The adoption of artificial intelligence by companies reached a record level in 2024. The question now is no longer about the spread of these technologies, but their actual performance. According to the McKinsey Global Survey on AI 2024, a large majority of organizations are already using AI in at least one business function.
The share of those who regularly use generative AI has significantly increased between 2023 and 2024. Despite this acceleration, the creation of financial value remains concentrated in a minority of companies.
Gap between AI adoption and measured profitability in business
The gap between widespread usage and impact on results is the key takeaway of 2024. The proportion of companies reporting a positive effect on their EBIT related to AI remains almost stable year on year, at just over one-third of respondents, according to McKinsey.
| Indicator | 2024 Findings (McKinsey) |
|---|---|
| AI adoption in at least one function | Large majority of organizations |
| Regular use of generative AI | Significant increase between 2023 and 2024 |
| Reported positive impact on EBIT | About one-third of respondents, stable |
| “High performers” (significant share of EBIT attributed to AI) | Marginal fraction |
This table highlights a concrete paradox. AI is spreading into processes, internal applications, and customer data management. However, the translation into tangible financial results does not follow the same curve.
Companies that derive measurable benefits from AI often share a common point: they have restructured their business processes around the technology, rather than simply adding a layer of AI on existing workflows. Several analyses published on www.bart-magazine.com detail this logic of operational transformation applied across different sectors.
Data and security strategy: prerequisites that generative AI does not replace
The rise of generative AI has highlighted a structural weakness in many organizations: the quality and governance of data condition the return on investment. A language model deployed on poorly structured or siloed data produces unusable results.
Managing trust, risk, and AI security (an approach sometimes referred to by the acronym AI TRiSM) has emerged as a strategic focus in 2024. Technological trends related to cybersecurity are no longer just about perimeter protection. They now encompass the reliability of outputs generated by artificial intelligence.
Three concrete initiatives for business leaders
- Audit of the quality of datasets used by AI applications, with traceability of sources and bias detection before production
- Implementation of continuous threat exposure management (CTEM), which assesses vulnerabilities in real-time instead of relying on one-off audits
- Regulatory framing, particularly in Europe with the obligations of the AI Act, which mandates the classification of AI systems by risk level and transparency requirements
Without data governance, generative AI amplifies errors instead of creating value. This observation partly explains why the fraction of “high performers” remains so small.
Product innovation and customer experience: blockchain, mixed reality, and personalization
Innovation in the business world in 2024 is not limited to artificial intelligence. Several technologies are maturing simultaneously and changing the consumer experience.
Blockchain is finding concrete applications beyond decentralized finance. The traceability of food products, management of digital rights, and authentication of legal documents are among the use cases that have developed this year. Blockchain secures the value chain where trust is lacking.
Mixed reality has also matured. Online applications for customers now allow them to visualize a product in a real context before purchase, whether it’s furniture, a vehicle, or industrial equipment. This evolution reduces return rates and improves customer satisfaction.
AI-driven personalization: the balance between utility and intrusion
Consumers expect a personalized experience. Companies leverage behavioral data to tailor product recommendations, interfaces, and online shopping journeys. Personalization generates loyalty when it remains transparent about data usage.
The risk lies in excess. Overly granular personalization, perceived as intrusive, leads to rejection. Brands that clearly document their data collection policies and offer real control to users achieve better engagement rates than those that personalize in an opaque manner.
Skills and organization: the human factor hinders or accelerates transformation
The World Economic Forum highlights that the skills sought by employers are rapidly evolving due to AI. Profiles capable of combining business expertise with mastery of artificial intelligence tools are in highest demand.
This pressure on skills is reflected in recruitment and training strategies. Companies that invest in upskilling their existing teams achieve faster and more sustainable adoption of new tools.
- Continuous training of business teams on AI applications specific to their field, not just generic tools
- Recruitment of hybrid profiles (data analysts with industry experience, managers trained in interpreting predictive models)
- Reorganization of teams to integrate feedback loops between AI users and technical teams
The performance gap between companies is more attributable to organization than to technology itself. The tools are accessible to all. The ability to integrate them into rethought processes makes the difference.
The trends and innovations of 2024 outline a landscape where available technology exceeds the absorption capacity of most organizations. The key takeaway remains from the McKinsey Global Survey: despite massive adoption, only a marginal fraction of companies attribute a significant share of their results to AI. The lever for transformation is not technical; it is organizational.
