More conversions, less routine: the role of AI in modern advertising automation

Artificial intelligence is no longer just an experimental technology – it is already changing the operational work of marketing and PPC. In this article, we will consider how AI improves the effectiveness of advertising campaigns, which business tasks should be automated first, how to build processes, and what tools to consider for integration into daily work.

What does AI bring to business in contextual advertising?

The benefits of integrating AI into PPC processes are not limited to «faster text generation». It is about a systemic change in approach:

• AI reduces the time spent on routine tasks – ad variants generation, creative adaptation to formats, basic localization, and initial screening of results are now much faster;
• AI helps to scale relevant content: instead of one or two variants, the team can test dozens, increasing the chances of finding the best one;
• analytical modules provide slices that previously required manual aggregation and time – automatic Ad-Scoring, detection of «top creatives» and anomalies in metrics speed up budget decisions;
• AI improves the accuracy of optimization of advertising rules and bidding programs by quickly processing a large array of signals.

Overview of tools

In order to build a reliable stack for campaign automation, it is important not only to know what is available on the market, but also to understand what role each tool plays in the workflow, what risks it carries, and how to integrate it into the existing pipeline.

Image generation

Midjourney

Midjourney is an independent service known for its ability to generate artistic, stylized, and detailed images from textual props. The platform is popular among designers and creatives for its deep styling options, fast iteration, and a strong community where users share their own prompts and variations.

The business benefit is the ability to quickly get several visual directions for brand concepts without the participation of a full-fledged art team. At the same time, it’s important to set clear restrictions in the brief, as Midjourney is subject to artistic interpretation and requires human validation of brand relevance and image rights.

DALL-E 3 (in ChatGPT)

DALL-E 3 in conjunction with ChatGPT allows business users to generate images right in the chat, receiving prompts to improve the promo and the ability to ask the model to «fix» the image already created. This workflow is a huge plus for marketing teams. Instead of using two separate tools, you get an interactive process of «idea → prompt → iteration» with instant adjustments.

In business use, it speeds up the creation of visual variants for A/B tests and allows you to refine the tone and composition at the same time. There are limitations – the need to control copyright and prohibited topics, as well as possible limits in tariff plans.

Leonardo.ai

Leonardo.ai positions itself as a «studio» for creating high-quality visuals with a focus on style flexibility, scalability, and model customization. The platform allows you to create both static illustrations and motion elements optimized for social media or product cards.

For business, Leonardo.ai is useful because it produces more uniform results during repeated generations (important when scaling a line of creatives) and has tools for fine-tuning/local style customization. This factor makes it a good choice for companies that plan to standardize visual approaches and maintain consistency across campaigns.

Adobe Firefly

Adobe Firefly is a generative platform from Adobe that integrates with Creative Cloud to create images, make generative edits, and get started with video and audio.

For business, the main advantage of Firefly is its tight integration with existing Adobe tools (Photoshop, Illustrator, Premiere), which speeds up the conversion of AI drafts to production quality. Firefly is also focused on commercial use with attention to licenses and access to templates. Its disadvantage is that some of the advanced features are tied to Creative Cloud subscriptions and may require additional resources to integrate into a production pipeline.

Stable Diffusion

Stable Diffusion is a family of open models that allows you to generate images locally or in the cloud with great flexibility in customization.

For businesses, this is an advantage in the form of control: you can deploy the model on your own servers, adjust it to your usage policy, and solve issues with training on your own data. Stable Diffusion is suitable for teams that want to avoid external limits and have resources for engineering support. But at the same time, you need to take into account the risks of training data validation and the need for additional filters for safe content.

Bing Image Creator

Bing Image Creator (Microsoft/Bing service) allows you to generate and edit images based on leading models and is integrated into search services and Microsoft 365.

For business users, it’s convenient: quick access through a browser/Office, easy options for creating illustrations and promotional materials, as well as security policies and Microsoft usage policies. It’s worth noting that Microsoft’s approach makes the tool useful for quick working prototypes and internal presentations, but for complex brand kits, it’s better to use highly specialized solutions.

Video generation and multimedia

Runway

Runway is a platform specializing in AI tools for video: from text-to-video generation to advanced editorial functions (workflows for background replacement, styling, and motion model generation).

Runway is suitable for production teams that want to speed up their editing cycles and get the first «set» versions of videos automatically, and then finalize them manually. The platform also offers models with high visual quality (Gen-series), which makes it useful for brands that want to test video creatives without incurring significant shooting costs. Integration into the production chain and license/attribution issues are the key technical issues in implementation.

PixVerse

Pixverse is positioned as a platform for generating high-speed video and interactive multimodal stories (text → image → video).

The service is suitable for marketers who need a large number of short videos for social networks, teasers, and dynamic versions of product demonstrations. The advantage of Pixverse is its focus on automating short video production and the possibility of real-time generation. Limitations – the quality and control over detailed scenes may require additional manual grinding.

Pika

Pika (Pika Labs / pika.art) is a tool for converting text and images into short social-ready videos with fast generation and a set of «effects» (lip-sync, Pikaffects, Pikaswaps).

For PPC campaigns, Pika is useful because it allows you to quickly test video formats on social media, create several variants for different audiences, and get the results of iterations in a time window close to the real one. Marketers should plan for human verification for localization and copyright, especially if the video uses realistic images of people or brands.

Automated creative builders

BannerBoo

BannerBoo is a tool for quickly creating HTML5 banners with ready-made templates, animations, and the ability to export them to various ad networks.

Business use includes the rapid preparation of a large number of options for display campaigns, seasonal promotions, and localized banners; the platform saves designers’ resources and significantly reduces the time from idea to placement. Limitations: very complex or brand-sensitive elements require additional design work and close integration with the company’s DAM/CI system.

Magic Studio by Canva (Canva Magic Studio)

Magic Studio is a set of AI tools within Canva that enables image and video generation, quick design adaptation, automatic translation, and stylized templates.

For businesses, it’s a great option when you need team workspaces, brand kits, and easy content localization. Many features are aimed at non-design users, which speeds up the production of marketing materials. Limitations: Advanced options are often available only in paid plans, and production-level design requires integration with external tools.

Image enhancement

Magnific.ai and similar services automate exposure correction, color correction, noise removal, and other operations that are always required when preparing stock or generated images for publication.

Business scenario – preparing a large batch of images for a catalog or large-scale banner output; this saves retouchers time and reduces costs when it comes to bringing heterogeneous sources to a single style. It’s important to keep the originals and check the results for accessibility (contrast for readability, correct color for product pages).

Ad Scoring and predictive analytics: how to automatically filter the best creatives

Ad Scoring is a way of machine ranking creatives by the probability of achieving KPIs. The model analyzes historical data (CTR, CVR, audience behavior, image features) and assigns a «score» to each option.

How to use it:

1. Collect a minimum amount of data (ad history, metrics, micro-segments of audiences).
2. Train the model or connect a SaaS solution that performs Ad Scoring.
3. Set up rules: for example, all ads with a score < X are automatically disabled; those with a score > Y receive an additional budget.
4. Conduct canary testing and only then scale.

Important! Such algorithms are strong where there is history and clean data. If the data is fragmented or incorrectly attributed, the predictions will be wrong – that’s why you need to audit your analytics before Ad Scoring.

An effective tool is Pencil, a GenAI platform for creating and testing advertising creatives that combines content generation with predictive analytics.

The platform is focused on performance marketing: it generates variations of creatives, assesses their chances of success based on a large database of media impressions, and helps prioritize testing. The platform is suitable for agencies and in-house marketing teams working with large budgets and needing to scale ads quickly.

Category	Tool	What it’s good for	Top tip
Image generation	Midjourney	Artistic, stylish concepts, quick iteration of ideas	Use for moodboards and ideas, always do fact-checking
Image generation	DALL-E 3	Interactive generation via ChatGPT, quick edits in chat	Convenient for briefs and quick edits, keep an eye on licenses
Image generation	Leonardo.ai	Style control and repeatability, good for maintaining brand integrity	Suitable for scaling visual identity
Image generation	Adobe Firefly	Integration with Creative Cloud, generation and correction	Use as part of the production chain
Image generation	Stable Diffusion	Open, customized generation – deploy on your own infrastructure	For teams with Dev resources, you need to control filters
Image generation	Bing Image Creator	Fast accessibility via Bing/Edge and Office	Ideal for prototypes and internal materials
Video	Runway	Powerful models for text-to-video, styling, and editing	Integrate into production pipeline, test on short videos
Video	PixVerse	Generate real-time/interactive video for social formats	Use for quick social tests
Video	Pika	Social media-ready videos, speech synchronization, personalized effects	Suitable for quick generation of short creatives
Creative builders	BannerBoo	HTML5 banners, templates, animations	Quick adaptation to formats, performance testing is required
Creative builders	Canva	Teamwork, brand kits, Magic Studio features	Use for quick localization and operational support of content
Scaling	Pencil	Generation and predictive ranking of creatives	Test on a fraction of the budget; compare with manual A/B
Improvements	Magnific.ai	Upscaling, removing noise, improving exposure	Final preparation before publication
Catalogs	Futuretools	Search for niche tools, comparison	Always check through free periods and internal pilots

Creative automation: practical tips for getting started with AI

Creative automation with AI is not just a matter of «pressing a button and getting a banner». For the process to work quickly and deliver the expected quality, it needs to be formalized: broken down into stages, quality criteria for each step, and clear roles.

Let’s take a closer look at each stage.

1. Briefing.

This is where all the key parameters of the future creative are laid down:

• • unique selling proposition
  • portrait of the target audience;
  • tone of voice;
  • mandatory brand elements (logos, colors, fonts, forbidden phrases);
  • technical limitations of the platform (size, file weight, video duration).

A clear brief reduces iterations. If the team creates a promo template, it should contain these parameters in a «machine-readable» form, for example, standard color markers (#hex), a list of forbidden words, and CTA templates. This allows AI to generate relevant concepts right away, rather than giving general ideas that have to be processed manually.

2. Concept generation.

At the concept generation stage, AI creates a set of initial ideas. Of course, not as a final product, but as quick sketches: 3-6 visual or text options that differ in composition, message emphasis, and CTA. It is important that at this stage, a person defines the evaluation criteria: what we consider a «winner» – a higher CTR, better CVR on the landing page, or a lower CPA. If the criteria are defined in advance, you can immediately send AI outputs to automated Ad-Scoring or conduct a short manual selection.

Example of a prompt:

«Create 4 300×250 banner concepts with the theme ‘20% discount on subscription’. Use brand colors #0044CC (primary) and #FFB400 (accent). The tone should be official and friendly. Do not use images of animals. For each concept – 1 title ≤ 30 characters, 1 description ≤ 90 characters, recommended by CTA.»

3. Adaptation to formats.

One concept is transformed into a set of artifacts for different placements:

• • size adaptation;
  • re-arranging text for mobile headlines;
  • weight optimization for banners.

Here it is useful to have a system of templates and feeds (CSV/JSON) for mass substitution, for example, prices, local CTAs, or conditional discounts. Automation can create dozens of versions based on the same concept, but be sure to keep the original and indications of which elements have been modified.

4. Preliminary testing.

Preliminary testing is necessary before launching to a wide audience. Ad-Scoring helps to select the variants with the highest potential according to the set KPIs. At the same time, it is useful to run variants through quick quality control, i.e. to check compliance with the brand guide, the absence of forbidden words, and check the face elements for copyright issues.

5. Canary launch and monitoring.

New creatives are shown to a small portion of traffic, usually 5-10%, to measure initial reactions without risking the main stream. At this stage, it is important to track basic metrics: CTR, CVR, CPA, landing page bounce rate, average time on page. If the platform allows it, you should also look at the cohort analysis for the first days of display.

6. Scaling.

Scaling starts only after clear success criteria: pragmatically, with a steady improvement in CTR or CVR on comparable traffic, for example, ≥10%, and/or a decrease in CPA, with confirmation of the statistical significance of the results. It is important to set business thresholds and automatic rules for which options will receive additional budget and at what percentage of growth in KPI achievement the expansion will occur. At the same time, you need to monitor long-term effects: whether the scale leads to audience fatigue or a decrease in quality metrics on the landing page.

To minimize post-generation edits, the practical steps are as follows:

• • store promo templates in a shared repository with versions;
  • keep a log of prompts and versions of creatives with an indication of how they performed in tests;
  • apply file naming and metadata standards for quick tracking;
  • automate basic rules checking (bulk scanning of text for prohibited phrases, checking color contrast for readability).

In addition, integrate the upload of final assets into the asset management system (DAM) so that the design team can quickly make manual adjustments if necessary.