Data teams need to be able to set up data pipelines that are as fast as business and make it easier for AI algorithms to find the data they need in order for AI to work in advertising.
We can all agree that a nice guitar is cool, but without the amp modelers (the systems you use), effects pedals (optimizing algorithms), or the years of work you put into every note, playing live wouldn’t be as real. The same is true for ads that use AI.
You need AI systems that can do things like the following to make a cutting-edge marketing product:
- Making pictures for artistic reasons
- Video synthesis for dynamic content
- Personalization in real time based on how the user interacts with the site
You need to connect with people on many levels. For example, the AI shouldn’t just make a headline; it should also interact with the image it made, fit the user’s context, and be given in the right format. All of this should happen before the site loads at scale without causing problems with your infrastructure.
Most companies are still trying to figure out how to keep their data clean, or worse, they don’t want to use the newest technology because they are too scared to take risks. AI is being used on data that is: kept on more than one system – not very accurate – updated on timescales that are measured in days instead of seconds
It’s like asking me to make a cake for my wife’s birthday. Is it possible? Of course. Is it a good idea? Not at all.
What This Means for You
If you work in AdTech or are making AI-powered ad systems, this is what really matters:
- You put money and time into the data infrastructure first. Before you can connect to those great models, make sure your data pipelines are working.
- Need for speed: Your data processing and business decisions need to be as fast as each other. Your AI can’t just be a nice piece of furniture. You might not be able to avoid using GPU-accelerated processing on a large scale, so keep an eye on those cloud costs.
- Diversify: To win, you might need to know how to combine different AI technologies into workflows that are easy for customers to use, instead of just using one model or provider.
- Don’t just hire data scientists; also hire data engineers. You need more than just people who can train models; you also need people who can make systems that work. The ratio needs to be at least three to one. If you really like cursors, maybe 2:1.
Since this is about data – being redundant is not a crime – AI needs a lot of data infrastructure to work well in advertising. Not the models. Not the rules. The pipes. These kinds of things don’t come up as primtetime often on tech blogs, in cringe-worthy LinkedIn posts, or at AI conferences where the models take center stage.
To make this more tangible –
- Continuous Data Quality Management: Data drift and data staleness make AI less effective in ad tech. Put money into real-time validation, automated cleaning, and anomaly detection all along your data pipelines, not just when you first get them.
- Unified Data Layer Across All Channels:
Your AI can’t give you useful insights or personalize scale if customer interactions, campaign performance, and content metadata are all stored in different places. Make a “customer 360” or unified data layer so that every model has a complete, up-to-date view. - Governance and Security Are Table Stakes:
Your basic data design should include privacy laws (CCPA, GDPR), consent management, and audit trails. They shouldn’t be added later. If AI needs sensitive data, compliance has to be easy and automatic. Make sure your legal and privacy teams are your friends, they will help you avoid more risks than you will realize. - Data Operations:
Make sure that every data pipeline has system-wide visibility into latency, freshness, and completeness. Use alerts and dashboards to find lags and mistakes before they happen that can stop real-time ad personalization. - Flexibility for AI Experimentation: Systems that are too rigid and over-designed stop new ideas from coming up. Allow for data sandboxing, fast integration of new sources (like clickstream, contextual signals, and new partner APIs), and quick deployment of new AI experiments without having to change the way pipelines work.
- Scalability and Cost Discipline: To do real-time processing, streaming analytics, and AI-driven creative optimization, you need infrastructure that can grow with your business. Put autoscaling architectures first like managed Kubernetes, serverless data pipelines, and cloud-native connectors. To avoid huge cloud bills, always keep an eye on unit economics.
- Teams Teams Teams:
For AdTech to work, there needs to be a close feedback loop between the engineering, data science, product, and business teams. Infrastructure becomes an enabler instead of a roadblock when people share a language, goals, and operational dashboards.
This is what makes companies with strong, long-lasting AI-driven advertising systems different from those that only have cool (but short-lived) demos.