2025 is shaping up to be the year of AI — or is it? Every martech vendor seems to be slapping an “AI-powered” label on their products, promising everything from hyper-personalization to predictive insights. Let’s be real: How much of this is genuine AI innovation and how much is just rebranded automation? It’s a question every marketer should be asking.
Beyond the buzzwords: What real AI in martech looks like
We’ve all been promised the holy grail by automation tools. Unfortunately, the “real-time personalization” and “predictive insights” often didn’t deliver on the promise. Whereas technology in the past was more based on static logic, true AI adapts dynamically, learning from behavioral signals, context and intent. It’s about moving beyond static “if-then” logic to something truly intelligent.
As a marketer, you’ll have to forgive me for being skeptical about the flood of AI-powered solutions. Not every tool shouting “AI!” actually has it. Many are just dressing up old automation in new clothes. The key is to discern the real deal from the imposters.
6 key considerations when evaluating AI in martech
Here’s how I evaluate if I’m looking at actual AI or old logic with a new label and higher pricing.
1. Look for adaptive learning, not just rule-based automation
What to look for: Martech platforms may combine rule-based workflows with machine learning components, and that’s OK. The key is whether the system improves its outputs over time based on new data. For example, look for machine learning models that adjust based on data such as behavioral patterns.
What to watch out for: The solution follows pre-set rules, such as “if X happens, do Y,” without evolving.
Questions to ask:
- Does the system dynamically retrain its models or operate on fixed rules? If so, how often?
- Does the system learn from behavioral data or just follow set workflows?
2. Ask for transparency on AI models and techniques
What to look for: Not every platform needs deep learning or reinforcement learning. You’ll want clarity on what type of AI it uses and, more importantly, why. Typical AI will use machine learning (ML), deep learning, natural language processing (NLP) or reinforcement learning.
What to watch out for
The product relies on decision trees, workflow automation or pre-programmed logic. Again, these simpler models are not wrong and, in some cases, are the right approach for your solution. But you’ll want to understand if the new feature is labeled AI.
Questions to ask
- Does it leverage deep learning or reinforcement learning or is it rule-based?
- Can you provide details on how the model updates itself?
- Why was this approach chosen for the specific use case?
Dig deeper: All the AI that glitters isn’t martech gold
3. Understand the training methodology
What to look for: It’s all about learning. A model’s sophistication depends on its training process. You’ll want to investigate the datasets (volume and variety) that train and improve the solution’s accuracy over time.
What to watch out for: Relies on pre-configured responses without meaningful learning.
Questions to ask:
- Is it training on real-time data, synthetic data or pre-fed rule sets?
- How often is the model updated and how is feedback incorporated?
4. Look for evolving insights, not static dashboards
What to look for: True AI-driven systems have evolving recommendations and provide insights that shift as behaviors change (e.g., predictive lead scoring that updates as new data comes in).
What to watch out for: Presents static reports and basic analytics without advanced modeling.
Questions to ask:
- Does the system improve its accuracy over time as new data comes in?
- Can it create content dynamically or does it rely on templates?
- Are the insights predictive or just descriptive?
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5. Watch for buzzwords without substance
Some vendors misuse terms like “machine learning” and “AI-driven” when using basic automation. Basic automation may get the job done — but be cautious of key red flags:
- Lack of clarity: No clear explanation of how AI functions within the product.
- No supporting evidence: Absence of technical documentation or case studies demonstrating AI-driven improvements.
- Vague understanding from the vendor: The vendor cannot clearly articulate the difference between AI and automation in their tool. (Tip: You may need to speak with someone from product marketing or engineering, as sales reps might not have this information readily available.)
6. Consult third-party sources
As with anything, don’t take the vendor’s word for it. Check independent analyst reports from reputable leaders like Gartner or Forrester Wave.
Use your network, LinkedIn groups or peer reviews for real user experiences of those who have already tried the product or solution.
Dig deeper: AI is poised to disrupt the world of martech vendors and users
Final thoughts
Many of the best martech solutions today blend automation with AI. Not every tool needs bleeding-edge AI to be valuable. But you need to know what you’re buying, especially if it comes with an increased price tag. By asking the right questions and looking for substance over marketing buzz, you can cut through the hype and choose tools that deliver.
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