Now that we have walked through the previous blogs covering unique vendor identification, taxonomy, and ML-based categorizations, we can see all of them come together to realize how the AI-powered Spend product delivers business value.

Traditional dashboards are not very useful if you manually analyze data to figure out the savings.

Don't work for the data – let the data work for you. The carefully crafted ML models ensure that the deep insights you receive help you realize maximum value. These models do the heavy lifting for you. Armed with meaningful actionable insights, business leaders can make decisions to drive opportunities for cost savings. They can run an intelligent procurement function that supports business growth. ML-based products enable sustainable spend management because they learn from your data continuously and provide new insights.

Pillar Four: Actionable Insights

It's all about the vantage point! Whether you want to review spend at an aggregated level or scrutinize spend data at a granular level – your ML-driven dashboard gives you a holistic view. It gives you insights to carve out savings in the direct spend categories where 80% of the spend happens. It is with these suppliers that you have the maximum negotiating leverage.

But then the devil is also in the detail. Consider the use-case of tail spend. Identifying spend belonging to the tail spend categories allows savings to be discovered in unexpected places. You can direct the tail spend towards a few preferred suppliers rather than suppliers scattered across and realize volume discounts. Businesses that proactively manage tail spend derive a competitive advantage by significantly pruning costs.

There are many benefits that you derive from an ML-based spend product.

Take the use-case of rationalizing vendor names. Suppliers may not have the same name across various disparate systems in your enterprise from where the spend data is pulled. Furthermore, with M&A, their identities may change over time AI-powered products can rationalize unique suppliers and the spend with each unique vendor. This rationalization opens the door for volume discounts, and this visibility helps you seek the most favorable agreement. You can read about it in the first post of this series- Pillar One.

Organizing data in meaningful structures allows you to look at suppliers at multiple levels of a category, giving you negotiating leverage. You can broaden your strategy with a supplier by broadening the width of your category. You can read about it in the second post of this series- Pillar Two.

Categorizing your spend into a predefined taxonomy provides an understanding of where your money goes - who spent it, on what, and with whom? Such visibility can help you spot inefficiencies and find savings opportunities. You can read about it in the third post of this series- Pillar Three.

ElectrifAi's SpendAi: 2-4% savings in 6-8 weeks

ElectrifAi is one of the US' leading ML products providers with a large library of pre-built products enabling our clients to capture tangible benefits quickly. We work with the C-suite to understand and solve business problems through data and machine learning. The insights generated from SpendAi help our clients realize 2-4% savings in 6-8 weeks. In addition, it provides specific recommendations to mitigate supply chain risks. All this is done with zero data quality requirements and zero need for a data science team.

Our product does not require investment in a new platform, infrastructure, or data science team. Instead, we leverage the data existing in your system to power the ML models to deliver business outcomes.

We are the last mile product that sits on the top to solve specific business problems and bring about savings.