Building Your Sustainable AI Strategy and Implementation

Building Your Sustainable AI Strategy and Implementation

A review of best practices and steps for projects to produce sustainable AI implementations.

Artificial intelligence (AI) continues to surpass what experts thought was possible. Governments and companies of all industries are pouring billions into implementing the technology. In the energy industry, AI has thoroughly permeated the exploration and production sector. Now it’s popping up in many forms across midstream and downstream as well. The power and complexity of AI lends itself to making AI projects complex as well as different from other IT projects. When getting started with AI, including a defined AI governance and other AI-specific considerations leads to sustained value from the powerful technology.

It All Starts with AI Project Governance

AI projects lend themselves to an Agile methodology. Since AI is new to the business world, AI projects are more likely to experience setbacks and course corrections during implementation. Agile minimizes costs of redirection while delivering benefit sooner, making Agile a better approach for AI implementation. 

Effective AI governance also involves choosing your battles wisely. Choose a relatively small and specific business use case to enable and enrich AI at first, as this reduces risk while allowing the enterprise to prove the business utility of the tool. If you start with an activity that has broad effects across the company, bumps in the road will be more costly and time-consuming. Win a smaller battle before trying to win the whole war.

After you’ve won your first AI battle, you can begin to leverage the resulting AI solution to solve other business problems. As business needs evolve, AI governance plays an ongoing role to ensure that AI projects and applications are prioritized effectively and continuously evaluated. When plotting the course for the company’s future with AI, resource constraint and business benefit must be weighed against each other to inform where it is wisest to apply your AI technology next. 

A good way to accelerate AI adoption through the organization is spreading the word about these projects. If AI successes have been effectively evangelized, when company leaders have a potential AI need, they are more likely to think of the project team, generating more work for them.

What Skills/Personnel Do You Need?

In addition to what IT projects typically include, here are some of the roles that can accelerate an AI project:

  • Executive Sponsor – An Executive Sponsor provides the funding for the project and maintains alignment with the company strategy. Successful AI implementations benefit from support from the executive office.
  • Product Owner – The Product Owner is ideally from the business unit instead of the IT group. Business alignment ensures the products are helpful to the business, and lets the business see where the products can be applied to add value. This prevents solutions from becoming just what IT thinks is the next coolest toy.
  • Project Management – Project Management takes the lead on organizing the governance, personnel and requirements. Filling leadership roles with individuals that have experience on AI projects ensures that project decisions and design will be informed by the proper insight.
  • Data Engineers – Data Engineers will extract and integrate the necessary data into the AI platform. Having several data engineers on the project is necessary due to the size and time-consuming nature of the task. 
  • Data Scientists – In an AI project, data scientists develop and train the AI. The algorithms the scientists design are what give AI the ability to “learn,” and to operate with reduced supervision. Data Scientists are usually employed by the provider of the software or by another third party. 

These key personnel will continue to be important as AI use cases spread throughout the company. Largely the same group should be on each new AI project. From these projects, the participants will pick up skills that other employees won’t have. For this reason, it is most efficient to treat these personnel as your AI “factory” to be called upon for future projects that apply the use case to other areas.

Helping Employees Adapt to The New Tool

With any disruptive, technology-driven change comes resistance. Harvard Business Review cites AI’s lack of transparency, the hype surrounding AI, fear of losing control over work, and the disruption of familiar work patterns as reasons why employees may hesitate to adopt. These impressions leave employees reluctant to fully utilize new AI applications, which subtracts from the value of technology investments.

Despite employee discomfort, AI often does not replace jobs, and only makes existing jobs more enjoyable. One common use for AI is to automate tasks that are repetitive, time-consuming, and don’t add value, which leaves the more enjoyable value-added tasks for the humans. Therefore, an AI solution may actually make employees happier once they understand what it does. To move toward this understanding, visual demonstrations of the process an AI app uses has shown to be an effective means of gaining employee trust. Most people are visual learners and, when people understand something, they can begin to trust it.

Bringing in External Help

Oil and Gas consultants can be a catalyst for organizational improvement and innovation, and AI projects are no exception. Bringing a blend of energy expertise with technology delivery experience, they can introduce a value-added Project Management capability to lower delivery risks and better ensure successes are delivered early. In addition, energy consultants can be effective change agents, helping the organization understand the impact of AI on jobs and harness the power that AI presents. Finally, they can assist the organization with moving beyond project-based governance into a business-led AI governance framework which is essential for sustaining and expanding the benefits of AI.

Consultants and subject matter experts assist in bridging the gap between software and client. It isn’t feasible to build AI software in-house, so an existing AI product should be brought in by a software company and customized to meet requirements. However, not all AI software can be customized to fit the task or industry as desired. Consultants can leverage their industry experience and technical know-how to help determine the right AI software for the job, and how to customize the software to meet a client’s unique needs. 

Source: Medium
Source: Medium

Furthermore, consultants can help prepare the client to support the application in the long term by assisting with data cleansing and data integration, setting up ample computing power, helping establish controls and compliance methods for AI, training employees to use the AI system, and more. These activities provide the foundation crucial to a sustainable AI solution.


AI projects are unique and require new approaches and skills. Like all projects, the right governance, leadership and knowledge can unlock enormous value. Contact Opportune today to see how we can help drive your AI implementation journey.

This is the final installment of a three-part series discussing AI’s potential and critical role in oil and gas, how a company can prepare for an AI platform, and important steps to executing a sound AI implementation.

Author Profile

Ian Campbell is an intern with Opportune LLP’s Process & Technology group. He is pursuing an undergraduate degree in management information systems with a certification in business analytics at Baylor University.

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