Mining organisations are under pressure to modernise. Ore bodies are harder to access, operations are more complex, stakeholders are less patient, and productivity expectations keep rising. At the same time, the promise of digital and innovation investments is compelling: better decisions, safer work, lower waste, and more resilient value chains. The World Economic Forum and Accenture estimated that digitalisation could generate more than $425 billion of value across the mining and metals ecosystem over a decade, alongside safety improvements, but they also flagged a potential displacement risk of roughly 330,000 jobs that has to be anticipated and mitigated.

That tension, value on one side and disruption on the other, is why change management in mining cannot be treated as a supporting activity. In a sector defined by heavy assets, safety critical work, layered contractor ecosystems, and deep operational routines, adoption is the value. If people do not use the new ways of working consistently, safely, and competently, the technology becomes an expensive pilot that never reaches routine production.

Across the research we reviewed, a clear common thread stands out:

Mining change succeeds when innovation and project delivery are designed around people as active agents, and when change practices are translated into mining language, mining realities, and operational rhythms.

This is not a soft statement. It is a practical operating principle that shows up repeatedly in mining-specific studies of resistance, digitalisation, and innovation adoption. One mining innovation study developed a three-phase process model that explicitly centres people as the agents of innovation and embeds activities like stakeholder involvement, communication, training, role redesign, and incentive alignment into the adoption path. Another study of managers in the mining sector shows how resistance is fuelled by practical constraints and human realities: communication breakdowns, conflict, planning strain, limited resources, low commitment, and job security fears linked to new technology. And a South African study on digitalising mining project management reinforces that change management and employee buy-in are not optional. They are positioned as core enablers, supported by training, incentives, and recognition.

In this Change Diary, we connect these threads into a practical, Prosci-informed point of view for mid-level and executive change leaders: how to manage resistance and adoption in mining digital and innovation programmes, without losing sight of trust, fatigue, conflict, and job security concerns.

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Why resistance in mining is predictable, and why it is often rational

In many industries, resistance is framed as a mindset problem. In mining, it is often a systems problem that shows up in human behaviour.

Managers in the mining sector describe communication as difficult because mining work is labour-intensive, team-based, and complex, and because individuals interpret change through their own expectations and risks. They describe conflict as a common outcome of uncertainty and fear, and they note that conflict delays the change process. They also describe planning as strenuous because mining work is complex, acceptance happens at different rates, and continuous contingency planning is costly.

These are not excuses. They are conditions. When those conditions exist, resistance becomes a signal that the organisation’s change system is under-designed for the reality on the ground.

Add the economic and job security dimension, and resistance becomes even more understandable. In the same study, managers report that employees quickly interpret new technology as a threat to their jobs and future job security. Another section reinforces that employees can be sceptical about technology-driven change strategies because they perceive them as a direct threat to job security, which then becomes a major management challenge. When global research openly discusses potential job displacement from digitalisation, even if projections differ by region and commodity, the fear is reinforced and travels quickly through informal networks.

A Prosci lens helps here: people do not resist change because they dislike progress. They resist when the change threatens something they value, when the path feels unclear, when they do not believe leadership will protect them, or when they lack the capability and support to perform in the new environment.

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Adoption lives inside the work, not beside it

One of the most useful contributions from mining-focused innovation research is that it refuses to separate innovation from adoption. It presents innovation as a phased process that includes stakeholder engagement and the people-side work required to move from idea to routine production.

The model describes three phases, each with three steps:

• Phase 1: Ideating and strategising (identifying opportunities, examining options, making the case)
• Phase 2: Developing and piloting (developing/testing/piloting, proving the concept, preparing to implement)
• Phase 3: Implementing and embedding (transitioning to new operations, new routine production, scaling out)

What matters for change managers is what sits inside those steps. The model includes actions that look like change management, because they are change management: ramping up communication with site staff, celebrating wins, handing ownership to operations, allocating dedicated support roles with KPIs, redesigning workflows and role descriptions, training, revising incentives, and retaining skills through induction and job sharing.

This aligns strongly with what we see in digitalisation research in mining project environments. Buthelezi and Naidoo’s framework positions resistance to change, employee buy-in, and change management itself as explicit disruptors and challenges that must be managed, alongside technical issues like infrastructure and legacy integration. It also identifies success factors that sound like a change leader’s checklist: leadership support, training and skills development, stakeholder engagement, scalability, and continuous monitoring and evaluation.

Mining change becomes feasible when the adoption path is built into the operating model of the work, and when leaders treat resistance as a predictable outcome of job impact, uncertainty, capability gaps, and trust conditions.

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A Prosci-informed adoption path for mining innovation and digital programmes

Prosci’s ADKAR Model provides a practical structure for individual change: Awareness, Desire, Knowledge, Ability, Reinforcement. (Prosci) In mining, ADKAR works best when it is not delivered as training content, but when it is used to design the change system around the operational rollout.

Below is a mining-ready adoption path that maps the three-phase mining innovation model into a Prosci-oriented approach.

Phase 1: Ideate and strategise (build belief, not just a business case)

Mining innovation research emphasises scanning for opportunities, examining options with stakeholders, and building a business case that addresses benefits and risks, including selecting a project champion and publicising senior leadership support.

What this means in practice for change leaders:

1. Translate “why” into operational truth.
   A corporate-level rationale will not land unless it connects to site-level constraints: downtime, rework, near misses, variability, maintenance backlogs, compliance demands, or ore recovery challenges. Site teams want to know what problem is being solved and whether it is a real operational problem.
2. Surface job impact early, and treat it as design input.
   Job security fears are not fixed. They increase when leaders avoid the topic. The WEF’s own framing acknowledges that digitalisation can threaten traditional roles and create inequitable benefits if poorly designed. Make job impact visible early: what changes, what stays, what new skills are required, and what support will be provided.
3. Anchor sponsorship in active visibility.
   Prosci’s research consistently identifies active and visible sponsorship as a key contributor to change success. (Prosci) In mining, visibility includes time on site, not only broadcast messages. It also includes coherent decisions when trade-offs arise between production pressure and adoption discipline.
4. Design the stakeholder system, not just the comms plan.
   Innovation research highlights early stakeholder involvement and frank discussions about opportunities and barriers with both vendors and internal groups. In mining contexts, stakeholder systems often include operations leadership, shift supervisors, maintenance, safety and risk teams, unions or employee forums, contractors, and central functions like IT/OT.

ADKAR focus in Phase 1: Awareness and Desire. (Prosci)
Your goal is not agreement in principle. Your goal is credible belief that the change is necessary, workable, and fair.

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Phase 2: Develop and pilot (co-create adoption, prove the concept, and protect trust)

In the mining innovation model, Phase 2 includes assembling a project team with key stakeholders, co-designing work plans and KPIs, proving the concept through increasing scales, and preparing to implement by ramping up communication, celebrating wins, and handing ownership to operations.

Digitalisation research complements this with a warning: resistance to change and lack of employee buy-in disrupt digitalisation, and leadership and organisational support matter.

What we recommend in Phase 2:

1. Treat pilots as operating model rehearsals, not technical trials.
   Many pilots prove the tool and fail the adoption system. In mining, that is common when pilots are run by a specialised team that shields the site from the real workflow implications. By the time the rollout reaches routine production, the protective bubble disappears and resistance spikes. Build pilots that include real shift patterns, real constraints, and real handovers.
2. Build a supervisor-led change spine.
   Mining teams often interpret change through their immediate leader. If supervisors are not confident, aligned, and equipped, employees will experience mixed signals. The resistance study highlights how communication difficulties and uncertainty make it stressful to explain benefits to employees who fear job loss. Equip supervisors with practical talking points, escalation paths, and feedback loops.
3. Plan for conflict, do not wait for it.
   Managers describe conflict as a primary challenge because uncertainty and fear trigger frustration, which then delays the change process. Build conflict handling into the change plan: clear issue resolution routes, defined decision rights, and facilitated problem-solving sessions that do not punish people for raising risks.
4. Resource the change like it is real work.
   The resistance study is blunt: when resources are inadequate, it is difficult to manage resistance because people do not see the benefit and the organisation cannot meet new requirements. This includes time, backfill, training capacity, and on-the-job support. If adoption matters, resource it.

ADKAR focus in Phase 2: Knowledge and Ability. (Prosci)
Training must be role-specific, timed to the work, and reinforced with coaching. Mining teams often need competence, not only awareness.

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Phase 3: Implement and embed (make the new way the easy way)

In the mining innovation model, Phase 3 includes allocating dedicated support staff with KPIs, redesigning workflows and roles, training, communicating benefits, revising incentives, retaining skills, and scaling out across operations.

Buthelezi and Naidoo’s digitalisation framework highlights continuous monitoring and evaluation as a key success factor. They also position training, stakeholder engagement, and leadership commitment as part of the success foundation.

What embedding looks like in practice:

1. Operational ownership with defined KPIs and support capacity.
   Handovers fail when ownership is symbolic. The innovation model explicitly calls for dedicated support staff with KPIs and operational ownership transfer. This is where many mining rollouts falter: the project team leaves, and adoption becomes “extra work” for already stretched supervisors and crews.
2. Workflow redesign and role clarity.
   If the new tool creates additional steps, dual capture, or unclear boundaries between functions, it will be worked around. Make role descriptions and workflows explicit, and communicate them clearly.
3. Incentives, recognition, and reinforcement.
   The resistance study recommends incentives, training, and the appointment of change agents to encourage acceptance and minimise resistant behaviour. Buthelezi and Naidoo similarly highlight that culture, training, incentives, and recognition support positive attitudes toward digitalisation. In mining, recognition that respects operational credibility often matters as much as monetary incentives.
4. Continuous monitoring that is meaningful to operations.
   Monitoring is not reporting for the sake of governance. It is rapid learning: what is working, where bottlenecks sit, and what corrective actions keep the initiative on track.

ADKAR focus in Phase 3: Reinforcement. (Prosci)
Reinforcement is achieved through aligned measures, local leadership routines, visible follow-through, and the removal of practical barriers.

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Where safety fits, without making it the only story

We deliberately do not frame this diary as a safety article, but in mining, safety is never far from any operational change. Digitalisation can improve safety outcomes at scale, including fatigue monitoring and connected worker solutions, and the WEF analysis explicitly links digitalisation to lives saved and injuries avoided when designed and implemented correctly.

From a change management viewpoint, safety plays a second role: it shapes trust. When people believe a change could compromise safety, resistance increases, often correctly. When leaders demonstrate that safety-critical controls, competencies, and verification mechanisms remain strong, trust rises.

This is where “people side” and “technical side” stop being separable. Safety-critical work requires disciplined adoption, not partial adoption.

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A practical resistance map for mining change leaders

Based on the studies and what we see repeatedly in complex, high-risk industries, resistance in mining programmes tends to cluster into five patterns. Each pattern has an associated design response.

1) Job security and identity threats

Employees interpret technology as a threat to job security, and managers describe this as a central challenge.

Change response: early job impact clarity, reskilling pathways, credible commitments, and transparent trade-offs.

2) Planning strain and change fatigue

Planning is described as strenuous because change is uncertain, work is complex, and acceptance is uneven, requiring ongoing contingency planning.

Change response: reduce concurrent change load, sequence releases around operational rhythms, provide backfill, and design adoption in waves rather than a single “big bang”.

3) Communication breakdown and informal narratives

Poor communication fuels uncertainty, disjointed teams, and difficulty explaining benefits to employees worried about losing jobs.

Change response: supervisor enablement, two-way channels, consistent messaging, and visible leadership decisions that match the messages.

4) Conflict and trust erosion

Conflict is described as a major change challenge because uncertainty and fear get displaced into interpersonal tension and delay.

Change response: issue resolution pathways, facilitated working sessions, and psychological safety for raising risks without repercussion.

5) Practical constraints (resources, infrastructure, integration)

Lack of resources makes it hard to motivate staff and meet new requirements, and digitalisation disruptors include infrastructure limitations and legacy integration.

Change response: resource the change, remove blockers fast, and avoid promising benefits that depend on infrastructure the site does not yet have.

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The executive question that changes outcomes

For mining executives, the most useful shift is to move from asking:

“How fast can we implement?”
to asking:
“How fast can we reach safe, consistent, routine production?”

The mining innovation model explicitly uses the language of “new routine production” as an embedding step, and then “scaling out” once the new way is stable. That phrasing is powerful because it centres what mining operations actually need: a reliable routine.

This also reframes governance. When governance measures progress by milestones completed, adoption becomes negotiable. When governance measures progress by routine performance and capability, adoption becomes the point.

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Key takeaways for change managers working in mining

• Build adoption into the innovation and project lifecycle. Mining research embeds communication, training, role clarity, incentives, and operational handover into the innovation path for a reason.
• Treat resistance as a predictable signal. In mining, resistance is often a rational response to job impact risk, uncertainty, conflict, and resourcing constraints.
• Invest in buy-in as a capability, not a campaign. Digitalisation research positions change management, employee buy-in, training, incentives, and recognition as core to success.
• Use ADKAR to design the rollout, not to decorate it. Awareness and Desire must be earned early, Knowledge and Ability must be built in the work, Reinforcement must be operationalised. (Prosci)
• Be explicit about job impacts and mitigation. Value creation and job displacement risk can coexist. Leaders who face that tension directly build trust faster.

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Closing: the point of digital and innovation change in mining

Mining does not need more pilots. It needs more innovations that become routine, and more digital investments that translate into daily work in a way people trust, understand, and can perform.

When we design change as if mining is a generic corporate environment, we create friction. When we design change as if mining is a living system of people, shifts, routines, risks, controls, and constraints, we create adoption.

If you are leading a digital, operational, or innovation programme in mining and want a Prosci-led approach that takes resistance seriously, builds supervisor capability, and drives measurable adoption, we would welcome a conversation.