A little education re: AI...learning what to believe and what may be less believable!  Philip Jay LeNoble, Ph.D.

Commentary

The Hidden Risk In AI-Driven Marketing: Not Knowing the Data Behind the Model

• by Danyelle Horrillo , 4 hours ago

AI is involved in every step of the media process, generating output faster than a human ever could. The risk isn’t AI itself; it’s how much we rely on it without understanding the data behind the models shaping decisions.

What “AI Data” Actually Means

When marketers hear “AI uses data,” it’s easy to assume it means campaign results alone. It’s far more layered than that.

Most AI-driven tools rely on some combination of:

• Training data: Historical datasets used to teach the model, often a mix of public, platform-level, or proprietary datasets.
• Input data: Briefs, targeting parameters, budgets, performance metrics, and prompts.
• Reference or enrichment data: Benchmarks, modeled audiences, third-party datasets, or performance trends.
• Feedback loops: Past results that influence future recommendations, sometimes reinforcing old patterns.

Every tool has its own nuances, and vendors vary in transparency levels.

Why This Matters

Knowing AI data sources isn’t a technical exercise for the ops team; it has implications for how media strategies are built and optimized. AI models learn from patterns, and those patterns shape strategy. If the underlying data over-represents certain platforms, formats, or attribution models, recommendations will naturally skew in that direction, even if it’s not the best strategic move. The output may look like “optimization,” even when it no longer aligns with business goals.

Optimization Without Context Is Still Optimization

AI is good at optimizing toward whatever it’s instructed to value. But KPIs are incomplete without human interception:

• CTR doesn’t equal brand impact.
• Cheap conversions don’t equal quality.
• Short-term efficiency doesn’t equal long-term growth.

Without context, incrementality, offline performance, seasonality, or real-world constraints, AI can confidently optimize in the wrong direction, without understanding why those decisions matter.

Bad Data Gets Scaled         

AI can scale bad data. Inconsistent naming conventions, incomplete UTMs, blended attribution models, and platform-analytics gaps will still produce an output: “garbage in, garbage out faster and at scale.”

The Transparency Gap with AI-Powered Tools

Many AI-powered tools operate as black boxes - recommendations without rationale, or insights without clarity into inputs. There isn’t always a clear breakdown of training data sources, data influencing results, or how frequently models are updated. Yet marketers are expected to act on and defend those outputs.

What We as Marketers Should Be Asking About AI

Knowing the data sources is part of the job. Ask vendors:

• What data sources train this model?
• How are attribution methodologies handled?
• Can recommendations be audited?
• How frequently is the model updated?
• Is there any data intentionally excluded?
• Can we customize this data used?

Vague answers are a red flag.

AI Is An Assistant, Not A Strategist

AI is a powerful tool, but it’s still a tool. AI processes data, marketers provide meaning. Human judgement sets the objectives, applies business context, and pressure tests recommendations.

Understanding where AI gets its data is a strategic responsibility. Marketers who get ahead won’t be the ones who use AI blindly. They’ll be the ones who question it, validate it, and use it intentionally, knowing the data behind the model.