While AI continues to dominate headlines, reshaping how industries think about the future, there’s another “A” word that’s quietly delivering transformation in the present: automation.
Automation is advertisers’ great underused asset. Savvy teams have been putting it to work to shape how campaigns are planned, launched, measured, and optimized – not just as a time-saver or cost-cutter, but as a force multiplier. And while today’s zeitgeist tends to lump automation and AI together, they’re not interchangeable. AI can power automation, but automation doesn’t require AI to be valuable–or transformative.
In fact, some of the most effective automations in use today rely on adhering to pre-determined rules that don’t require machines to do anything. Many tasks in advertising need precision and predictability, and can do without the unexpected flourishes or creative tangents that sometimes come with generative AI (GenAI).
For example, we might take it for granted, but a fundamental innovation of programmatic advertising is automation–making it possible to advertise to audiences at scale with lower transactional and logistical complexity in a complex media landscape.
The final mile of campaign execution still relies heavily on manual setup, trafficking, and QA – ad operations work highly susceptible to errors that can lead to wasted media spend, make-goods, or flawed measurement. Automation helps reduce those risks and improve efficiency: APIs enable programmatic control over workflows like IO ingestion, trafficking, and data sync across DSPs, ad servers, and measurement tools. Robotic process automation (RPA) can step in where robust APIs are lacking, using headless bots or scripted macros to configure placements or enforce naming conventions inside user interfaces and forms.
Nearly 78% of agencies said they would look to automate manual tasks to drive cost and operational efficiencies, according to a Forrester Consulting study commissioned by Amazon Ads. There’s broad recognition that thoughtful automation makes systems built on human expertise speedier and less error-prone, freeing teams from the drudgery of repetitive tasks so they can focus on the strategic, creative, and analytical work that drives growth.
You don’t need to wait on a future release–you can and should use automation now.
Here’s the right playbook for embracing automation today
Audit the automation you already have
Too many teams overlook the automation features embedded in the tools they already use. Most major ad platforms offer rule-based automation that can handle common tasks like ad scheduling, dynamic budget allocation and pacing, and recurring report generation without manual intervention. Yet it’s still common to see teams typing in targeting lists, manually naming creatives and placements, or rebuilding reports from scratch in spreadsheets or slide decks.
To get started with automation, take stock of the capabilities already at your disposal, educate your teams on how to use them, and ensure you’re exploiting their full potential.
Identify where automation can bring more value
Once you’re taking advantage of automation that’s already paid for, look for areas where it could create even more value.
Begin with a solid understanding of how work really gets done – across tools, teams, and timelines – to root out repetition, errors, and bottlenecks and assess whether they can be improved through automation. Is your team logging in and out of a warren of different platforms to coordinate creative rotations, audiences, and bidding strategies? Do last-minute launches always augur a pastiche of mismatched names in dashboards? How much time is expended on discrepancies or delays? Does reporting require orchestrating a cottage industry of stakeholders?
Armed with that diagnosis, you can evaluate the trade-offs of partnering, buying, or building. Start by communicating pain points to current vendors to understand whether there are solutions you can already pilot or whether they’d be willing to create one for you Next, explore new technologies or partners with built-in capabilities that can automate away your pain. If you have technical resources and important needs unmet by the marketplace, consider whether to develop your own internal tools. Whatever path you take, make sure to measure the benefits so you can demonstrate tangible business value.
Decide what roles GenAI should play in automation
Forrester predicts that fewer than 1% of core business processes will be orchestrated by GenAI in the near term. But over the long run, it will undoubtedly catalyze further automation in more complex advertising workflows that require creativity and expert judgment.
The latest agentic models–capable of executing multi-step decisions independently–are best understood as copilots, not autopilots: tools that can quickly discern patterns and accelerate tasks to inform the decisions of experienced practitioners.
When you’re ready to consider leveraging GenAI to further automate your workflows, avoid common pitfalls by putting the following principles to work:
- Define clear guardrails for AI behavior. GenAI can be a wildcard, so give it structure. Use structured prompts to ensure copy aligns with brand tone or regulatory language. Provide templates to control your taxonomies. Ground AI-generated reports with plenty of previous examples and reliable data sources. Clear constraints make GenAI more reliable.
- Prioritize low risk use cases first. Start where the stakes are low – i.e. where missteps won’t be expensive, impact customers, or damage your brand – and where human review is built-in. Multi-modal models can help you draft briefs for internal use and training, create early-stage mockups for use in testing, or scan campaign setup to flag inconsistencies – accelerating work without introducing unnecessary risk.
- Design human–AI collaboration intentionally. Don’t try to replace your experts–elevate them! Sure, GenAI can rapidly iterate on media plans or flag underperforming creatives, but your seasoned strategists and creatives should interpret those signals and make the final calls. Human experts have their own pattern-matching algorithms, layered with a nuanced understanding of stakeholders, competing priorities, and information outside of models’ context windows. Build your workflows so humans review and adapt AI outputs.
- Foster user trust and set expectations. Your teams need to know when AI is being used—to automate setup, suggest targeting, manage pacing, reallocate budget, explain performance—but also what its limitations are, when humans are expected to review or intervene, and how to do so. Summaries and dashboards should clearly indicate when they’re AI-generated and link to source data. And training and onboarding is essential to create transparency, build trust, and drive adoption.
You Don’t Need to Wait on AI to Get More Out of Automation
Bleeding-edge AI isn’t the only path to better performance.
Forward-thinking organizations aren’t deferring automation until their GenAI capabilities mature—they’re embracing it now to reduce manual error, improve efficiency, and free up talent for high-impact work that drives long-term results. And when the time is right, they’ll augment that foundation with AI.
Before investing in AI pilots, ask a more foundational question: Are we making full use of the automation that’s already within reach?