As the years go by, corporate governance finds itself caught between an increasingly complex and rapidly changing environment and the structural limitations of its operating model.
On the one hand, there is an explosion of data, multiplying crises, and the complexity of geopolitical, social, and climate phenomena.
On the other, we have increasingly formal and rigid governance bodies, decision-making processes that are more political than effective, and analytical methods designed for stable, linear worlds.
The result is much the same everywhere, with authorities struggling to make decisions or make the right decisions, not because of a lack of information, but because of a lack of clarity, cross-functionality, and constructive debate.
In this context, AI appears to some as the light at the end of the tunnel, as a possibility to finally restore the impact of what governance is no longer able to mobilize and articulate: weak signals, tensions, and a form of collective and contextual intelligence.
But to do this, we must move away from the fantasy of providential technology that will take the reins of business without people making the effort to question themselves or the way they operate, because AI will not work miracles without them, but with them. Or rather, it is they who will work miracles with AI.
But to move away from techno-solutionism (To solve anything, click here), we still need to understand what AI can and must bring to governance, because we are not talking about governance by AI, but governance enhanced by AI.
In short:
- Corporate governance is caught between the growing complexity of its environment and its structural limitations, which hinder its ability to make relevant decisions despite the abundance of information available.
- Artificial intelligence can strengthen governance by helping to detect weak signals, simulate scenarios, automate tasks, and synthesize contributions, provided that it is linked to a clear and shared vision.
- Leaders do not need to be technical experts in AI, but they must be able to identify the objectives, formulate concrete use cases, and cooperate with experts to implement them.
- The use of AI in governance carries risks, including loss of transparency, reproduction of biases, and human disengagement, which require ethical vigilance and governance of the tools themselves.
- AI does not replace collective intelligence but can reinforce it if used as a tool for insight and debate, in a clear-headed, reflective, and responsible manner on the part of decision-makers.
What leaders need to know about AI
Let me start with a few clarifications.
We don’t expect executives to become experts in AI from a technical standpoint. Yes, it’s important to have a basic understanding in order to talk to those who are proficient in the technology, but when I see some people trying to acquire expertise by watching very “deep tech” online videos and then proudly announcing that they’ve cobbled together something in Python that sort of works, it’s completely pointless.
What we ask of them is to know what they expect from AI, the objectives they set for themselves, to formulate use cases, and then to be able to collaborate with those who know how to make things happen so that it becomes a reality.
To each their own expertise and role.
The first thing to keep in mind is that deploying AI in business is not just about putting ChatGPT on a server (Why enterprise AI can’t keep up with consumer AI: beyond ChatGPT, a more complex reality).
Next, we need to stop being blinded by generative AI, which currently accounts for most of the hype but only a fraction of the possibilities (AI for dummies who want to see a little more clearly).
And then we need to keep in mind, for inspiration, a few very concrete use cases that can be considered in a very realistic way, most often by combining different types of AI.
What AI can really bring to corporate governance
We will start with a few very concrete and illustrative cases that I will detail before taking a broader look at what AI can bring to what is known as augmented governance, hoping that I have correctly understood the articles I have read.
Decision support
Predictive analysis
Objective: to anticipate changes in key indicators (HR, performance, reputation, risks, etc.).
Type of AI required: supervised machine learning (regression, decision trees, neural networks), time series models(ARIMA, Prophet).
Example: predicting turnover in a strategic business unit based on historical HR data.
Simulation of complex scenarios
Objective: to aid decision-making by evaluating different possible futures.
Type of AI required: expert systems (logical rules simulating business reasoning), probabilistic modeling (Bayesian networks, Monte Carlo), hybrid AI combining rules + data.
Example: simulating the combined effects of restructuring and regulatory change.
Detection of weak signals
Objective: identify emerging trends or latent risks.
Type of AI required: unsupervised machine learning (clustering, anomaly detection), natural language processing (NLP)to analyze verbatim comments or texts.
Example: detect organizational malaise based on employee feedback.
Management, transparency, and compliance
Reporting automation
Objective: automatically generate reports or dashboards.
Type of AI required: RPA (Robotic Process Automation) to extract and consolidate data and NLG (Natural Language Generation) to generate summary texts.
Example: automatically generate a monthly ESG report for the board of directors.
Analysis of complex documents
Objective: read, understand, and summarize governance documents (minutes, policies, regulations).
Type of AI required:LLM (Large Language Models) such as GPT-4, Claude, Gemini, NLP to extract entities, relationships, intentions, OCR if the documents are scanned.
Example: summarize the last 10 CSR committee meetings in 5 bullet points.
Continuous auditing and smart compliance
Objective: Automatically monitor regulatory, ethical, or internal deviations.
Type of AI required:supervised machine learning (fraud detection, behavior classification), expert systems, or rule engines for strict standards (GDPR, SOX, etc.).
Example: Automatically detect deviations in purchasing processes from internal policies.
Optimization of governance processes
Monitoring the implementation of decisions
Objective: Automate the monitoring of the execution of decisions made.
Type of AI required: Conversational agent + LLM to interact with managers, intelligent workflows with prioritization learning.
Example: AI automatically follows up with action takers if a decision has not been implemented within the expected timeframe.
Assisted summarization and writing
Objective: save time on writing notes, summaries, or reports.
Type of AI required: LLM for writing, rephrasing, summarizing, NLG if automatic structuring from tabular data.
Example: generate a strategic analysis note from reference documents.
Support for collective intelligence
Summary of contributions and debates
Objective: summarize contributions from workshops, forums, and working groups.
Type of AI required: LLM + NLP to structure ideas and detect convergences/divergences.
Example: summarize contributions to an internal consultation on CSR strategy in real time.
Semantic analysis of verbatim transcripts
Objective: extract themes, emotions, and signals from free-form text.
Type of AI required: NLP for pre-processing (lemmatization, tokenization, classification), supervised ML to categorize feedback according to internal reference systems.
Example: automatically classify verbatim quotes by satisfaction or alert theme.
Multilingual translation and cultural adaptation
Objective: streamline governance in international contexts.
Type of AI required: multilingual LLM (GPT-4, DeepL AI, Meta’s NLLB), NLP with tone, style, and cultural expression detection.
Example: automatically translate and adapt governance policies for subsidiaries on multiple continents.
Continuous augmented governance
Real-time monitoring and feedback loop
Objective: Continuously monitor performance and risks through intelligent alerts.
Type of AI required: Real-time AI embedded in information systems (ERP, HR tools, etc.)
Example: Be automatically alerted to a cybersecurity incident or a spike in turnover in a critical team.
More broadly, here is a summary of possible use cases.
| Type of AI | Main function | Examples of tools/tech | Use cases in governance |
|---|---|---|---|
| Supervised machine learning | Prediction based on labeled data | Linear regression, Random Forest, XGBoost | Prévision de turnover, estimation de risque financier ou réputationnel |
| Unsupervised machine learning | Clustering or anomaly detection without labels | K-means, DBSCAN, Isolation Forest | Clustering de feedbacks, détection de signaux faibles, anomalies dans les processus |
| Time series models | Forecasting based on historical data | ARIMA, Prophet, LSTM | Prévision d’indicateurs clés (CA, engagement, absences…) |
| Probabilistic modeling | Uncertainty simulation, risk management | Bayesian networks, Monte Carlo | Simulation de scénarios de crise, modélisation de dépendances entre risques |
| Expert systems | Logical rule-based reasoning | Business rule engines, fact-based/if-then systems | Vérification de conformité réglementaire, audit éthique automatisé |
| Natural Language Processing (NLP) | Human text comprehension and processing | Tokenization, classification, entity extraction | Analyse de verbatims, compréhension de documents, extraction de thématiques |
| Natural Language Generation (NLG) | Generation of structured text from data | Yseop, Arria, AX Semantics, GPT applied to tables | Génération automatique de rapports (ESG, RH, finance), bulletins de gouvernance |
| Large Language Models (LLM) | Generation and understanding of unstructured text | GPT-4, Claude, Gemini, LLaMA | Synthèse de documents, rédaction assistée, préparation de réunion, aide à la prise de décision |
| RPA (Robotic Process Automation) | Automation of repetitive manual tasks | UiPath, Blue Prism, Power Automate | Extraction de données, génération de reporting, automatisation du suivi de décisions |
| OCR (Optical Character Recognition) | Reading scanned documents or image PDFs | Tesseract, Adobe OCR, Google Vision | Lecture de PV, contrats ou règlements papier, analyse juridique automatisée |
| Chatbots / Conversational agents | Contextualized human-machine interaction | ChatGPT, internal AI agents, voice assistants | Suivi des actions de gouvernance, réponse aux administrateurs, aide au pilotage en temps réel |
| Image generation AI (diffusion models, GANs) | Visual design | DALL·E, Midjourney, Stable Diffusion | (moins courant en gouvernance mais utile en communication ou support visuel aux décisions) |
AI for augmented governance is therefore often a combination of several types of AI.
For example:
- Generating a governance report = RPA + NLG + NLP.
- Summarizing strategic workshop feedback = LLM + NLP.
- Monitoring continuous compliance = expert systems + supervised ML.
- Simulating a merger scenario = probabilistic modeling + rules engine.
So if you think that generative AI is a gimmick with little value once the “magic” wears off, you can see that by combining it with other things, you can achieve some pretty interesting results.
The risks of AI-driven governance
When discussing technology, it is impossible to avoid the subject of governance, and AI applied to governance is no exception if we want it to function optimally and, crucially, if we want its users to trust it.
We can tolerate going back over AI’s work when the text it has generated for a marketing campaign is unsatisfactory, but when it comes to augmented governance, there is zero tolerance for risk.
Black boxes and lack of transparency
As predictive and prescriptive models become more powerful, their operation relies on massive volumes of parameters, correlations, and statistical model optimization to such an extent that even their designers cannot always explain why a recommendation was made ([FR]Anthropic lost control of its AI and does not know how it works).
This “black box” phenomenon causes a loss of transparency in decision-making processes. If a governance committee validates a decision based on an algorithmic analysis whose foundations and margins of error they do not understand, the trust of the entire business is at risk and the principle of executive accountability is set aside.
Moreover, the European AI Act insists on the mandatory auditability of models used in contexts with a high social, environmental, or economic impact (The European AI Act for dummies).
Algorithmic bias
AI models only “learn””” by being trained on existing data. When this happens, they logically reproduce pre-existing biases found in the data, without any malicious intent. This can affect recruitment, risk identification, prioritization of issues, or even the attention paid to certain stakeholders.
In other words, if your business has only ever promoted white men over the age of 50 to management positions and women have been discriminated against in hiring, don’t expect AI to suddenly help you become more inclusive. Worse, it may even reinforce existing behaviors. To avoid this, you will need to train it on specially generated data sets that show it the behavior you would like it to exhibit, and under no circumstances on your historical data.
Disengagement and dilution of responsibility
When a model provides a credible answer, there is a strong temptation to follow its result without questioning it. This is a form of “passive delegation,” often unconscious, which occurs particularly when the tool produces elements that are a priori rational and well presented.
The more effective a tool is in form, the more it produces a cognitive halo: a credible form prevents us from questioning questionable content.
By placing blind trust in these systems without taking a critical look at them, we reduce governance to a validation chamber, which is what it was criticized for being before. Only the source has changed.
If AI helps collective intelligence, collective intelligence also helps AI.
Towards enhanced and humanized governance
The real lever for transforming governance therefore lies not in technology itself, but in how it is used to promote responsible, humane, and deliberative governance, if not democratic governance.
It is not, and must not be, AI that makes decisions, but rather an infrastructure that informs, questions, and fuels debate.
AI is a tool to assist, not to delegate
AI used in the context of augmented governance must remain a tool for highlighting, synthesizing, contextualizing and putting things into perspective, without ever reducing complexity to a single answer.
Generative AI can produce a summary of a multitude of reports, but it is up to the collective to debate them. Descriptive AI can identify a trend, but it is up to governance to decide whether it is a priority.
Augmented governance speeds up the preparation of work, but not its execution: above all, it is expected to be more lucid, more substantiated, and more reflective.
Restoring collective intelligence through AI, without abdicating responsibility
If used correctly, AI can reintroduce quality into the debate, even where decision-making processes have caused it to disappear.
It can highlight contradictions, propose alternative scenarios that no one had spontaneously thought of, and provide a basis for discussion around several possible paths, but never to decide for us. It equips us to compare perspectives but leaves us in control of the final decision.
This requires a strong engagement on the part of leaders: not to give in to the easy solution, not to abdicate in the face of powerful models, but, on the contrary, to debate, question, and arbitrate in good conscience.
AI will never do the political work for them and will never have the soft skills to do so.
So contrary to certain fears, AI does not spell the end of collective intelligence. On the contrary, it gives it new meaning (Does AI spell the end of collective intelligence?) and, in my opinion, in order to implement augmented governance, the first task is to focus on collective intelligence and AI governance in this context before thinking about technology.
Bottom line
AI will not save corporate governance if we expect it to be a miracle solution. However, if we use it as a systemic lever to structure and support a renewed commitment to governance, it will be very useful.
Conversely, if it fails to reconnect with reality, refuses to see complexity and accept uncertainty, governance will become nothing more than a game of procedures, committees and rules.
But with the right attitudes and a clear vision of its role, it can once again become what it should never have ceased to be: a demanding, lucid, and humane way of making decisions together.
In any case, AI will not transform governance if the latter does not do the necessary work on itself to know what it wants to become, why, and how. This is a task that requires a mindset focused on collective intelligence, which will always precede technology.
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