{"id":2073255,"date":"2026-08-21T14:09:23","date_gmt":"2026-08-21T12:09:23","guid":{"rendered":"https:\/\/www.luxsure.fr\/2026\/08\/21\/ai-in-business-technology-advances-execution-makes-the-difference\/"},"modified":"2026-08-21T14:20:19","modified_gmt":"2026-08-21T12:20:19","slug":"ai-in-business-technology-advances-execution-makes-the-difference","status":"publish","type":"post","link":"https:\/\/www.luxsure.fr\/en\/2026\/08\/21\/ai-in-business-technology-advances-execution-makes-the-difference\/","title":{"rendered":"AI in Business: Technology Advances, Execution Makes the Difference"},"content":{"rendered":"\n

At VivaTech, IBM, Orisha, and EY reiterated a fact that many organizations are still discovering: artificial intelligence does not fail due to a lack of technological capability. It fails due to a lack of operational integration.<\/p>\n\n

The days of impressive demonstrations seem long gone. In large companies, the question is no longer whether AI can write, summarize, code, or assist in decision-making. It can. The real challenge begins afterward: how can this capability be integrated into actual processes, existing systems, work habits, approval workflows, security constraints, and business models?<\/p>\n\n

It was precisely this transition from promise to value that shaped the discussion between Anna Paula Assis, IBM\u2019s head of EMEA and Asia-Pacific, Alexandre Fr\u00e9ty, CEO of Orisha, and Jad Shimaly of EY.<\/p>\n\n

The End of \u201cDecorative\u201d AI<\/h2>\n\n

For two years, many companies have been piling up use cases: an assistant for HR teams, a co-pilot for developers, a content generator for marketing, and a summary tool for meetings.<\/p>\n\n

These experiments sometimes yielded localized gains. But they rarely transformed the company.<\/p>\n\n

For Anna Paula Assis, the mistake lies in treating AI as an add-on. Yet value does not come from a technological layer superimposed on an unchanged operational framework. It emerges when a company is willing to reevaluate its workflows, data, responsibilities, and metrics.<\/p>\n\n

The analogy with electricity is illuminating. At first, electricity made it possible to light factories. They became cleaner, safer, and more efficient. But their production logic did not change. The breakthrough came later, with the assembly line.<\/p>\n\n

AI is at the same stage. As long as it merely enhances existing processes, it improves them. When it forces us to rebuild them, it transforms them.<\/p>\n\n

The real problem isn\u2019t the model, but the organization<\/h2>\n\n

Alexandre Fr\u00e9ty puts it bluntly: many companies are adding AI to an unchanged operational model. As a result, they achieve incremental gains, not a transformation.<\/p>\n\n

The right question isn\u2019t: \u201cWhere can we add AI?\u201d<\/p>\n\n

Rather, it is: \u201cIf we had to rebuild our operations today, would we design them the same way?\u201d<\/p>\n\n

In most cases, the answer is no.<\/p>\n\n

The obstacles are well known: fragmented data, legacy systems, business silos, insufficient governance, and difficulty linking AI initiatives to financial results. AI promises to cut across functions, but the company is often still organized to keep them separate.<\/p>\n\n

Yet value lies precisely in these areas of friction: between finance and sales, between HR and operations, between customer relations and the supply chain, and between product development and field support.<\/p>\n\n

ROI too often remains a matter of belief<\/h2>\n\n

One of the most striking findings from the session concerns the gap between ambition and measurement.<\/p>\n\n

According to the data shared, 80% of companies expect AI to significantly boost their growth. But only 20% can explain precisely where and how this growth is expected to materialize.<\/p>\n\n

Even more concerning: fewer than 15% clearly link their AI programs to their financial statements.<\/p>\n\n

In other words, many organizations believe in the potential but do not yet know how to translate it into margins, revenue, productivity, or cash flow.<\/p>\n\n

IBM offers an interesting counterexample here. The company had set an initial goal of $2 billion in productivity gains over three years. It reports having achieved $4.5 billion. The method: map out processes, define a target model, create operational prototypes, and then engage the relevant functions.<\/p>\n\n

In human resources, an agent now handles 94% of the inquiries directed to the HR department. The most interesting aspect isn\u2019t just automation\u2014it\u2019s the improvement in the employee experience: the NPS is reported to have risen from around 20 to 74.<\/p>\n\n

When properly integrated, AI does more than just cut costs. It can improve service.<\/p>\n\n

The Hidden Cost of AI<\/h2>\n\n

The session also brought up a topic that is still not discussed enough: the true cost of AI.<\/p>\n\n

Many companies monitor token prices. This is necessary, but not enough. Reducing the cost of AI to the consumption of models is like managing a factory based solely on its electricity bill.<\/p>\n\n

The most significant costs lie elsewhere: governance, security, oversight, training, change management, errors, dependence on suppliers, staff redundancy, system maintenance, and regulatory risks.<\/p>\n\n

For Orisha, the issue is very real. The cost of AI in software development accounted for about 2% of developers\u2019 salaries last year. It could reach 5% this year, with some developers already spending the equivalent of 40 to 50% of their pay on AI tools.<\/p>\n\n

This isn\u2019t necessarily a problem if the value is there. But it still needs to be measured.<\/p>\n\n

Alexandre Fr\u00e9ty offers a useful comparison: AI agents will need to be managed like employees. A company wouldn\u2019t accept two employees doing exactly the same thing. It won\u2019t be able to accept dozens of redundant, opaque, and costly agents for long.<\/p>\n\n

From the Fear of Missing Out to the Fear of Making a Mistake<\/h2>\n\n

The market\u2019s psychological state has shifted.<\/p>\n\n

Yesterday, FOMO\u2014the fear of missing out on the AI bandwagon\u2014dominated.<\/p>\n\n

Today, another concern is rising: the fear of doing things wrong. Deploying too quickly. Losing control. Letting an agent operate without supervision. Multiplying tools without an architecture. Creating new risks instead of new opportunities.<\/p>\n\n

This shift is healthy. It marks AI\u2019s entry into a more mature phase.<\/p>\n\n

Leaders are no longer just asking themselves how to adopt AI. They\u2019re asking how to govern, monitor, explain, secure, and maintain it.<\/p>\n\n

This requirement is all the more pressing because AI is probabilistic. At two different points in time, a system can produce two different responses. For a company, this variability demands new standards for observability, traceability, and accountability.<\/p>\n\n

AI Is Becoming a Critical Infrastructure<\/h2>\n\n

Another point deserves attention: operational dependence.<\/p>\n\n

Alexandre Fr\u00e9ty cites a recent outage of large language models that nearly paralyzed some developers. The message is clear: when AI becomes central to operations, its unavailability is no longer just a technical incident. It\u2019s a business risk.<\/p>\n\n

Companies will therefore need to establish contingency plans, diversify their business models, coordinate their workforce, and avoid excessive reliance on a single vendor.<\/p>\n\n

AI is no longer a peripheral tool. It is becoming a core part of the workplace.<\/p>\n\n

Leadership as a Decisive Factor<\/h2>\n\n

Ultimately, this session conveys a simple message: AI is not an IT department issue. Nor is it an innovation lab issue. It is becoming a matter for senior management.<\/p>\n\n

For the next twelve months, Alexandre Fr\u00e9ty identifies one key quality: courage.<\/p>\n\n

The courage to start with a blank slate.<\/p>\n\n

The courage to reassess talent.<\/p>\n\n

The courage to train, but also to demand.<\/p>\n\n

Finally, the courage to lead by example. A leader cannot drive an AI transformation unless they have personally tested the tools, understood their limitations, and internalized their logic.<\/p>\n\n

Anna Paula Assis, for her part, emphasizes culture. Productivity gains, cost savings, and financial metrics aren\u2019t enough. If employees don\u2019t understand how AI is changing things for them, they\u2019ll become a hindrance. If they see its value, they can become the driving force behind the transformation.<\/p>\n\n

What this session really reveals<\/h2>\n\n

The debate hardly touched on business models. This is undoubtedly the most interesting sign.<\/p>\n\n

Competitive advantage is no longer determined solely by access to technology. It is determined by the ability to translate that technology into operations, governance, measurable value, and trust.<\/p>\n\n

AI will not reward the companies that have launched the most pilot projects. It will reward those that have had the discipline to rebuild their processes around it.<\/p>\n\n

The promise is now well known.<\/p>\n\n

The real issue is execution.<\/p>\n\n

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