Retail is moving through a third major wave of digital change. The first wave made information accessible, the second wave was defined by mobile and social platforms, and the third is being driven by artificial intelligence. The real disruption is not the technology itself, but how quickly it is resetting customer expectations.

This creates a strategic risk for any organisation still planning for “today’s customer” rather than the customer “the day after tomorrow”. The competitive baseline is shifting in real time.

Many organisations have started using AI as an assistant—tools that help individuals work faster and access information more efficiently. The next stage is the use of agents as task-focused colleagues, where humans instruct systems to complete defined work.

The future state is more profound: organisations building agentic teams that can run workflows and projects with oversight rather than step-by-step instruction. This requires a new operating model—one where leaders set direction, define guardrails, and monitor outcomes while autonomous capability executes at speed.

Consumer expectations vary by region, but convenience-led markets (including the GCC) amplify demand for speed and frictionless experiences. That expectation is pushing retailers to rethink everything from fulfilment strategy to service design.

AI and agents make this acceleration possible by enabling real-time decision-making, predictive operations, and always-on capability across functions—far beyond traditional forecasting.

In volatile conditions—trade disruption, policy uncertainty, shifting costs—retailers are under pressure to protect margin while maintaining growth. One response is a renewed focus on monetising first-party data and strengthening consumer relationships through new business models.

At the same time, trend cycles have compressed dramatically. Internet phenomena can reshape demand globally in days, not months. AI becomes a practical tool for leaders who need to identify emerging signals, understand cultural momentum, and translate it into commercial opportunity.

Three shifts matter for retail strategy going forward:

• Ecosystems over isolation: winning increasingly comes from partnerships and alliances rather than trying to do everything alone.

• Data quality over data volume: AI capability depends on the quality, structure, and reliability of data—not the size of the dataset.

• Sustainability as a requirement: circularity and sustainability are moving from “nice to have” to an expectation from consumers, cities, and regulators.

A core reality stands out: most AI initiatives don’t fail because the technology is weak. They fail because the organisation is not prepared. Without clear business goals, ownership, change management, and the right data foundations, AI becomes an experiment instead of an operating advantage.

The organisations that succeed tend to use a disciplined approach: define a business objective, select the right use case, ensure data readiness, and adopt a lean, iterative methodology that builds capability over time.

Innovation is not a one-off initiative. It is a process that compounds—through curiosity, learning, and repeated experimentation. Leaders need to build a culture where teams are encouraged to ask better questions, test ideas, and evolve operating models continuously.

A practical lens is “jobs to be done”: consumers are not buying products in isolation; they are “hiring” solutions to solve a problem—often driven by both practical and emotional needs. This perspective helps organisations innovate around what truly matters.

Even in an agentic future, differentiation won’t come only from automation. Brands that win will combine intelligent capability with human experience design.

A simple example: a memorable service moment can outperform expensive technology because it signals care, attention, and empathy—qualities consumers continue to value, especially in an attention-saturated world.

The shift ahead is not just adopting AI tools—it is building an AI-first organisation with the courage to change, the discipline to focus on outcomes, and the maturity to balance automation with the human experience.