Emerging technologies: transformer models, multimodal AI systems, AutoML.
Activity/Exercise:
Group Work:
Participants analyze the most common use cases in their respective industries.
Creation of a short presentation: How can AI/ GenAI/ AI agents deliver concrete improvements?
Module 2: Technologies and Tools for AI Solutions (3 hours)
Contents:
AI Technology Basics:
Difference between supervised, unsupervised, and reinforcement learning methods.
Structure of neural networks and how they recognize patterns in data.
Functionality of generative models such as GANs (Generative Adversarial Networks) and transformer architectures (e.g., GPT).
Hardware and Software Resources:
GPUs, TPUs, and other specialized hardware for AI training and inference.
Cloud services (e.g., AWS, Azure, Google Cloud) for AI projects.
Open-source libraries: TensorFlow, PyTorch, Hugging Face Transformers.
Integrations and APIs:
Overview of RESTful APIs and SDKs.
Ways to integrate AI models into existing software landscapes.
Security and privacy considerations when using AI services.
Activity/Exercise:
Practical Demonstration:
Participants work in groups with a simple generative model application, such as text or image generators.
They compare results and discuss implementation opportunities and challenges.
Day 2: Sales and Customer Focus
Module 3: Selling AI Products and Services
Contents:
Understanding Products and Services:
Differences between AI services (e.g., APIs, consulting), software products (e.g., pre-built AI solutions), and hardware solutions (e.g., AI-optimized hardware).
Use cases and benefits for various target audiences.
Communicating Customer Value:
Presenting success stories and case studies.
Addressing common customer concerns (e.g., data sovereignty, implementation costs) and how to handle them.
Industry Examples:
Using GenAI in marketing campaigns.
AI agents for process automation in call centers.
Hardware solutions for AI-powered image analysis in healthcare.
Activity/Exercise:
Role-playing:
Participants practice sales conversations with different customer types.
Peers act as customers, raising typical questions and objections.
Feedback session: Strengths and improvement areas.
Module 4: Strategies and Best Practices for AI Sales
Contents:
Developing Sales Strategies:
Market segmentation: How to identify potential customers.
Targeted outreach: Personalizing offers based on customer profiles.
Up-selling and cross-selling: Building a portfolio that extends beyond a single solution.
Building Customer Relationships:
Ongoing engagement with customers: Collecting feedback and deriving improvements.
Long-term customer retention through training and support.
Best Practices:
Case studies of successful AI implementations.
Key dos and don’ts in the sales process.
Adapting to technological changes and continuous learning strategies for sales teams.
Activity/Exercise:
Workshop:
Participants create a short pitch deck for an AI product or service.
Presentation in front of the group, followed by a feedback session.
Goal: Develop a convincing sales presentation that is practical and engaging.
Summary and Wrap-Up
Open discussion about lessons learned.
Addressing any remaining questions.
Participants receive a brief summary of the discussed topics and links to additional resources.
