Generative AI A Valuable Catalyst for Growth and Performance

Tunisia Seeks to Combine Growth and Efficiency

Tunisia is currently seeking to combine growth and efficiency. Generative artificial intelligence appears to be a promising lever for increasing productivity, improving economic and social performance, and accompanying changes in the world of work. Maher Kallel, an expert in organizational transformation, details the challenges and best practices to be adopted in order to fully benefit from this technological revolution.

The Press — Productivity: A Central Issue

The issue of productivity has now become a central challenge. At the heart of this debate, the emergence of generative artificial intelligence opens up new perspectives, provided it is thought of as a strategic choice for transforming work and economic organization. According to Maher Kallel, an expert in organizational transformation and applied artificial intelligence for productivity, the recent emergence of generative AI in the workplace requires Tunisia to confront this question head-on. "It's not just a technological issue, but a real societal and economic model choice," he emphasized.

A Silent Revolution in Work

In an economy subject to severe budget constraints, growth can no longer come from public spending, debt, or low-cost labor that has become non-competitive compared to other regions. The only remaining structural lever is productivity, i.e., the ability to create more value per hour worked. However, for over a decade, Tunisian productivity has been stagnant. This situation largely explains the weakness of growth, the persistent pressure on wages, and the inability to sustainably finance social policies.

Generative AI: A Major Breakthrough

In this context, generative AI constitutes a major breakthrough. For the first time, a technology allows for rapid productivity gains, accessible to SMEs, without heavy investments, and immediately applicable to tertiary jobs, which represent a growing share of the economy. It acts precisely where the main bottlenecks are located: administration, management, commerce, services, engineering, communication, or finance. Unlike previous technological waves, generative AI does not primarily replace machines, but rather increases the cognitive abilities of workers. It enables the reduction of time spent on repetitive tasks, improvement of analysis and decision quality, and acceleration of execution without degrading work quality.

Visible Effects

In many Tunisian companies that have adopted these tools in a structured manner, the effects are already visible: significant time savings often exceeding one hour per day, improvement of work quality, and refocusing of teams on higher-value tasks. At the national level, the generalization of such gains represents a potential for growth equivalent to several points of GDP, without increasing public debt.

Keys to Success

However, as Maher Kallel reminds us, AI is not a magic tool: it amplifies well-structured organizations and reveals, in contrast, the dysfunctions of others. Telecommuting, flexible hours, and objective-based work are often presented as miracle solutions to improve productivity. In reality, they can produce the opposite effect when poorly implemented.

The Main Obstacle

In Tunisia, the main obstacle is not technological or legal: it is primarily managerial and cultural. The dominant model is still based on physical presence, control, and a vertical hierarchy, whereas hybrid work requires precisely the opposite: clear objectives, measurable indicators, and a culture of responsibility and trust. Without this shift, telecommuting becomes a source of disorganization, suspicion, and ultimately, decreased performance.

Regulatory Framework

This is added to the need to establish a clear and reassuring regulatory framework, an effective right to disconnection, and social protection adapted to new forms of work. Hybrid work is therefore not an end in itself; it only becomes productive when thought of as a coherent system, supported by adapted digital tools, including generative AI.

Productivity and Employment

Kallel later stated that the most widespread fear is known: increasing productivity would destroy employment and weaken purchasing power. This fear is understandable, but it is based on a major confusion. The real danger to employment is not productivity, but its absence. Economies that stagnate ultimately destroy jobs brutally and compress wages. The question is therefore not whether to increase productivity, but how to do so. First rule: replace tasks, not people. AI primarily eliminates repetitive and low-skilled tasks; used intelligently, it increases the value of human work instead of making it obsolete. Second rule: share productivity gains. Without improving wages, working conditions, or training, the transformation becomes socially unsustainable. Third rule: support traditional sectors. In textiles, agriculture, or classic services, AI should be used to reduce losses, improve quality, and promote upgrading, rather than brutally automating at the expense of employment. Finally, fourth rule: make training a national priority. The real protection of employment is no longer the job itself, but employability. Massively training in the concrete uses of AI is now a full-fledged economic and social policy.

Tunisia at a Crossroads

Tunisia is at a crossroads. Either it considers generative AI as a technological gadget or a threat to be contained, or it treats it for what it really is: a decisive lever to get out of economic stagnation, improve productivity, preserve employment, and recreate social margins. This choice does not concern only engineers or entrepreneurs. It involves the state, companies, unions, and, more broadly, the entire Tunisian society. The question is no longer whether this transformation will take place: it is already underway. It remains to be determined whether Tunisia will undergo it or finally be able to organize it, concluded Maher Kallel.