Here are the 5 essential components for artificial intelligence to truly create value in your business.
You were perhaps thinking of “connecting” a artificial intelligence like installing new software. You were hoping for an immediate gain in productivity, reduced costs or better decision-making. However, the results you have seen are often disappointing. The tool remains little used, the benefits remain invisible, and sometimes unexpected problems appear. Language models offered on the market (by Mistral, OpenAI, Anthropic or others) mainly provide the model itself, accessible via an interface or technical connection. They also offer remote computing power. On the other hand, they do not deliver the integration of the company’s internal data, nor the understanding of its business processes, nor the necessary governance, security and sovereignty. These elements remain to be fully constructed.
Artificial intelligence is not a magic solution out of the box. To truly transform the functioning of an organization, it requires technical, organizational, ethical and governance mastery. Here’s why, and how successful businesses do it.
Five essential dimensions
An effective artificial intelligence system in business is based on several elements that must work together:
- Computing power (computers and servers capable of processing large amounts of information).
- Data integration and quality (bringing together and cleaning information scattered across the enterprise).
- Understanding the business meaning (connecting this data to the organization’s real processes: what a customer is, a production defect, an urgent order, etc.).
- The artificial intelligence models themselves (the programs that learn from the data).
- Governance, confidentiality and sovereignty (protecting sensitive information, maintaining control over processes and decisions, and ensuring that the company remains in control of its data and its choices).
Neglecting one of these dimensions renders the whole fragile or useless.
Computing power and data integration
Without reliable and well-organized data, artificial intelligence cannot do anything useful. However, in most companies, information is scattered: management software here, Excel files there, machine sensors elsewhere, customer bases elsewhere.
The UPS example illustrates this point well. For more than ten years, the American delivery company has developed a route optimization system called ORION. This is based on hundreds of data points collected every day from tens of thousands of vehicles (speed, stops, fuel consumption, traffic conditions, etc.). Through this massive, continuous integration of information, the system has saved approximately 100 million miles traveled each year, $300 to $400 million in operational costs and 10 million gallons of fuel.
Without this in-depth data work, the system would never have produced results on this scale. Many companies that simply purchase an “off-the-shelf” artificial intelligence tool without connecting their own information find exactly the opposite: very low usage and no measurable gain.
Giving meaning to data: the organizational dimension
Raw data is not enough. They must be linked to the reality of the profession. What is a “production defect” in a factory? How to distinguish a priority order from an ordinary order? What decision rules does the company already apply?
This is where what we might call the business sense layer comes into play. Successful industrial companies often use “digital twins”: complete virtual models of a factory or process. Siemens and several of its customers (particularly in the automobile and chemical industries) are showing concrete gains. In some cases, designing a new factory using these models has achieved 20% higher productivity compared to traditional factories. Simulations allow changes to be tested before implementing them in reality, reducing risks and accelerating improvements.
This step requires the active participation of industry experts, not just technical specialists. Without them, the system remains abstract and of little use.
Artificial intelligence models: one tool among others
The models themselves (whether open or closed) are often featured as the centerpiece. Yet, when used alone, they generally produce responses that are too generic or difficult to control. Their true value appears when they are integrated into a broader system that includes the company’s data and its operating rules.
Many organizations have discovered the hard way that a simple conversational assistant, even a high-performance one, does not automatically transform operations if it is not linked to internal processes and information.
Governance, confidentiality and sovereignty
This fifth dimension is essential, even if it is often underestimated. It brings together the protection of sensitive information, control over the decisions made by the system, and the ability of the company to remain in control of its data and its processes.
Without clear governance, the risks are real: leakage of confidential information, loss of control over important decisions, excessive dependence on an external supplier, or non-compliance with data protection rules.
The case of Amazon remains emblematic on an ethical level. Between 2014 and 2018, the company developed a recruitment tool based on artificial intelligence. Trained on historical recruitment data (largely male in the technical sector), the system began to systematically penalize female candidates. It lowered the rating of CVs which contained the word “women’s” or which came from exclusively women’s universities. Amazon eventually abandoned the tool.
Beyond bias, the question of sovereignty arises forcefully: who really owns the data? Who can use them? Who controls automated decisions? Good governance involves clear rules, human oversight, regular audits and the company’s ability to recover or move its systems if necessary.
A methodical approach rather than a gimmick
Companies that truly benefit from artificial intelligence don’t treat it as just another technological add-on. They build a complete system: computing power, reliable data, understanding of the business, adapted models, and a solid governance framework.
The possible gains are significant: cost reduction, improvement in quality, optimization of resources, and even reduction in environmental impact, as the example of UPS shows. But these results only appear when artificial intelligence is integrated methodically and responsibly.
The question is therefore no longer whether a company should use artificial intelligence. The real question is whether she is ready to master it in all its dimensions.
