Training course · Generative AI · Retail & E-Commerce

Generative AI for Retail

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Generative AI with the everyday processes of Retail & E-Commerce. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

Whether bricks-and-mortar stores, online shops, wholesale or omnichannel, retail works with large ranges, thin margins and high customer expectations. Generative AI helps wherever there is lots of text, lots of data and lots of customer contact. It writes product descriptions and category pages, maintains product master data, answers customer service enquiries, analyses reviews and return reasons and supports promotion planning and campaigns. AI agents can check order status, pre-sort complaints or bundle supplier requests. For buying and category management, AI becomes a tool for commenting on sales data and preparing range decisions. The key is balancing efficiency, brand voice and legally accurate product information.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • E-commerce and online shop teams
  • Buying and category management
  • Store and area managers
  • Customer service and returns management
  • Retail marketing and CRM

Typical challenges we address

  • Large ranges requiring heavy effort for product copy and master data
  • High volumes of customer enquiries, complaints and returns
  • Seasonal peaks and short-notice promotions
  • Data from tills, web shop and merchandise systems that is hard to combine

What you will be able to do

  • You can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • Produce product copy and campaign content at scale with AI in a consistent brand voice.
  • Analyse customer feedback, returns and sales data with AI in a structured way.
  • Identify service cases and processes that can be automated with AI agents.

Course outline

Module 1 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
Module 6 Practice lab: Retail & E-Commerce
  • Product copy at scale
  • Cleaning master data
  • Supporting customer service
  • Analysing reviews and returns
  • Promotions and campaigns
  • Commenting on sales data
Module 7 Data protection, quality & transfer to daily work
  • Customer data from web shop, CRM and loyalty programmes is subject to the GDPR, and marketing activities to consent and unfair competition rules. Product information, price displays and advertising claims must comply with consumer protection law, and customer-facing AI chatbots must be disclosed as such under the EU AI Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Product copy at scale

Manufacturer data and attributes become consistent, search-optimised product descriptions, bullet points and category pages in several languages, with mandatory details carried over accurately. Spot checks happen before publishing.

Cleaning master data

AI helps standardise product data, detect missing attributes, normalise colours and sizes and assign products to the correct categories – with spot checks by the team.

Supporting customer service

Enquiries about delivery status, exchanges, warranty or product details are classified and given draft replies; AI agents can handle standard cases directly. Complex cases are handed to staff with a summary.

Analysing reviews and returns

Customer reviews and return reasons are grouped by themes such as fit, quality or inaccurate descriptions in order to improve copy, range and suppliers in a targeted way.

Promotions and campaigns

Promotion plans, newsletters, social posts and copy for leaflets or displays are created for each target group and aligned with seasonal occasions. Results of past promotions inform the planning.

Commenting on sales data

For buying and category management, AI summarises sales data, explains variances against last year and plan and prepares arguments for supplier negotiations. Figures come from the merchandise system.

Prerequisites

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. Professional experience in store-based or online retail; no technical background required.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

Data protection & compliance

Customer data from web shop, CRM and loyalty programmes is subject to the GDPR, and marketing activities to consent and unfair competition rules. Product information, price displays and advertising claims must comply with consumer protection law, and customer-facing AI chatbots must be disclosed as such under the EU AI Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 Online Request
  • Thu, 3 December 2026 Kaiserslautern Request
  • Wed, 9 December 2026 Online Request
  • Thu, 7 January 2027 Online Request
  • Fri, 22 January 2027 Augsburg Request
  • Thu, 28 January 2027 Online Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

How do I make sure AI product copy is accurate?

Feed the AI only verified product data, specify which details must not be changed and run systematic spot checks. Dimensions, materials, mandatory labelling and advertising claims are critical. In the course you develop a workflow with templates and review steps for producing copy at scale.

Is AI worthwhile for smaller retailers too?

Smaller teams in particular benefit, because AI takes on tasks there is otherwise no capacity for – such as product copy, newsletters or customer replies. Many features can be used without major investment. The course shows pragmatic starting points that work without an IT department.

How much does “Generative AI for Retail” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

More courses for Retail

More Generative AI courses

All 45 Generative AI courses

Generative AI · MunichGenerative AI · SalzburgGenerative AI · BerlinGenerative AI · AugsburgGenerative AI · HanoverGenerative AI · BremenGenerative AI · StuttgartGenerative AI · KaiserslauternGenerative AI · Online