The first thing Generative AI (GenAI) did for enterprises was not write code, but draft emails, summarize reports, and analyze data. Business users, like customers, used GenAI to automate communication, glean insights, and optimize search at volume and velocity.
This explains why marketing was the frontrunner for GenAI adoption. However, with the rampant, unchecked use of GenAI in customer-facing outreach came compliance risks to the brand’s tone of voice, ethical posture, and unintended data exposure. GenAI tools, using prediction and pattern recognition, were limited in their ability to meet standards for content consumed by end-users.
- False and plagiarized: A tech news website had to issue corrections on 41 of the 77 news stories it published using GenAI because of glaring inaccuracies and ‘not entirely original content’, effectively pausing the AI tool.
- Ethical biases and embarrassment: A Microsoft AI chatbot trained using Twitter interactions, meant to interact with Twitter users using machine learning and natural language processing, posted 95,000 overtly racist, misogynistic, and anti-Semitic tweets in less than 24 hours of its launch. Microsoft had to suspend the bot and ultimately pull the plug.
- Ungoverned actions and penalties: Air Canada paid hundreds of dollars to a passenger after its AI chatbot gave incorrect information on its bereavement pricing policy. Air Canada maintains that, the chatbot is a separate legal entity and the airline cannot be held liable – a stance that is not admissible under the US and EU’s AI regulations.
When AI interacts with customers, it speaks for the brand. As businesses outsource interactions to AI, they cannot ignore the consequences of the AI flywheel flying off the rails.
What could brand managers do to drive positive outcomes from customer-facing GenAI?
Outsource Outcomes, Not Just Interactions
If GenAI is the generative engine, Agentic AI can be the overseer. Plugged into marketing workflows, Agentic AI can enforce guidelines around tone of voice, ethical and legal considerations, and data safety, while freeing up human bandwidth to focus on more strategic tasks.
As the branch of AI that can contextually understand, reason, learn, and orchestrate an execution plan by decomposing complex tasks into subtasks, Agentic AI works within an enterprise ecosystem, pulling data from siloed knowledge systems to achieve marketing outcomes.
AI agents do this with:
- Access across the enterprise content stack: Marketing content is stored across digital asset and product information management systems. The real challenge comes with access to non-marketing-specific content stored in Shared Drives as Word documents, PowerPoint presentations, and meeting notes, which often have deeper insights into product value. AI agents need access to the entire content stack for holistic messaging.
- Integration with marketing-allied data: In most settings,marketing operates in silos with sales, product development, and delivery teams, which maintain their own databases (CRM, production data). This data can be mined for insights based on active customer feedback, expectations, and product-market fit. An AI agent must source insights into personas, customer segments, and pain points, which can only happen if the marketing-allied functions are integrated.
- Composability to orchestrate campaigns: For a hyper-personalized campaign, the AI agent will need access to disparate systems and tools to take actions and the right to execute omnichannel campaigns. Composability is key for combining separate information, data, and tech stacks into new workflows. A robust API architecture is critical for agentic marketing.
Ensuring marketing agent wherewithal requires clearly defined brand guidelines, content style guides, and a corporate-endorsed ethical policy. With Persistent, all of this is a few clicks away.
Introducing Agentic AI Marketing Assistant, Powered by AWS
Persistent’s Agentic AI marketing assistant, available as an AWS Marketplace offering, is designed to deliver on measurable marketing KPIs, with the potential to accelerate product launches up to three times faster, boost engagement by as much as 25%, and reduce the manual effort content teams require by up to 40%.
Leveraging the AWS AI stack, our Agentic AI assistant comprises:
- Intelligent knowledge bases: The agentic system has an intelligent, searchable knowledge base at its core, connecting it with the end-to-end enterprise content stack. The AI agent aggregates documentation and product websites and crawls related content via a Web Crawler connector. With Amazon S3 for vector storage, teams can build scalable, cost-effective vector stores. By applying caching and vector optimization strategies, retrieval latency is minimized, ensuring faster contextual responses within agentic marketing workflows.
- The digital conductor: Powered by Amazon Bedrock, the AI agent orchestrates everything behind the scenes, turning plain prompts into executed actions by coordinating multiple AI components, such as:
- The retriever: Understands context and relationships using semantic search. It is governed by brand guidelines and tone of voice that ensure content meets expectations. It extracts key features, value positioning, and product insights to feed into the next step.
- The composer: Converts raw insights into polished, platform-specific content using Amazon Nova Models, while staying true to brand voice and optimizing for social media platform formats. This ensures content does not sound like AI and retains a human touch. If the generated output fails predefined quality checks, the system leverages fallback models or retries with alternate prompts to ensure reliable, high-quality content delivery.
- The publishing powerhouse: Triggers a secure AWS Lambda function to authenticate for accuracy and brand guidelines, before publishing the content directly to the company’s social media handles.
Our AI marketing agents create auditable workflows, so brand managers have a trail to ensure the campaign is run according to standards and meets expectations, something that was not the case with pure-play GenAI-based content creation.
Why AWS
Building a truly autonomous agent that can retrieve knowledge, reason over it, create content, and publish requires more than just a language model. We need an orchestration method to connect retrieval, reasoning, and action seamlessly, along with security for enterprise API publishing. Utilizing best-in-class language models like Claude and Titan is essential, as is a no-code setup that allows us to focus on user experience rather than infrastructure. With Bedrock Agents, we can create secure, versionable knowledge bases from various sources without extensive development effort, ultimately enabling us to generate large-scale, high-quality, on-brand marketing content. With Bedrock Agents and AWS Lambda operating on a pay-as-you-go model, enterprises benefit from elastic scaling without the burden of always-on infrastructure, making experimentation and large-scale rollouts both cost-efficient and resilient.
Transform your marketing operations with Persistent Agentic AI Marketing Assistant, now available at the AWS Agentic AI Marketplace.
