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Industry is adopting artificial intelligence at a growing pace; however, many organisations are struggling to move beyond pilot phase. How can industry scale the use of artificial intelligence in 2022, and streamline and operationalise its use? We explore five new adoption strategies and technology focuses that are promising to help industry in its efforts to scale AI and start generating return on investment.

1. CENTRALIZED MULTITALENTED TEAMS

Siloed specialised business units are increasingly being replaced with centralised multitalented teams, comprised of highly skilled data scientists, subject matter experts, engineers, and operators. By combining a diverse group of skill sets, teams can work together on business-critical matters, rapidly producing, testing, and implementing insights from AI models. These teams encourage collaboration and experimentation and encourage the use of advanced analytics throughout an organisation.

2. EXPLAINABLE AI

Being able to explain a prediction or model result is critical to building trust and ultimately to scaling AI. No longer just a black box, artificial intelligence needs to be explainable. Platforms that drill down into the rational and root cause and can produce a DNA map of all contributing factors overtime will win confidence and assist in the adoption of AI across a business. Being able to produce an AL model is no longer sufficient, professionals need to explain the insights aquired for it to drive business decisions.

3. AUTOML

No-code machine learning and low-code machine learning tools are helping industry to scale the use of artificial intelligence. These tools allow machine learning algorithms to be produced without any coding, programming knowledge or experience. The end result is an AI model that the user can put into a live production environment to be used for predictive maintenance and advanced analytics of industrial equipment and processes. VROC combines both autoML and MLOps in its automated process, whereby models can be deployed straight into a live production environment, where they automatically update, learning from newly ingested data on a continuous basis, providing on-going live monitoring and predictions.

4. MLOPS

The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even thousands of models in operation, MLOps becomes essential to streamline and automate this process. Without MLOps, model deployment can take many businesses months, and often change ownership to IT, limiting the ability to scale and operationalize the use of artificial intelligence. VROC has combined both AutoML and MLOps into one automated processes, allowing models to be developed and deployed seamlessly.

5. RAPID EXPERIMENTATION

Rather than committing to a few pilot projects, industry is adopting a fast-fail rapid AI experimentation approach, which will be enabled by the adoption of AutoML and MLOps. AI models will be able to be produced within hours, allowing businesses to rapidly test theories and predict outcomes. Switching the focus off model creation onto execution of the insights obtained from the models.

These new approaches and advances in technology will help industrialise the use of AI. Businesses will be able to scale the use of their data across their business for advanced analytics and improved decision making. It is these adoptions which will lead to the realisation of the value that researchers such as McKinsey have reported, including as predictive maintenance which will generate $260 billion to $460 billion by 2030 across industries, and for Oil and Gas specifically, operational improvements that would lead to $80 billion to $300 billion in economic value annually by 2030.

As we move into a new year, it’s a good opportunity to revisit what has and hasn’t worked for your organisation so far and consider if any of these new approaches could be implemented by your team.

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