Revitalizing Growth: AI's secret sauce for manufacturers made easy

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.

The smartest manufacturers are already implementing AI in their front-end guidance tools for their sales teams with great success.

Contrarily, many projects fail. Gartner, Inc. cited by Forbes has estimated that 85% of artificial intelligence (AI) and machine learning (ML) projects fail to produce a return for the business.

However, The MIT-BHI survey showed that intermediate size and large companies that undertook AI-driven pricing transformations achieved more than $100 million of revenue improvement 70% more often than companies that focused on another area.

So, how do the 15% of handpicked manufacturers who succeed with AI approach it differently?

The secret to success ??

The key to success in AI dynamic pricing and selling projects lies in following a productive work sequence, both simple yet surprisingly underutilized. It consists of three steps.

First, choose an AI use case that is truly useful for the business, such as how artificial intelligence technology is utilized to achieve tangible outcomes for sales and margins improvement, e.g. optimizing price while improving the win rate on project business in the building sector.

A work session on how RADICAL Technologies will concretely impact the business, and what data are necessary, is often a very productive start as I described in a recent article

Second, organize for over 95% of salespeople to validate the proof-of-concept and apply the AI recommendations.

Third, industrialize guidance for salespeople in a simple manner within existing front-end systems.

A few of the most profitable use cases ??

Since COVID-19, the manufacturing sector is brimming with successful and easily testable cases that can be implemented much faster than typical lengthy IT projects.

Reviewing the use cases for Resilient Growth Consultants' clients, I have noted several clever cases for manufacturing:

- Project pricing in coatings: Improve net sales and EBITDA by 5% by recommending net prices per project that maximize the chances of winning the quote.

- Abrasives product pricing: Improve price by 3-5% by breaking down customers' willingness to pay into value per attribute such as cutting speed, strength, or CO2 reduction.

- Tendering business in chemicals: scan thousands of tenders and improve prioritization by 10%, and optimize pricing by up to 5% according to the tender's purchasing scenario, while increasing revenues by up to 20%.

- Specialized steel distribution: Improve net sales and margins by 5% for over 10,000 quotations per day.

- Repricing of long tail customers in Chemicals: automatically calculate and optimize the price, for approval before sending to the customer, thereby increasing sales and net margins by 5%.

- Demand Forecasting for tire fitters: 80% of walk-in customers getting the right tire, combined with enhanced revenue management.

Why results may vary? ????

When it comes to AI-driven price and sales optimization, the number one reason for failure, in my opinion, is rejection by sales teams, due to insufficient early experimentation and validation by the sales team.

The other reasons often cited by Gartner, Inc. for the high failure rate include poor scope definition, bad training data, and organizational inertia, lack of process change, mission creep.

But at a deeper level, what are the underlying reasons leading mature professionals to failure?

I identify five essential reasons for failure:

1. Non-specific use case for the industry: Trying to apply AI to vague business problems such as “AI discounting” without proper consideration.

2. Unclear data strategy: Insufficiently defining which business decisions need to be made and in what sequence, and from there, not targeting the right data to train the algorithm in a useful way.

3. Illogical deployment sequence: Choosing a specific software solution too quickly without having tested and proven the concept.

4. Sub-optimal decision trees: The most effective AI models generate a large number of scenarios. Trying to associate them with predefined linear calculation rules does not work

5. Choosing an ill-fitting tool: A mistake is to select software before testing it on a small scale when activating the right AI model in the front-end has become straightforward. The added value comes from the right dataset, the right core model, and proper algorithm training.

Taking a more positive angle on this reflection: In the 15% of successful AI projects in sales, manufacturers find that the necessary data already exists or can be created within a few weeks, much easier than initially imagined. Obtaining 95% compliance from sales teams is often much simpler than expected, greatly facilitating deployment. It often takes just 6 to 8 weeks to prove the concept and gain the sales team's buy-in, and it is definitely worth it.

For practical insights and to discuss how AI can be simplified for your sales team, feel free to reach out to me or my team directly at Resilient Growth Consultant ?

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