With extensive expertise in artificial intelligence and machine learning, Dominic Jainy has spent his career exploring how technology can solve complex business problems. Today, he joins us to discuss a critical shift happening in the digital marketing world: the move from manual campaign management to AI-driven automation for agencies running paid social ads. He’ll explore how this technology tackles common agency pain points like team burnout and inconsistent client results, ultimately reshaping the way agencies scale and succeed.
In our conversation, we delve into the practical ways AI enhances Facebook advertising for agencies. Dominic explains how these tools move beyond static targeting to create dynamic audiences based on real-time user behavior, significantly reducing wasted ad spend. We also touch upon the automation of creative testing, a process that identifies winning ads and combats creative fatigue without overwhelming the team. Furthermore, he illuminates how continuous, real-time optimizations in budget and bidding provide a crucial speed advantage over traditional, scheduled campaign reviews. Finally, we discuss how agencies can scale their client load without sacrificing quality or control and how AI makes even small budgets work harder, concluding with a look at the future of AI in the agency landscape.
Many agencies face team burnout and unnoticed performance drops with manual campaign management. How exactly do AI tools address these operational headaches, and what metrics should an agency track to measure the improvement in efficiency and campaign stability?
Those operational headaches are precisely why AI is becoming so essential. The core issue with manual management is that it’s relentless. Your team has to constantly watch performance, and even the most skilled media buyer can’t be everywhere at once, 24/7. That’s where AI steps in. It acts as a tireless digital team member, monitoring ads around the clock and processing performance signals continuously. It spots trends and potential issues long before a person doing weekly check-ins would, preventing those performance drops that go unnoticed for too long. To measure the impact, an agency should look at the stability of key metrics like CPA or ROAS over time; you’ll see fewer drastic dips. You can also track the reduction in time your team spends on repetitive, manual tweaks versus strategic planning. The goal is to see campaign performance become steadier while your team’s focus shifts from reactive fixes to proactive strategy.
Traditional Facebook targeting often relies on preset interests that can become outdated. How do AI tools refine audiences based on real-time user interactions, and can you walk us through how this reduces wasted ad spend for a typical client?
This is one of the most powerful shifts AI brings to the table. Traditional targeting is based on assumptions—you pick interests you think your audience has. But user behavior evolves rapidly. What worked last month might not work today. Instead of this static approach, AI tools watch how real users actually interact with the ads in real-time. The system learns from clicks, conversions, and engagement, continuously refining the audience based on actual performance data, not just preset categories. For a typical client, this means the ad spend is dynamically allocated toward audience segments that are actively converting. If a particular subset of an audience isn’t responding, the AI reduces spend there automatically, preventing you from throwing money at low-quality traffic. It’s a move from educated guessing to data-driven adaptation, which makes every dollar of ad spend work significantly harder.
Creative fatigue is a major issue, yet manual testing is time-consuming. How does an AI platform automate the process of testing ad creatives? Could you describe the steps it takes to identify and scale winning ads while pausing underperformers?
Creative fatigue is a constant battle, and manual testing is a bottleneck. An agency simply doesn’t have the bandwidth to test every headline, image, and video combination effectively. An AI platform completely streamlines this. It starts by allowing you to run multiple creative variations simultaneously without a complicated manual setup. As the ads run, the AI analyzes performance data in real time. When it sees a creative that is showing early promise—getting better engagement or conversions—it will begin to allocate more budget toward it to validate that success. Conversely, when a creative is underperforming, the system will automatically pause it, stopping the spend before it becomes a significant waste. This creates a continuous feedback loop where the best ideas are identified and scaled quickly, allowing agencies to test more and learn faster without increasing their workload.
Manual campaign reviews often happen on a schedule, while ad performance can change in hours. What specific real-time optimizations, like budget shifting or bid adjustments, do AI tools make, and what impact does this speed have on overall campaign ROI?
The difference between scheduled reviews and real-time optimization is like the difference between reacting to yesterday’s news and making decisions as events unfold. Facebook ad performance is incredibly volatile; it can change in hours due to competitor actions, audience sentiment, or platform algorithm updates. An AI tool operates in this real-time environment. It’s constantly making micro-adjustments. For example, it can automatically shift budgets from an underperforming ad set to a better-performing one within the same campaign, maximizing the use of the daily budget. It will also reduce spend or adjust bids when it detects that conversion rates are slowing down. This speed is a massive competitive advantage. It ensures that money is always flowing to what’s working right now, which directly leads to stronger, more consistent ROI than campaigns that rely on delayed, manual decisions.
As agencies scale, managing multiple client accounts manually can lead to errors. How do AI tools centralize campaign management while still allowing agency experts to maintain strategic control? What’s the ideal balance between automation and human oversight?
Scaling is often where manual processes begin to break down into chaos. With more clients comes more complexity, and the risk of small mistakes—a missed setting, a budget allocated to the wrong campaign—grows exponentially. AI tools like Plai.io solve this by centralizing campaign management into a single dashboard. You can apply consistent optimization rules across multiple accounts, which reduces repetitive work and ensures a baseline of quality and performance. The ideal balance is to let the AI handle the tactical, moment-to-moment execution. It’s brilliant at the day-to-day bid adjustments and budget shifts. However, the agency expert must always maintain strategic control. Your team sets the goals, defines the overall strategy, chooses the creative direction, and interprets the results for the client. The AI is a powerful tool for execution, but it’s the human expertise that provides the ‘why’ behind the campaign.
For agencies with smaller-budget clients, every dollar counts. In what specific ways do AI-driven tools help maximize a limited budget? Please provide an example of how automation prevents wasted spend on ads or audiences that are not converting.
This is a scenario where AI tools can be particularly impactful. When a budget is tight, there is absolutely no room for waste. AI-driven tools help by being relentlessly focused on performance. They don’t get attached to a particular ad or audience; they follow the data. A perfect example is an ad that starts strong but begins to fatigue. Manually, an agency might not catch this dip for a day or two, and in that time, precious budget is spent on an ad that is no longer converting effectively. An AI tool would detect the drop in performance almost instantly and automatically pause the ad or reduce its budget, reallocating those funds to another creative that is performing better. This prevents money from being spent on what was working and ensures it’s always directed toward what is working, stretching every dollar of a small budget as far as it can possibly go.
What is your forecast for the future of AI in agency ad management?
Looking ahead, I see AI-driven tools becoming a practical necessity for agencies, not just a luxury or a competitive edge. The ad environment is only getting faster and more data-intensive. Agencies that try to manage everything manually will struggle to keep up. The future isn’t about AI replacing agency experts, but rather about forming a powerful partnership. The most successful agencies will be those that seamlessly combine the strategic, creative, and client-facing expertise of their human teams with the speed, precision, and data-processing power of AI-powered execution. They won’t be working longer or harder; they will simply be working smarter and delivering stronger, more consistent results for their clients.