What growth levers produce measurable results for French companies approaching the second half of the decade? With the accelerated adoption of generative AI, restructuring of financing channels, and the emergence of the Revenue Operations model, the options are plentiful. Their effectiveness varies depending on the size of the company, the sector, and digital maturity.
Generative AI and RevOps: Comparative Table of Growth Levers in 2024
Two approaches are capturing the attention of SME and mid-sized company leaders: integrating generative AI into daily operations and aligning sales, marketing, and customer service teams under a RevOps model. Their mechanisms, entry costs, and return timelines differ significantly.
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| Criterion | Generative AI (SME Use Case) | Revenue Operations (RevOps) |
|---|---|---|
| Adoption in France | More than one-third of SMEs have integrated at least one use case in less than a year (Bpifrance Le Lab, Nov. 2024) | Adoption concentrated in high-growth companies, still a minority in traditional SMEs |
| Main Use Cases | Internal chatbots, assisted writing, customer support | Sales-marketing-service alignment, unification of customer data, management by common indicators |
| Perceived Impact Timeline | Positive effect on productivity observed within the first few months by the majority of user leaders | Visible results after restructuring processes (several quarters) |
| Initial Investment | Low to moderate (SaaS subscriptions, internal training) | Moderate to high (overhaul of CRM tools, recruitment or upskilling) |
| Main Risk | Dependence on third-party providers, data privacy issues | Resistance to organizational change |
This table highlights a timing gap. Generative AI produces quick gains on specific tasks, while the RevOps model deeply restructures the value chain. The two do not exclude each other, but their sequencing matters.
Specialized resources in supporting growing companies, such as the NewCom inc website, allow for evaluating these levers based on the development stage of each structure.
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Funding Digital Transformation: European Funding Avenues for SMEs
The issue of funding remains a bottleneck for SMEs looking to industrialize AI or overhaul their technology stack. The Digital Europe Programme from the European Commission has strengthened its co-funding avenues for AI, cloud, and cybersecurity projects led by innovative SMEs.
European Digital Innovation Hubs: A Local Network
Targeted calls for projects, open since 2024 through the network of European Digital Innovation Hubs, explicitly aim at the industrialization of digital solutions to support the growth of small structures. The mechanism relies on co-funding, which requires a contribution from the company.
- Eligible projects cover applied AI, sovereign cloud, and cybersecurity, three pillars identified by the Commission as priorities for SME competitiveness
- The network of Digital Innovation Hubs operates as a local funding office: each hub assists companies in preparing the application and defining the use case
- Funding is accessible via the European Commission’s Funding & Tenders portal, with regularly updated submission timelines
European co-funding targets industrialization, not experimentation. An SME that has already validated an AI prototype internally is better positioned than a company that is still at the technology watch stage.
Generative AI in French SMEs: Insights from the Bpifrance Le Lab Study
The study published in November 2024 by Bpifrance Le Lab provides specific insights into the French economic fabric. More than one-third of SMEs report having integrated at least one use case of generative AI into their daily operations, less than a year after the democratization of ChatGPT.
This adoption rate is remarkable for structures that have historically lagged in digital transformation compared to large companies. Three use cases dominate: internal chatbots for knowledge sharing, assisted writing (emails, reports, marketing content), and automated customer support.
Perceived Productivity and Methodological Limitations
Leaders who regularly use generative AI mostly believe it has a positive effect on productivity and growth. However, this perception is based on statements, not on standardized impact measurements.
The gap between perception and objective measurement constitutes a blind spot. Few companies have indicators isolating the contribution of AI from other productivity gains related to simultaneous investments (training, reorganization, new tools).
Growth Strategy: Sequencing Investments Rather Than Stacking Solutions
The temptation to activate all levers simultaneously (generative AI, RevOps, European funding, new distribution channels) often produces the opposite effect of what is sought. Teams become scattered, budgets dilute, and tracking indicators become unreadable.
A sequential approach yields better results. A company that starts with a low-entry generative AI use case (assisted writing, for example) can measure an initial return before committing to a more structural RevOps overhaul.
- Phase 1: deploy a generative AI use case in a specific function (marketing, support, administration) and measure the impact over a quarter
- Phase 2: align sales and marketing teams around shared indicators, leveraging data collected in phase 1
- Phase 3: seek European co-funding to industrialize the validated solution, with a proposal supported by concrete results
Each phase feeds the next with data and internal legitimacy. The capital invested in phase 1 serves as proof of concept to unlock budgets for subsequent phases.
Companies that rigorously document their intermediate results find themselves in a better position to convince their funders, whether it be equity, bank financing, or public grants. Sustainable growth in 2024 relies less on the number of technologies adopted than on the rigor of sequencing and the quality of measurement.