Evaluate and scale AI use cases

From measurable successes to new value streams

AI use cases in companies - diverse & promising for the future

Every company is as unique as the people and processes that shape it. This also means that the possible applications of artificial intelligence (AI) cannot be generalized. Every company – whether a medium-sized business or a large corporation – has special processes that are designed very differently depending on the industry, product and internal structure. In order to exploit the full potential of AI, it is essential to analyze precisely these processes and avoid risks that could cause the project to fail. From experience, we attach particular importance to a sound database, comprehensive change management and sustainable financial benefits. Especially in large companies and production-intensive areas, customized AI solutions can achieve enormous efficiency and added value. This is where we come in: We support you with an in-depth consulting approach and develop AI strategies that are tailored to your individual processes and challenges. Our focus is particularly on the evaluation of AI potential, because there are plenty of AI use cases – but what really counts is the selection and prioritization of projects that are not only feasible, but also promise a high ROI and generate future-oriented value streams. We have created our AI Assessment Center to achieve precisely these goals. In this way, we ensure that your AI use cases start right from the beginning exactly where your company will benefit the most – and that the use of AI grows step by step as you progress.

Scaling AI - Why AI is failing in companies

The full potential of artificial intelligence (AI) opens up enormous opportunities for companies to increase efficiency, innovation and value creation. However, the path to comprehensive AI enablement is often accompanied by challenges. It is completely normal for companies to encounter hurdles on the way to successfully scaling AI solutions, as the implementation and integration of new technologies entails structural, technical and organizational requirements:

Model accuracy: AI projects often fail because the models developed are not precise enough to deliver reliable results in complex business processes. The fault here usually lies in the quality, quantity and preparation of the individual data required for a reliable AI business model.

Security and compliance risks: Security and compliance are often not given sufficient consideration. Companies must ensure that AI applications comply with both internal security standards and regulatory requirements – for example, the provisions of the EU AI Act.

Compatibility with existing IT systems: The integration of new AI technologies into existing IT infrastructures poses a further challenge. A lack of compatibility can mean that AI implementations do not function smoothly or cannot be successfully integrated into existing processes.

Lack of success measurement: Without clear KPIs and effective monitoring, the actual benefits of AI and the associated investments often remain unclear. Central KPIs are also important for determining the ROI and evaluating the investment expenditure, as this is not necessarily immediately visible.

New processes and data management: As artificial intelligence and its models are fundamentally dependent on data, data management and data security must be a top priority. It is therefore necessary to manage large volumes of data in various formats – such as numbers, text, images or videos – securely and efficiently.

Talent acquisition and development: The search for specialists with specific AI and domain knowledge is often difficult and expensive, as qualified experts in this field are in high demand. In particular, skills in the development, training and provision of machine learning models require technical knowledge that is not always available internally.

Choosing the right tools: The tools used to scale AI cover three areas: Tools for data scientists to develop models, IT tools for data and resource management, and platforms that allow employees to interact with AI outputs. In addition to the overarching goal of producing the best possible results using AI, it is all the more important to ensure that the AI model has a good basis. Data scientists need tools that make it easier to train and optimize the models, and data engineers need special IT tools for managing and processing large volumes of data in order to organize the data efficiently and make it available to the AI model. This often raises the question of “buy or make”. Through our technology partners, we have the best tools in the industry at our disposal to quickly benefit from the new AI process for results and added value – a decisive competitive advantage for agile companies and their time-to-market.

Use of time and resources: The implementation and scaling of artificial intelligence often require a lot of patience – it can take months or even years from data collection to successful integration. Especially when it comes to pilot projects, valuable time can be lost without strategy and planning until the use of AI can be fully established in the process. In the strategy phase, it is therefore particularly essential to evaluate the project potential and decide on the right project – because if the project fails, the competition will take the lead.

With targeted support from Ventum Consulting, you can successfully overcome these challenges and scale the use of AI individually and according to your needs. In addition to expertise in project management and process automation, Ventum Consulting has over 20 years of experience in data management and is particularly strong in creating a solid database – a crucial step in anchoring AI in your company in a scalable and effective manner. In this way, we help you to exploit the full potential of AI and create real added value for your unique business processes.

The path to becoming an AI-driven company in 3 stages

Every company is on an individual path to integrating artificial intelligence and has specific requirements that it needs to address in order to fully exploit the potential of AI. The implementation of AI usually takes place in three main stages, into which companies can be categorized.

Level 1: Empowering

The first stage on the path to becoming an AI-driven company is also known as the experimental phase. It is characterized above all by the testing of new technologies for which there are no major investment risks. AI takes on the role of a personal assistant that provides support in day-to-day work and promotes the efficiency of existing workflows and processes. The result is increased productivity and higher employee satisfaction.

Examples:

Chatbots such as CoPilot or ChatGPT as a “personal assistant”
- Formulating e-mails
- Summarize texts or white papers
- Research and brainstorming
- Create meeting summaries
AI coding or coding assistants like GitHub Copilot
- Code suggestions
- Debugging
Image creation using tools such as DALL-E, MidJourney or Adobe Firefly

Stage 2: Transform

In this phase, the use of AI is scaled. The focus here is not on the individual added value of AI, but on solutions integrated into the company. Different forms of AI solutions are combined to automate and optimize entire business processes. AI-supported process optimization and seamless integration into existing systems enable data-based decisions, optimize ROI and increase efficiency and cost savings.

Examples:

Customer service in sales or aftersales (customer service, call center, end-to-end support)
Generative design in product development, for example AI powered packing decision engine
Fraud detection and identity verification for banks and insurance companies
HR automation through candidate pre-selection and process optimization

Level 3: Reimagine

In the final stage, companies begin to write visionary and innovative stories. AI becomes a central driver of corporate strategy and paves new ways to rethink technologies. People and technologies work hand in hand to develop new business models and create opportunities for growth and progress. The results of the final stage are innovative products, an optimized user experience and new value streams that secure long-term competitive advantages and open up new target groups.

Examples:

Data-driven products and services
Hyper-personalized customer experience
- Creation of personalized suggestions and recommendations based on user habits and individual preferences
- AI-controlled advertising placement
- Adaptation of learning content to the progress of users in educational institutions
Autonomous driving with AI-controlled systems that influence the entire business model
Agricultural machines with AI-controlled planting agent treatment and weed detection that recognize planting cycles and reduce the amount of fertilizer
AI portfolio management
- Data evaluation
- Optimization
- Forecast
- Risk management

Thinking big about AI? This is how we proceed

Ventum Consulting helps companies to realize the potential of artificial intelligence through strategic planning and implementation. Our AI Assessment Center offers comprehensive consulting services to assess the current status of your data structure and processes and to identify potential for AI-supported optimization. The overarching goal of the AI Assessment Center is to prioritize AI use cases, determine the potential ROI, weigh up the feasibility and ultimately achieve the greatest possible impact for your company. In addition to these consulting services, Ventum Consulting also provides ready-to-use solutions, including communications mining, AI-driven data analysis and lead to offer. These customized solutions enable you to immediately benefit from the advantages of AI and seamlessly integrate data-based decisions into your business processes.

AI Assessment Center - Our consulting services

AI use cases

A successful AI strategy begins with the targeted identification and evaluation of the relevant use cases. This first step helps companies to clearly define the potential uses of AI and identify the best projects to achieve the greatest benefits and minimize investment risks. We adapt to the existing situation and the current status in your company. So, regardless of your AI and data analytics skills or existing infrastructure, we can either support you with data collection and analysis through to the implementation of your use cases and guide you through your individual end-to-end process. For companies that require technical and analytical validation of their solution, on the other hand, we carry out a proof of concept (PoC) to determine the technical feasibility of an initial assessment.

Impact:

Increases ROI by focusing on business-critical AI projects.
Identification of “quick wins” and strategic long-term projects.
Anchors AI initiatives in the company’s objectives.
Creates real added value for companies, users and customers instead of just following a trend.
Performs detailed impact and risk analysis to assess utility, feasibility and value.
Develops customized roadmaps that identify prioritized use cases for maximum return on investment.
Reduces bad investments through clear assessment of feasibility and impact on the company.

AI integration & implementation

Successful integration of AI requires the seamless linking of AI models with existing systems such as ERP and CRM software as well as scalable platforms. Continuous monitoring and optimization play a crucial role in ensuring the long-term performance of AI processes. We support you in the targeted design of platforms and services as well as in the creation of a flexible, future-oriented concept to ensure smooth and adaptable integration.

Impact:

Ensures smooth integration and continuous adaptation to company systems.
Increases scalability and performance through ongoing optimizations.
Reduces integration problems and minimizes the risk of operational failures.
Maximizes strategic benefits and efficiency through architectural optimization and flexibility.
Integration of AI in processes based on a value assessment enables maximum added value.
Cost optimization and greater efficiency through suitable architecture and technology approaches.

AI Data Readiness (Data for AI)

The ability to use data in a semantically structured, high-quality and accessible form forms the basis for successful AI projects. Together with Ventum Consulting, a well thought-out information and data management system is created to ensure that data is available and accurately prepared. The result is a clean database for AI models that enables efficient work and delivers reliable results.

Impact:

Creates company-wide knowledge structures and a stable “fixed point” for AI models through semantically structured data.
Ensures continuous data availability and quality, which optimizes the predictive power and accuracy of AI models.
Identifies and closes data gaps early to minimize integration issues and speed up the data pipeline.
Reduces delays in AI projects through optimized, cross-system data flows and smooth data integration.
Anchors data and IT governance structures that increase clarity in data processes and pave the way for future-proof AI processes.

Use case from practice:

Through an as-is analysis of an automotive manufacturer’s current IT governance, data models and processes, we were able to identify potential for improvement in order to meet new requirements. This creates the basis for a modern and more efficient IT governance landscape that is better aligned with future challenges.

AI Governance

Responsible AI use requires clear guidelines and standards that integrate ethical principles and regulatory requirements into the development, implementation and monitoring of AI systems. Establishing and adhering to such guidelines ensures that AI solutions remain transparent, trustworthy and legally compliant while meeting ethical and legal requirements.

Impact:

Protects the company from regulatory and ethical risks through clear guidelines on the use of AI.
Promotes transparency and trust in the decision-making processes of AI systems,
Enables adherence and compliance with global data protection standards such as GDPR and legal requirements, for example through the AI EU Act
Strengthens trust in AI initiatives both internally and with external stakeholders and underlines the company’s innovative strength.

Use case from practice:

This AI governance project focuses on the transformation of an automotive company’s data protection. Instead of the previous principle of “need-to-know”, where data is only accessible when needed, there is now a switch to “need-to-share”, whereby data is openly available by default and specific protection measures are targeted. This change is crucial in order to make data available for networked queries, such as AI-supported LLM searches. In addition, the project defines the guidelines for the harmonized use of AI platforms and regulates which platforms may be used within the company.

Culture & Change Management

Ventum Consulting supports your company in shaping the cultural change required for successful AI integration. The use of AI technology requires not only technical training, but also a profound change in ways of thinking and working. With targeted digital upskilling, enabling for innovation processes and early involvement of stakeholders, Ventum Consulting ensures that new technologies are accepted and embedded in the corporate culture and day-to-day work processes in the long term.

Impact:

Accelerates the acceptance and effective use of new technologies through customized training and education.
Promotes an open attitude towards AI innovations through early involvement and transparent communication with stakeholders.
Anchors AI solutions in the corporate culture in the long term and strengthens data-based decision-making processes.
Promotes employee loyalty and creates an innovation-friendly environment that strengthens the willingness to use new technologies.
Creates an understanding of the skills required by employees and shows which specialists and HR measures are necessary to support and successfully drive change.

Your contact for AI Scaling & Use Cases

Tobias
Reuter

Principal and expert for AI scaling

"Together with you, we ensure that your EAM provides the framework for building and expanding your IT architecture in a targeted manner and flexibly adapting it to new requirements."

– Tobias Reuter, Ventum Consulting

Our AI solutions

Communications mining - more automation in customer service

AI-supported recognition of customer concerns (intent recognition) analyzes unstructured customer inquiries in order to clearly identify their intent – i.e. the customer’s concern – and thus increase the self-resolution rate and the degree of automation in customer service. Text-based inquiries – such as emails, chats and tickets – are often difficult to understand in a business context, as conventional approaches usually only use keywords and therefore overlook relevant nuances. With UiPath’s communications mining platform, however, it is possible to precisely pre-classify customer inquiries contextually, read out relevant parameters and generate machine-processable customer orders even from complex, unstructured messages.

Impact:

Increases the self-resolution rate and speeds up the processing of customer inquiries.
Improves service quality through precise and context-based processing of unstructured data.
Reduces manual effort and increases the degree of automation in customer service.
Promotes greater customer satisfaction through faster and more efficient processing.

Use case from practice:

As part of a proof of concept, an intent analysis was carried out in the company’s Customer Care department. The aim was to create transparency about the frequency and type of customer inquiries received via the contact form and by email. With these findings, the company can prioritize inquiries and thus make processing more efficient. Based on the prioritizations, simple customer inquiries are automated, which speeds up customer service and frees up resources for more complex requests.

Lead to Offer - evaluate and respond to tenders more securely and quickly

Responding to tenders and initially assessing whether participation makes sense often involves a lot of effort. As a rule, the time of the employees involved is valuable and cannot be invoiced for such tasks. Answering the requirements, searching for and reusing suitable documents from previous tenders and writing new concepts and texts often add up to substantial cost and time wasters. In addition, this also severely limits the number of possible offers. Many of these tasks can now be automated and simplified very efficiently, either partially or completely, using the capabilities of AI; the reuse of content from existing documents can also be significantly improved. With AI-supported lead-to-offer processes, companies that regularly participate in tenders or process a large number of individual requests for quotations can efficiently automate the processing and evaluation of tenders and offers. Artificial intelligence enables faster analysis of tender requirements and the creation of customized offers, which speeds up the entire sales process up to the submission of offers and can increase the chances of winning.

Impact:

Increases the precision and relevance of bids through data-based analysis of tender texts and requirements.
Increases the chances of winning through accurate, AI-supported bid submissions.
Saves valuable capacity in the tendering phase and shortens the tender processing time.
Enables an increase in sales through a higher win rate with reduced manual effort.
They can submit more bids without sacrificing time or quality and open up new sales opportunities for potential projects that were previously considered unsuitable.
Customize our basic product through an open plug-in ecosystem to integrate it individually and automatically into your processes and tools (e.g. CRM, staffing, skill management, etc.)

AI-driven data analysis - making data-based decisions more easily

AI-based data analysis platforms offer companies valuable insights in real time and enable predictive and prescriptive analyses that can be used to play out future scenarios and make well-founded business decisions. By using predictive models, companies can identify trends and risks at an early stage, enabling them to make more accurate predictions and act strategically. Prescriptive analyses supplement the predictions made and provide concrete recommendations for action in order to make the best possible decision. The platforms therefore create transparency and efficiency throughout the entire data processing chain – from collection to analysis and application.

Impact:

Increases the precision and speed of decision-making and promotes well-founded strategic decisions.
Uses predictive models to identify trends and risks at an early stage and ensure competitiveness.
Enables deeper insights into customer and market behavior, which supports data-driven innovations and process optimizations.
Creates both competitive advantages and cost savings through better planning, which strengthens the company in the long term.

Generative product design - efficient product development thanks to AI

The development of innovative and efficient product solutions is made considerably easier by automating the design process. Companies that rely on AI-supported design automation can create products that are not only more efficient and cost-effective, but are also tailored precisely to customer requirements. Because the process is built on interconnected and consistently structured data, it provides access to novel design solutions that go beyond traditional approaches. Together with Ventum Consulting, you lay the foundation for your generative product design by modelling networked data into consistent structures and optimizing your data readiness.

Impact:

Reduces development costs through optimized designs and material savings.
Shortens design and development cycles so that you can react more quickly to market needs.
Enables creative and innovative design solutions that go beyond conventional methods.
Promotes more environmentally friendly production through efficient use of resources.

Scaling AI - Why AI is failing in companies

Data is at the heart of every AI application – without it, models remain ineffective and, depending on the area of application, do not deliver usable results. Internal data is particularly crucial in the enterprise environment, as it contains specific information about unique processes, customer interactions and market conditions. This internal data enables AI models to make precise and contextual predictions that ultimately create real added value. Careful data preparation and detailed data analysis are therefore essential to ensure the quality, relevance and consistency of the data that feeds into AI models. Only if the data is well structured and prepared can AI systems be integrated into company processes in a scalable manner and develop their full potential. This is precisely where Ventum Consulting’s strength lies: with over 20 years of experience in data management, Ventum Consulting works to create solid and reliable databases for companies that enable sustainable and successful AI implementation. In this way, we ensure that AI projects not only get off the ground, but also create added value in the long term.

Technology partner

IBM

IBM is a valuable partner for Ventum Consulting in particular due to its comprehensive portfolio of AI and cloud technologies, including IBM Watson for machine learning and data analysis. With IBM as a partner, we benefit from scalable cloud solutions that enable customized applications for various industries.

Liquid AI

Liquid AI is a revolutionary AI technology based on generative models that offers high performance and scalability. This is also used for lead-to-offer projects, for example, to increase automation and efficiency. The technology works in a similar way to neural networks, but offers additional flexibility and efficiency through dynamic, time-based adjustments. AI models based on Liquid AI can thus react dynamically to real-time information, work precisely and up to date and increase the success rate.

UiPath

UiPath supports Ventum Consulting with a wide range of business automation tools that are particularly valuable for the automation of end-to-end processes. UiPath’s AI-supported functions, such as reading and classifying customer inquiries, enable Ventum Consulting to significantly improve the efficiency and speed of customer processes.

Defining and scaling AI use cases - investing in tomorrow today

With Ventum Consulting as your partner, you can successfully implement and scale artificial intelligence. With a tailored approach that addresses the individual needs and goals of your company, we support you in identifying and prioritizing the specific AI use case. This allows us to focus on the projects that offer the greatest added value and highest ROI.

With a clear strategic focus and sound risk management, Ventum Consulting ensures that your AI projects also develop the analyzed potential to future-proof your company and achieve real success on the basis of AI.

FAQs on the topic of AI use cases

Why is AI relevant for companies?

Artificial intelligence enables companies to make processes more efficient, analyze data better and make informed decisions. AI can help to increase sales, reduce costs and create competitive advantages through automation and innovative technologies.

How do I integrate AI into my company?

The path to AI implementation involves several stages: First, we identify individual use cases, analyse existing processes and prioritize projects with high added value. We then support the implementation with change management strategies to ensure sustainable integration. Every process and every result is unique to each company.

What is an "AI use case"?

An AI use case describes a specific application of artificial intelligence in a company process that aims to create added business value. By identifying use cases, targeted AI initiatives can be developed that meet the needs and objectives of your company.

How do you identify AI use cases?

We analyse companies and their processes and business models in order to prioritize individual and scalable AI use cases that deliver profitable and measurable success. To do this, we focus on identifying the use cases with the highest potential for AI-supported optimization.

Why is data so important for AI in companies?

Data, especially internal company data, contains specific information about processes, customer interactions and market conditions that are essential for accurate, contextualized predictions. Without this data, AI models often remain too general and cannot meet the specific requirements of the company.

What is AI governance and why is it important?

AI governance comprises the guidelines and rules that ensure the responsible use of AI. AI governance ensures compliance with ethical standards and regulatory requirements in order to minimize risks such as bias in AI models and to make the use of the technology transparent and safe.

When does AI make sense in companies?

AI makes sense when it creates concrete, measurable added value. This is the case when AI processes can be made more efficient, decision-making can be improved and costs can be reduced. AI unfolds its full potential particularly in data-intensive areas such as customer service, sales, production and analysis processes. However, the prerequisite is that the company has sufficient, high-quality data and has defined clear use cases in which AI can increase productivity and competitiveness.

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