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Case Study Mining Gen AI Large Language Models

A Leading African Mining Company's
AI-Powered Transformation: From Concept to Implementation in Record Time

Client
Leading South African Mining & Energy Company
Industry
Mining
Location
South Africa
Solution
Generative AI & LLM Digital Assistants

The Requirement

The client sought to deploy AI-driven digital assistants to enhance operational efficiency, compliance, and decision-making across HR, safety, production, and investor relations functions.

The Solution

Decision Inc. deployed four tailored GenAI-powered digital assistants — for HR, safety, production, and investor relations — in just six months, using Meta Llama 3.3 on Azure Databricks at just $9 per day.

The Impact

Expected efficiency gains, cost reductions, and improved compliance exceeding 30% ROI. HR query times expected to drop 30-40%, manual data processing reduced 20-25%, and annual cost savings of R2-5 million anticipated.

01 · Client Overview

A Leading South African Mining & Energy Company

The client is a leading South African mining and energy company with a growing focus on renewable solutions and operations across Africa. With sustainability at the core of their strategy, they are committed to creating long-term value while embedding environmental responsibility across operations.

In response to the evolving demands of the industry, the organisation has made digital transformation a key priority — leveraging technology to boost efficiency, enhance safety, and support data-driven decision-making across the business.

Mining Operations
Upload cs_mining_ai_overview.jpg
02 · Executive Summary

Six Months from Concept to Implementation

In 2024, Decision Inc. partnered with the client to drive a transformative AI initiative aimed at improving operational efficiency, compliance, and strategic decision-making. In just six months, Decision Inc. successfully designed, tested, and implemented a suite of AI-powered digital assistants, demonstrating a scalable and repeatable framework for AI-driven operational excellence.

This initiative focused on leveraging Generative AI (GenAI) and Large Language Models (LLMs) to enhance key business functions across HR, safety, production, and investor relations. The rapid implementation was achieved through a structured approach combining agile delivery, optimised AI model selection, and proactive change management.

A robust measurement framework has been established to track key performance indicators (KPIs) over the next 12-18 months. Based on initial results, the AI solutions are expected to deliver a significant return on investment with expected efficiency gains, cost reductions, and improved compliance exceeding 30%.

03 · The Challenge

Overcoming Four Operational Bottlenecks

The client operates in a highly regulated and complex environment, where efficiency, compliance, and decision-making are critical. The company identified four key operational challenges that needed to be addressed:

1. HR Query Management & Employee Support

HR systems were fragmented, requiring employees to search multiple platforms for policies and procedures. This resulted in inefficiencies, delays in query resolution, and lower employee satisfaction.

2. Safety Compliance & Regulatory Adherence

With a multilingual workforce, accessing and understanding safety protocols was a challenge. Lack of clarity in safety documentation increased non-compliance risks, potentially leading to operational disruptions and penalties.

3. Production Decision-Making & Efficiency

Manual production data analysis delayed operational adjustments, reducing efficiency in responding to real-time changes in mining operations. A real-time analytics solution was essential to enhance productivity.

4. Investor Relations & Stakeholder Reporting

Financial and market data required for investor communication was compiled manually, leading to inefficiencies and slower reporting cycles. A more automated and accurate reporting mechanism was needed.

04 · The Solution

Four GenAI-Powered Digital Assistants

Decision Inc. deployed four tailored GenAI-powered digital assistants, each designed to automate, streamline, and enhance key business functions across the organisation.

Assistant 01
People & Performance Digital Assistant
  • Integrated with the company's HR systems to provide employees with instant, accurate responses to HR queries
  • Leveraged LLMs to interpret policies conversationally, reducing the need for manual HR interventions
  • Designed to handle complex policy-related questions, improving accessibility and compliance with company policies
HR Assistant Diagram
Assistant 02
SHEQ Safety Assistant
  • AI-driven multilingual support to translate and explain safety protocols in real time
  • Ensured that employees could easily access compliance documents, reducing misinterpretation of safety requirements
  • Helped mitigate safety risks and improve adherence to regulatory standards
SHEQ Safety Assistant Diagram
Assistant 03
Production Analytics Assistant
  • Integrated real-time analytics and AI-driven insights to support faster operational adjustments
  • Provided predictive analysis for optimising production schedules and resource allocation
  • Reduced reliance on manual data interpretation, enabling quicker response to production issues
Production Analytics Assistant Diagram
Assistant 04
Investor & Liaison Newsletter Assistant
  • Automated the aggregation and summarisation of financial and media insights, significantly improving reporting speed
  • Ensured real-time access to key investor data, allowing for more informed engagement with stakeholders
  • Reduced the time spent on manual reporting processes, freeing up resources for strategic financial planning
Investor Assistant Diagram
05 · Implementation

A Structured, Agile AI Deployment Model

The project followed a rigorous, structured delivery approach that balanced speed with quality — achieving full implementation across four enterprise functions in just six months.

Agile Development & Rapid Deployment

The project followed a phased, sprint-based development model that enabled fast prototyping and continuous improvement. Short, focused iterations allowed working models to be delivered within weeks, accelerating feedback cycles and reducing time-to-value.

AI Model Optimisation & Cost Efficiency

After evaluating various architecture options, the solution was built using Meta Llama 3.3 on Azure Databricks. This combination delivered strong performance and kept operational costs to just $9 per day, making the model highly cost-effective and scalable for long-term use.

Change Management & User Adoption

A structured change management process supported widespread adoption, including ongoing stakeholder workshops, targeted training sessions to transition teams from manual to AI-driven workflows, and a staggered rollout strategy incorporating user feedback at every stage.

06 · Business Impact

Expected ROI and Measurable Benefits

A robust measurement framework has been established to track KPIs over the next 12-18 months. Based on initial results, the AI solutions are expected to deliver significant and sustained returns across every function deployed.

30%+
Expected Overall ROI Efficiency gains, cost reductions, and improved compliance across HR, safety, production, and investor relations are expected to deliver combined ROI exceeding 30%, tracked via a robust KPI measurement framework over 12-18 months.

HR Process Efficiency

  • Expected reduction in HR query resolution times by 30-40%, improving employee experience
  • HR teams expected to reallocate 14% of time to strategic initiatives rather than handling routine queries

Safety & Sustainability

  • Expected to enhance adherence to safety protocols, reducing risk exposure and compliance incidents
  • Early feedback indicates improved accessibility to safety documents among field workers

Production Optimisation

  • Anticipated increase in operational efficiency by enabling faster, data-driven production adjustments
  • Expected reduction in manual data processing time by 20-25%, leading to improved responsiveness

Investor Relations & Reporting

  • Expected time savings of 30% in investor report preparation, ensuring more timely and accurate communication
  • Anticipated improvement in data accuracy and consolidation, reducing reporting errors

Cost Savings & Scalability

  • AI model optimisation led to an immediate cost reduction of over 90%, making AI-driven operations financially viable at scale
  • Overall AI implementation expected to contribute to an annual cost saving of R2-5 million across HR, production, safety, and investor relations

Conclusion: A Blueprint for AI-Driven Business Transformation

The successful implementation of GenAI-powered solutions at this mining company underscores the potential for AI-driven transformation across industries. This initiative demonstrates that AI can deliver real business value — quickly, effectively, and at scale.

  • AI can be implemented rapidly, with measurable impact within six months
  • Strategic AI model selection is critical to balancing cost and performance
  • Change management is essential — technology alone does not guarantee success
  • AI-driven automation can unlock millions in savings while improving efficiency and compliance

This case study serves as a model for businesses seeking to integrate AI strategically, ensuring not only cost reductions but sustained operational excellence. Decision Inc. remains committed to delivering high-value AI solutions that drive business transformation and measurable ROI.