Artificial Intelligence (AI) is a hot topic. Businesses in most industries are including it in their technology portfolios. The push is on.
The mining industry is no different. Mining companies are evaluating how they can use AI – namely advanced analytics, machine learning, and automation – to add long term value to their operations. These tools hold the promise of improving processes and enhancing productivity while at the same time reducing costs and improving safety. Should mining companies go all-in on AI? If so, how?
Norcat has a unique perspective on mining technology and how companies should implement AI. We recently collaborated with Deloitte to produce an in-depth study of AI in mining entitled “Future of mining with AI: Building the first steps towards an insight-driven organization.” What’s more, our Norcat Underground Centre positions us as a one-stop shop for mining tech firms to develop, test, and demonstrate their technologies in an operating mine. So, when mining companies seek our opinion on how they should approach AI, our message, rooted in our observations, is: slow down! True, mining is undergoing a technology and innovation transformation that has put the industry on an uncharted path.
That’s a good thing. Companies are excited to implement technologies that have the potential to increase competitiveness and drive shareholder value. However, as with all other industries that have undergone a technological transformation, existing procedures and processes need to be designed correctly and the right team with the right skillsets must be in place. It is only then that businesses can find an effective and tempered way to implement and use AI – otherwise, their value propositions and promises of new technologies will be a lost cause.
Assessing the landscape: Is the company ready for AI implementation?
While AI has the potential to offer the mining sector a wide range of benefits, companies, for a myriad of reasons, first need to grasp the importance of fully understanding the components and pathway to a successful integration. The opportunities for innovation are plentiful for those who plan accordingly, which is why we recommend taking the time to assess processes and personnel first.
Preparing for AI implementation requires companies to review their structures, expectations and options. Here’s how:
- Align people, processes, and technologies from the top down: Companies should explore how they want to operate and determine if and how emerging technology can help achieve their desired outcomes. Prior to implementation, it’s important for leaders to understand the capacity and competence of their workforce and what skills and level of confidence they will need to both implement the technology and ensure sustained utilization and operational success.
- Build the case for AI and set expectations appropriately: Companies need to ask fundamental questions about the purpose of AI, as well as its prerequisites and constraints, and ensure that there is clarity on the roadmap for AI’s implementation. Leaders should plan for the transition and account for periods where they may lack tangible results and face challenges in data quality. Implementation can often be more difficult, time consuming and costly than anticipated.
- If you must choose, choose wisely: There is no shortage of emerging AI tech firms with an exceptional array of solutions poised to transform the global mining industry. This “tech buffet” is overwhelming for any company looking to procure and integrate technology solutions. That said, leaders should not get caught up in the innovation excitement bandwagon, and instead should employ a thoughtful, methodical, and careful process to assess options on where to invest.
The data issue: Is it ‘big’ or not?
It’s no secret that a vast array of quality data is a critical ingredient when introducing AI into any business. Without it, the ability to develop complex correlations and pattern recognition algorithms is limited. Mining companies already have a lot of data; however, unlocking it and validating its integrity are key barriers when it comes to recognizing the benefits of AI. As I have previously argued (Feb/Mar 2019 CMJ), the mining industry does not have data problem, it has a data utilization problem.
The industry revolution is quickly changing the narrative on gathering and effectively utilizing data to inform and integrate with AI solutions. Sensors are now reliably gathering data to the point where these systems are seen as commodities, but to win, companies need to take things to the next level by using the data more effectively and creatively than their competitors.
Playing the long game: Looking at tomorrow in the context of today
For mining companies that are looking to win at the technology game, it’s careful and thoughtful planning that will get them the gold medal – not speedy implementation. The future of AI in mining rests on the ability of businesses to get the most value from the technology platforms they use today. For this reason, they must apply the brakes, pause, and connect the parts to maximize the whole. Then, they can decide how best to use AI in their operations and adapt as needed. AI is flourishing, and mining companies that set the stage for it will flourish too.
DON DUVAL is the CEO of Norcat. For more, follow Don on LinkedIn and Twitter (@don_duval).