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Top 12 SAP Conversational AI Use Cases in 2024

Updated on Jan 11
5 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Top 12 SAP Conversational AI Use Cases in 2024Top 12 SAP Conversational AI Use Cases in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

Business leaders are exploring conversational AI adoption in different departments due to its ability to facilitate processes and handle time consuming issues. SAP software which allows connection and collaboration across business departments can benefit from conversational AI capabilities.

The aim of implementing conversational AI in SAP is to make the user’s experience easier and simplify business interactions. Chatbot implementation in SAP enables better customer service, quick on-demand insights about business resources and data, facilitate issue solving, and simplify notification and alerts.

SAP Conversational AI solutions, which implement natural language processing, combine:

  • Using digital assistants to guide users through SAP processes
  • A chatbot building platform to build and test chatbots for different business purposes.

Conversational AI SAP solutions

System Applications and Products (SAP) is an Enterprise Resource Planning (ERP) software. SAP consists of business-specific models that allow users to collect and share data across business departments.

SAP acquired the French company Recast.AI in 2018 to develop their natural language processing (NLP) performance and to support >20 languages.

SAP digital assistant

SAP digital assistants leverage machine learning algorithms to allow the chatbot to learn from practice and become more contextually aware. Using a digital assistant within an SAP software enables:

  • Smooth collaboration across SAP modules (also called applications or models) by
    • providing faster access to information across business departments
    • Answering FAQs
  • Task automation by customizable workflows. Users can create custom chatbot intents to perform specific tasks
  • Better decision-making by providing operational insights
  • Personalized recommendations according to user behavior

Based on the user’s role/position, an SAP digital assistant can display reports, news, or alerts, as well as schedule meetings and invite different users to participate in a process.

Chatbot building platform

Chatbot building platforms allow users with minimal or no coding experience to build, train, and deploy chatbots. Nonetheless, in order to build a chatbot, the user must grasp the concepts of skills and intents which we explained in our article Intent Recognition in Chatbots in 2021.

SAP Conversational AI provides a platform to build or adapt end-to-end chatbots and integrate them with SAP ecosystem. The user can also use the platform to create chatbots from scratch to automate specific tasks in customer support, IT service, or purchasing. However, in order to build a chatbot, the user must grasp the concepts of skills and intents.

Leveraging SAP chatbot creating platform enables:

  • Faster chatbot creation, training, and deployment
  • Chatbot connection to multiple SAP solutions, external communication channels, or back-end systems.
  • Analysis of customers’ and employees’ communications to further improve users’ experience.

What are some conversational AI use cases in SAP?

Conversational AI can be implemented in the following SAP modules. We provide in-depth examples of their usage and briefly mention the business use cases these bots can serve. For more information on specific chatbot use cases, please refer to our articles on chatbot usecases by industry and by department.

IT department

First level IT support

Many IT issues, that SAP users face, can be solved by simple solutions found in tutorials or SAP Help documents. However, users need the assistance of an IT professional to provide these documents and guidelines. Digital assistants in SAP systems can handle these simple tasks.

For example, MOD Pizza, an American fast casual pizza restaurant chain, used SAP CoPilot digital assistant to facilitate IT support processes. When a SAP system user requires IT support, the digital assistant provides links to tutorials or SAP Help documentations. The digital assistant can also direct the user to a live IT agent and send them information about the context of the problem, such as screenshots to facilitate problem-fixing.

The user interface of SAP system.
Source: SAP user experience community

HR department

HR employees handle an enormous amount of employee information to which they may require access on the spot. An HR chatbot can retrieve employee information and visualize the data using SAP features. With this functionality, an HR chatbot can be used to answer:

FAQ on HR policies

Complete HR requests like reserving Paid Time Off (PTO)

Guide users through onboarding

For example, Nestlé, the world’s largest food & beverage company, used SAP conversational AI platform to create an HR chatbot. The HR chatbot provides self-service access to HR department data, such as headcount or full-time versus part-time hire ratios, in a secure and consistent approach.

A map of the SAP conversational AI architecture tutorial.
source: SAP conversational ai tutorial

Customer service

SAP users in customer support department need access to different documents and information when responding to customer inquiries. Implementing a chatbot in customer support enables:

Management of multiple inquiries at the same time

Provide accurate responses about products and services

For example, Groupe Mutuel, a Swiss insurance company used SAP conversational AI platform to develop a chatbot that can:

  • Respond to customer’s inquiries 24/7, in French and German through the company website
  • Enable end-to-end process integration and self-service scenarios for insurance and health plan members

For more on chatbots uses in healthcare, feel free to read our article Chatbot Applications / Use Cases in Healthcare in 2021

Sales & marketing

The sales and marketing departments can leverage SAP to measure marketing campaign results, and automate different marketing processes, such as email responses after cold calls.

Conversational AI in SAP marketing models can:

Generate leads

Leads are potential customers who visit company websites looking for products or services. Chatbots can engage these customers, collect their information and inquiries, and direct them to products/services based on their inquiries and demographics. For more on chatbots in marketing, feel free to read our article Chatbot Applications / Use Cases in Marketing in 2021

Serve as SDR

Sales development representatives (SDR) are the ones who initially speak to customers and book a product demo in B2B context. The demo would be run by a more experienced sales rep.

Chatbots can handle these tasks. they can converse with customers, provide product/service information such as pictures, videos, or links and schedule appointments for demos or trials.

Serve as sales reps

In B2C e-commerce, bots can even book tickets and flights.

For example, Expedia, an online travel shopping company, utilizes a Facebook messenger chatbot to offer customers a 24/7 agents who can book and manage trips, as well as provide COVID-19 updates about travel restrictions and airport openings.

An image of Expedia's Facebook Messenger chat.
source: expedia facebook messenger

Supply chain management

SAP models for supply chain management (SCM) have the following features:

  • Ability to collect data about different supply chain resources such as warehouses, inventories, shipments, and storage places.
  • Ability to connect suppliers, customers, manufacturers, business partners and retailers in one platform
  • Include different planning applications related to Advanced Planning and Optimization APO
  • Include applications for supply chain networking, supply chain planning and coordination, and supply chain execution.

Chatbots in supply management SAP models can:

Provide instant and accurate data about SCM resources

Process supply chain employees’ requests

Chatbots can process requests based on supply chain data, such as tracking numbers and customer ID

Manage orders

Chatbots can directly collect new orders from customers, manage old/cancelled/delayed orders, and automatically update the supply chain database.

What to expect in the future?

According to our chatbot / conversational stats, 31% of executives said that virtual assistants have the largest impact on their business. Additionally, 75-90% of queries is projected to be handled by chatbots by 2022. This data suggests that SAP software will depend on conversational AI heavily across multiple SAP business models in the future.

Furthermore, implementing RPA along with conversational AI into SAP applications can drive the automation process in enterprise resource planning to the point where the user will only has to ask the chatbot what the next step is, and have the RPA bot take care of it.

For more on RPA in SAP, feel free to read our article Top 5 RPA Use Cases / Application Domains in SAP in 2021

For more on conversational AI

To learn how conversational AI and chatbots work, read our articles about natural language understanding, and top 10 voice recognition applications and use cases

For more on conversational AI successes, failures and market, feel free to read the following articles:

If you think your business can benefit from conversational AI, let our data-driven list of chatbot vendors and chatbot platforms can show you which vendors you can start talking to.

And if you have questions about how chatbots can help your business, we can help:

Find the Right Vendors

This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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