AI chat systems are used in the regular work of ESG-areas have long since become indispensable. The author uses AI-based chatbots in everyday ESG life - Specifically ChatGPT, Perplexity and Le Chat. And not as a replacement for the classic search engine, but in day-to-day work: when texts need to be created, when workshops need to be organised and when risk logics need to be structured.

This article is deliberately written as an experience report: with clear strengths, clear weaknesses and a focus on the insights gained from working with sustainability managers. „Strength“ here means: noticeable time saving and/or new, useful answers (new in the sense of = the author would not have easily conceived this himself on the basis of his experience). „Weakness“ means: Quality problems, No time saving or human effects such as Cognitive offloading - In other words, the risk of outsourcing thinking and thus being less controllable at the crucial moment.

Topics relating to data protection and information security in AI applications are not addressed separately in this article. There is very good work on this from the State Commissioner for Data Protection and the Chambers of industry and commerce.

The observations described come from the author's project experience and from feedback from sustainability managers in joint projects. In order to also Perspective of AI experts from software providers we recommend you read our other article.

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1. brief introduction of the author

The Author is 40 years old, has a Master's degree with a focus on finance and behavioural economics, is a certified credit analyst and has completed internal training in AI prompting. The author has worked in around 100 projects Company to Sustainability reporting, sustainability risk assessment and strategy development - including a large number of workshops.

Important for the categorisation: The threshold for defining a strength or weakness always depends heavily on the respective benchmark. In this article, which focuses on time resources, quality of results and human bias, the assessment is therefore largely dependent on the author's level of knowledge and time efficiency without the use of AI. In order to better categorise this for all readers, three specific application situations are presented below: Sustainability reports, workshops and risk management.

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2 AI-based chatbots in everyday ESG life in the sustainability report

Initial situation: Preparation of sustainability reports in accordance with ESRS and VSME. ESRS are the European reporting standards in the CSRD-environment; VSME is the European voluntary standard for companies not subject to CSRD.

Strengths: text drafts, variants, tonality - in minutes instead of days

Thesis: AI provides the greatest leverage for text production in reporting.
Justification: Just a few key points are enough to produce an easily readable draft within seconds. Feedback loops are standard in reporting anyway - the review of an AI draft is therefore not an „extra effort“, but an earlier start to quality assurance.
Example: The „Climate reduction plan, GHG neutrality 2040, top management target agreement“ becomes a structured chapter that can be technically reviewed and concretised.

The author sees a clear quality booster when the prompt not only delivers content, but also the Target group and the Intended use of the report:

• EcoVadis logic: If the report is to serve as a verification document, evidence-based texts (process + evidence + responsibility + review rhythm) work much better than „corporate storytelling“.
• Banking/ESG risk assessment: If the report is used as input for credit-related ESG audits, texts should be risk- and control-orientated (criteria, controls, plans, time horizons). The expectation logic from the EBA guidelines fits very well here as a „tonality filter“.

Additional benefits that the author regularly sees in ESG reporting:

• Consistency checks: AI can mark contradictions (time horizons, definitions, roles) if „facts“ are provided as a bullet list.
• Executive Summary: The summarisation for the management/board can be done quickly without the team having to „rewrite the report“. Alternatively, a short summary presentation can also be quickly generated from the report content.

Weaknesses: Regulatory interpretation - plausible, but often not resilient

The greatest weakness in the preparation of reports arises with Detailed regulatory issues. As soon as it comes to the concrete interpretation of individual passages - plus cross-references to other specifications - chatbots often provide answers that sound valid but cannot be clearly substantiated in the rules (Hallucination risk).

In everyday ESG work, this was particularly evident when ESRS/VSME questions were mixed with other sets of rules (e.g. SFDR or taxonomy contexts). For example, answers as to whether data points in different sets of rules follow the same or different definitions often contained errors or were incomplete.

The time advantage of a quick and often logical-sounding answer was then quickly offset by an increased review effort. For teams with a high level of rule routine, errors can be recognised quickly, which is why an appropriate net review saves time. For less experienced teams, this creates a significant Additional expenditure, because practically every legal statement has to be cross-checked - and thus the time advantage is no longer given.

Deep research: fewer errors, more effort

Deep research modes noticeably reduce hallucinations because research, source evaluation and citations are more integrated - albeit at the expense of time and resources (= physical resource consumption of an AI search). Especially in the case of „hard“ regulatory issues, it is worth taking a look at regulations, as this often makes it easier and quicker to adapt to subsequent changes. The best example of this is dealing with the Changes to the ESRS from the drafts in 2025.

Author's practical rule for reporting: AI can structure, formulate and deliver variants. The following applies to regulation: Primary source beats AI.

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3. strengths and weaknesses in the workshop conception and realisation

Initial situation: Conception and moderation of workshops as part of strategy development processes - often over months, sometimes years, with changing stakeholders, political dynamics and conflicting objectives.

Strengths: Conception of new workshops becomes drastically faster

The author sees AI chatbots as particularly strong when a workshop format new or variants are needed quickly:

• Agenda design (minute grid, logic, transitions)
• Mix of methods (creative vs. analytical vs. hybrid)
• Moderation guidelines (introduction, key questions, decision logic)
• Working documents (matrices, canvas, evaluation grid, result templates)

In the author's experience, the quality of the method suggestions is often surprisingly consistent - but only if the prompt reflects reality: Participant profile (functions, level of knowledge, interests), target image (output format, decision vs. input) and desired characteristics (tempo-, data- or consensus-orientated). The quality of results for AI-based chatbots in everyday ESG work depends on a well thought-out and comprehensive prompt.

Weakness 1: Context load eats up the time saved

Workshops are rarely isolated. A good workshop builds on the knowledge gained from previous steps and pays into the next steps. If the author wants to map this entire process logic in an AI prompt, the prompt quickly becomes a project file. Particularly in advanced strategy processes, the time saved compared to an experienced human concept is often less than expected.

Concrete sample: The more preparatory work, lines of conflict, key figures, Stakeholders-The more sensitivities and decisions that have already been made are to be taken into account, the more „feeding“ the chatbot needs - and the smaller the net advantage becomes.

Weakness 2: Cognitive offloading - the risk arises in people

According to the author, the most critical weakness does not necessarily arise from poor AI quality, but from the interplay between AI output and moderation expertise:

• The longer a workshop lasts, the more likely it is that the need for a intuitive redesign during implementation (resistance from participants, schedule does not work out, workshop objective changes just-in-time, etc.).
• The more the main conception is outsourced to an AI system and the less experienced the moderation, the higher the risk that this redesign will fail to materialise or be too weak.

This pattern matches Cognitive offloadingMental work is outsourced, which feels relieving in the short term, but in the long term reduces the ability to control the situation - especially in the case of inexperience.

Author's recommendation for action: The longer the workshop, the more sensitive the topic and the less experienced the moderator, the more human preparation must dominate. AI remains a very good sparring tool - but not the autopilot.

Mini template: prompt that works with several workshop concepts

The author often uses this structure (short but effective) for workshop conception:

 Target group: [roles, seniority, prior knowledge, conflict lines] Aim of the workshop: [concrete output, decision/agreement/ideas] Framework: [duration, format, number, remote/onsite] Character: [data-driven/creative/conflict-sensitive/fast-paced] No-gos: [e.g. no exposure, no evaluation without data] Delivery: Agenda, methods, moderation guide, working documents + please provide 2 alternative paths for "time missing" and "conflict escalates" 

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4. strengths and weaknesses in sustainability risk assessment

Initial situation: Integration of sustainability risks into an existing operational risk management system for SMEs. The relevance is increasing because expectations regarding the integration of ESG risks into management and risk organisation are becoming more concrete - among other things due to the EBA guidelines for financial institutions, which indirectly increases the requirements for companies.

Strengths: Gap analysis and structured risk lists - fast, clear, comprehensible

The author reports very stable productivity gains here:

• Gap analysis existing risk lists with regard to ESG risks, especially if categories are considered separately (e.g. climate risks, biodiversity risks, human rights-related supply chain risks).
• Evaluation according to criteria (e.g. high damage potential is to be assumed in the event of a reduction in sales of more than 2.5% or an increase in material costs of more than 5%) provides comprehensible suggestions. In the use cases, these served as input for internal experts, not as a final decision on the risk assessment.

The quality increases visibly when a clear Context structure (business model, financial structure, etc.). The most recently published annual report, supplemented by information on supply chains and strategy, can be used as an excellent basis for providing AI-based chatbots with a comprehensive picture of the company relevant for the risk assessment in everyday ESG work.

Weakness 1: Risk management measures often remain too superficial

The author sees a recurring weakness in the development of measures: The chatbots often deliver correct but generic suggestions („policy“, „training“, „monitoring“, „supplier code of conduct“). Even after a large number of iteration rounds, the result is often not a portfolio of measures that is more innovative or of higher quality than what in-house experts develop in the same amount of time. The difference is that the measures developed by internal experts generally enjoy significantly greater acceptance within the company.

Why this is plausible: Good risk management is highly contextualised: budget, responsible parties, process and system landscape, contractual power, CAPEX/OPEX logic, internal controls - all of this is rarely fully integrated into the prompt. AI cannot „seriously“ close this gap without slipping into hallucinations.

Weakness 2: Context limitation - „forgetting“ in long risk threads

When working in depth on risk clusters, it is noticeable, according to the author, that the chatbot no longer consistently takes information from the beginning into account. This is not a problem in practice, but it requires a working pattern that many teams first have to learn:

• Summarise interim results (1 page „project memory“)
• Start new chat (clean context basis)
• Versioning like a working paper (V1, V2, V3)

It's „just like human sparring“ - except that the chatbot doesn't automatically ask when the context changes.

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5. recommendations for action: What has helped to limit weaknesses?

Some simple recommendations for action from the previous chapters are summarised below.

5.1 The 10 most effective guardrails (short list)

1. Target group and purpose always first: EcoVadis, Bank, Audit, Board of Directors - the prompt starts with „For what?“ and „For whom?“.
2. Regulation never without a primary source: AI may structure, but interpretation is verified in ESRS/VSME/SFDR.
3. Use deep research in a targeted manner: for complex research with sources, not for standard text.
4. assumptions can be marked: Enter „If you are not sure, tick Accept/Uncertain instead of guessing.“ in the prompt.
5. Red Team question mandatory: „What would be wrong if the opposite were true?“ (30 seconds, great effect).
6. Cognitive offloading self-protection: The less experience you have with a topic or situation, the more time you should invest in your own thought processes and not rely too much on the „sweet fruit“ of a quick AI solution.
7. Manage context: summarise, restart, version - instead of endless chat.
8. Always „ground“ risk measures in the company: Owner, budget, systems, milestones - without that it remains generic.

5.2 Overview: strengths, weaknesses, countermeasures (at a glance)

Use case	Strengths	Weaknesses	What limits the weaknesses?
Sustainability report (ESRS/VSME)	Very fast text drafts, variants, executive summary, better readability	Regulatory interpretation prone to error; cross-references risky	Precise target group; primary source requirement; deep research only if required
Workshop conception & realisation	Novel processes quickly; mix of methods; guidelines & templates	Context load eats up time; cognitive offloading with inexperience	Summarise context; ramp up human preparation
Sustainability risk assessment (SMEs)	Gap analysis strong; modular risk structure; scoring as input	Measures often generic; context limitation in long threads	Treat risk categories individually; finalise measures in the specialist team; summarise & restart

5.3 Three quick steps the author recommends to ESG teams

So that AI-based chatbots in everyday ESG life The author recommends three steps that can realistically be implemented in one week:

1. Establish further training formats: Create formats with already experienced internal users and simply make external offers available to all users (e.g. AINAUTES)
2. Standardise prompt templates: Target group + purpose + output format + quality rules (mark assumptions, name sources).
3. Define review workflow: Who checks the facts? Who checks regulations? Who takes final responsibility?

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6. conclusion

AI-based chatbots in everyday ESG life In the author's experience, they deliver the greatest benefit when they are used as a turbo for preparatory work and sparring partners: Start texts more quickly, vary workshop design more quickly, structure risk lists more quickly. The crucial point is not „AI or not“, but rather: where AI is used and Which guardrails/run parallel to the crash barriers.

The greatest risks arise where outputs sound „serious“ but are not resilient (regulation) or where human expertise is outsourced too much (cognitive offloading). With clear target group logic, mandatory primary sources, modular working methods and clean context management, AI remains a productivity lever - without any loss of quality.