It’s Scale or Fail: How AI Can Drive Cost Optimization in Manufacturing (Italy & Germany Focus)

In recent years, manufacturing companies worldwide have faced unprecedented challenges: geopolitical instability, rising energy costs, and increasing competitive pressure.

Italy and Germany, in particular—two economies driven by the manufacturing industry and deeply interconnected—now stand at a crossroads: embrace innovation or be left behind.

Following the energy crisis and the slowdown of the Chinese market, Germany has seen its industrial model falter, with direct repercussions on Italy. The latter, already struggling with stagnant productivity for over two decades, now faces an increasing competitive risk in the manufacturing sector.

In this scenario, cost optimization is not just a necessity but a strategic opportunity to free up resources and reinvest them in growth.

As I told in this article, there are four key principles for achieving this successfully:

1. An “always-on” approach, integrating cost control into all business activities.

2. A strong company culture focused on efficiency.

3. An integrated supply chain management.

4. The use of advanced technologies to boost productivity.

It is precisely this last lever that I want to focus on today—together with you.

AI ADOPTION: WHAT ITALIAN AND GERMAN CEOs THINK

Although much is said about the potential of AI, its impact on cost optimization remains largely unexplored. Let’s take a closer look at how AI can help streamline cost management and what room for maneuver exists in the short term, particularly for Italy and Germany.

According to the results of our survey, 93% of companies worldwide are either already using or planning to adopt artificial intelligence within the next 18 months.

In Italy and Germany, while there is widespread awareness that AI will be a key driver of success in the future, companies remain particularly cautious and unconvinced that it can already serve as a concrete value driver today.

In Italy, for instance, only 28% consider AI significant in the short term for achieving cost optimization goals (vs. 42% of EMESA - Europe, Middle East, South Africa - companies), while 14% believe AI currently plays no role at all (vs. 7% in EMESA).

Looking at the medium term, only 40% see AI as crucial (vs. 55% at the EMESA level), while in the long term, confidence grows to 44%, narrowing the gap with other countries.

Germany, on the other hand, takes a more cautious approach in the short term regarding strategic AI adoption but accelerates significantly in the medium to long term, with 58% of companies (vs. 50% in EMESA) believing they will be able to optimize costs through AI.

The risk of this hesitation in adopting disruptive technologies? Slower innovation and a widening productivity gap compared to other countries.

3 IMPACTFUL AI USE CASES

From my long experience at BCG, I have witnessed firsthand how the application of Gen AI in industrial companies can bring tangible benefits across multiple areas—from supply chain and engineering to production processes, marketing, sales, and after-sales operations. Here are a few examples:

· In today's landscape, where the traditional cost-minimization approach must align with a resilient supply chain capable of withstanding geopolitical shocks, implementing a control tower to monitor and automate material flow management has proven to be a winning strategy.

· Similarly, the adoption of AI agents capable of automating highly complex engineering processes (generative design) has shown great potential. One of the key advantages of this approach is the efficient use of raw data collected over time. These data points can provide engineers with crucial insights for designing new products and machinery—insights that are often difficult to access and utilize effectively.

· Finally, in production, AI-powered Predictive Maintenance and Remote Optimization technologies enable companies to enhance plant efficiency, reducing downtime and energy waste.

5 AI IMPLEMENTATION MISTAKES AND HOW TO AVOID THEM

Many companies, even those led by visionary CEOs who recognize AI’s transformative potential, struggle to turn it into a tangible competitive advantage. Here are five common mistakes and how to avoid them:

· Organizational silos. AI cannot be an isolated project. A cross-functional approach is essential, with Business Development and Operations teams in place and P&L - Profit & Loss - leaders fully accountable for results.

· Too many initiatives with no real impact. Too many ideas without a clear strategy lead to wasted resources and effort. Prioritizing high-ROI projects is key.

· Endless doubts about feasibility and implementation. Overanalyzing and debating slow down innovation. It's better to set top-down objectives and measure outcomes concretely.

· Fragmentation into small-scale projects. Focusing only on individual use cases limits AI’s full potential. A holistic, end-to-end transformation is needed.

· Over-engineering the technology. While IT infrastructure planning is important, companies cannot afford to wait years before taking action. AI must be developed in parallel with real use cases.

To conclude, adopting AI is not just about cost reduction—it’s about redefining how manufacturing companies operate and compete. The companies that successfully scale AI will lead the industry. Those that hesitate may not get a second chance.

I’d be truly interested in hearing your experience and vision on how you are tackling AI adoption. I look forward to reading your comments!