AI consulting for companies
AI Consulting: Why AI Projects Often Fail and How Ventum Consulting Approaches Them
Many artificial intelligence (AI) projects in companies fail not because of the technology itself. They fail because the use cases chosen are too generic, teams aren’t fully on board, and industry requirements are taken into account too late.
Ventum Consulting takes a different approach: We focus on specific industries and develop solutions tailored to the unique challenges facing your company and its processes. This enables us to identify use cases that have a proven impact. The result is artificial intelligence initiatives that don’t get stuck in the pilot phase but actively contribute to your AI strategy, operate productively, are utilized by teams, and deliver measurable ROI. Our promise: Individual use cases. Structured implementation. Empowered teams.
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We take full responsibility from planning through to implementation—this ensures efficiency and reliability.
More than 1,500 projects completed
Over 20 Years of Transformation Expertise at
100% Dedicated to Your Company's Success
From AI Strategy to "
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AI consulting use cases by industry
Every industry has its own processes, regulations, and value drivers. Generic AI solutions fail precisely because of this. We start with your industry and work with you to identify AI use cases that have been proven to work there.
Here is a selection of example industries and their specific AI use cases:
Industry & Manufacturing
- Predictive maintenance – detect failures before they occur. Reduce downtime costs, extend machine running times.
- AI-powered production control – Dynamic order allocation based on real-time data rather than fixed rules. Higher throughput, more stable processes.
- Quality control with computer vision – automatically identify defects and deviations in real time – without manual inspection.
- Process Optimization – Targeted reduction of bottlenecks and waste using AI algorithms. Increase efficiency along the entire production line.
- Energy efficiency – intelligent control of energy flows in production and administration. Measurably reduce costs and CO₂ emissions.
- Workforce Augmentation – Empowering teams with AI-powered assistance systems and AR solutions. Boost productivity and mitigate the skills shortage.
Automotive
- Predictive maintenance in production – analyze vehicle data, proactively identify maintenance requirements. Avoid breakdowns before they occur.
- Manufacturing Optimization & Robotics – Automate production processes with AI and robotics. Improve quality and efficiency.
- Simulation Data Management – AI Readiness in Engineering: Laying the Groundwork for Scalable AI in Verification and Validation.
- E2E Data Management – Consolidate fragmented data processes. Transparency, clear roles, higher data quality and AI readiness.
- Supply chain resilience – plan requirements with foresight, manage risks automatically. Making supply chains more resilient.
- Cybersecurity & threat detection – protecting vehicles and systems with AI-based monitoring and attack detection
Healthcare
- Diagnostics & Decision Support – AI-powered analysis of medical images and patient data. Improve diagnostic accuracy and reduce errors.
- Administrative automation – automate documentation and billing. Relieve staff, eliminate sources of error.
- Predictive analytics & prevention – recognizing risks at an early stage, enabling targeted interventions. Shaping patient care proactively.
- Clinical workflows & AI agents – scheduling, resource management and workflow automation by autonomous agents.
- Remote Monitoring & Wearables – Monitoring chronically ill patients in real time. Reduce hospitalization rates, increase quality of care.
- Data Quality & Interoperability – Using AI for Data Cleaning and System Integration. Improving Accuracy, Optimizing Data Flows.
Financial Services
- Fraud detection in real time – recognize fraudulent patterns in transactions before damage occurs. Minimize losses.
- Automated credit assessment – speed up credit decisions. Objective, data-based evaluation instead of manual checks.
- Predictive analytics for markets – analyze market trends and risks with foresight. Base investment decisions on real-time data.
- Risk Management & Compliance – AI for continuous monitoring of policies, reporting requirements, and regulatory guidelines.
- Dynamic pricing & product design – adapt customer-specific conditions and prices in real time. Increase competitiveness.
- Back-Office Automation – Fully automate documents, invoices, and compliance processes using AI.
Logistics & Supply Chain
- Route optimization – reduce costs and delivery times. AI selects the most efficient routes, taking traffic, weather and real-time data into account.
- Demand forecasting – optimize stock levels, prevent bottlenecks. Precisely forecast requirements and seasonal peaks.
- Automated warehouses – automate storage location allocation and picking with AI-compatible robot systems.
- Real-time tracking – transparency for every shipment, at all times. AI-supported IoT data for maximum supply chain visibility.
- Supply chain risk management – proactively identify risks, develop alternative strategies with AI. Securing supply chains.
- Sustainability optimization – optimize loading capacities, routes and energy consumption for green logistics. Measurably reduce emissions.
Retail & Retail
- Personalized product recommendations – Generate individual suggestions from purchase data in real time. Increase sales and customer satisfaction.
- Dynamic price adjustment – automatically adjust prices to market and demand data. Maximize margins, secure competitiveness.
- Inventory management & forecasting – avoid overstocking and understocking. Precisely forecast demand, reduce storage costs.
- Fraud Detection – Monitor transactions and internal processes for anomalies and fraudulent activity.
- Supply Chain Optimization – Predict bottlenecks, optimize transportation routes, and increase transparency throughout all supply chains.
- Store layout optimization – data-based optimization of walkways and product placements. Increase sales per square meter.
Our Services: AI Consulting Services
AI Strategy, Technology, and Implementation
AI use case identification & evaluation
AI Implementation & Integration
Proof of concept & piloting
Scaling & value maximization
AI Enablement & Training
AI training for teams
Executive AI Workshops
AI Organization & Operating Model
Change management
AI Data Foundation & Analytics
Data Architecture & Readiness
Analytics & Reporting
Data-driven operations
Increased Efficiency & Cost Reduction
AI Governance & Compliance
AI Governance & EU AI Act
Risk & compliance management
AI Cybersecurity
Optimal AI Tools & Technology Stack
Your AI Consulting experts
Success Stories - Successful AI consulting projects from Ventum Consulting
Scaling AI Adoption Across the Enterprise: From AI Pilots to Production Deployment
: AI was used on an ad hoc basis but was not systematically integrated into work processes—there was a lack of a uniform level of expertise and a scalable methodology.
Our Approach
A modular enablement program ranging from AI fundamentals and structured prompting to full Copilot integration into the operational toolchain.
Result
-wide adoption of Copilot across all key workflows. Measurably higher efficiency in documentation, task management, and daily planning.
Highlight
: End-to-end integration into existing business processes rather than isolated tool training. To the success story
Generative AI in Sales and Communication: Translating Existing Experience into Targeted Application Skills
The company was already familiar with generative AI tools. What was missing for the next step was a common framework. The focus was on specific use cases in sales and communications. Equally important was the question of how to save time, streamline processes, and improve results.
Our Approach
Our field-tested AI training combined technical understanding with typical tasks from everyday work. Participants worked with ChatGPT and Copilot on analyses, texts, proposals, and concepts. Content on LLMs, prompting, meta-prompting, and AI agents provided the necessary methodological knowledge. Exercises and real-world use cases allowed participants to experience the potential firsthand.
Results
Participants were better able to identify, evaluate, and implement suitable AI applications on their own. Their existing experience with these tools led to a structured and goal-oriented approach to generative AI.
Highlight
The direct connection to daily tasks. Not just isolated knowledge transfer, but collaborative experimentation with familiar tools and relevant use cases. This is how technological potential was transformed into immediately applicable expertise.
Building AI Readiness: From Understanding to the Productive Use of Agents and Automation
A newly established department needed a robust AI foundation within a short period of time—not just theory, but the ability to take productive action.
Our Approach
Structured skill development, ranging from an understanding of LLMs and prompting methodologies to agent-based workflows and end-to-end automation.
Result
A unified understanding of AI, independent use of prompting and AI agents from day one, and noticeably higher efficiency in day-to-day operations.
Highlight
Not just imparting knowledge, but fostering the ability to take action—from initial orientation to the next stage of maturity. To the success story
AI-Based Production Optimization: From Bottleneck Analysis to Real-Time Decision Support
Bottlenecks on the production line were holding back capacity utilization and output—the complexity of the equipment, production program, and data quality made conventional optimization impossible.
Our Approach
On-site process analysis, data-driven identification of improvement potential, and development of an AI-powered real-time simulation for dynamic feed optimization—based on live production data.
Result
Over 5% increase in output during normal operation, up to 8% in critical high-load situations—while simultaneously transferring know-how to the customer’s employees to ensure sustainable implementation.
Highlight
In-house development instead of a standard solution—deep process understanding combined with AI, mathematical optimization, and real-time data. To the success story
AI in IT Governance: From Manual Compliance to Automated Enterprise Management
: Increasing compliance requirements, redundant documentation obligations, and a lack of transparency regarding IT services made manual governance increasingly inefficient, error-prone, and unscalable.
Our Approach
Group-wide analysis of governance processes, development of a user-centered “Governance as a Service” framework with a semantic data model, and piloting of AI-powered automation for machine-readable, compliance-compliant documentation.
Result
Digitized, automated governance processes with full transparency regarding status, risks, and regulatory requirements—scalable, flexibly adaptable, and ready for immediate rollout.
Highlight
Governance consistently designed with the user in mind—combined with AI automation and a scalable data model instead of rigid, document-based frameworks. To the success story
AI Readiness in Engineering: Simulation Data Management as the Foundation for AI in Product Development
: Over 16 simulation disciplines with varying data requirements, a lack of integration between geometric and functional data, and no AI-enabled data structures—all while aiming for a rollout to over 2,500 employees.
Our Approach
Systematic requirements analysis across all disciplines, development of validated use cases, and a scalable architectural vision for cross-functional simulation data management—serving as the direct foundation for AI agents and machine learning.
Result
AI-enabled data structures established, a viable architectural concept for non-geometric data derived, and modern workflows embedded—scalable from a pilot team to an industrial rollout.
Highlight
In-depth engineering expertise combined with data-driven architectural expertise—not AI for AI’s sake, but the right data foundation as a prerequisite for real AI benefits. To the success story
Data-Driven Feature Management: From Idea to Market-Ready Innovation in Record Time
: Rising customer expectations, limited budgets, and a lack of data-driven management of the feature pipeline made it difficult to prioritize digital innovations and bring them to market.
Our Approach
A structured gate and feature funnel process with a data-driven evaluation system, a maturity catalog, and real-time reporting—focused on customer value and cost-effectiveness.
Results
: 30% more features released, reduced rework, and faster time-to-market. Investments are directed specifically toward the most impactful innovations—from concept to over-the-air update.
Highlight
: Cross-functional feedback loops and full transparency regarding budgets, resources, and maturity levels. To the success story
E2E Data Journeys: Transforming Fragmented Data Management into a Controllable Framework
Inconsistent practices, a lack of collaboration between data providers and users, and fragmented architecture led to redundancies and a lack of controllability—over 30 overlapping processes with more than 250 process steps.
Our Approach
: Development of an “E2E Data Journey” as an analytical template for harmonizing existing frameworks—from an “as-is” analysis through user-centered target design to a gap analysis with an implementation plan. Consolidated within 2 months.
Result
A harmonized, company-wide data management framework with clear responsibilities, standardized processes, and AI-enabled data structures—the foundation for scalable AI use cases.
Highlight
: Data requirements are consistently derived from the business strategy rather than conceived from an IT perspective—lightweight and quickly scalable.
AI in Controlling: Transforming Analysis, Forecasting, and Management Reporting
Controllers from several companies were looking for a systematic way to integrate AI into analysis, planning and reporting beyond individual experiments.
Our Approach
A domain-specific AI program designed around real-world controlling processes—from automated commentary and forecast support to AI-driven management storylines.
Result
Significant time savings in reporting. Greater consistency in management reports. Initial standardized AI workflows established across the entire organization.
Highlight
Precisely tailored to the controlling value chain—with results you can apply right away. To the success story
From an umbrella organization model to a self-sufficient, AI-ready IT operation
. Dependence on central IT and the parent organization, as well as a lack of structures for governance, operations, and integration, hindered the strategic deployment of AI.
Our Approach
Development of an AI-ready IT operating model that harmonizes IT operations, data, and AI governance and integrates AI applications into existing ITSM processes in compliance with the EU AI Act and the GDPR.
Result
Strategic ownership of IT and AI, structured evaluation of business use cases, and a robust foundation for their secure and scalable implementation.
Highlight
End-to-end governance and support structures connect applications, data, and AI within an integrated operating model. To the success story
AI in Sales, Development, and Purchasing: From Isolated Applications to End-to-End Process Acceleration
Committed teams had already been using AI to great effect. A structured framework was needed to consolidate these experiences and scale them company-wide using uniform standards, aligned competency profiles, and binding compliance requirements.
Our Approach
In four hands-on workshops, we combined foundational AI knowledge and responsible AI use directly with specific tasks from sales, engineering, and procurement.
Result
Today, participants use AI in a compliant and value-adding manner. Independently developed agent prototypes significantly shorten key workflows. Binding guidelines and documented qualification certificates form the foundation for company-wide rollout.
Highlight
: Building expertise that delivers immediate value, leading to faster processes, operational AI applications, and a robust knowledge base for sustainable scaling. Learn More
AI-Powered Knowledge Platform for Decentralized Organizations: From a Patchwork of Data to Networked Access
Relevant knowledge was fragmented across locations, mailboxes, and individual filing systems. Contributors without access to central resources created their own templates and processes. AI was used in isolated instances, but was neither systematically nor scalably integrated.
Our Approach
We consolidated all dispersed document collections and made them searchable across locations and accessible within the interface via the Ventum AI Hub. At the same time, we equipped all stakeholders with an AI assistant that provides immediate support with texts, meeting minutes, and event planning.
Results
Today, a RAG-powered search infrastructure provides organization-wide access to consolidated knowledge. Inconsistent document versions are a thing of the past. The AI assistant has become an integral part of daily work for employees at all levels of the organization.
Highlight
Digitization with immediate effect: a networked knowledge platform and a personal AI assistant as a common foundation for a decentralized organization.
From an umbrella organization model to a self-sufficient, AI-ready IT operation
. Dependence on central IT and the parent organization, as well as a lack of structures for governance, operations, and integration, hindered the strategic deployment of AI.
Our Approach
Development of an AI-ready IT operating model that harmonizes IT operations, data, and AI governance and integrates AI applications into existing ITSM processes in compliance with the EU AI Act and the GDPR.
Result
Strategic ownership of IT and AI, structured evaluation of business use cases, and a robust foundation for their secure and scalable implementation.
Highlight
End-to-end governance and support structures connect applications, data, and AI within an integrated operating model. To the success story
AI in Sales, Development, and Purchasing: From Isolated Applications to End-to-End Process Acceleration
Committed teams had already been using AI to great effect. A structured framework was needed to consolidate these experiences and scale them company-wide using uniform standards, aligned competency profiles, and binding compliance requirements.
Our Approach
In four hands-on workshops, we combined foundational AI knowledge and responsible AI use directly with specific tasks from sales, engineering, and procurement.
Result
Today, participants use AI in a compliant and value-adding manner. Independently developed agent prototypes significantly shorten key workflows. Binding guidelines and documented qualification certificates form the foundation for company-wide rollout.
Highlight
: Building expertise that delivers immediate value, leading to faster processes, operational AI applications, and a robust knowledge base for sustainable scaling.
AI training & education from Ventum Consulting
AI expertise is not a nice-to-have – it is the difference between a pilot that collects dust on the shelf and a solution that creates value every day.
Our training courses, including in the field of artificial intelligence, are industry-oriented, practical and tailored to specific roles. No abstract theory – just methods and tools that your employees will be able to use tomorrow.
Training courses at a glance:
Agentic Coding Training - AI app development process
Participants will learn how to transform the entire software development lifecycle into a systematic, reproducible process using AI-powered agent tools —from precise requirements specifications and the automatic generation of source code to quality control and scalable deployment. The goal is to reduce development effort and significantly increase productivity.
AI Security Assessment
With the AI Security Assessment, you’ll receive a comprehensive, well-founded evaluation of your AI infrastructure—including identified security vulnerabilities, risks prioritized by urgency, attack scenarios tested under real-world conditions, and a transparent, verifiable action and governance roadmap that ensures your AI applications are secure, compliant, and scalable as they are deployed into enterprise-wide production operations.
Application Modernization & Migration Workshop
This workshop focuses on methodically analyzing complex legacy systems, transforming them into a future-oriented architectural and operational structure, and, building on that foundation, developing concrete modernization and migration strategies—with the goal of achieving more stable systems, reduced risks, and a technologically up-to-date, AI-ready application environment.
ChatGPT & Copilot training - save time with AI and prompt engineering
Hands-on application of ChatGPT and Microsoft Copilot to achieve lasting productivity gains in day-to-day work.
ChatGPT training - using AI professionally in everyday work
Turn repetitive tasks into well-thought-out prompts, increase efficiency, and consciously use AI as a supportive tool.
Design sprint for AI - from business case to prototype in 5 days
Within five days, an artificial intelligence (AI) concept is transformed into a testable prototype with a validated business model. Perfect for teams that want to drive innovation and make well-informed investment decisions.
AI Champion Training
In this training program, employees and managers develop the skills to use artificial intelligence confidently, identify relevant AI opportunities within the company, and actively support change processes. The AI Champion training program combines practical AI knowledge with strategic implementation, governance, and change management—ideal for companies that want to embed AI sustainably and build internal expertise in a targeted manner.
AI workshop - Develop your own use cases
Systematic identification, assessment, and prioritization of industry-specific artificial intelligence (AI) use cases. The result: a concrete roadmap with a profitability assessment and defined follow-up actions.
AI workshop for SMEs
An application-oriented introduction to artificial intelligence with a focus on business implementation and scalability. Ideally suited for medium-sized companies seeking to achieve tangible value creation.
AI workshop for companies - successfully anchoring innovation
A well-founded analysis of current AI methods and their targeted application in business practice. Ensures transparency, confidence in decision-making, and a solid foundation for systematic implementation.
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FAQs on the topic of AI Consulting
We advise companies from a wide range of sectors, such as industry & manufacturing, automotive, financial services, healthcare, logistics & supply chain and retail (excerpt) – with use cases that have been proven to work in these sectors.
That depends on the scope. A use case workshop is completed in one day. A proof of concept typically takes 4-8 weeks. Full implementations usually take 3-6 months. We always recommend starting with a clearly defined first step when using artificial intelligence.
Yes – and this is a central component of our approach. We offer certified training courses (e.g. ChatGPT, Copilot, Prompt Engineering), industry-specific workshops and individual curricula for different roles. For us, enablement is not an add-on, but part of every project.
The costs depend on the scope, duration and team size. We recommend a use case workshop or a non-binding initial consultation to get started – so that you can quickly obtain a realistic assessment.
Three things: Firstly, we always start with your industry and your processes – not with generic frameworks. Secondly, we integrate team enablement right from the start. Thirdly, we don’t deliver strategy papers that end up in a drawer – we support you right through to productive operation.
Governance and compliance are not an afterthought for us – they are considered right from the start of the project. We create guidelines, roles and verifiability in accordance with the EU AI Act, GDPR and industry-specific standards (e.g. MDR in the healthcare sector, regulatory requirements in the finance sector).
A successful start begins with clear business goals and a thorough assessment of the current situation. The guide to implementing AI outlines the entire process, from prerequisites to scaling. Building on this foundation, a robust AI strategy, structured AI strategy consulting, and the systematic identification and prioritization of suitable AI use cases help ensure that investments are targeted toward measurable value creation.
A clearly defined AI proof of concept first assesses technical feasibility and business value using real-world company data. Subsequently, a structured AI implementation integrates the validated solution into existing processes and systems. A suitable AI infrastructure provides the foundation for secure, cost-effective, and scalable operations.
Success should be defined before the project begins using clear baseline values and a few meaningful metrics. The guide to measuring the success of AI projects explains suitable financial, operational, and technical metrics. In addition, the articles on calculating and maximizing AI ROI and on cost optimization through AI show how companies can assess and sustainably realize economic potential.
Data-intensive, recurring, or multi-step processes that require a significant amount of manual effort are particularly well-suited for this. AI Automation Consulting combines process analysis, intelligent workflows, and existing systems to create scalable solutions. The examples of AI agents in the enterprise —from areas such as contract management, controlling, compliance, knowledge management, and quality assurance—provide concrete guidance.
Companies need both organizational risk management and technical safeguards. AI Risk Management establishes clear roles, controls, human oversight, and verifiable processes. AI Security provides additional protection against specific threats such as prompt injection, data exfiltration, model manipulation, and attacks on agent-based systems.
Employees need role-specific knowledge to safely apply and critically evaluate AI results. The guide to building and scaling AI competencies shows how learning paths, internal champions, and communities can embed knowledge throughout the organization. In addition, leaders need leadership AI skills to contextualize decisions, take responsibility, and confidently guide teams through change.
Today, AI supports the entire development process, from requirements analysis through programming and testing to deployment and maintenance. This overview of AI in software development explains its areas of application, opportunities, and risks. AI-Driven Development goes a step further and demonstrates how specialized AI agents collaborate within structured, quality-assured development processes.
Yes, AI solutions can be integrated into existing IT environments—a separate infrastructure is not required. Ventum Consulting implements them in a way that ensures they run stably, are maintainable, and are scalable during routine operations; tools and the technology stack are selected based on the specific use case.
In AI projects, Ventum Consulting embeds compliance before implementation rather than addressing it as an afterthought: Guidelines, role models, and auditability in accordance with the EU AI Act, the GDPR, and industry-specific standards are developed alongside the solution. Risks are systematically identified, assessed, and mitigated throughout the process, ensuring that the solution remains audit-ready.