• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the sales team is already behind schedule, trying to catch up on last week's numbers and prepare for the upcoming meeting with the category manager to discuss quarterly targets, while also considering how AI productivity in the enterprise for sales can help them stay on track.

The difference between time saved and time redeployed

The introduction of AI-powered tools in sales teams is often touted as a way to save time, but what does that really mean? In practice, it usually means that tasks such as data analysis and reporting are automated, freeing up time for sales representatives to focus on more strategic activities. However, the reality is that this saved time is not always redeployed effectively, and may simply be absorbed into existing workflows without resulting in tangible benefits.

A shift supervisor, for example, may find that they have more time to spend on coaching and developing their team members, but if this time is not explicitly allocated and prioritized, it may not lead to noticeable improvements in sales performance. The category manager, on the other hand, may be able to use the extra time to negotiate better deals with suppliers, but if the sales data is not accurate and up-to-date, these efforts may be misguided.

Why self-reported time savings overstate the effect

When sales teams are asked to estimate the time they save by using AI-powered tools, their responses are often overly optimistic. This is because they tend to focus on the specific tasks that have been automated, without considering the broader context of their work. In reality, the time saved on one task may be offset by increased time spent on other tasks, or by the need to learn new skills and adapt to new workflows.

Furthermore, self-reported time savings are often based on hypothetical scenarios, rather than actual observations of how time is being used. A more accurate assessment of time savings would require careful measurement and analysis of how sales teams are using their time, both before and after the introduction of AI-powered tools. This is where sales teams can benefit from a more nuanced understanding of how AI productivity can support their work.

The sales data this depends on

The effective use of AI-powered tools in sales teams depends on access to high-quality sales data, including historical sales at planning grain, order book and pipeline, inventory positions, and promotional calendar. This data is essential for identifying trends and patterns, and for making informed decisions about sales strategies and tactics.

A sales representative, for example, may use historical sales data to identify opportunities for upselling and cross-selling, while the category manager may use order book and pipeline data to optimize inventory levels and minimize stockouts. The promotional calendar, meanwhile, can help sales teams to plan and execute targeted marketing campaigns, and to measure their effectiveness.

What it changes about: a forecast nobody can interrogate and inventory out of step with demand

One of the key benefits of using AI-powered tools in sales teams is that they can help to create more accurate and transparent forecasts. By analyzing large datasets and identifying patterns and trends, AI-powered tools can provide sales teams with a more detailed understanding of future sales, and help them to make more informed decisions about inventory and resource allocation.

For instance, a forecast that is based on AI-powered analysis of historical sales data and market trends can help sales teams to avoid stockouts and overstocking, and to ensure that inventory levels are aligned with demand. This, in turn, can help to improve customer satisfaction and reduce waste, while also minimizing the risk of lost sales and revenue.

Measured with: forecast accuracy, win rate, stock cover

The effectiveness of AI-powered tools in sales teams can be measured using a range of key performance indicators (KPIs), including forecast accuracy, win rate, and stock cover. By tracking these metrics over time, sales teams can gain a better understanding of how AI-powered tools are impacting their performance, and make adjustments as needed to optimize their use.

For example, a sales team that is using AI-powered tools to improve forecast accuracy may track their forecast accuracy over time, and compare it to historical benchmarks. If the data shows that forecast accuracy is improving, the sales team may be able to use this information to negotiate better deals with suppliers, or to optimize their inventory levels and minimize waste. To learn more about how to implement AI productivity in your sales team, you can contact us for more information.

What this does not do

While AI-powered tools can bring significant benefits to sales teams, they are not a silver bullet, and there are certain limitations and potential drawbacks to consider. For instance, AI-powered tools may not be effective in situations where the sales data is incomplete, inaccurate, or biased, and they may require significant investment in training and support to ensure that sales teams are using them effectively.

Additionally, AI-powered tools may not be suitable for all sales teams, particularly those that are very small or have limited resources. In these cases, the costs and complexity of implementing AI-powered tools may outweigh the potential benefits, and other solutions may be more effective. By understanding these limitations, sales teams can make more informed decisions about how to use AI productivity to support their work, and avoid potential pitfalls and challenges.