AI: Beyond industrial automation

Are you ready to turn efficiency gains to revenue growth?

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From cost savings to growth

AI is transforming industrials - but are you leveraging it to drive growth?

Artificial Intelligence is becoming increasingly embedded in industrial operations, from streamlining processes and optimizing supply chains to reducing downtime. But AI efficiency is just the beginning. The next phase demands a shift in strategy. Leaders must think beyond cutting costs and embrace industrial AI strategy to drive revenue, improve margins, and unlock new growth opportunities.

Companies that go beyond automation are already seeing measurable impact in areas like dynamic pricing, AI-powered customer segmentation, and predictive sales strategies. But for many, uncertainty remains:

  • Where does AI add the most value in an industrial setting?
  • How do you balance automation with human expertise in decision-making?
  • What’s the right AI adoption strategy to maximize ROI?

Navigating these questions requires a structured approach, aligning investments with business goals, industry-specific trust factors, and commercial impact.

Where does your company stand in this shift? The AI Maturity Curve provides a roadmap for moving beyond industrial automation and toward revenue-generating AI strategies.

The AI maturity curve: Where does your business stand?

Move from process automation to AI-driven revenue.

AI adoption is a journey. Companies progress through distinct stages as they integrate AI into their operations and commercial strategy. While many companies today use AI, only those further along the maturity curve achieve true AI transformation – unlocking new opportunities in pricing, sales, and revenue growth.

Explore the five stages of AI maturity in industrials:

Exponential graph showing the 5 stages of the AI maturity curve from automation to agentic AI
Stage 1: Automation

AI is used to automate repetitive, rule-based tasks, reducing manual effort and improving accuracy. At this stage, it is embedded in quality control, document processing, and routine operational workflows.

Examples: AI-assisted defect detection in manufacturing, automated invoice processing.

Stage 2: Machine learning

AI begins to analyze patterns and predict trends, assisting decision-making but not executing actions autonomously. Real-time analysis helps optimize maintenance schedules, forecast demand, and improve supply chain efficiency, but human oversight remains critical.

Examples: Predictive maintenance in industrial equipment, AI-driven demand forecasting.

Stage 3: Human-AI hybrid

AI works alongside human decision-makers, executing predefined actions within clear parameters. At this stage, artificial intelligence supports pricing recommendations, sales enablement, and operational adjustments, with final decisions made by humans.

Examples: AI-driven pricing optimization with human approval, automated supply chain adjustments within set limits.

Stage 4: Generative AI

AI moves beyond analytics and decision support to generate new content, strategies, and solutions. This phase includes AI-driven product design, customer engagement, and sales strategy optimization.

Examples: AI-assisted R&D for industrial components, AI-generated personalized B2B sales proposals.

Stage 5: Agentic AI

AI operates autonomously, making complex decisions across multiple business functions with minimal human intervention. Companies leverage this to dynamically optimize revenue, pricing, and operational strategies in real time.

Example: AI-powered revenue management, autonomous supply chain optimization adjusting to market shifts in real time.

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4
Question 1/4
How much human effort is required to execute repetitive or routine processes in your organization?
1 ▲ Processes are fully manual Processes are fully manual; employees handle all tasks step-by-step.
1 Processes are fully manual; employees handle all tasks step-by-step.
2 ▲ Technology assists with routine tasks Technology assists with routine tasks, but humans oversee every step.
2 Technology assists with routine tasks, but humans oversee every step.
3 ▲ Systems handle routine tasks Systems handle routine tasks, and humans validate or make adjustments as needed.
3 Systems handle routine tasks, and humans validate or make adjustments as needed.
4 ▲ Systems handle most processes Most processes are handled by systems, with occasional human involvement for exceptions.
4 Most processes are handled by systems, with occasional human involvement for exceptions.
5 ▲ Minimal human intervention Processes operate end-to-end with minimal human intervention or oversight.
5 Processes operate end-to-end with minimal human intervention or oversight.
Next question
! Please pick an answer before proceeding.
Question 2/4
To what extent do your systems or tools make decisions without requiring manual input?
1 ▲ Systems perform only as programmed Systems perform only what they are explicitly programmed to do.
1 Systems perform only what they are explicitly programmed to do.
2 ▲ Systems can suggest actions Systems can suggest actions but require approval for every decision.
2 Systems can suggest actions but require approval for every decision.
3 ▲ Systems work collaboratively Systems work collaboratively with employees, sharing insights and decisions.
3 Systems work collaboratively with employees, sharing insights and decisions.
4 ▲ Systems make independent decisions Systems make decisions independently in most cases, with humans monitoring exceptions.
4 Systems make decisions independently in most cases, with humans monitoring exceptions.
5 ▲ Systems handle complex decisions Systems independently handle decisions, even in complex or unpredictable scenarios.
5 Systems independently handle decisions, even in complex or unpredictable scenarios.
Next question
! Please pick an answer before proceeding.
Question 3/4
How often do your processes contribute to the creation of new strategies, solutions, or outputs?
1 ▲ Focus is solely on predefined tasks Processes focus solely on executing predefined tasks without creating anything new.
1 Processes focus solely on executing predefined tasks without creating anything new.
2 ▲ Processes provide data or insights Processes provide data or insights that help inform new ideas or strategies.
2 Processes provide data or insights that help inform new ideas or strategies.
3 ▲ Processes and employees co-create Processes and tools help employees co-create or innovate within defined parameters.
3 Processes and tools help employees co-create or innovate within defined parameters.
4 ▲ Processes generate new solutions Processes regularly generate new solutions, designs, or ideas without significant human involvement.
4 Processes regularly generate new solutions, designs, or ideas without significant human involvement.
5 ▲ Processes develop novel solutions Processes independently develop novel solutions or strategies with little to no human input.
5 Processes independently develop novel solutions or strategies with little to no human input.
Next question
! Please pick an answer before proceeding.
Question 4/4
How well do your systems or processes adapt to changes or recover from disruptions?
1 ▲ Processes require manual updates Processes require manual updates and intervention to handle changes or issues.
1 Processes require manual updates and intervention to handle changes or issues.
2 ▲ Processes adjusted with guidance Processes can be adjusted with guidance from employees.
2 Processes can be adjusted with guidance from employees.
3 ▲ Processes improve over time Processes improve over time based on feedback but still require human monitoring.
3 Processes improve over time based on feedback but still require human monitoring.
4 ▲ Processes automatically adapt Processes automatically adapt to changes or disruptions with minimal input.
4 Processes automatically adapt to changes or disruptions with minimal input.
5 ▲ Processes continuously learn Processes continuously learn, improve, and recover independently.
5 Processes continuously learn, improve, and recover independently.
Submit
! Please pick an answer before proceeding.
Your AI maturity level is:
Automation (stage 1) → Machine Learning (stage 2)
1 1.9

AI is automating routine tasks, but decision-making still relies on human input. Moving to Machine Learning means using AI to generate insights and assist decisions.

What to focus on next:
1
Identify Data-Driven Opportunities: Focus on processes that generate useful data (e.g., sales trends, customer feedback) and explore how insights from this data could improve decision-making.
2
Reduce Decision Bottlenecks: Look for areas where repetitive decisions slow down the business and explore tools to provide smarter, faster recommendations.
3
Standardize Process Metrics: Ensure your business is consistently measuring performance, so you can start using historical patterns to guide future decisions.
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Your AI maturity level is:
Machine Learning (stage 2) → Human-AI Hybrid (stage 3)
2 2.9

AI provides insights, but humans still act. The next step is enabling AI to execute within guardrails and build trust through collaboration.

What to focus on next:
1
Improve Decision Quality: Use systems to support employees in making more accurate, data-informed decisions, such as forecasting demand or optimizing resource allocation.
2
Foster Collaboration Between People and Systems: Create workflows where employees can validate, adjust, or enhance system outputs to build trust and improve results.
3
Expand Smarter Processes Across Teams: Identify additional areas—such as marketing, customer service, or operations—where smarter tools could improve efficiency and outcomes.
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Your AI maturity level is:
Human-AI Hybrid (stage 3) → Generative AI (stage 4)
3 3.9

AI supports decisions, but not creation. Advancing means using AI to generate content, ideas, and solutions that drive commercial impact.

What to focus on next:
1
Automate Creative Tasks: Focus on processes like drafting customer communications, developing marketing materials, or creating business proposals to save time and scale output.
2
Encourage Innovation Through Technology: Use generative tools to help teams develop new ideas, like product concepts or personalized customer experiences.
3
Streamline Content-Heavy Workflows: Identify functions like sales, customer service, or marketing where significant time is spent on content creation and explore tools to automate these efforts.
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Your AI maturity level is:
Generative AI (stage 4) → Agentic AI (stage 5)
4 5

AI creates content but needs oversight. Agentic AI acts independently, adapting in real time and driving decisions across the business.

What to focus on next:
1
Empower End-to-End Autonomy in Complex Areas: Identify workflows that are dynamic and time-sensitive (e.g., real-time customer support, inventory management) and allow systems to act independently with clear guardrails.
2
Adapt to Rapid Market Changes: Leverage systems that can monitor and respond to changing market conditions or customer behaviors without manual intervention.
3
Focus on Strategic Innovation: Use self-sustaining systems to continuously drive innovation in areas like product development, service delivery, or operational efficiency.
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AI for industrials: From insight to impact

Your guide to AI transformation

Many companies struggle to transition from efficiency gains into AI-driven commercial impact. Our latest report explores:

  • The AI maturity curve: Understand where your company stands and how to progress
  • Key opportunities and challenges: Leverage insights from predictive analytics to AI-driven pricing
  • Practical steps to AI adoption: Learn how to structure AI investments for long-term growth

Explore AI’s next frontier in industrials and discover your AI roadmap to success.

Access our full report
AI in industrials: Two men in high viz jackets and hard hats looking at a bank of screens

Unlock AI for industrial growth

For complete AI transformation, leaders must adopt an industrial AI strategy that drives revenue growth. Companies moving up the AI maturity curve with a structured, data-driven approach will outpace competition just using AI for process optimization. 

By combining 40 years of industrial expertise with the digital capabilities of Simon-Kucher Elevate, we help industrial leaders harness AI effectively. Our team will help you identify high-impact AI applications, optimize pricing and revenue strategies, and build AI-driven commercial models that deliver impactful results.

Let’s discuss how AI can drive better growth for your business.

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