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Optimally exploiting the business benefits of artificial intelligence

AI and AI strategies are already in practical use in the business and everyday lives of most managers and employees without them actually realizing it… for not everything that has AI in it also has AI on it. We’re accompanied by AI throughout the day without consciously noticing it: This can be anything from the “Good morning” of the smart speaker, or the cell phone in your pocket, to the well-deserved after-work chill-out on the couch when you indulge in a little online shopping.


AI application scenarios from sales, marketing, production and HR

Artificial intelligence is currently one of the most promising subdomains of computer science, mimicking human decision-making structures and striving to automate intelligent behavior. AI projects aim to cut time and costs, boost efficiency, enhance the customer experience and even establish new AI-based business models.

To best realize this value add, AI projects must be embedded into a long-term, company-wide corporate strategy. Practicable AI strategies are already leveraging concrete added value for companies today, with successful, real-life examples from the business world providing impressive proof of this.

27 AI application scenarios from sales marketing, production and HR which are already generating concrete value add today:


  • Automated mediation of sales leads
  • Optimized pricing
  • Real-time recommendations for special customer groups using pattern recognition
  • Automated customer service agents that understand natural language and refer information
  • Cross- and up-selling by determining customer propensity for system updates
  • Automated opportunity scoring
  • Business closure forecasts


  • Customer service chatbots
  • Expediting internal reporting
  • Increasing advertising performance
  • Improving content marketing
  • Optimizing / assistance in the preparation of text content
  • Real-time customer behavior analytics
  • Optimizing the understanding of the customer
  • Customized newsletters that take usage and purchasing behavior into account


  • Predictive maintenance based on anticipated machine failures
  • Collaborating and context-aware robots
  • Maximizing production yield
  • Automated quality testing
  • Simplifying human-machine communication
  • Intelligent data analytics for detecting irregularities with the help of using operating data and sensors
  • Image recognition for production safety


  • Digital recruitment assistants, e.g. video interview tools for simultaneously testing several applicants
  • Profile analyses for staff further development measures
  • Career assistants that coach staff and recommend bespoke further training programs
  • Applicant selection support, applicant appraisal and reporting
  • Preparing training documentation and materials

Which specialist areas and processes benefit from AI?

As banal as the answer may sound, it is: Every one and everywhere. No in-house programmers are needed to use AI. Many off-the-shelf AI solutions and applications are already available on the market and can be adapted to your organization’s unique needs, making your entry to the AI world a quick and easy one.

AI consulting and AI strategy

Is your company ready for the AI sensation?

The path to a data-driven business that works with intelligent machine support in all areas and processes is not a sprint, but typically split into the four phases: (1) Kickoff toward AI, (2) Targeted experimentation, (3) Establishment of enterprise-wide AI expertise, and (4) AI expertise becomes embedded in the corporate DNA.

Two core competencies are crucial in each of these phases and successively refined: (1) The analytical core competence of your organization includes high-quality data, adequately qualified employees (such as data scientists and AI experts), and AI tools optimally aligned with your business goals and application scenarios; and (2) these analytical competences must be complemented by business competence – your AI strategy should be endorsed and promoted by C-level management and all business units.

Phase 1: Kickoff toward AI

Companies that are in the start-up phase will generally have kicked off an initial AI initiative, but have not yet identified AI deployment scenarios across the business. A meaningful level of AI competence and AI expertise has still to be established within the company. The focus is on quick results.

Phase 2: Targeted experimentation

In Phase 2, companies start to build up core analytic capabilities centering on data, AI staff and AI tools. Data scientists are recruited, and initial pilot projects have delivered promising results. The company looks for further AI application scenarios that promise real value add, now also involving the business divisions in these endeavors.

Phase 3: Establishment of enterprise-wide AI expertise

High-quality data and AI algorithms have become fixed elements of the corporate strategy. Making data-driven decisions has become the new modus operandi in all business units, and using the new AI tools an integral part of everyday work.

Phase 4: AI expertise becomes embedded in the corporate DNA

AI self-services and analytics self-services are accessible to all employees. Staff are trained in how to use them, have confidence in the forecasts generated by the systems, and perceive them not as a threat but as an aid. A culture of learning has been established. Security and compliance regulations for using AI have been defined and are binding throughout the company.

Herald the starting shot with us!

valantic helps you to proceed strategically and choose the right initial step. With the identified action areas as the starting point, we first accompany you in updating your data structures to create a suitable basis for transformation; this is followed by the dedicated choice of suitable processes that generate the fastest value add and serve as the first pilot project to produce concrete results. valantic’s and INTARGIA’s AI strategy consultants support your business in every phase of the AI lifecycle. If you’d like to learn more, we’d be happy to offer you with a non-binding AI consultation.

Data science and artificial intelligence: Optimizing products, services and processes

Data science has become indispensable for securing competitive advantages in an innovative market. The truth is, however, that data science is not yet being used at many companies and, while most have recognized its benefits, they are unsure about how to use it constructively.

Data science has the goal of extracting knowledge from structured, semi-structured and unstructured data (Big Data). Machine learning models are created which are used for pattern recognition or forecasting, resulting in a wide range of potential applications.

Realizing direct benefits

Data science is already used today to optimize products, services and processes, to identify customer needs, to improve customer loyalty, to forecast sales and inventory levels and to detect fraudulent activities. This results in improvements in the quality of products, services and processes, in enhanced customer satisfaction and in lower costs.

A practicable use case lies at the core of every successful data science project. Besides realizing  standard use cases, valantic also works closely with its customers on developing bespoke use cases for specific business departments and industries. Our data science experts offer companies holistic advise on developing and implementing their data science strategies – ranging from the identification of practicable use cases and selection of suitable tools to the productive operation of machine learning models.

With an experienced partner like valantic, you can define the starting points and generate value add for your own company, and benefit from valantic’s business expertise and data competence acquired with numerous customers over many years.

Business benefits of chatbots and voicebots in customer service

Today’s chatbots and voicebots are already realizing high efficiency gains for their users. A common aspect of all these digital assistants is their reliance on natural language processing (NLP) technology. NLP enables software assistants to “understand” natural language and respond accordingly. Chatbots and voicebots are effectively the same, but use different channels to connect to this technology: With chatbots, the system is addressed via written language, with voicebots, via spoken language.

Image of a woman with a headset, next to it an image of a networked structure and behind that an image of a live chat with a chatbot and an image of a robot, valantic Blog Robotic, Chatbots and Conversational AI
Conversational AI – The human-machine interface

With conversational AI, we address networked systems and resources by means of naturally spoken dialogs.

Learn more about conversational AI with valantic now!

How chatbots work...

A chatbot is an interface that allows human actors to access a business process using natural language: Instead of e.g. completing a form manually, the chatbot requests the user for the relevant data and then passes this information on to the business process.

What can a chatbot do?

Digital assistants can be used for a wide range of use cases, with chatbots serving as the first point of contact for call center customers and customer service center callers, or for providing basic advice. This offers two benefits: Chatbots relieve human employees of routine tasks, allowing them to focus on more complex technical matters; and customers can avoid long waits on hold and be served as quickly as possible.

Convincing interpersonal communication with chatbots is still very limited, however. Chatbots are nevertheless a great help for companies, and, when used e.g. in a customer service environment, can recognize if a user is dissatisfied with the communication and refer the conversation to a human service agent.

How quickly is a chatbot deployable?

That depends on the scope of the project. A simple FAQ bot can naturally be deployed much faster than is the case for a complex process automation. But the average time to set up a chatbot is only about a month. The valantic AI consulting team would be happy to provide you with a detailed calculation.

Using SAP business applications more successfully – with artificial intelligence

The value of critical business applications rises for users when they are enhanced with AI. SAP offers a comprehensive portfolio of AI solutions to leverage this value for your organization. These range from embedded AI, intelligent robotic process automation and conversational AI / chatbots to individual AI business services with which customers can use business processes via the “AI as a Service” procurement model.

  • SAP SaaS applications from the cloud offer more and more embedded intelligence that only needs to be activated to provide you with direct value add.
  • Conversational AI enables customers and employees to use natural language to communicate with your company in a familiar and straightforward manner
  • Intelligent robotic automation uses machine learning, conversational AI and robotic process automation to help relieve your workforce of repetitive and monotonous tasks.
  • AI business services let you solve specific business problems in a various areas using reusable, off-the-shelf AI services such as image recognition and classification.

valantic’s AI consulting team places particular emphasis on using artificial intelligence as a problem-solving tool. As a process and technology expert, valantic helps you improve or re-engineer intelligent processes with a sharp focus on usable results. Benefits that can be realized with AI include more informed decisions, improved efficiency, lower costs, faster value creation, and an enhanced customer experience.

Significant efficiency improvements with artificial intelligence – Automating business processes with RPA

valantic regards robotic process automation (RPA) as a bridge technology that allows significant value add to be generated relatively quickly and economically in a matter of weeks, while simultaneously establishing a solid foundation for more advanced hyperautomation technologies at the company. Full automation is achieved in three steps:

  1. AI consulting: Partial automation (Attended Automation)

    The RPA bot acts on instruction and assists the employees. Both parties engage in a cooperation work model in which humans and bots work together as a team in which both sides benefit. The bot is responsible for the standardized work steps.

  2. AI consulting: Full automation (Unattended Automation)

    In this step, the RPA bot works entirely autonomously and responds to predefined trigger events such as the arrival of an email containing e.g. specific content or certain trigger words. The bot performs activities and processes fully automatically without human interaction, even when employees are logged out of the system.

  3. AI consulting: RPA and conversational artificial intelligence

    The RPA bot is enhanced with artificial intelligence and language recognition algorithms and able to handle even complex situations appropriately. Where artificial intelligence is supplemented by self-learning neural networks, the bot becomes increasingly confident over longer periods of operation and draws on experience gained from already successfully completed activities to handle the current tasks.

Humans nevertheless remain responsible for quality assurance and for formally accepting the results, and intervene whenever the results are unsatisfactory. valantic cooperates with top technology providers to find the fastest and best bespoke solutions to realize your company’s automation and business goals.

Your contact

You’d like to enjoy the maximum benefits of artificial intelligence, machine learning, business analytics and neural networks? valantic’s AI strategists would be happy to offer you a free and non-binding AI consultation.

Bild von Martin W. Vierrath, Senior Sales Manager bei valantic

Martin W. Vierrath

Senior Sales Manager
valantic Business Analytics