78% of companies now use AI, compared to just 55% a year ago. The integration of artificial intelligence into businesses is expanding rapidly and is no longer a competitive advantage in itself. Generic assistants like GPT have become commodities, easy to integrate but rarely differentiating.
According to BCG, only 26% of companies manage to move beyond the proof-of-concept stage to transform AI into a concrete performance lever.
Without structured data, AI remains an empty shell
Companies successfully transitioning to an AI-driven model share a common foundation: they adopt a resolutely data-driven approach through structured and contextualized analysis. This raw material, this new "black gold," once refined, enables the training of useful and relevant models capable of enhancing business processes in a targeted manner.
Most organizations are overwhelmed by volumes of scattered, poorly formatted, or outdated information. It is by intelligently combining internal data (contracts, usage history, financial reports, etc.) with external signals such as benchmarks, market trends, and price alerts that AI becomes truly operational and effective for the company.
In finance, the operational use of AI enables, for example, the development of benchmarks tailored to the company's specific needs. In procurement, it facilitates the anticipation of price increases by automatically analyzing contractual clauses and market trends. In HR, data-driven AI provides a precise projection of recruitment needs and salary budgets, based on the growth trajectory.
Therefore, it is not the AI assistants themselves that create the impact and performance, but their integration with qualified business data. Useful AI improves a specific business indicator: reduced acquisition cost, better budget allocation, optimized conversion rate, more reliable forecasts, or increased productivity.
Step into the cockpit of financial excellence
Prioritize company culture over the race for models
AI only becomes a strategic lever when it is part of a gradual transformation. Companies that leverage it proceed methodically, starting by integrating simple assistants into existing tools. Next, they test targeted use cases led by pilot teams, then develop a more ambitious roadmap by involving data governance, operations, the IT department, and senior management before incorporating it into a comprehensive strategic roadmap.
According to BCG, the success of an AI project depends 70% on teams and processes, 20% on technology and data, and only 10% on models. Business alignment, data governance, and clear use cases are therefore the key drivers that allow AI to move beyond being a mere augmented gadget or simple commodity.
Tomorrow, companies will no longer pay for a generic AI assistant. They will invest only in enhanced services, powered by differentiating, cross-referenced, enriched, and modeled data tailored to their specific strategic challenges. Ultimately, the real value will no longer lie in "AI-powered" solutions, but in the ability to design "business-native" AI , deeply embedded in companies' internal processes.
