Applying AI for sales growth: Where to start & how to scale

Data is a growing, ever-evolving business resource that can be an analytical nightmare or a company’s greatest asset. Because large enterprises use massive amounts of data, this critical asset can quickly become unmanageable and can sabotage the accuracy and efficiency of hard-working sales teams. But with the thoughtful application of AI, it doesn’t have to be this way.

Not only can AI tame unmanageable data but it can exponentially increase the value add of this critical asset across the business. Document processing, querying data, and making recommendations are just a few business cases where AI can streamline operations, enhance decision-making, and drive competitive advantage. This article will unpack what technical foundations are needed to get started using AI and how trained AI is a competitive differentiator.

Leveraging SAP

Many organizations already run SAP and this makes AI that much more accessible. While most enterprise data will not be perfectly clean, thankfully, AI does not need a perfect dataset to create value. This is because AI itself is a great tool for parsing through incomplete and poorly formatted data to identify trends and fill in missing gaps. AI uses weightings to logically identify what pieces of information are most important.

This means when a sales representative is looking for a specific product, AI doesn’t need perfect data to identify the correct material number. For example, a traditional search engine would have a difficult time finding the correct material number for the query “2-inch steel pipe 5 feet” if the long description in the SAP material data is “5ft. 2in. DIA steel pipe”. While rules can be written to explicitly convert “feet” to “ft.” other material descriptions including fractions, units of measure, units of sale, etc. create an infinite number of possible queries that are impractical for mapping technologies to accommodate.

But with the application of AI, these discrepancies can be identified and standardized in a matter of seconds. A search engine using AI would recognize that the differences between the query and the material description are referring to the same product. Not only that, but AI can analyze multiple data fields and apply historical trends to discriminate between materials that have very similar product descriptions. While this does not guarantee 100% accuracy, it means that AI is significantly more powerful at retrieving data than the typical search query and will improve the likelihood that sales reps can find the data they need when they need it.

Scaling with trained AI

With trained AI models, businesses can reap powerful benefits that are not possible with generic AI. Machine learning engineers and data scientists can refine generic AI algorithms through the use of parameters on SAP datasets to render unparalleled accuracy. Extensive training allows the AI to learn over time the intricate patterns, nuances, and correlations of a specific dataset and its business application.

Through ML lifecycle management, results continuously improve through the stages of planning, data collection, training, pipeline development, release, deployment, monitoring, feedback, and improvement. This process creates AI models that have adapted to the dataset and a business’s unique needs allowing the AI to learn and grow with the organization. This is especially important for organizations that operate in a diverse market with emerging products and those that serve customers with complex needs. Bottom of Form

Tying it all together

When working with simple, straightforward datasets, generic AI can provide a huge advantage. However, realistically the modern business is dealing with large and complex data, and this is especially true of those serving global markets. But this doesn’t mean the benefits of AI are out of reach!

The simple foundation of SAP is enough to begin deploying and training models into sophisticated customized solutions. Within just a few weeks, the valuable patterns reflecting industry knowledge will begin to form and shape the AI’s accuracy and performance. Our team has seen leading organizations in the energy space deploy AI frameworks in under a month and reap the benefits of customized models in less than 2 months. This is all feasible through the strong foundation of SAP systems and the application of trained AI.

If process innovation is a key initiative for your organization, consider how to best organize the technical, operations, and executive stakeholders that will have buy-in on the project. It may be beneficial to create an AI committee or a technology and cyber board that facilitates the discussion, planning, and organizing of leaders passionate and ready to support technological innovations.

This team can also help identify and prioritize where AI can be applied to meet challenges throughout the business and what projects will see the highest value. The SAP marketplace is full of industry-specific solutions to leverage your data in the application of intelligence automation.

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