Video Overview and Key Themes
In a recent YouTube episode hosted by Samuel Boulanger, Fatih Nâyebi, VP of Data & AI at the Aldo Group, explains how retailers can move past hype and make AI a practical business driver. He outlines concrete use cases, from improved demand signals to smarter pricing, and places those cases within a larger conversation about operational change and measurement. As the conversation progresses, the episode frames the evolving technical landscape and the organizational choices leaders must make when adopting advanced tools.
Moreover, the discussion emphasizes real-world constraints and tradeoffs rather than idealized promises. Consequently, viewers gain a balanced sense of both opportunity and complexity when integrating AI into retail operations. This perspective helps executives weigh short-term wins against long-term investments in people, data, and governance.
AI in Practice: Aldo’s Use Cases
Nâyebi highlights how Aldo uses demand forecasting and markdown optimization to improve inventory flow and margin management. For instance, more accurate forecasting reduces overstock and clearance needs, while optimized markdowns preserve margin and clear inventory faster. These improvements require solid data pipelines and cross-functional collaboration between merchandising, supply chain, and stores.
However, the episode also stresses that implementing these systems involves tradeoffs. While sophisticated models can raise forecast accuracy, they also demand higher data quality and maintenance costs, and they introduce model risk that teams must monitor. Therefore, retailers should balance model complexity with operational readiness to ensure sustainable value.
Agentic AI, GenAI, and Automation: Differences and Tradeoffs
The video distinguishes three broad approaches: traditional automation, generative AI, and what Nâyebi calls agentic AI. In contrast to simple workflows, agentic AI can act more autonomously by combining tools, planning, and execution, which makes it useful for complex retail scenarios that require multi-step decisions. Meanwhile, generative models excel at language and creativity tasks but may need grounding and control when used to recommend business actions.
Consequently, each approach brings tradeoffs in control, transparency, and risk. Autonomous agents can boost productivity, yet they increase governance challenges and the potential for opaque decision-making. Thus, teams must design clear guardrails, testing regimes, and human-in-the-loop checks to harness power while limiting unintended outcomes.
Measuring Impact Beyond Revenue
Nâyebi argues that retail leaders should measure AI impact using broader metrics than top-line sales alone. For example, improved forecast accuracy shortens decision cycles, reduces markdown depth, and improves in-store availability, all of which affect customer experience and operating cost. He also highlights metrics like cycle time for decisions, forecast error reduction, and reduction in excess inventory as meaningful indicators of operational improvement.
At the same time, measuring these outcomes involves tradeoffs in what to prioritize and how to attribute results. Faster metrics can show early wins but might miss long-term risks, while financial outcomes can lag and obscure process improvements. Therefore, combining short-term operational KPIs with long-term financial measures helps create a clearer picture of value.
Starting the AI Journey: Governance, Skills, and the Future of Work
The episode closes by outlining practical steps for retailers to begin their AI journeys, including governance frameworks, pilot projects, and upskilling programs. Nâyebi stresses the need for clear data ownership, ethical guardrails, and explainability so teams can trust model outputs and act on them. He also points to the importance of investing in skills for frontline employees and analysts to interpret insights and make faster decisions.
Nevertheless, the path forward includes difficult choices and tradeoffs between speed and safety. Rapid pilots can deliver early value, but without governance they may create technical debt or compliance gaps. Conversely, heavy upfront controls slow adoption; therefore, a staged approach that pairs agile pilots with gradually tightening governance often proves most effective.
Conclusion: Practical Adoption with Balanced Expectations
Samuel Boulanger’s episode with Fatih Nâyebi offers a grounded playbook for retail leaders who want to adopt AI in ways that truly move the business. It combines case studies, technical distinctions, and governance advice to illustrate both the gains and the thorny tradeoffs retailers must navigate. As a result, the video serves as a pragmatic resource for teams planning pilots, measuring success, and building the capabilities necessary for long-term change.
Overall, the discussion encourages leaders to start with well-scoped problems, measure the right mix of operational and financial outcomes, and pair innovation with clear controls. In this way, retailers can capture value from modern technologies while managing the risks that come with greater autonomy and complexity.
