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Four Barriers in 2021 to AI Adoption in Oil and Gas

Updated: Nov 17, 2020

There are many barriers to AI adoption in the Oil and Gas industry that are slowing the use of proven technologies that can help improve business operations and reduce cost. Here, we look at four of the most common and significant barriers every company will face as they imagine, explore and commit to an AI roadmap for the next few years.

A Lack of Skilled Resources

There continues to be a shortage of skilled resources that can bridge the gap between business requirements, statistical modeling, and technology. These skills reside within different groups and functions in organization and, therefore, collaboration and awareness on common business challenges remains an issue.

Most organizations are addressing the skills gap by bringing different functions together in a hub and spoke model in a single centre of excellence (COE) structure.

The COE is a great model. However, to develop the mandate, build organizational support, and gain commitment from executives takes time and resources that are scarce commodities in most organizations. Read more on COE and their function here.

Evolving Technology

The traditional software procurement models in Oil and Gas favour "off the shelf" and proprietary software. This is for good reason. It ensures ongoing support, enterprise integration, and long term value generation. However, most innovation in the AI space is happening within the open source space, where software is free but comes with limited integration and support. It is a big challenge for Oil and Gas companies to formally incorporate these components into their landscapes and keep up with the maintenance.

Some open source software are starting to offer a commercial version of their solution to fill this gap and support enterprise customers. For example, a commercial version of the open source software Superset is available on A commercial version of Spark is available at Databricks.

Still, the integration challenges between various platforms remain to be resolved on a case by case basis. What if an organization requires Superset and Databricks? Or other packages that might be hosted on different clouds?

At, we have taken a different approach to enabling organization with access to open source innovation. We offer an integrated platform that brings, not just one open source software, but packages several open source technologies in an integrated fashion. That way, they work on the same data, with the same data base, and the same security schema.

If you are looking to start your AI/ML journey in Oil and Gas, and get your users access to open source software without compromising the security and integrity of your existing architecture, you should take a look at features.

Data Challenges

This is an area that organizations have been struggling with for a decade, since the inception of data warehouse, reporting, and business intelligence software. The same traditional issues of data quality, completeness, and accuracy are affecting AI adoption and the types of use cases that can be explored. Organizations are addressing these challenges in multiple ways, by establishing data governance (policies), and data lakes (technologies). Unfortunately, that also takes time and requires significant investment with low initial return on investment.

The good news is that developing AI solutions does not mean you need big data, or a data lake. Most data science projects can get a head start with flat files to demonstrate value and expand their data requirements over time.


Given the current macroeconomic conditions and effect of COVID-19 on market demand for energy, justifying expenditures on data and analytics is a challenge in every organization. Most Oil and Gas companies are very careful with every dollar they spend, so doing research and development, which is required for this space, does not make it to the top of the budget list.

Organizations need to create a portfolio of AI/ML use cases and address the needs of multiple groups and users, which means incremental technology expenditure. If you are successful, the internal demand for services will grow.

Great - but, how can you sustain the demand for technology?

Digital Hub has a solution for managing the cost of data science projects. By leveraging on-demand cloud native infrastructure, you can enable many projects in your organization without incurring additional costs.

Digital Hub projects only consume resources on-demand, so companies and their users only pay for what they use and when they use it.

This means many projects can be launched, but the cost will only accumulate when these projects are in use. This is great for enabling broader organization access to technology without incurring significant upfront cost.

For more information on use cases that you can enable on Digital Hub visit Integra's website.

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