In the previous post, we explored how marketing agencies are leveraging Artificial Intelligence across five functions: content creation, campaign optimization, personalization, data analysis, and workflow automation.

In this post, we’ll break down the most commonly used tools, like Jasper, Surfer SEO, and Midjourney, and show how agencies integrate them into real workflows. We’ll also look at the difference between platform-native tools (like Meta’s Advantage+ or Google’s Performance Max) and third-party solutions, including where each shines, where they fall short, and why some agencies are starting to build their own.

Please note that the list of tools is to give you an overview of the currently available tools. If you are looking to adapt AI and automation, it is important to set clear goals first to identify what exactly should be improved or automated. A strategic approach to AI tool selection leads to real results, not just more complexity.

1. Tools for AI-Powered Content Creation and Ideation

Most tools used for content creation are third-party solutions, giving agencies flexibility across channels. In contrast to platform-native tools, like Meta’s Advantage+ for ad creatives, these tools aren’t tied to a single advertising ecosystem. That makes them useful for cross-platform workflows, where content needs to be repurposed or adapted to different formats.

For LLMs and writing assistance, general-purpose tools like ChatGPT are widely used for brainstorming, drafting, and idea generation. More marketing-focused tools like Jasper, Copy.ai, and Writesonic offer templates tailored to ads, blogs, and social posts. All of them are third-party, subscription-based products designed for speed and scale.

In SEO and content optimization, third-party tools like Surfer SEO and Clearscope provide keyword guidance and content scoring based on what’s ranking in search. Larger suites like SEMrush now incorporate their own AI features for content briefs and rewriting.

For visual content, Canva (with its Magic Studio features) is a go-to for fast social media and presentation design, while AI image generators like Midjourney and Adobe Firefly offer more creative control over visuals. All of these are independent tools, no dominant platform-native option exists here yet.

For video creation, tools like Synthesia and HeyGen use AI avatars to create talking-head videos without cameras or studios. Lumen5 helps automate short-form content from text. Again, these are third-party tools, often used to support top-of-funnel content or explainer videos.

Finally, for writing enhancement, Grammarly continues to be a staple. It now offers generative rewriting suggestions in addition to grammar and tone checking.

General Strengths: These tools significantly accelerate content generation, help overcome writer's block with diverse ideas, and assist in optimizing content for search engines and specific audiences. The ability to quickly generate visual and video content also democratizes multimedia production.

Common Challenges: A primary concern is that AI-generated content can sometimes be generic, lack nuance, or require substantial human editing to meet quality and brand standards. Accuracy of the information presented by AI can also be an issue, necessitating careful fact-checking. There's also the risk of content feeling "AI-written" if not carefully refined. While many tools offer free tiers or trials, the most powerful features often come with significant subscription costs. Finally, ethical considerations around image and video generation, such as intellectual property rights and the potential for creating deepfakes, are ongoing discussions in the industry.

2. Tools for AI in Advertising and Campaign Optimization

Like for content creation, AI tools for advertising fall into two broad categories: platform-native tools, which are built into ad networks like Google and Meta, and third-party platforms, which help agencies manage, optimize, or scale across channels.

Platform-native AI is increasingly dominant, especially in performance marketing. Google Ads uses AI for campaign types like Performance Max and features like Smart Bidding. Meta Ads employs similar AI models for creative testing, audience segmentation, and budget optimization. These native tools have access to user behavior data that third-party tools don’t, which gives them an edge in real-time optimization.

Nevertheless, there are many use cases for third-party tools in this space. For creative generation and optimization, tools like AdCreative.ai focus on producing large volumes of ad variations and scoring them for predicted performance. Albert.ai takes this further with autonomous campaign management, adjusting targeting, creatives, and budget allocation across multiple channels.

Cross-channel tools such as Smartly.io and The Trade Desk help agencies manage campaigns across multiple ad platforms. Smartly.io is widely used for social media campaigns, offering creative automation and testing. The Trade Desk, primarily for programmatic display and video ads, uses its Kokai AI engine to optimize media buying based on real-time performance signals.

For language optimization, Phrasee and Persado generate and test ad copy variations using models trained on past campaign performance. These tools are typically used by larger teams focused on performance at scale.

Strengths: These tools allow for faster iteration, automated A/B testing, and improved return on ad spend (ROAS through more relevant targeting and dynamic creative. Especially with native tools, performance gains can be substantial due to deeper data integration.

Challenges: Native AI tools often operate as black boxes which means agencies may not fully understand how decisions are made. Optimization depends heavily on the quality and volume of historical data, and misaligned goals or poor data hygiene can lead to underperformance. Third-party tools may struggle to match the depth of data that platforms like Google and Meta have access to. Lastly, enterprise-level platforms can be cost-prohibitive, and human oversight remains key for strategy, brand safety, and ethical concerns.

3. Tools for AI-Driven Personalization

This segment is largely dominated by third-party tools, as most personalization workflows rely on integrating data across CRM, website, and marketing channels to achieve high levels of personalization. This scope is broader than what ad platforms alone can offer.

CRM and Marketing Automation platforms with AI capabilities form the backbone of many personalization strategies. Tools like HubSpot and Salesforce (via Einstein AI and Marketing Cloud) are widely used for personalized email flows, lead scoring, dynamic content, and behavior-based segmentation. Marketo (Adobe) also supports advanced audience targeting and cross-channel personalization.

Personalization engines such as Dynamic Yield focus on website and app-level personalization, including real-time product recommendations, behavioral targeting, and A/B testing at scale.

Conversational AI tools like Drift, ManyChat, and Tidio AI enhance personalization through chat interfaces, often triggered by user behavior or purchase history, and provide real-time assistance or guided sales flows.

For customer journey orchestration, Ortto AI offers tools to create branching experiences based on user actions, combining email, on-site messaging, and other touchpoints into unified, personalized flows.

Strengths: These tools allow agencies to go beyond rule-based segmentation, using real-time behavioral and demographic data to tailor communications and experiences. AI models improve audience targeting, timing, and message selection across the customer lifecycle. Chatbots increase responsiveness and can capture data that feeds into broader personalization systems.

Challenges: Effective personalization requires a clean, unified customer data layer which is often a bottleneck. Missteps in targeting can come off as intrusive, and data privacy compliance (e.g., GDPR, CCPA) must be tightly managed. Additionally, implementation often requires close coordination between marketing, data, and development teams, especially for tools with deeper orchestration or website integration.

4. Tools for AI in Data Analysis and Insights

In this category, platform-native tools play a key role again, especially those embedded in analytics ecosystems like Google or Microsoft, because they’re often closest to the raw data and offer tight integration with ad platforms and web traffic sources. But third-party tools expand capabilities, especially when custom dashboards, deeper analysis, or more flexible data wrangling are needed.

Web & Marketing Analytics platforms with AI like Google Analytics have been adding features like anomaly detection, predictive metrics, and automated insights. These tools are powerful for analyzing site behavior, attribution paths, and campaign performance. HubSpot Analytics adds value on the CRM side, especially for inbound marketing.

Business Intelligence (BI) platforms such as Tableau (often paired with Salesforce’s Einstein AI), Microsoft Power BI, Qlik Sense, and Domo bring AI into more flexible reporting, data blending, and interactive exploration. They often also support natural language querying and smart visualizations that surface hidden patterns

For specialized AI analytics and research, tools like Metabase and Rows (an AI-enhanced spreadsheet) offer lightweight ways to analyze and share insights quickly. Data prep tools like Trifacta Wrangler (Alteryx Designer Cloud) help clean and shape messy data before analysis. And for consumer research, tools like Quantilope and GWI Spark use AI to automate survey insights and audience profiling.

Strengths: AI tools here excel at sifting through large datasets quickly, identifying trends, forecasting outcomes, and automating reports. Native tools tend to offer seamless integration with data sources, while third-party platforms often provide greater customization and cross-source analysis.

Challenges: Poor data quality limits the value of these tools as they can’t fix bad inputs. There’s also a risk of users accepting AI-generated trends at face value without context. Many platforms require time and expertise to set up, and enterprise tools often come with high costs. Finally, data governance and compliance are essential, especially when customer data is involved.

5. Tools for AI-Powered Workflow Automation

This category is broad and includes platform-native automation tools, third-party integrators, and full-blown marketing automation suites. Together, they help agencies reduce manual work and scale processes efficiently. The right mix depends on whether you’re automating internal workflows, marketing campaigns, or customer touchpoints.

Integration and process automation tools like Zapier are go-to solutions for stitching together apps with minimal code. With growing AI support (e.g. natural language flow creation), these tools now go beyond triggers and actions to offer intelligent automation logic.

Marketing automation platforms with AI, such as HubSpot, Adobe Marketo, and ActiveCampaign, bring AI into lead scoring, personalization, and campaign orchestration. These are especially useful for teams looking to automate the full customer journey.

Project management tools with AI, including ClickUp (with ClickUp Brain) and Asana, are starting to support smart task suggestions, content generation, and workflow recommendations, providing a boost for teams managing complex marketing operations.

Social media automation platforms are leaning hard into AI. Tools like Hootsuite (with OwlyWriter AI), Buffer, FeedHive, Lately.ai, and Sprout Social now generate post copy, recommend hashtags, and analyze performance, cutting down content workload and helping teams stay consistent.

Strengths: These tools free up time by automating repetitive tasks across marketing, content, and project workflows. AI brings added intelligence to automation by enabling smarter lead handling, content creation, and campaign triggers. Teams can scale without necessarily increasing headcount.

Challenges: The biggest friction is often in setup. Building workflows, ensuring data is flowing correctly, and managing integrations can become complex. There’s also a risk of over-automation leading to robotic experiences or unintended actions. API reliability, tool fragmentation, and escalating subscription costs can also become issues over time.

Conclusion

The AI marketing stack is evolving fast, offering tools for almost every part of the workflow, from content creation to analytics to automation. But more tools doesn’t always mean more value.

As we’ve seen, there are plenty of powerful options already making a big impact. Still, the abundance of choices can be overwhelming. Before jumping into any new platform, it’s essential to define the specific outcome you’re aiming for. What process needs improving? What task can be automated or simplified? Starting with clear goals makes it much easier to pick tools that truly move the needle.

Being strategic about tool adoption, choosing the right solution for the right problem, at the right time, is how agencies can get the most out of AI.

If you’re looking to streamline your marketing workflows or make sense of the tool landscape, I’m developing solutions to help and also offer consulting for agencies. Feel free to reach out if that sounds useful.