Training course · Generative AI · Consulting

Generative AI for Consultants

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 Consulting. 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.

Consulting thrives on rapid onboarding, structured analysis and persuasive communication – exactly where generative AI makes a difference. Consultants use AI assistants to get up to speed on new industries, synthesise interview notes, build hypothesis trees, analyse client data and develop storylines for steering committees. AI agents support desk research, document comparison and first slide drafts. At the same time, AI is reshaping the business model: clients expect faster results and ask what value consultants add beyond what they could generate with AI themselves. Consultants who use AI confidently shift their time from grunt work towards judgement, implementation support and relationship building.

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?

  • Management and strategy consultants
  • IT and process consultants
  • In-house consulting teams
  • Independent consultants
  • Analysts and junior consultants

Typical challenges we address

  • Short ramp-up time for new clients, industries and topics
  • Large volumes of interviews, documents and data under time pressure
  • Heavy effort for slides, reports and proposals
  • Confidentiality of client information when using AI

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.
  • Accelerate onboarding, research and synthesis in consulting projects with AI.
  • Develop structured storylines, slides and proposals with AI support.
  • Use AI in client work in line with confidentiality obligations and communicate its use transparently.

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: Consulting
  • Rapid onboarding
  • Synthesising interviews
  • Hypotheses and issue trees
  • Storylines and slides
  • Proposals and bids
  • Faster data analysis
Module 7 Data protection, quality & transfer to daily work
  • Client information is usually covered by confidentiality agreements and may only be processed in AI environments cleared contractually and under data protection law. AI use should be agreed with the client, and personal interview data must be handled in line with the GDPR.
  • 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

Rapid onboarding

AI produces industry overviews, explains terminology, value chains and typical KPIs and drafts key questions for the kick-off – as a starting point to be verified with reliable sources.

Synthesising interviews

Notes from stakeholder interviews are organised by theme, pain point and quote, patterns and contradictions are drawn out and the results prepared as a basis for hypotheses.

Hypotheses and issue trees

Problems are broken down with AI into mutually exclusive, collectively exhaustive sub-questions, hypotheses are formulated and the required analyses and data sources derived. This quickly yields a robust workplan for the team.

Storylines and slides

Analysis results become pyramid-structured storylines, action titles and slide drafts, which the team then sharpens and builds out in the house style. The core message stays at the centre of every slide.

Proposals and bids

Tender documents are analysed, requirements turned into checklists and proposal sections on approach, work packages and team profile written with a clear focus. The client's evaluation criteria are addressed directly.

Faster data analysis

Client data is cleaned, analysed and visualised with AI support, for example for benchmarks, cost analyses or process KPIs, with documented calculation steps. Sanity checks safeguard quality before client presentations.

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. Experience in consulting projects or analytical project roles; no programming skills 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

Client information is usually covered by confidentiality agreements and may only be processed in AI environments cleared contractually and under data protection law. AI use should be agreed with the client, and personal interview data must be handled in line with the GDPR.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Wed, 9 December 2026 Salzburg Request
  • Fri, 18 December 2026 Online Request
  • Fri, 8 January 2027 Bremen Request
  • Tue, 19 January 2027 Online Request
  • Mon, 1 February 2027 Augsburg 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.

Can I use client data in AI tools?

Only if the contract, NDA and your firm's policies permit it and the tool is covered contractually. It is often sensible to agree AI use with the client. The course shows how to work with anonymised data and set clear rules for project teams.

Will AI make consultants redundant?

Grunt work such as desk research or first slide drafts becomes cheaper. Judgement, industry experience, facilitation and implementation support remain in demand. Firms that use AI well deliver faster and can invest more time in real impact for clients. Value shifts towards advice quality and outcomes.

How much does “Generative AI for Consultants” 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.

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