Check the interview in Qatar Tribune hier.
For the past two years, most conversations around AI have focused on copilots and chatbots, systems designed to assist humans with content generation, recommendations, and productivity. These systems largely remained passive as they advised humans, but humans still made the decisions and executed the work.
A shift is already underway.
We are now entering the phase of autonomous enterprises where AI systems do not simply support workflows, but increasingly coordinate them, make operational decisions, trigger actions across systems, and continuously optimize outcomes in real time.
This transition is being enabled by the convergence of generative AI, agentic systems, workflow automation, enterprise data platforms, and real-time computing infrastructure. Moreover, modern AI systems can interpret context, reason probabilistically, and adapt dynamically to changing environments. This marks a major shift from automation to autonomy.
In practice, this evolution is already visible across industries. We are already developing and implementing autonomous AI systems across multiple sectors. In retail, we deployed AI agents capable of autonomously conducting preliminary market and financial analyses for store expansion opportunities in minutes instead of months. Other agentic systems validate supplier tender documents, detect discrepancies in financial closing processes, and orchestrate customer reimbursement workflows with minimal human intervention, delivering productivity gains of up to 50%.
In travel and tourism, we implemented autonomous concierge platforms that move beyond traditional chatbot interfaces by dynamically building itineraries, rebooking disrupted journeys, and coordinating bookings across multiple providers in real time. In the energy sector, we supported large-scale AI deployments across operations, supply chain, customer service, and infrastructure management, significantly accelerating operational decision-making and reducing inefficiencies.
The shift is equally visible in government and healthcare environments, where we developed autonomous systems supporting claims processing, risk scoring, data governance, and software development lifecycles through agentic coding workflows designed to accelerate engineering productivity.
However, the transition toward full autonomy will vary significantly in pace and maturity across business functions, depending on how suited they are to autonomous execution. High-volume, repetitive operational workflows are already moving rapidly toward AI-led orchestration. Strategic decisions, ethical trade-offs, crisis management, and reputation-sensitive interactions remain firmly dependent on human judgement.
This creates one of the defining leadership challenges of the AI shift: governance.
Most governance models today were designed for AI systems that recommend. Autonomous enterprises require governance for systems that act. Organizations will need clear frameworks defining decision boundaries, escalation mechanisms, auditability standards, and accountability structures. Human oversight cannot disappear, it must evolve from direct execution toward supervisory control.
Ultimately, the future enterprise will not be defined by how much AI it deploys, but by how effectively it balances autonomy with trust, speed with accountability, and machine intelligence with human judgement.
