How to Create Value with AI for Your Organization

Artificial Intelligence is no longer a buzzword; it’s a core capability that will determine how successful organizations are in the future. Yet for many companies, the biggest challenge isn’t adopting AI but actually delivering measurable value.

At Cadran Analytics, we see every day that organizations with a strong ERP foundation, such as JD Edwards or NetSuite, are excellently positioned to benefit from AI — provided they know where to start. During our recent presentation in Sweden, we showed how AI becomes a concrete driver of business value when you follow a structured and pragmatic approach.

The Value of AI: from Hype to Results

AI is everywhere — at conferences, in boardrooms and on every LinkedIn timeline. But behind the hype lies a real business case. Research consistently shows that organizations further along in AI maturity outperform their competitors. They achieve higher revenue growth, stronger return on investment and greater market valuation.

AI creates value through three primary mechanisms:

1. Automation and optimization — Streamlining repetitive tasks so employees can focus on higher-value work.
2. New products and business models — Developing new services, pricing models or propositions using predictive insights.
3. New insights from data — Discovering patterns and opportunities that previously remained hidden.

In practice, this means, for example: predicting production breakdowns before machines fail, calculating which customers are likely to reorder, or understanding which of your 100,000 products are truly profitable. These aren’t future scenarios; they are achievable today — provided the fundamentals are in place.

The Challenge: Why Many AI Initiatives Fail

Despite all the promises, only a small share of AI projects reaches production. According to Gartner and McKinsey, only about half of models are implemented — and fewer than 30% deliver demonstrable business value. The main reason? Lack of structure.

Too often, organizations “experiment” with AI without clear goals, use cases or the technical foundation needed for success. They start from enthusiasm rather than from a concrete value hypothesis.

That’s why we at Cadran Analytics developed a disciplined framework that bridges the gap between ambition and execution.

Our 4-Step Approach

Creating business value with AI doesn’t happen by accident. It requires a clear roadmap that takes you from exploration to results.

Our four-step approach helps organizations do exactly that:

1. Check the prerequisites: Is your organization AI-ready?
2. Choose the right starting point: Where is the greatest value?
3. Develop iteratively: Build, test and refine your use cases.
4. Scale and embed: Turn early wins into lasting impact.

Let’s briefly explain each step.

Step 1: Check the Prerequisites: Is Your Organization AI-Ready?

Before jumping into models and machine learning, the basics must be in order. Being AI-ready isn’t about following the hype; it’s about data, systems and people.

There are four essential building blocks:

1. AI-ready data: Clean, well-linked and secure data structures. Think of converting your ERP data from “Julian” to readable dates (literally).
2. Infrastructure: A data platform that can combine ERP, IoT and external data. We often use Azure Databricks for its scalability and security.
3. Business intelligence tools: BI tools such as Power BI or Tableau to visualize insights and set priorities.
4. An AI champion: Someone in the organization with the energy and influence to truly drive AI forward.

At Cadran Analytics, we often accelerate this step with Analytics4JDE, our ready-made data models and integration layer. This approach saves more than 250 development days and gets you from raw data to AI-ready insights faster.

Step 2: Decide Where to Start — the Benefit Assessment

Once you know you’re ready for AI, the next question is: where do you begin?

Instead of starting with the loudest voice in the room, we use a structured method we call the Benefit Assessment. It maps business challenges — such as supply chain disruptions, staffing shortages or working-capital constraints — based on two factors:

• Potential value: What do we gain if we solve this?
• Degree of difficulty: How easy is it to implement?

By visualizing these factors, it quickly becomes clear which opportunities sit in the top-right quadrant — where value and feasibility meet.

This approach is not just analytical; it’s connective. It brings IT, data and the business together around shared priorities. For our clients, this often turns a vague “we should do something with AI” notion into a concrete action plan.

Step 3: Develop Iteratively — from Idea to Use Case

Once you’ve chosen a promising theme, it’s time to move from concept to execution. The key: start small, learn fast. Co-design your first use case on paper with your AI champion and data experts. Translate it into a business case, check whether the necessary data is available, and only then start building. Multidisciplinary teams — combining business expertise and data science — ensure every model is not only technically sound but also practically applicable.

Example: in manufacturing, a predictive maintenance use case can combine machine data (temperature, vibration, oil pressure) with ERP data (orders, spare-parts inventory). The model predicts when a component will fail and automatically generates a work order in JD Edwards. This iterative cycle — design, test, learn — increases the odds of real impact with the very first use case.

Step 4: Scale and Embed — from Experiment to Organization-Wide Value

Success with one use case is only the beginning. The next step is to anchor what you’ve learned and scale it up.

Three elements are crucial:

1. Keep monitoring. Data changes, people change — your model needs to evolve with them.
2. Capture lessons learned. Document the insights from the first project; they form the foundation for future initiatives.
3. Build governance after success, not before. Too many organizations start with policies instead of practice. First results, then rules.

As you grow, repeat the Benefit Assessment. Use what you’ve learned to discover new high-value areas and gradually build an AI operating model. Over time, AI will also influence your organization: the skills you need, the tools you use and even how decisions are made.

Accelerating the Journey: How Cadran Analytics Helps

For organizations ready to move forward with AI, Cadran Analytics offers three routes tailored to where you are today:

1. AI Readiness Assessment — For companies that want to know where to start. In a single day, we assess your infrastructure, strategy and data landscape. You receive a clear plan to become AI-ready.
2. Configuring the Basics — For organizations that already know what’s missing. We help set up AI-ready data (via Analytics4JDE), the BI environment or cloud infrastructure with Databricks on Azure.
3. Proof of Concept — For teams that want to experiment. Together we run ideation workshops, define use cases and build testable AI models that demonstrate value quickly.

Whichever route you choose, our goal is the same: helping your organization extract measurable value from data and use AI as an engine for growth.

In Conclusion

The future of AI isn’t about technology; it’s about value — about teams that can make decisions faster, smarter and with more confidence. AI doesn’t replace people; it amplifies their impact. But it only works when you apply it with a clear purpose, discipline and the right foundation.

Wondering where to begin, or how to turn your first experiments into results? We’re happy to help. And Tableau gives you the tools to better understand your customers and grow your business.