In recent times, Artificial Intelligence (AI) has become a buzzword, often discussed for its far-reaching implications.
At its core, AI promises to deliver three pivotal advancements: accelerating economic value across societies, propelling business growth, and cultivating more creative and meaningful work environments.
As a Manager, the challenge - and opportunity - lies in harnessing AI to achieve these objectives within your team.
Yet, amidst the widespread discourse on AI's potential, practical guidance on developing and effectively utilising AI within business contexts remains scarce. This is partly due to the evolving nature of the technology and our collective learning curve in leveraging its full potential.
The following guide aims to provide you, as a manager, with a pragmatic approach to deploying AI in your teams, focusing on tangible results and strategic integration.
Focusing on Business Benefits, Not Just Technology
In the resources sector, the key to effectively leveraging Artificial Intelligence (AI) lies in your ability to focus on its business benefits, rather than getting entangled in the allure of the technology itself. It's crucial to approach AI as a strategic investment that brings substantial value to your organisation, rather than just another technological advancement.
There’s a common pitfall where managers become enamoured with the technology's potential and fall into a 'resource intensive rabbit hole', investing heavily with uncertain returns.
This tech-centric approach often leads to sophisticated solutions that fail to integrate into daily business operations, either because they don't address real business needs or due to lack of a solid adoption strategy.
The result? A shiny, new technology that remains underutilised or completely ignored by the team.
Your primary objective as a manager should be to circumvent these issues. Focus on making AI a tool that your team not only understands but is eager to embrace. This involves understanding the specific challenges and opportunities within your team and aligning AI solutions to meet these needs.
Remember, successful implementation of AI is not just about the system you install; it's about the people who use it and the processes it enhances.
Your role is to ensure that AI is seen not as a replacement but as an enhancement to your team's capabilities, driving efficiency, accuracy, and innovation in your everyday operations.
Step One: Understand the Technology
When considering AI, remember it’s part of a broader spectrum of emerging technologies, including AR, advanced robotics, biosynthesis, and quantum computing, among others.
These technologies are developing rapidly and should be considered collectively to avoid siloed thinking and fully reimagine business processes.
Below is a summary of the new technologies that have practical application in the mining and oil and gas sectors.
Technology | Maturity | Description | Use in Resources and Oil & Gas Sector |
Quantum Computing | Experimental | A field of computing based on quantum theory principles, using quantum-mechanical phenomena to perform operations on data. | Potential to revolutionise data analysis and problem-solving in exploration and operational efficiency. |
AI (Artificial Intelligence) | Emerging | A branch of computer science dealing with the development of machines capable of intelligent behaviour. | Enhances decision-making, efficiency, and safety through predictive analysis and automation. |
Biosynthesis | Emerging | The use of biological systems, or components thereof, to develop or manufacture products, mimicking natural biological processes. | Can be applied in material science for developing new, efficient, and environmentally friendly materials. |
VR/AR (Virtual and Augmented Reality) | Emerging | Simulated experiences that can be similar to or completely different from the real world. | Used for training, remote collaboration, and visualisation of complex data or geological formations. |
Energy Storage and Renewable Energy Technologies | Emerging | Technologies for storing energy produced at one time for use at a later time, and renewable sources of energy like wind and solar power. | Reduces operational costs and carbon footprint through efficient energy use and sustainable practices. |
5G Technology | Maturing | The fifth generation technology standard for broadband cellular networks, offering higher speed and more reliable internet connectivity. | Facilitates real-time data transfer and remote operations, enhancing operational efficiency. |
Machine Learning and Predictive Analytics | Maturing | A subset of AI focused on algorithms that can learn from and make predictions or decisions based on data. | Predicts equipment failures, optimises exploration efforts, and improves operational planning. |
IoT (Internet of Things) | Advanced | Network of physical objects embedded with sensors, software, and other technologies to exchange and act on data they collect from their environments. | Monitors equipment health, environmental conditions, and operational efficiency in real time. |
Blockchain | Advanced | A system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system. | Ensures data integrity in supply chain management and transactional processes. |
Cloud Computing | Advanced | The delivery of different services through the Internet, including storage, databases, networking, software, analytics, and intelligence. | Provides scalable data storage and powerful computing capabilities for data analysis and management. |
Big Data Analytics | Advanced | Techniques and technologies used for analysing large and complex data sets. | Enables deep insights into operational processes, market trends, and strategic planning. |
Robotics and Automation | Advanced | Machines capable of carrying out complex tasks autonomously or semi-autonomously, often used to perform duties that are hazardous or not feasible for humans. | Automates hazardous or complex tasks, increasing safety and operational efficiency. |
Drones and Aerial Surveillance | Advanced | Unmanned aerial vehicles and systems used for monitoring, surveying, and mapping. | Offers remote monitoring and surveying capabilities for inaccessible areas. |
The trick as a manager is to immerse yourself in these technologies and know their potential. That way you can see intersections between the issues your teams face and the potential these new technologies bring.
The Expense of AI Development: Understanding your options
For ambitious managers in the resources sector looking to pioneer AI implementation, understanding the cost implications is a critical step in preparing a compelling business case.
Currently, there are three broad strategies for AI development, each with its own cost structure and benefits:
Developing New Foundation Models: This approach involves building a bespoke AI model from the ground up. The costs for such an endeavour can range from $50 million to over $90 million. While this option comes with a hefty price tag, it offers unparalleled customisation and complete control over the AI model's capabilities and applications. It's a strategic choice for organisations seeking to develop highly specialised AI solutions that align closely with their unique operational needs.
Enhancing Existing Models: An alternative route is to collaborate with providers of Large Language Models (LLMs) to enhance existing AI models. This process typically costs between $1 million and $10 million. By taking this path, you can tap into the power of proven AI technologies while introducing a level of customisation to address specific business challenges. It's a cost-effective way to leverage AI's benefits without the significant investment required for building a new model from scratch.
Fine-tuning Existing Models: The most budget-friendly option involves adapting pre-built AI models to perform specific tasks relevant to your operations. This can cost anywhere from $10,000 to over $100,000, depending on the complexity of the customization. This approach is ideal for companies looking to explore AI capabilities without committing to the substantial financial and resource investment of developing or significantly enhancing an AI model.
As AI technology continues to evolve, costs are likely to decrease, making it more accessible. However, for managers aiming to be early adopters and drive innovation in their teams, it’s important to have a clear understanding of these current cost structures.
This knowledge will enable you to make informed decisions and put forth persuasive business cases that align with your organisation’s strategic goals and budgetary constraints.
Step Two: Identifying your “golden” use cases
For managers in the resources sector looking to integrate AI into their operations, the key lies in identifying what we call “golden use cases.” These are scenarios where the application of AI or related technologies could significantly enhance efficiency and drive business value.
Uncovering High-Value Opportunities
Begin by analysing your current processes, particularly those that are resource-intensive and time-consuming. While not every task is suitable for AI application, this review may reveal potential golden use cases.
These are high-value tasks performed frequently, where AI's impact can be transformative—either through significant cost reductions or revenue enhancements. Consider the following examples:
AI in Exploration: Utilising predictive analysis for mineral discovery to increase efficiency and reduce exploration costs.
AI-Enhanced Safety: Implementing AI to predict potential hazards, thereby saving costs related to accidents and enhancing safety standards.
AI in Process Optimisation: Using AI for predictive maintenance to avoid costly equipment failures and reduce downtime.
AI in Environmental Management: Employing AI to monitor and manage environmental impacts, preventing regulatory fines and reputational damage.
Data-Driven Decision Making: Leveraging AI-powered analytics for strategic decisions that lead to cost savings.
Workforce Management: Optimising labor allocation with AI, resulting in significant labor cost savings.
Compliance and Regulation: Streamlining regulatory compliance processes with AI, thus avoiding fines and legal costs.
Evaluating the Business Case
The feasibility of pursuing an AI solution hinges on understanding the cost implications of the current problem. This requires benchmarking the costs and revenues of existing practices and precisely determining how AI might reduce those costs or increase revenues.
If the potential value offered by AI is several times the estimated development cost, you may have a compelling business case to present to your leadership.
To effectively assess these opportunities, utilise your in-depth knowledge of your team's activities. Think critically about how AI and other emerging technologies could address the challenges your team faces.
It’s essential to weigh the benefits against the costs – are the potential improvements and savings worth the investment in AI?
Step 3: Addressing the Non-Technology Aspects of AI Implementation
Integrating AI into your business operations is a multifaceted challenge that goes beyond just the technological aspects. It involves ensuring that the AI solutions are seamlessly woven into your existing business processes and workforce dynamics.
Here's an approach to effectively manage this crucial aspect of AI implementation:
Encouraging Adoption and Identifying Beneficial Processes
Process Evaluation: Carefully assess how AI tools can enhance your team's daily operations. Identify specific tasks where AI can bring about improved efficiency and outcomes.
Technology Alignment: Evaluate existing AI tools and technologies to determine which ones align best with your team's activities and can effectively augment their capabilities.
Training and Addressing Workforce Concerns
Developing Skills: Create a comprehensive training program designed to equip your team with the necessary skills to effectively use AI tools.
Managing Perceptions: Address potential resistance head-on. While many workers recognise the value of AI, job security concerns are common. It’s important to communicate transparently about the role of AI and how it's intended to complement human skills, not replace them.
Establishing Decision Rights
Decision-Making Framework: Clearly define who will be responsible for making decisions related to the adoption and use of AI. Establish transparent decision-making processes and delineate rights to ensure smooth implementation.
Navigating Stakeholder Perception and Leadership Expectations
Risk Mitigation: Plan for and address risks associated with transitioning from human-operated to machine-driven processes. Effective risk mitigation demonstrates to leadership that the implementation is well-considered.
Stakeholder Engagement: Understand who needs to be informed about the AI integration. Utilise a stakeholder map to address concerns and plan communication strategies.
Engaging Leadership: Identify which members of the leadership team might have reservations. Proactively engage with them about your plans, showing that you are considering potential concerns and including them in your strategy.
By focusing on these non-technology aspects, you ensure a holistic and well-considered approach to AI implementation. This approach not only fosters a culture that embraces AI but also ensures your team is adequately prepared for the transition.
Addressing concerns and risks proactively is essential in demonstrating to leadership that your AI strategy is comprehensive, pragmatic, and aligned with both business goals and workforce dynamics.
Step 4: Keeping Up with Ethical and Regulatory Developments in AI
As AI technology rapidly advances, regulatory frameworks are evolving at a similar pace to address new challenges and ethical considerations. For managers in the resources sector, staying informed about these developments is crucial. It’s not just about leveraging the power of AI but doing so responsibly and in compliance with emerging regulations and ethical standards.
Here's how you can ensure that your AI initiatives align with these evolving considerations:
Staying Informed on AI Regulations
Regular Updates: Establish a routine for keeping abreast of the latest developments in AI regulations. This can involve subscribing to industry newsletters, attending relevant conferences, or participating in regulatory forums.
Legal Consultation: Engage with legal experts specialising in technology and AI. Their insights can help you navigate the complex regulatory landscape and ensure compliance with current laws.
Implementing Ethical AI Practices
Ethical Frameworks: Develop and adhere to ethical guidelines for AI use in your operations. This includes considerations around data privacy, bias in AI algorithms, and the impact of AI decisions.
Training on Ethical Use: Educate your team about the ethical implications of AI. Ensure they understand the importance of ethical AI practices and how to apply them in their work.
Ensuring Ongoing Compliance
Review and Audit: Implement regular review points to audit your AI initiatives against the latest industry best practices and regulatory requirements. This process should be dynamic, allowing for adjustments as regulations evolve.
Leadership and Team Confidence: Demonstrating a commitment to ethical and regulatory compliance in AI not only builds confidence among your leadership but also fosters trust within your team. It shows that you are responsibly and thoughtfully embracing AI technology.
By staying updated on the ethical and regulatory aspects of AI and incorporating these considerations into your AI strategy, you position yourself and your team as responsible adopters of this transformative technology.
This approach not only safeguards against potential legal and ethical pitfalls but also reinforces your reputation as a forward-thinking and conscientious leader in the field.
Ready to Embark on Your AI Journey?
Navigating the complexities of AI in the resources sector can be a challenging yet rewarding endeavour.
As you consider integrating AI into your operations, remember that it's a journey of continuous learning and adaptation. If you're looking for more guidance or have specific questions about how to effectively implement AI in your team, we're here to help.
Get in Touch with Us at Transformation Partners
At Transformation Partners, we specialise in empowering leaders like you to harness the full potential of emerging technologies like AI. We are dedicated to providing tailored advice and solutions that align with your unique business needs.
Whether you're just starting out or looking to refine your existing AI strategies, we invite you to reach out to us.
Together, we can explore innovative approaches to optimise your operations, enhance team performance, and drive sustainable growth in the resources sector.
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