Leading with AI insights

Welcome to the Leading with AI blog, where we explore practical AI applications in pricing, decision-making, and leadership. Join us as we delve into real-world case examples, data-driven strategies, and ethical considerations to help you create measurable business value.

Data-Driven Decision-Making In Industrial Manufacturing And Supply Chains

Learn how to apply AI to improve decision-making in your industrial manufacturing and supply chain operations. We target supply chain and industrial manufacturing professionals, strategy leaders, and those keen to implement AI in a value-driven manner. Gain concrete ideas, frameworks, and tools that you can apply immediately.

Practical AI For Pricing And Commercial Strategy

Discover how AI can revolutionize your pricing and commercial strategies. We provide insights and practical applications for business leaders, pricing managers, and practitioners looking to leverage AI for real value. Explore case studies, frameworks, and decision-making tools to improve your outcomes.

AI-Supported Leadership And Change Management

Understand how AI can support leadership and change management within your organization. This section aims to provide business leaders and transformation leaders with insights into leveraging AI for better leadership outcomes. Explore trustworthy, modern strategies grounded in real industrial experience.

Why I Wrote A to Z of Pricing

Pricing is one of the most powerful — and misunderstood — levers in industrial organizations.

After years of working across manufacturing, supply chains, and aftermarket services, I repeatedly observed the same pattern: pricing decisions were often disconnected from data, strategy, and real customer value.

A to Z of Pricing was written to close that gap. The book brings together practical experience, structured thinking, and modern AI-driven approaches to help leaders and practitioners treat pricing as a strategic capability — not just a calculation

 

Figure: A to Z of Pricing – From History to the AI Future for Manufacturing. The book explores pricing as a strategic capability in industrial organizations.

Available on Kindle: https://www.amazon.com/dp/B0GCGSDXQ7

 

How AI Is Changing Pricing Decisions in Manufacturing

Why pricing decisions need to evolve

In many manufacturing organizations, pricing is still treated as a calculation exercise—cost plus margin, adjusted occasionally for market pressure. While this approach worked in the past, it is no longer sufficient in today’s environment.

Volatile supply chains, fluctuating demand, regional cost differences, and increasing customer expectations have made pricing far more complex. Decisions based only on historical data or static price lists often lead to lost margin, inconsistent pricing, and missed growth opportunities.

This is where artificial intelligence is beginning to change the game.

From static pricing to data-driven decisions

AI in pricing is most effective when applied pragmatically. Some proven examples include:

  • Price consistency checks
    AI identifies pricing anomalies across regions, customers, or channels, reducing margin leakage.

  • Demand-based price guidance
    Instead of reacting late, AI detects early demand signals and supports proactive price adjustments.

  • Lifecycle pricing optimization
    Different pricing strategies are recommended depending on whether a product is new, mature, or nearing end-of-life.

  • Scenario simulations
    Pricing teams can test “what-if” scenarios before implementing changes, improving confidence and alignment.

The key is not automation for its own sake, but decision support grounded in business reality.

The role of leadership in AI-driven pricing

Technology alone does not create value. Leadership plays a critical role in ensuring AI is adopted correctly.

Successful organizations:

  • Treat pricing as a strategic capability, not a back-office task

  • Invest in data quality before advanced tools

  • Encourage cross-functional collaboration between pricing, sales, finance, and supply chain

  • Focus on transparency, not black-box decisions

AI should support trust and clarity—not replace human judgment.

What this means for the future

AI will continue to evolve, but its real value in pricing lies in better decisions, not full automation. Organizations that start with practical, well-defined use cases will gain a sustainable advantage.

Pricing leaders who embrace AI thoughtfully can improve profitability, consistency, and customer value—without losing control.

 

Leading with AI focuses on practical, experience-based insights into how artificial intelligence can support pricing, decision-making, and leadership in industrial organizations.

 

Practical AI Use Cases for Commercial Leaders

Artificial Intelligence is no longer a future concept reserved for data scientists or technology teams. For commercial leaders, AI has already become a practical tool that can improve pricing decisions, customer focus, and overall profitability—when applied correctly.

The real challenge is not whether to use AI, but how to use it in a way that delivers tangible business value. Below are practical, proven AI use cases that commercial leaders can apply today without turning their organizations into experimental labs.

1. AI-Driven Pricing Optimization

Pricing remains one of the most powerful profit levers, yet it is often managed using static rules, spreadsheets, or historical averages. AI changes this by continuously analyzing large volumes of data to recommend optimal prices.

Practical applications include:

  • Identifying underpriced and overpriced products across portfolios

  • Recommending price corridors based on customer behavior and willingness to pay

  • Adjusting prices dynamically based on demand signals, cost changes, and competitive movements

For commercial leaders, the value lies in decision support, not full automation. AI provides recommendations, while leaders retain strategic control over pricing governance.

2. Customer Segmentation and Value Differentiation

Traditional customer segmentation often relies on size, geography, or industry. AI enables segmentation based on actual behavior and value drivers.

AI can help:

  • Group customers by price sensitivity instead of volume alone

  • Identify customers willing to pay for service speed, reliability, or customization

  • Detect early signs of churn or declining engagement

This allows commercial teams to tailor pricing, discounts, and service models more precisely—improving margin without damaging customer relationships.

3. Sales Forecasting and Demand Prediction

Forecast accuracy directly impacts revenue planning, inventory levels, and working capital. AI enhances forecasting by combining historical sales data with external variables such as seasonality, economic indicators, and customer order patterns.

Practical benefits include:

  • More reliable sales forecasts at product and customer level

  • Early detection of demand shifts

  • Reduced reliance on subjective sales estimates

For leaders, this creates stronger alignment between sales, operations, and finance—one of the most persistent challenges in industrial organizations.

 

4. Discount Governance and Deal Intelligence

Discounting is often where margin leaks occur. AI can analyze past deals to identify patterns that lead to successful—or unprofitable—outcomes.

Use cases include:

  • Flagging deals that deviate from healthy margin benchmarks

  • Recommending discount ranges based on customer and deal context

  • Supporting approval workflows with data-backed insights

Instead of slowing sales down, AI helps create smarter guardrails that protect profitability while maintaining commercial agility.

5. Cost Transparency and Margin Analytics

Many pricing decisions fail because cost data is fragmented or outdated. AI can integrate data from ERP systems, suppliers, and operational sources to provide near-real-time margin visibility.

Commercial leaders gain:

  • Better understanding of true product and customer profitability

  • Early warnings when cost increases threaten margins

  • Stronger input for pricing adjustments and contract negotiations

This shifts pricing discussions from opinion-based debates to fact-based decisions.

6. Leadership Decision Support, Not Automation

One common misconception is that AI replaces leadership judgment. In reality, the most successful organizations use AI as a decision accelerator, not a decision maker.

Effective leaders:

  • Use AI insights to challenge assumptions

  • Combine data-driven recommendations with market intuition

  • Maintain accountability and ethical oversight

AI works best when paired with strong leadership, clear governance, and transparent communication.

Final Thoughts

AI does not need to be complex or disruptive to be valuable. The most impactful use cases are often incremental, focused, and tightly aligned with commercial objectives.

For pricing and commercial leaders, the goal is not to “implement AI,” but to solve real business problems—improving profitability, decision quality, and strategic clarity.

When applied pragmatically, AI becomes not a buzzword, but a competitive advantage.