AI and Agents: Toward a New Operating System for Retail Planning 

In a context of retail transformation, artificial intelligence is gradually becoming a major lever for improving operational and financial management. It is no longer limited to isolated experiments but is now being embedded at the core of planning processes. 

Retail operates in an environment characterized by increasing complexity. Demand volatility, the multiplication of channels, the growing number of SKUs, and the explosion of data volumes are making management increasingly demanding. Organizations must now analyze multiple signals, make rapid trade-offs, and take decisions within ever shorter timeframes. 

In this context, the limits of traditional approaches are becoming clear. Conventional tools are no longer able to effectively leverage all available data or anticipate changes with sufficient responsiveness. Artificial intelligence provides a direct response to this complexity. 

Organizations deploying intelligent agents within their planning processes are already seeing significant impacts. Decision cycles are shortening, team productivity is increasing, and manual interventions are decreasing, enabling smoother and more efficient management. 

AI is therefore becoming a performance accelerator, capable of continuously processing volumes of information that are impossible to analyze manually. 

“AI forces customers to review their data and get their house in order. It is also a broad church that makes things, such as optimization engines and predictive analytics, more accessible despite them being available for a long time already.  

We see context, role-based AI being applied in specific processes accelerating decision making speed and improving quality, but importantly maintaining human lead accountability.” – Tom Owen, Managing Director UK, Better Decisions Group  

Beyond forecasting: toward decision-oriented AI 

In an uncertain environment, performance no longer depends solely on forecast accuracy, but on the ability to make decisions quickly and consistently. Value is therefore shifting toward decision-making. 

The most advanced solutions now go far beyond simple forecasting. They are capable of simulating different scenarios, identifying risks upstream, and recommending concrete actions adapted to the context. 

In practical terms, AI makes it possible to: 

• process a large number of variables in real time 
• integrate operational and financial constraints 
• propose trade-offs based on scenarios 

Artificial intelligence is thus evolving from an analytical tool into a true decision-making assistant. This transformation is fundamental, as it enables the shift from an analysis-driven approach to an action-driven approach, directly connected to business challenges.

Tom Owen

UK Managing Director Better Decisions Group

AI adoption also needs to be considered and deliberate. It’s important to understand that AI is not creating anything new – it can only work with the data it is provided and can only do it in a way that it is prompted.

Measurable and structural performance gains 

Organizations scaling AI adoption are already seeing tangible and measurable benefits. 

By 2026–2028, companies industrializing the use of AI can significantly accelerate their decision-making processes and reduce operational costs. These gains are driven by two major transformations. 

• The automation of repetitive analyses 

• The ability to rapidly explore a large number of scenarios 

This results in: 

• The elimination of low-value tasks 

• Greater focus on strategic activities 

• Improved decision quality 

This dual capability—automation and simulation—enhances both the speed, accuracy, and robustness of management.

Tom Owen

UK Managing Director Better Decisions Group

Your plan is probably a good one, but then a war/pandemic/waterway blockage happens and destroys it. It's not about getting Version 1 perfect, its about how fast you can make Version 2, and then 3, and then 4, then jumping back to 2.

A profound transformation of roles and organizations 

The integration of artificial intelligence does not only transform tools. It fundamentally reshapes roles and organizations. The role of planners is evolving significantly. They are no longer solely responsible for producing analyses or forecasts. They become key decision-makers, capable of interpreting system-generated recommendations and making trade-offs based on context. 

This evolution is part of a collaborative model between humans and machines. Systems analyze data, identify trends, and propose options, while teams validate, adjust, and make final decisions. 

The machine enhances analytical capacity, while humans retain responsibility for decisions. This repositioning strengthens the value of teams by refocusing them on strategic analysis, trade-offs, and the management of complex situations. 

“Humans are still, and will be for the foreseeable future, accountable for the outputs in the planning process. AI can lead the process of checking, processing and analyzing data but the output will always need to be assessed and curated, and thus a non-black box approach is needed.  ” – Tom Owen, Managing Director UK, Better Decisions Group

Concrete use cases already in operation 

This transformation is no longer theoretical. It is already visible in many organizations. Some solutions integrate specialized AI agents capable of supporting business teams on a daily basis. These agents continuously analyze performance, identify anomalies, and recommend corrective actions. 

Board’s AI Merchandiser agent is a strong example. It enables the analysis of product performance, detects risks of overstock or stockouts, and recommends adjustments to allocation, pricing, or replenishment. This type of tool illustrates the shift from an analytical approach to a decision-support model. 

The objective is not to replace teams, but to enable them to make better decisions, faster and with a higher level of confidence. 

“The role specific and contextual nature of Boards Merchandiser agent is the right approach. From speaking to CTO/CIOs we know theres a large challenge around which AI provider to deal with, it’s a constantly changing situation as to which provider has the best model. We see agents within SaaS to be a great way to mitigate this, but have agents that have role based context to work directly with each role in the organization. Finance agents for finance, HR agents for HR and Merchandising agents for merchandisers.” – Tom Owen, Managing Director UK, Better Decisions Group

2026–2030: toward augmented and partially autonomous planning 

By 2030, the role of artificial intelligence in retail management is expected to intensify significantly. The most advanced organizations will rely on systems capable of continuously analyzing data, simulating different scenarios, and proposing decisions adapted to operational and financial constraints. 

In this context, a growing share of decisions will be generated automatically or semi-automatically. AI agents will handle a large part of analysis and decision preparation, enabling teams to focus on higher value-added activities. 

Retail management will evolve toward a hybrid model, combining the analytical power of machines with human judgment, in a continuous performance optimization approach. 

Tom Owen

UK Managing Director Better Decisions Group

The cost of AI is a huge cause of uncertainty. Providers are losing billions of dollars every year in setting up infrastructure and trying to keep up with demand. This creates uncertainity around the economics for users. It gives peace of mind that Boards agents are provided without a token model and therefore you don’t have to worry about someone burning through the allocation and getting unexpected bills.

Download the full retail trends analysis and discover how to transform your planning by 2030.