• Written by Azhar Aliyar, Director

This week, a sales manager at a large retailer must decide how to allocate stock to different stores, a decision that currently relies on a combination of intuition and manual analysis of sales data. However, with the advent of agentic AI workflows for sales, this decision can be informed by a more nuanced understanding of the complex interactions between sales, inventory, and customer demand.

What separates an agent from a chatbot

An agentic AI workflow for sales is distinct from a chatbot in that it is capable of taking multi-step actions, using various tools and systems to achieve a specific goal. This is made possible by the agent's ability to maintain state and use this information to inform its decision-making. In the context of sales, this means that an agentic AI workflow can analyze historical sales data, current inventory levels, and upcoming promotions to make informed decisions about stock allocation.

A chatbot, on the other hand, is typically limited to answering a single question or providing a simple response, without the ability to take action or maintain state. While chatbots can be useful for providing customer support or answering frequently asked questions, they are not equipped to handle the complex decision-making required in sales.

Where a human approval step belongs

One of the key benefits of agentic AI workflows for sales is their ability to automate complex decision-making processes, reducing the need for human intervention. However, this does not mean that human approval is never necessary. In fact, removing the human approval step altogether can be premature, as it is still important to have a level of oversight and control in place.

A more effective approach is to use agentic AI workflows to provide recommendations and insights, which can then be reviewed and approved by a human decision-maker. This allows the benefits of automation to be realized, while still maintaining a level of control and accountability.

The sales data this depends on

Agentic AI workflows for sales rely on a range of sales data, including historical sales at planning grain, order book and pipeline, inventory positions, and promotional calendar. This data is used to inform the agent's decision-making, allowing it to make predictions and recommendations about stock allocation and other sales-related activities.

For example, a shift supervisor at a retail store might use an agentic AI workflow to analyze sales data and determine the optimal stock levels for different products. The agent would take into account factors such as seasonal demand, promotional activities, and inventory levels to make its recommendations.

What it changes about

Agentic AI workflows for sales have the potential to revolutionize the way sales teams operate, providing a level of insight and automation that was previously unavailable. One of the key areas where this can have an impact is in forecasting, where agentic AI workflows can provide a more accurate and nuanced understanding of customer demand.

Currently, many sales teams rely on forecasts that are based on simple extrapolations of historical data, without taking into account the complex interactions between different factors. This can lead to inventory levels that are out of step with demand, resulting in stockouts or overstocking. Agentic AI workflows can help to address this issue, providing a more accurate and dynamic understanding of customer demand.

For more information on how agentic AI workflows can support sales teams, please contact us.

Measured with

The effectiveness of agentic AI workflows for sales can be measured in a range of ways, including forecast accuracy, win rate, and stock cover. These metrics provide a clear indication of the impact that the agent is having on sales performance, allowing for ongoing evaluation and improvement.

By using these metrics to evaluate the performance of agentic AI workflows, sales teams can gain a better understanding of the benefits and limitations of this technology, and make informed decisions about how to deploy it in their organization.

What this does not do

While agentic AI workflows for sales have the potential to provide significant benefits, they are not a panacea for all sales-related challenges. In particular, they are not a replacement for human judgment and decision-making, but rather a tool to support and augment these processes.

Additionally, agentic AI workflows may not be the best fit for all sales organizations, particularly those with very simple sales processes or limited data availability. In these cases, other approaches may be more effective, such as using traditional sales analytics tools.