The 3 Stages of AI Implementation for a Company in the Steel Sector

Sales and marketing departments are often the first to benefit from AI due to their familiarity with digital tools and tasks suited to generative AI. Here are five concrete examples for this stage:

1.1 Automated meeting recording and summarization

All prospecting and client follow-up meetings can be automatically recorded and summarized with tools like Otter or Firefly, or through AI features integrated into Microsoft Teams or Zoom. These tools generate meeting summaries, transcriptions, and follow-up emails with actionable tasks. This information can also be directly transferred into the CRM without manual entry.
Fictional example: SteelFlow Inc., a steel processing firm, saved over 15 hours a week by using AI to automatically summarize sales calls and update their CRM, reducing manual errors and improving pipeline tracking.

1.2 Refining your marketing plan with ChatGPT

AI, particularly ChatGPT, can refine your marketing plan. You can brainstorm with AI to define your personas, establish your go-to-market strategy, and receive advice on optimizing your campaigns.
Fictional example: SteelTech Solutions used ChatGPT to identify untapped market segments, like solar panel manufacturers, and develop tailored marketing campaigns that boosted their lead generation by 20%.

1.3 Website improvements and bio rewriting

ChatGPT can review your website, suggest improvements, or even offer a complete redesign. It can also craft compelling LinkedIn bios, enhancing your professional online presence.
Fictional example: IronWave Industries revamped its website content using ChatGPT, improving SEO and increasing website traffic by 30% within three months.

1.4 Proposal writing

AI can automate much of the process of writing commercial proposals, creating structured documents tailored to client needs. This allows for quicker responses to requests while maintaining quality.
Fictional example: ForgeIt Steel cut proposal response times in half by using AI to generate proposals customized for different sectors, such as automotive or infrastructure.

1.5 Data analysis for requests for proposals (RFPs)

Using tools like NotebookLM, you can analyze RFP data quickly and efficiently. AI can generate a 10-minute podcast summarizing key points or even create a FAQ or table of contents to organize information effectively.
Fictional example: BlueMetal Co. used AI to analyze an RFP for a government bridge project, creating a comprehensive summary and identifying key compliance requirements within hours instead of days.

Administrative functions are naturally suited to AI-driven automation. Processes are often repetitive and well-defined, making them ideal for optimization through AI. Here are three use cases for this stage:

2.1 Invoice automation and data entry

Managing supplier invoices and manually entering data can be largely automated with AI, reducing the time required by up to 95% and significantly lowering errors.
Fictional example: SteelWorks automated its invoice processing, reducing errors by 80% and freeing up the accounting team to focus on financial planning.

2.2 Automated processing of compliance documents

In the steel industry, Mill Test Reports are essential to ensure material compliance. AI can extract relevant data from these technical reports and send it to clients automatically.
Fictional example: IronCore Solutions automated Mill Test Report processing, cutting document handling time from five hours to 30 minutes per batch, ensuring faster compliance checks.

2.3 Automated email writing

Routine client communications can be automated via ChatGPT, standardizing responses while saving valuable time on daily email drafting.
Fictional example: SteelEdge Systems implemented AI email automation to handle routine order updates, reducing response time to client inquiries by 60%.

This is where the most significant challenges and optimization opportunities for AI lie, but it’s also where companies often face difficulties. Technical teams, often leading AI initiatives, may lack firsthand knowledge of on-the-ground operations, client interactions, and the real pain points of production processes. That’s why it’s crucial to involve people who deeply understand operations to fully leverage AI’s potential. Here are four concrete examples for this stage:

3.1 Forecasting material orders

AI can anticipate raw material needs by analyzing ongoing orders and future projects, helping avoid stockouts while optimizing storage costs.
Fictional example: AlloyChain Ltd. implemented AI to forecast steel demand for construction projects, reducing overstock costs by 15% and preventing delays due to material shortages.

3.2 Scheduling optimization

AI optimizes production scheduling based on machine availability, team resources, priorities, and deadlines.
Fictional example: MetalLogic Industries used AI scheduling to balance workloads across its manufacturing lines, increasing machine utilization by 25%.

3.3 Automated quality inspection

AI systems combined with computer vision technologies can automate parts of product quality inspection, detecting anomalies or manufacturing defects before shipment.
Fictional example: SteelVision AI flagged microscopic cracks in steel beams at BrightForge Inc., reducing returns and rework by 10%.

3.4 Automated client communication

AI can draft automated client interaction messages, such as production status updates or delivery notifications, ensuring smooth and regular communication.
Fictional example: IronClad Solutions introduced AI-driven notifications to keep clients informed about order progress, increasing customer satisfaction scores by 20%.

AI is a powerful technology, but to maximize its benefits, it’s essential to work with partners who specifically understand the challenges of the steel industry. Collaborating with an expert partner, like Code&Steel, provides access to best practices in AI while leveraging their knowledge of industry-specific processes and challenges. As specialists, they can offer tailored solutions that address real on-the-ground problems and maximize potential gains for every department, from sales to production.

AI implementation in a company should not be undertaken without careful thought. It must progress step by step, starting with departments most open to technology, such as sales and marketing, before extending to administrative services and then operations and production, where the greatest gains can be achieved.

However, it’s crucial for technical teams, often leading AI initiatives, to be supported by individuals with in-depth knowledge of operations, real client needs, and concrete problems AI can solve. Ultimately, these individuals will enable AI to truly transform the company and maximize its impact.