Introduction:
This letter is addressed to the leaders in manufacturing and the PE firms that own these groups. It aims to share practical experiences based on recent or ongoing reflections and case studies with other industrialists. Please feel free to share your feedback or contact me.
This month, I’ve noted growing interest in the following topics linked to AI:
– Which AI use case to start with in pricing and sales?
– How to develop AI as a core competence within teams?
– What are the risks of failure and how to avoid them?
AI-Based Pricing: Where to Start?
In the manufacturing sector, it has been proven that the most profitable pilot projects for dynamic pricing generate 5 to 8% additional net sales to EBITDA compared to traditional pricing methods, and up to a 20% increase in revenue, according to BCG and McKinsey as well as my own project experience.
🔍 Which Simple AI Use Case Should Your Team Start with in Pricing?
A useful starting point is accurately quantifying elasticity and optimal prices per SKU and client. To create credible curves and insights, ensure you:
– Neutralize seasonality effects.
– Neutralize promotional effects unless measuring promotional effectiveness.
– Understand competitors’ reactions to price changes and generate non-linear elasticity curves.
Case Study:
A large OEM in Agriculture parts adapts its sell-in price by part based on sell-out prices movements and sell-in price elasticity. Although the price is adjusted less dynamically than sell-out prices, price monitoring is constant, and sell-in price is adjusted to follow the trend in sell-out prices.
How to Turn AI-Based Sales and Pricing Optimization into a Core Competence?
After successfully implementing pilots for long-tail repricing and sales next best actions using AI, many manufacturers find it challenging to scale these solutions into core competencies once the initial data science team departs. Here’s a practical roadmap to achieve this transition:
1. Build an Initial Business AI & Data Team:
– Data & AI Manager: Oversees processes and strategy.
– Data Engineer: Manages data pipelines and infrastructure.
– Data Scientist: Develops and refines algorithms.
– Ensure business profiles are well represented in that team.
2. Recruit the Right Talents within and outside the organization:
– Within the Organization: Identify problem solvers who integrate coding into their tasks or create workflows with tools like Alteryx.
– Outside the Organization: Talent in manufacturing may be limited. Look for expertise in fields such as actuarial sciences, energy forecasting, or banking derivatives.
3. Establish Effective Routines:
– Generate Python Code: Leverage tools like ChatGPT but ensure you have robust quality checks and technical proficiency to enhance prompts.
– Manage Data Securely: Use secure platforms for data management.
– Create Workflows and Quality Loops: Establish routines to maintain and improve processes.
4. Seamlessly Integrate AI-Based Optimization:
– Ensure that AI-driven pricing and sales optimizations are fully integrated into your end-to-end processes to maximize efficiency and effectiveness.
By following these steps, manufacturers can transform AI-driven price and sales optimization into a sustainable core competency. I invite you to share your thoughts and experiences to enrich this discussion.
Why AI Projects Fail and How CEOs and Commercial Leaders Can Address It
AI projects fail in most cases for the following reasons:
– Mismatch with Traditional Software Projects: Unlike traditional software projects, AI projects handle constant data changes and variability, requiring a different approach.
– Dependence on Data: AI projects rely heavily on data, which is always changing. What works today might not work tomorrow.
– Transition from Pilot to Real World: Many AI projects fail when moving from a controlled pilot phase to real-world application due to unexpected variability.
– Organizational Readiness: Successful AI projects need a capable organization that can handle ongoing changes, not just a one-time effort.
– Clear Communication: Misaligned goals and expectations across different levels of the organization can derail AI projects.
– Data Management: Properly managing and preparing data is crucial. Underestimating this can lead to project failure.
– Business Case and ROI: Ensure there is a strong business case and clear return on investment before starting an AI project.
– Adapting Project Management: Traditional project management models (like agile and waterfall) often don’t suit the experimental nature of AI projects.
– Continuous Monitoring: AI systems need ongoing monitoring and adjustments to maintain performance over time.
– Scaling and Operationalizing: Moving from successful pilots to full-scale AI requires investment in tools, processes, and skilled talent to manage AI effectively.
Each of these points has a solution, and when addressed, the ROI of AI projects can be extremely high. Successful industries often combine the expertise of their business teams and data scientists with temporary support from professional data science consultants who implement solutions to avoid common pitfalls.
—
Conclusion:
I strongly encourage industrialists to grow their teams by integrating AI. Though the journey has risks, a technical approach can address them. The rewards are substantial and highly profitable when successful.
Please feel free to share your feedback or contact me to discuss these topics in more detail.
Best regards,
Olivier
Founder & Managing Partner,
Resilient Growth Consultants