• Written by Muhammed Abdulla NC, CEO

This week, the operations manager at a regional power company must decide whether to bring in a contractor to perform maintenance on a substation, and the decision will be based on a manual review of the maintenance log and the contractor's availability. The current process is time-consuming and prone to errors, highlighting the need for more efficient and reliable systems. In the energy and utilities sector, the adoption of enterprise AI can be hindered by the unique challenges of asset-heavy operations and regulatory scrutiny.

Unique Challenges in Energy and Utilities

The energy and utilities sector presents distinct challenges that can hinder the adoption of new systems, including enterprise AI. Outage planning, for instance, requires careful consideration of multiple factors, including weather forecasts, crew availability, and equipment maintenance. The regulatory reporting burden also adds complexity, as companies must comply with strict guidelines and submit detailed reports. Furthermore, geographically dispersed assets can make it difficult to implement and maintain new systems, as they often require significant infrastructure investments.

These challenges can make it difficult for companies to adopt new technologies, including enterprise AI. The cost and complexity of implementing such systems can be prohibitively high, especially for smaller companies or those with limited resources. However, the potential benefits of enterprise AI, including improved efficiency and decision-making, make it an attractive option for companies looking to stay competitive.

Attaching New Capability to Existing Routines

One way to overcome the challenges of adopting new systems is to attach new capabilities to routines that already exist. For example, a shift supervisor can use an AI-powered system to analyze the maintenance log and identify potential issues before they become major problems. This approach allows companies to build on existing processes and infrastructure, reducing the complexity and cost of implementation. By integrating new capabilities into existing routines, companies can also reduce the risk of disruption and minimize the impact on ongoing operations.

A good example of this is a category manager comparing two suppliers using an AI-powered system to analyze pricing, quality, and delivery schedules. The system can provide real-time data and insights, enabling the category manager to make informed decisions quickly and efficiently. This approach can also help to build trust in the new system, as users can see the benefits of the new capability in the context of their existing routines.

Who Needs to Ask, Not Just Who Needs the Answer

When implementing enterprise AI, it is essential to consider who needs to ask questions, not just who needs the answer. In many cases, the people who need to ask questions are not the same as those who need the answer. For instance, a field technician may need to ask questions about the status of a particular asset, while the answer may be needed by the operations manager. By considering who needs to ask questions, companies can design systems that meet the needs of all users, regardless of their role or level of technical expertise.

This approach requires a deep understanding of the users and their needs, as well as the ability to design systems that are intuitive and easy to use. Companies can achieve this by working closely with users and stakeholders to identify their needs and preferences. By doing so, companies can create systems that are tailored to the specific needs of their users, increasing the likelihood of successful adoption and use.

The First Wrong Answer and Rebuilding Trust

Even with the best-designed systems, mistakes can happen, and the first wrong answer can be a significant setback. When this occurs, it is crucial to rebuild trust with users and stakeholders. This can be achieved by acknowledging the mistake, identifying the root cause, and taking corrective action. Companies should also provide transparent and timely communication, keeping users informed of the progress and any changes made to the system.

Rebuilding trust requires a commitment to continuous improvement and a willingness to learn from mistakes. Companies should also prioritize user feedback and engagement, using this input to refine and improve the system. By doing so, companies can demonstrate their commitment to delivering accurate and reliable results, rebuilding trust with users and stakeholders. For more information on how to implement enterprise AI in your organization, visit our website.

When Enterprise AI is Not the Right Choice

While enterprise AI can be a powerful tool for improving efficiency and decision-making, it is not always the right choice. In some cases, the complexity and cost of implementation may outweigh the potential benefits. Companies should carefully consider their specific needs and circumstances before deciding whether to adopt enterprise AI. They should also evaluate the potential risks and challenges, including the need for significant infrastructure investments and the potential for disruption to ongoing operations.

In cases where the potential benefits are unclear or the risks are too high, companies may want to consider alternative solutions. For example, they may be able to achieve their goals through process improvements or the implementation of simpler technologies. Companies can contact our team to discuss their specific needs and determine whether enterprise AI is the right choice for their organization. By carefully evaluating their options and considering their unique circumstances, companies can make informed decisions about whether to adopt enterprise AI.