• Written by Muhammed Abdulla NC, CEO

When a sales team overhauls its budget, the changes often look good on paper but fail to stick when production kicks in, leading to a costly mismatch between forecast and actual sales. This is because the team has not considered the total cost of its AI workflow, only the unit cost. The difference between these two costs is crucial, as it determines whether the budget reductions will survive contact with production.

The difference between unit cost and total cost of an AI workflow

The unit cost of an AI workflow refers to the cost of running a single instance of the workflow, whereas the total cost takes into account the cumulative cost of running the workflow multiple times. Most sales teams focus on reducing the unit cost, without considering the impact on the total cost. This approach can lead to unintended consequences, such as increased latency or decreased accuracy, which can negate the benefits of the cost reduction.

A sales team that understands the difference between unit cost and total cost can make informed decisions about where to allocate its budget. For example, a team may choose to invest in a more expensive AI model that can handle a higher volume of requests, rather than opting for a cheaper model that may need to be run multiple times, resulting in a higher total cost.

Caching, batching and model routing as the three real levers

Caching, batching and model routing are the three key levers that sales teams can use to optimize the cost of their AI workflows. Caching involves storing the results of expensive computations so that they can be reused instead of recalculated. Batching involves grouping multiple requests together to reduce the overhead of running the workflow. Model routing involves directing requests to the most suitable AI model, based on factors such as the type of request or the required level of accuracy.

By using these levers, sales teams can reduce the total cost of their AI workflows without compromising on performance. For instance, a team can use caching to store the results of frequent requests, such as sales forecasts, and then use batching to process multiple requests in a single batch. This approach can help reduce the latency and cost associated with running the workflow.

The sales data this depends on

The effectiveness of AI cost optimization for sales depends on the availability of high-quality sales data. This includes historical sales data at the planning grain, order book and pipeline data, inventory positions, and promotional calendar data. By analyzing this data, sales teams can identify areas where they can optimize their AI workflows to reduce costs.

For example, a sales team can use historical sales data to identify seasonal trends and adjust their AI workflows accordingly. They can also use order book and pipeline data to predict future sales and optimize their inventory levels. By integrating this data with their AI workflows, sales teams can make more informed decisions and reduce costs.

What it changes about

AI cost optimization for sales can have a significant impact on a company's sales operations. It can help to create a more accurate forecast, one that can be interrogated and refined by the sales team. It can also help to ensure that inventory levels are in step with demand, reducing the risk of stockouts or overstocking.

By optimizing their AI workflows, sales teams can move away from a forecast that is essentially a black box, and towards a more transparent and accountable process. This can help to build trust between the sales team and other stakeholders, such as the production team and the executive team. As sales teams become more data-driven, they can make better decisions and drive more revenue.

Measured with

The effectiveness of AI cost optimization for sales can be measured using a range of metrics, including forecast accuracy, win rate, and stock cover. By tracking these metrics, sales teams can see the impact of their optimization efforts and make adjustments as needed.

For example, a sales team can use forecast accuracy to evaluate the performance of their AI workflows, and make adjustments to improve the accuracy of their forecasts. They can also use win rate to evaluate the effectiveness of their sales strategies, and stock cover to ensure that they have sufficient inventory to meet demand.

What this does not do

AI cost optimization for sales is not a silver bullet that can solve all of a company's sales problems. It is a tool that can help sales teams to optimize their AI workflows and reduce costs, but it requires careful planning and execution to be effective. It is also not a replacement for human judgment and expertise, but rather a way to augment and support the sales team's decision-making processes.

If you are interested in learning more about how AI cost optimization for sales can help your company, please contact us to discuss your specific needs and challenges. We can help you to evaluate your current sales operations and identify areas where AI cost optimization can have the greatest impact.