rework
- Analyst capacity and cost per task
- Model or API cost
- Adoption and time to decision
08
Roadmap Ownership
The roadmap becomes a live management tool rather than a once-off presentation.
- Initiatives, owners and priority
- Status and dependencies
- Risks and technical requirements
- Value case and implementation cost
- Governance requirements
- Pilot results and scale decisions
Turn the highest-priority workflow into a governed, measurable implementation.
Discuss an AI Transformation EngagementMining-Specific Opportunities
Start with real mining workflows.
Every organization is different. These are examples of workflows that may warrant assessment.
Studies & Project Development
- Technical-document review
- Study precedent searches
- Assumption benchmarking
- Project comparisons
- Basis-of-design retrieval
- Risk synthesis and lessons learned
- Action tracking and reporting
Engineering
- Engineering document search
- Specification comparison
- Design-criteria retrieval
- Equipment-data extraction
- Vendor-document review
- Technical-query triage
- Standards retrieval and change analysis
Geology & Resource Teams
AI must not replace required professional geological judgement or formal resource-estimation responsibilities.
- Historical-report search
- Technical literature synthesis
- Drill-result retrieval
- Document classification
- Exploration knowledge management
Metallurgy & Processing
- Testwork search and organization
- Comparable flowsheet research
- Processing benchmark development
- Technical literature review
- Plant knowledge retrieval
- Operating-procedure support
Corporate Development & M&A
- Asset screening and peer research
- Public technical-document review
- Transaction intelligence
- Target comparison
- Due-diligence information organization
- Management briefing preparation
Corporate & Shared Services
- Contract review support
- Procurement and supplier research
- Meeting and action management
- Internal knowledge and policy search
- Reporting and document classification
- Repetitive administrative processes
Responsible Implementation
AI should support professional judgement, not bypass it.
LOMExcel does not advocate uncontrolled deployment into safety-critical, legally controlled or professionally regulated decisions. Appropriate human accountability remains essential.
AI can support
- Information retrieval and synthesis
- Workflow acceleration and comparison
- Drafting and classification
- Document analysis and structured research
- Repetitive process automation
AI should not autonomously replace
- Qualified Person or Competent Person responsibilities
- Engineering sign-off
- Safety-critical operational decisions
- Formal resource or reserve estimates
- Legal advice or regulatory accountability
- Investment decisions
- Professional judgement required by law or professional standards
Three Ways to Engage
Start where your organization actually is.
01 · Diagnose
AI Transformation Diagnostic
Understand where to act.
From CAD $4,500
Defined-scope assessment & 90-day roadmap
For organisations that need to determine where AI can create meaningful value, which opportunities should be prioritised, and what should happen next.
- Leadership discussion and selected stakeholder interviews
- Current-tool and workflow review
- AI use-case inventory
- Technical and organisational feasibility assessment
- Governance-gap assessment
- Quick-win identification
- Use-case prioritization
- Recommended first pilot
- 90-day transformation roadmap
- Executive readout
02 · Implement
AI Pilot & Implementation Sprint
Prove one high-value use case.
From CAD $8,500
Defined-scope implementation
For organisations that have identified a priority workflow and want to move it from concept into controlled practical implementation.
- Workflow mapping and requirements definition
- Business-case and baseline metrics
- Build-versus-buy evaluation
- Solution design
- Prototype or appropriate automation
- Vendor coordination where required
- Testing and validation
- Human-review framework and controls
- SOP and operating guidance
- Measurement framework
- Scale, modify or stop recommendation
Typical delivery window: 3–6 weeks depending on scope, integration requirements and client dependencies.
03 · Lead & Scale
Fractional AI Transformation Lead
Create ongoing ownership and momentum.
From CAD $6,500/month
Ongoing executive AI leadership · Typically 3-month initial engagement
For mining and engineering organisations that need senior ownership of their AI transformation program without immediately creating a full-time internal AI leadership position.
- AI transformation roadmap ownership
- Executive and stakeholder alignment
- Use-case prioritization and investment decisions
- Workflow discovery and opportunity assessment
- Pilot and implementation oversight
- Build-versus-buy and vendor evaluation
- AI governance and internal standards
- Technical direction across implementation teams
- Progress, value and risk reporting
- Internal AI capability development
- Additional implementation work scoped separately where required
Engagements are scoped around outcomes and required leadership capacity rather than fixed weekly attendance. Fractional engagements provide reserved senior AI transformation capacity, while hands-on implementation beyond the agreed leadership scope can be added as a defined project.
Not sure which engagement model fits? Start with the business problem.
Discuss Your RequirementWhat Progress Looks Like
The first 90 days should produce evidence, not just strategy.
Days 1–30
Understand, prioritize and establish guardrails.
- Stakeholder discussions and workflow assessment
- Tool inventory and risk review
- Use-case backlog and prioritization
- Current-state governance
- Pilot selection and success metrics
The organization should know which opportunities matter, which do not, what to pilot first, what governance is required and who needs to be involved.
Days 31–60
Move the first priority workflow into implementation.
- Detailed workflow design
- Tool selection and baseline measurement
- Prototype or pilot
- User testing and refinement
- Documentation and adoption planning
Subject to scope and organisational readiness, at least one priority opportunity should move beyond presentation into practical testing.
Days 61–90
Measure, decide and build the repeatable model.
- Pilot and business-value assessment
- Process and governance refinement
- Adoption support
- Second-use-case selection
- Roadmap refresh and executive report
Management should have evidence to decide: Scale it. Change it. Stop it. The next phase is based on organisational experience rather than speculation.
Build a practical first 90 days around your organisation’s real constraints.
Discuss the First 90 DaysThe Role Before the Headcount
You may need AI leadership before you need an AI executive.
For many mining and engineering organisations, creating a permanent Chief AI Officer or dedicated AI transformation team is premature.The organisation may still be determining where AI creates genuine value, which capabilities should remain internal, what should be purchased, where custom development is justified, what governance is required and whether the long-term workload ultimately warrants permanent specialist headcount.A fractional model provides senior ownership during this transition without requiring the organisation to establish a permanent executive role before the scope of that role is understood.
Senior capability without immediate permanent headcount
Access experienced AI transformation leadership while the organisation determines the scale and structure of its longer-term requirements.
Flexible engagement scope
The level of involvement can evolve according to transformation priorities, active initiatives and implementation requirements rather than a fixed weekly attendance model.
Independent technology perspective
Build-versus-buy and vendor decisions can be assessed against the organisation's actual requirements rather than a software sales target.
Designed for capability transfer
The objective is not permanent consultant dependency. Processes, governance, documentation and internal capability should progressively transfer into the client organisation.
Not a Generic AI Consultancy
Mining context on one side. Hands-on AI implementation on the other.
Technical transformation requires understanding how project information is controlled, how engineering decisions are made, what constitutes appropriate evidence and why professional accountability cannot be automated away.
Mining & Engineering
Experience across engineering, construction, commissioning, operations and mining-sector work.
Project & Study Management
Experience across growth projects and mining studies, bridging technical teams, management and commercial decisions.
Business & Strategy
MBA-level commercial perspective supporting prioritization, business cases, investment decisions and organisational change.
Practical AI Systems
Hands-on commercial AI-system development, automation and AI-enabled decision-support experience.
AI Transformation Is Not Shadow IT
Work with your technology environment, not around it.
The role works with internal IT, cybersecurity, data teams and enterprise architecture rather than creating an uncontrolled parallel technology stack.
- Approved tools and enterprise accounts
- Identity, authentication and permissions
- Data access, residency and confidentiality
- Auditability and cloud environment
- APIs and existing-system integration
- Vendor assessment
With an established IT department, LOMExcel acts as a business-technical bridge and workstream owner. Where internal capability is limited, more direct implementation support can be included in scope.
Controlled Adoption
AI adoption should not require giving up control of your information.
Mining organizations work with confidential technical, commercial and transaction information. Implementation must consider where information is processed, which tools are approved, who has access and what human review is required.
- Mutual NDA
- Tool and vendor assessment
- Information classification and confidential-data controls
- Approved-use and role-based guidance
- Human-review and source-verification requirements
- Governance documentation and escalation rules
Client information remains client information.
Engagements available under mutual NDA.
A Good Fit
This model works best when AI matters, but nobody owns it yet.
Mining Companies
Management is under pressure to respond to AI but has no dedicated internal transformation owner.
EPCM & Engineering Consultancies
Technical teams want productivity and better knowledge workflows without compromising quality or confidentiality.
Mining Services Businesses
The organization has repeatable information-heavy workflows that may benefit from automation.
Corporate Development & Technical Groups
Research, benchmarking, document review and technical intelligence consume senior professional time.
Innovation / Technology Leaders
IT leadership needs a partner with mining context to own the AI use-case and adoption layer.
Not Every Problem Needs This Role
A fractional transformation engagement is not appropriate for every organisation.
- You only need a one-hour introductory presentation
- You want a chatbot purely because competitors have one
- There is no executive sponsor for change
- You expect unreviewed engineering or safety-critical decisions
- You want tools without confidentiality or governance
- You already have a capable internal transformation lead with capacity
- You need a general outsourced IT helpdesk
Frequently Asked
AI Transformation FAQ
What is a Fractional AI Transformation Lead?
A retained senior leadership engagement providing ongoing ownership, direction, prioritization and oversight of the organisation's AI transformation workstream. Unlike a once-off adviser, the role remains accountable as initiatives are prioritized, tested, implemented and measured. Hands-on implementation beyond the agreed leadership scope is commissioned separately as a Pilot & Implementation Sprint or defined project.
Is this the same as a Fractional Chief AI Officer?
The functions may overlap, but LOMExcel intentionally uses AI Transformation Lead. The emphasis is practical transformation and implementation rather than creating an executive title where one is not yet required.
Do we need an AI strategy before engaging?
No. An organisation can begin with a Transformation Diagnostic or move directly into a fractional engagement where priorities are understood.
Will LOMExcel actually build AI workflows?
Where the use case fits LOMExcel's implementation capability, yes — typically through an AI Pilot & Implementation Sprint or a separately defined implementation project. For specialist enterprise engineering, LOMExcel can define requirements and manage work with internal teams or specialist providers.
Are you tied to one AI vendor?
No. Recommendations are based on the use case, enterprise environment, confidentiality, performance, cost and governance requirements.
Can you work with our IT team?
Yes. The role is specifically designed to bridge business and technical requirements.
What if AI isn't the right solution?
Then that should be the recommendation. The objective is improved business performance, not maximum AI deployment.
Can you help develop AI governance?
Yes. Governance can form part of a diagnostic, implementation or fractional transformation engagement.
Can you train our employees?
Yes. Role-specific enablement can be incorporated into implementation, and separate corporate AI training is available.
Can you work under NDA?
Yes.
Is there a minimum engagement?
Fractional engagements are typically structured around an initial three-month engagement — a reasonable period to establish priorities, initiate implementation and evaluate progress.
Do you replace our IT department?
No. The role owns the AI transformation workstream and collaborates with IT, management, technical teams and vendors.
From Discussion to Implementation
If AI matters to the business, someone needs to own it.
Whether you are deciding where to start or have disconnected initiatives underway, begin by defining the real problem. Bring the business problem, workflows creating friction, initiatives already underway and constraints that matter. LOMExcel will help determine whether the requirement is a diagnostic, focused implementation or ongoing fractional leadership.
AI Transformation Enquiry