The Inflection Point: What Changed in 2025
2025 will enter FMCG textbooks as the year when the gap between "lots of data" and "right decisions" reached critical mass. Commercial teams were drowning in spreadsheets and disconnected systems, leading to analysis paralysis precisely when market pressure intensified. The average employee spends 13 hours per week working with data.
Something equally important happened on the technology front. The inflection point was the emergence of Agentic AI. Unlike RPA, which executes a fixed sequence of actions, AI agents reason about a goal and independently determine the steps to achieve it — across multiple systems simultaneously, handling exceptions and adapting to changed conditions.
For FMCG teams this means a concrete revolution. A single AI agent can monitor demand signals in your WMS, update a replenishment request in SAP, verify supplier confirmation — and log the result — with no human involvement between steps. Previously this took days and multiple departments.
of organisations already use AI in at least one business process
McKinsey, 2025. A year ago the figure was 55%. The acceleration of adoption is the fastest in the history of enterprise technology. In FMCG specifically, the AI market will grow to $57.7 billion by 2033 at 22% annual growth.
What Is Agentic AI and How It Differs from Conventional AI
To understand the scale of change, it is essential to clearly distinguish three generations of technology:
The difference between the first and third generation is not quantitative — it is qualitative. Predictive AI will tell you "OOS expected at 12 outlets." Agentic AI will create the replenishment order itself, send it to the supplier, monitor execution, and alert you only if something goes wrong.
If after the system identifies a problem a human still needs to open another system and do something manually — that is not Agentic AI. Agentic AI executes the next step itself. That is the fundamental difference.
Why Excel Distribution Is Doomed
Excel was born in 1985. FMCG distribution still uses it — as the primary tool for managing field teams, orders, and analytics. This is not merely a technology lag — it is a structural vulnerability.
Consider a typical 2026 scenario without Agentic AI:
Now the same scenario with Agentic AI:
4 Stages of Evolution: Where Is Your Business Now
Based on our experience with FMCG companies, most move through four distinct stages of transformation. Understanding where you are now — and what the next stage looks like — is the starting point.
Stage 1: "We Run on Excel" — the baseline reality for most
Manual reports. Orders via WhatsApp. Analytics means a colour-coded Excel file. Decisions are made on yesterday's data and the manager's intuition. How many companies in the FMCG market are still here? Our estimate: the majority.
Stage 2: "We Bought a CRM/SFA" — the first step of automation
There is a mobile app for agents. There is some kind of dashboard. But data is still collected inconsistently, the system is not integrated with ERP, and analytics arrives once a week. Decisions are still predominantly manual.
Stage 3: "We Have Real-Time Data" — the modern standard
Vision AI collects shelf data. Dashboards update in real time. But the system does not yet act — it shows and signals, and a human still needs to make a decision and take action. This is where the most progressive FMCG companies sit today.
Stage 4: "We Have Agentic AI" — the competitive future
The system does not only see — it decides and acts. OOS → auto-order. Competitor price cut → ready response scenario. New outlet → lead in agent's route. Humans set the goal and review outcomes.
Concrete FMCG Examples: How Agentic AI Is Changing Operations Now
Example 1: Autonomous Inventory Management
An AI agent can monitor demand signals in your WMS, update replenishment requests in SAP, verify supplier confirmation, and log the result — without human coordination between each step. For FMCG this means your warehouse automatically maintains optimal stock levels without manual ordering.
Example 2: Real-Time Dynamic Pricing
Even in FMCG, brands are moving to AI-driven dynamic pricing engines that deliver higher margins through more accurate price elasticity modelling and rapid market response. A competitor cuts price — the AI agent detects it and within 2 minutes presents you with a recommendation and rationale.
Example 3: Autonomous Route Optimisation
Instead of a supervisor manually assigning routes every morning, an AI agent analyses customer priorities, road traffic, outlet stock levels, and automatically builds the optimal route for each agent. FMCG automation can reduce logistics costs by 5–20%.
Risks and What to Do Right Now
Agentic AI is not a tool reserved for large corporations with teams of data scientists. The market is moving toward accessible solutions for companies of any scale. But there are several risks worth avoiding.
Risk 1: Buying "AI-washing" instead of a real solution
Many vendors today write "AI" on every product — even if it is just a simple "if X then Y" rule. Ask specifically: what decision does the system make autonomously? What exactly is automated without human involvement?
Risk 2: Automating chaos instead of order
If your data is poor quality, processes are chaotic, and the team does not follow standards — AI will only amplify the problem. Automation magnifies both the good and the bad. Standards and clean data come first.
The era of Excel distribution is ending not because someone decided to replace spreadsheets. It is ending because competitors moving to Agentic AI gain such an advantage in response speed and decision accuracy that Excel-based companies simply cannot compete in the same price segment.
The question is not "whether to adopt AI" — it is "when." The later the start, the more expensive the catch-up.