Shadow ai, why the CMOs are on the front line

Generative AI is invited to marketing practices with unprecedented speed. She writes, synthesizes, translates, automation. It produces images, optimizing emails, structure of the countryside, nourishes the CRM. However, an increasing part of these uses escape formal validation circuits. They take place outside of official tools, IT processes, even the knowledge of the CMO itself. We then talk about Shadow ai.

This phenomenon is not marginal. He is structural, diffuse, and deeply linked to the dynamics of contemporary digital marketing. And he poses a simple, but decisive question: how to supervise innovation, without stifling it?

An invisible, but very real adoption dynamic

Unlike the AI ​​deployed officially (in a CRM, a collaborative sequence or a recommendation engine), the Shadow AI takes discreet forms: a prompt in Chatgpt, a visual generated in Midjourney, a summary obtained via Claude, a automation piloted by Zapier, or even an assistant integrated with concept, Canva or Hubspot.

These uses are born as close as possible to the field: a content manager who seeks to produce faster, a traffic manager who wants to test A/B tests on a large scale, a project manager who saves time on a presentation. Nothing illegal, nothing ill -intentioned. But practices which, cumulative, become opaque and uncontrollable.

Autonomy, a key virtue of marketing teams, becomes a risk factor here if it is not accompanied.

Three types of risks not to be overlooked

1. Exposure of sensitive data

It is enough that a collaborator between a prompt containing information on a confidential campaign or on customer data in a model hosted in the United States to generate a Potential data leakage. These models sometimes store inputs. Some IA even reuse the requests to refine their results. The border between contextual aid and involuntary compromise is tenuous.

2. Brand image alteration

Contents generated automatically, badly reread or poorly calibrated can be found online in a few clicks. A clumsy wording, a visual that escapes the charter, a factual error in an email … as many discreet but harmful slippagesin a context where each point of contact weighs on the brand’s consistency.

3. Multiplication of unlikely tools

By authorizing – or by tolerant – these non -supervised initiatives, the company is found with a stack of solutions without support, without contract, without visibility IT. This technical fragmentation leads to hidden additional costs, functional duplicates, or even incompatibilities with measurement systems or regulatory requirements.

Why the CMO must act now

Marketing is both the laboratory and the AI ​​distribution vector in the company. It concentrates high impact uses, sensitive data (customers, analytics, conversions) and teams exposed to Delivery pressure. Let the Shadow have developed without a frame comes back to Delegate in the informal responsibility for innovation.

Conversely, supervising these practices does not mean returning to a rigid model. This isSupport, rationalize, integrate. To transform wild experimentation into a controlled strategic capacity.

Governance to set up

Step 1: Call the uses

• • Ask the teams: what tools do they use? What ends?
  • Identify generative tools (texts, images, prompt, SaaS assistants).
  • Remember the potentially manipulated data.

Step 2: Assess the risks

• • What models are used? Where are they hosted?
  • Are there customer or owners data in prompt?
  • Are these tools subject to the GDPR? Do they have a non-learning clause?

Step 3: Offer a supervised alternative

• • Provide validated solutions: internal APIs, models deployed locally or on controlled clouds, contracts with compliant publishers.
  • Set up a simple, clear, revisable usage charter.
  • Create a channel for dialogue between IT, legal and marketing.

Essential interfunction coordination

The CMO cannot act alone. Supervise the Shadow AI supposes a transverse work:

• • With the CTO / CDO : to audit tools, centralize requests, standardize models.
  • With the DPO : to verify the lawfulness of uses (prompt, transfer of data outside the EU).
  • With cybersecurity : To include AI in the attack surface, define preventive measures.
  • With HR : to train teams at the risk, the quality of prompt, biases and content verification.

What to do according to the size of the organization?

Startups

• • Advantage: agility, short decision circuit.
  • Risk: Massive use of unsecured external tools (freemium, unpleasant prompt).
  • Good practice: Name an AI referent in the marketing team, set up a monthly review of the tools used.

SME

• • Advantage: Quick adaptation to new IA marketing tools.
  • Risk: absence of formal governance between marketing, IT and legal.
  • Good practice: establish a quick validation grid (use, type of data, location, security), with monthly arbitration.

Large groups

• • Advantage: existence of an CIO, internal security policies, and legal services.
  • Risk: proliferation of micro-initiatives in independent subsidiaries or BU.
  • Good practice: integrate the Shadow AI in technological review processes, label the validated IA tools, integrate an AI component in brand policy.

The 8 questions that all CMO should ask

1. 1. What tools of my teams use without validation?
   2. Are sensitive data (customers, strategic, products) entered in external models?
   3. Are these models subject to the GDPR? Do they have a policy of reuse of prompt?
   4. Is AI used to produce publicly disseminated content?
   5. Do I have a list of tools validated by the CIO?
   6. Is an alert or reporting device in place in the event of an incident?
   7. Am I able to explain, document and justify IA uses in my perimeter?
   8. Are my teams trained in reasoned use and responsible for these tools?