AI is being positioned as the magic bullet for nearly every problem in advertising. You’ve probably heard claims like “Our AI understands your audience better than even the best analyst or “No bias. Just machine logic.” As companies rush to include AI in their product suite, alarm bells should signal when you hear these phrases in investment pitches, keynote speeches, and sales presentations. As a software engineer, I work closely with backend systems and see the promise and the downside of AI. If used correctly, AI sharpens and hones your skills but there are several critical factors like accuracy, bias, data quality, and privacy that need to be evaluated by human intelligence.
From an engineering perspective, it’s a highly complex situation but there are a few things that I’ve learned when it comes to AI in ad tech. Here are my top tips to help you separate the reality from the hype.
AI Augments, Not Replaces Human Intelligence in Ad Tech
Intelligent algorithms can optimize bids and budget but you need human input for context. Human knowledge in conjunction with AI’s intelligence, avoids stale strategies or campaigns over-optimizing short-term outcomes. To streamline and enhance campaigns with AI tools, you will need to set clear strategic guardrails and refresh them no less than quarterly.
Along with AI’s performance analysis and insights into market shifts, human judgment needs to drive top-level decisions like campaign goals, targeting criteria, and budget allocation. This ensures that AI aligns with changing brand and market goals.
The Good & the Bad of AI in Ad Tech
At its core, Ad Tech is a highly evolving and dynamic discipline and because our products interact with the general public, we need to be sensitive issues like data, privacy, and security. AI certainly adds value, but being cautious to its limitations will help avoid misuse.
Garbage in, garbage out still holds true
Poor quality, biased, or incomplete data results in flawed AI predictions or automated decisions. Remember that AI merely replicates insights without regard to whether the data is erroneous.
To avoid this pitfall, invest in regular data hygiene processes like audits, deduplication, and diversity checks. If you haven’t already, designate a data quality steward or team to conduct monthly reviews on training and decision-making datasets. Be sure to monitor data representation across demographic and behavioral segments to remove potential bias.
Combatting bias in AI Models
An AI model’s cutting-edge and sophisticated engineering often conceals embedded biases. Identifying and addressing biases is a complex challenge, especially in sensitive areas like targeted advertising or content recommendations, where it’s crucial not to single out any particular group.
To create fairness and transparency, engineers need to use a comprehensive bias detection system, conduct regular fairness audits, provide detailed explainability reports, and have experts rigorously review our models. Model cards – short, plain-language “nutrition labels” for AI systems – give clear documentation on how a model was developed, the data used, its intended purpose, and any known limitations or trade-offs. Making these model cards accessible helps non-technical stakeholders understand how decisions are made. Models should be re-evaluated at least every six months, or after any significant updates, to ensure their continued effectiveness and fairness.
Automating tedious tasks using AI
Campaign experts who understand AI technology and prompts can streamline programmatic tactics such as fraud detection, user segmentation, and bidding effectiveness. AI is great at reducing time-consuming tasks that once took hours to mere seconds. For instance, AI models can immediately flag teams when unusual traffic patterns occur, such as a sudden burst of clicks from a single IP range, saving budgets from unnecessary ad spend. Automating segment users into intelligent groups like, “frequent mobile video viewers” versus “occasional desktop browsers” can be done without having to create several dozen rules. When combined with a savvy user, these tools save time and help refine results.
Using AI in Engineering Workflows
More and more, I’m relying on AI coding assistants within the development IDE. I utilize Windsurf within IntelliJ to create boilerplate code, recommend optimizations, construct unit tests, and refactor old logic more effectively. Rather than displacing my role, the tools speed up eye-glazing repetitive work, clear the cognitive space for architectural decisions, and in many cases become the real-time reviewer to call out potential edge cases that I may have overlooked. The trick is to view the assistants as collaborative colleagues while reviewing the recommendations critically and matching them with established engineering standards and privacy protections.
When evaluating AI Tools, ask yourself the following Questions
It’s easy to get caught up in what’s trending and emerging in AI but there a few underlying questions that you need to ask that truly matter when it comes to innovation.
- Is AI necessary to solve our problem or are we following a trend?
- What outcomes does the AI aim to improve? Which stakeholders benefit from these improvements?
- Is the data balanced, diverse, and relevant?
- How can we ensure our models make ethical decisions?
- Which areas require human judgment and how can you build it into your process and feedback loop?
- Is the tool designed to optimize compute usage and will it become more sustainable over time?
Collaborating with AI is the future of Ad tech
There’s no doubt that AI is already embedded into the ad tech ecosystem but it should not replace human expertise. Instead, it should augment skills and increase productivity. Ad tech is continually evolving, and AI works best when collaborating with human judgment. Let’s stop chasing hype and start using AI intentionally, transparently, and aligned with our goals.
