Mining companies are taking an increasing interest in adding components of AI and Automation into their digital landscape to cover a host of uses cases from document investigation on geological surveys, environmental impact studies, to complex reports and documents on safety and assets using natural language processing (NLP), as well as processing of old paper-based mapping and drill logs into digital formats to exploration targeting, for regional areas and local within the existing mining.
Solutions are being developed to enable staff to ask conversational questions such as “Where are our largest reserves in the region that are a particular grade?” and “What reports do we have around geotechnical safety incidents in the mine?”
Companies undertaking purposeful steps in the AI and Automation direction need to be aware of the fact that it is a complex landscape with many offerings in each functional area, with multiple vendors from the Big 4 to ISV to startups. Also, there needs to be awareness about the gaps in what AI/Automation solution vendors are providing, when starting to develop their systems. This has been the experience that companies that have embarked on this journey have found with clients when developing solutions with other AI & Automation platforms from AWS, Microsoft and Google.
What is needed to handle this situation is the AI & Automation ecosystem. Customers are looking for a single source of truth and governance for managing all type of AI/Automations. Customers are seeking out AI vendors to work together and bring combined AI capabilities to quickly fill the gaps and rapidly develop solutions.
System Integrators who can accelerate time to value with AI, with pre-baked industry solutions and BOTs, provide professional services skills in IBM, AWS, MS, Google, their own AI platform and Open-source, as well as provide technology training for the customers’ own staff into AI skills. Also, there is a growing need to gain access to crowdsourcing development capability like Freelander, Topcoder, UnEarthed etc. to bring capability on demand in AI and Advanced Analytics when needed.
Vendors that have collaborated on AI projects include Wipro, with its Holmes platform and IBM Watson for clients to deliver Holmes on Watson or Holmes + Watson solutions. One of these was where Wipro Holmes & IBM Watson collaborated to improve workplace productivity for a leading Australian oil & gas major. Another was a Natural Language Query (NLQ) for Enterprise for PetroTechnical Staff (Geoscientist) for Upstream, where Watson was used for language understanding tasks and Holmes for image intelligence, and the improvement in searching through structured and unstructured data went from 30 minutes to 2 minutes.
Going forward, the IT landscape in AI and Automation is not going to be a one-stop shop and customer IT organizations need to be aware of this complexity and look for organizations which are able to work together and have the platforms to manage the complex AI/Automation environment.