How To Integrate AI Into Metals Operations

Following are four stages of AI integration that when stepped through thoughtfully can result in better implementation.

A first, critical step in integration AI is providing education for executives, managers, and teams regarding expectations and guidelines for using AI and how AI can transform the company. While this stage does not have to involve using AI tools, helping all stakeholders gain foundational knowledge is vital for a successful journey with AI.

Metals companies that rush through or skip educating their teams often face employee resistance or a lack of companywide adoption. In addition, executives can delay making investments in AI tools when they’re uncertain about which one might be best for their companies. Education can bring clarity, certainty, and confidence as metals teams move forward with AI.

Creating a road map for how to strategically use AI is imperative for realizing benefits and gaining ROI. Metals companies must evaluate their business and potential use cases and then determine a course of action that can deliver the highest ROI.

Without a road map, the benefits of using AI won’t materialize. Even if employees use AI tools to capture savings here and there, companies might not see a significant effect on the bottom line without a standardized approach to tracking results and benefits of each use case. It’s critical for metals leaders to know where to apply small changes to see companywide results. AI initiatives must be targeted. Using a matrix to map the ROI and cost of several use cases can be helpful in strategic decision-making.

In this stage, metals companies implement AI tools to help expedite processes. A common place to start is back-office operations, where an AI assistant can create greater efficiency. Stage 3 might seem late in the game to begin using AI tools, but metals companies that skip Stage 1 and Stage 2 risk not realizing the benefits AI can offer.

Often, metals companies jump right to this stage and begin using AI without confirming if it’s the right tool or initiative to reap the greatest benefits. Metals leaders can be discouraged by a lack of results and experience added pressure during this stage if they have skipped the previous two stages.

In addition, it’s important to start small and choose AI tools that have a lower cost but produce exponential results. Examples include Microsoft™ Copilot for Finance or Copilot in Microsoft Teams™ Premium.

In Stage 4, metals companies use an AI-first approach. For example, an AI agent might receive information from a customer, pull data from within the company, and draft a response that’s ready for human review. The focus is on repetitive, data-oriented tasks that can be automated. While AI suggestions can be reviewed and edited, the goal is to minimize human review of AI-generated responses and actions.

Metals companies with data stored in multiple systems might experience setbacks if they attempt to move into this stage prematurely. When data is pulled in from several different sources to feed into an AI model, the process of using AI can become complex, require more attention from employees, and limit or even reverse the ROI.

Businesses operating with single-platform enterprise resource planning solutions might have a more seamless experience and see greater results.