Choosing an AI Tool Set for Content Personalization


Toptalauthors are vetted experts in their fields and write on topics in which they have demonstrated experience. All of our content is peer reviewed and validated by Toptal experts in the same field.

Today’s marketing teams are expected to provide personalized experiences. Discover how to meet customer demand with the latest developments in AI content personalization from Toptal’s Growth Marketing Practice Lead Jeff Gangemi.


Toptalauthors are vetted experts in their fields and write on topics in which they have demonstrated experience. All of our content is peer reviewed and validated by Toptal experts in the same field.
Jeff Gangemi
Growth Marketing Practice Lead
15 Years of Experience

Jeff is the Growth Marketing Practice Lead at Toptal. He holds a bachelor’s degree from Middlebury College and an MBA from Cornell University with an emphasis in leadership and innovation. Jeff has spent the past 15 years building demand generation, content marketing, and digital programs that drive meaningful transformation and growth for both internal teams and external clients. Before joining Toptal, he held senior management roles at Accenture Song, Material, and Telus International.

PREVIOUSLY AT

AccentureTelus International
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An untenable situation has taken root in marketing: B2B and B2C buyers alike increasingly expect Amazon-level e-commerce experiences with content personalized to their unique needs, preferences, and past buying and reading habits—not to mention their specific industries and functions.

That alone is a tall order, since not all firms are capitalized to compete with customer experience leaders like Amazon. Nevertheless, demand for personalization shows no signs of slowing. In its 2022 State of the Connected Customer study, Salesforce found that 73% of B2B buyers expect companies to understand their unique needs and expectations and 56% of consumers always expect personalized offers. Personalization has become table stakes.

For marketing teams, that expectation is reflected in the content a company produces. Personalizing a web page for different buyer personas or industry groups, for example, requires content including words and images, calls to action, and suggested product offerings.

From my perspective as the Growth Marketing Practice Lead at Toptal, this is where things can become unsustainable. A 2023 Adobe survey found that 66% of marketers expect demand for content to grow between five times and 20 times by 2025 and, as a result, 85% of marketing teams are under pressure to deliver more content and campaigns than ever before.

That pressure largely exists because content personalization works, and the companies that have adopted a personalization strategy believe it’s winning over consumers. Digital marketers whose brands provide a personalized experience were 215% more likely to say their marketing strategy was very effective compared to marketers at brands that don’t offer a personalized experience, according to Hubspot’s 2024 The State of Marketing report.

Teams with ample resources may be able to respond to this need for additional content. But in general, marketing budgets are decreasing and teams are being asked to do more with less. Generative artificial intelligence (Gen AI) can ultimately help marketers efficiently create quality content. While it’s not reasonable or feasible for AI-personalized content to be created en masse without human intervention, it can help bridge the gap between growing customer demands for personalization and shrinking budgets when properly deployed.

A diagram displays the six stages of a content supply chain: Choose Content Type, Identify Subject Matter Experts, Create Content, Distribute & Amplify Content, Monitor User Experience, and Measure Content Performance.
Content must pass through multiple stages to ensure quality and reach the right audience. This process can be time and resource intensive for marketing teams.

The Promise of AI-powered Personalization

Today, most buyers have a host of alternatives to your company, often with extremely low switching costs. Demonstrating an understanding of your buyers’ needs, behaviors, and challenges builds trust and compels prospects and customers to engage with your brand and product. Personalized content can build that trust in ways that drive tangible performance outcomes.

While AI offers world-changing technologies, so far there have been few high-profile success stories in marketing. Not because AI isn’t working, but because it simply takes time for teams to integrate a new technology into their tech stacks, data streams, and workflows. In practice, it’s more about changing a standard operating procedure than creating a Super Bowl ad. We are years away from realizing the full potential of AI in marketing.

When done right, however, AI has the potential to make teams more productive and efficient, and possibly even more creative. Instead of repurposing tired stock images, for example, teams can use AI to generate original imagery. A campaign manager can use AI tools to take a concept and quickly develop it into cross-channel content assets, then automate the process of building personalized versions of those assets for a brand’s key user groups. AI can turn your team’s ideas into unique, personalized content pieces that better engage customers with your brand—and it can do it quickly and efficiently via automation.

At Toptal we help marketing leaders, particularly VPs and CMOs, understand the potential path toward realizing the efficiency gains in AI-driven content personalization. Using AI in the right situations—and using the right tools—can help your business scale a content personalization, production, and orchestration process. After all, what good is building more personalized content if you can’t get it into the hands of the audiences who demand it?

In this article, I provide a relatively comprehensive roadmap, but I want to note that, depending on your existing tool set, you may be able to generate positive ROI in the near term–or it could take years and significant investment to realize benefits.

The Best Use Cases for AI-personalized Content

Gen AI is already helping marketing teams create more with less. According to the aforementioned HubSpot report, 77% of marketers who use Gen AI say it helps them create more personalized content, 84% percent are creating content more efficiently, and 85% say it has improved the quality of their content.

There are near-infinite ways to personalize content with AI. On the simpler side, teams can create industry pages on their websites that appeal to select users, or customize landing pages for specific user groups, buyer personas, or campaigns. On the more complex side, teams can generate and present tailored content, like product recommendations and thought leadership based on past engagements, or auto-generate timely social media posts.

The following use cases are high-impact, relatively easy-to-implement starting points to begin building a content personalization program using Gen AI:

  • Social media marketing: AI tools can scour social media channels to identify not just broadly trending topics, but also the ones most likely to engage and resonate with your segments and micro-segments. This allows your marketing team to quickly and effectively create content that is tailored to your audience.
  • E-commerce experiences: AI tools can leverage past behavior and searches to curate engaging buying experiences with conversational chat interfaces, personalized recommendations, and product descriptions tailored to a particular function or use case that increases the likelihood of purchase.
  • Email and SMS marketing content: AI embedded in customer relationship management (CRM) and marketing automation tools can analyze users’ past behaviors to send marketing emails that encourage repeat usage, or offer information and experiences that push users further down the purchase funnel faster.
  • On-site experiences (site pages, blog content, and landing pages): Many companies already customize ads to audiences, but AI can help personalize content experiences to particular user groups—even dynamically creating multiple versions of the same content piece—to boost conversion. For instance, platforms like Folloze can personalize landing pages and use OpenAI to tag content, recognize viewing patterns, and make real-time content recommendations that increase conversion rates.

These use cases are primarily focused on owned channels where you have reliable first-party data and tools that offer social media analytics and sentiment analysis, like Sprinklr and Buffer, and intent data, like 6sense and Demandbase. Using these tools, you can supplement your data to increase relevance and engagement.

Choosing Primary Gen AI Tools for Content Personalization

Any marketing leader who has delved into the world of martech in recent years understands its inherent complexity. The Martech for 2024 report found that there are more than 13,000 tools available in the market, with a net increase of 18.5% just six months prior to the report being published. New solutions are now competing with new offerings in existing tools to create an often confusing and overwhelming landscape for those deciding where to invest.

A diagram displays popular tools that are used by various marketing divisions, including project management, SEO, social media, and graphic design.
A small selection of popular marketing tools that can improve productivity and outreach. It can be difficult for marketing teams to navigate this diverse and highly competitive market.

Before you do so, I advise that you first try to understand what your team hopes to achieve with any new tool. Ask yourself, can we improve existing processes with a new tool, or will it require entirely new processes? If the latter, do you have the bandwidth and desire to create those new processes? Next, ask if you can accomplish those tasks with functionality in your existing tool set or if you require new ones. If you don’t start with this basic set of requirements, there will be too many options from which to choose, and you could unnecessarily inflate your martech stack.

For content personalization, these are the areas where you’re most likely to benefit from AI tool implementation:

  • Ideation: Even though it’s commonly thought of as a draft or outline generator, AI can also be an excellent idea generator and virtual researcher. For instance, AI tools can use real-time social media analysis to recommend content for specific audiences, helping marketers understand user preferences around particular topics to help create more effective content for your channels.
  • Content writing: AI tools can quickly generate first drafts of blog articles, turn a white paper into all the various content types needed for a full campaign, and assist with quick versioning of personalized content using vocabulary common to industry, role, geography, etc.
  • Production and distribution: Based on demonstrated visitor preferences, you can use martech to automate key areas of content production, like automatically building content in modular components, making it easier to serve that content to users according to past preference or current need.

Ideation and content writing are the most common AI tools used by marketing teams today, and there are hundreds of options out there. It’s tempting to provide some kind of exhaustive review, but by the time this article is published, it would already be out of date. That said, Jasper, Writer, ChatGPT, Grammarly, and Junia deserve mention. All of these tools, and the companies that produce them, have demonstrated a quality product and some degree of staying power.

ChatGPT is considered the OG of Gen AI for content creation because of its ability to use large language models (LLMs) to digest prompts and spit out customizable content and copy. ChatGPT Writer is its writing companion, which you can download as a Chrome extension to compose emails, rephrase text for tone and grammar, summarize content, and more.

Jasper and Writer offer similar value to ChatGPT, but they rely on different AI models and have markedly different routes to implementation and time to value. Jasper (a Toptal partner) offers the ability to create and activate full multichannel campaigns, adapting an anchor piece of content to different formats, content types, and word counts, then supplementing that with social media content, maintaining your brand’s unique voice throughout. Writer, another well-designed tool, is designed to integrate into a team’s existing content generation workflows to increase efficiency.

Grammarly is a well-known brand traditionally aimed at students and professionals who want to write cleaner, smarter copy, but it also offers a suite of tools for professionals by content type, offering assistance with creating Instagram captions, emails, articles, and executive summaries, among other features. But its sweet spot lies in its suggested revisions that help aspiring wordsmiths achieve strong grammar and overall writing, meaning even junior marketers can create better, more engaging copy for your personalization efforts.

Junia is another useful tool that allows users to generate multiple versions of blog articles, quickly write and rewrite e-commerce descriptions, and generate hundreds of social media posts. Junia integrates with WordPress and Shopify to import and publish content directly to your site with the added SEO benefit of automating internal and external linking.

Other Important Tools for AI-personalized Content

The Gen AI-driven solutions for the next phases of scaling production and distribution are equally important, especially for larger or more mature organizations. These are the areas in which to consider using Gen AI content platforms to generate the kinds of personalized experiences that customers say they want.

  • Workflow management: No matter your organization’s size, you’ll need some kind of work management system, like Trello, Jira, or Workfront. These tools enable teams to scale content production more effectively through better processes and workflows. You’ll also need some system for digital asset management (DAM) so that your teams can effectively manage and access assets to deploy in your content. A DAM provides centralized content access across teams, enabling easier versioning and storage of AI-personalized content. Without these tools, teams often spend a lot of unnecessary time communicating and searching for assets when they should be creating them.
  • Delivery: As customers engage with organizations across a broader selection of channels, you’ll need to assemble a comprehensive source of customer data. I recommend looking at a customer data platform (CDP) as a starting point. A platform designed to assemble the “golden customer record,” a CDP is a centralized location to store customer data across every single channel—including your e-commerce store—that can help process your audience segmentation to drive content across platforms and channels.

The CDP integrates with CRM systems to help create segmented workflows for personalized outreach, automated communications (like email), and prioritizations based on audiences. A CDP can also be linked to other tools like marketing automation platforms, allowing marketers to build and improve the customer experience across channels and lifecycle stages.

  • E-commerce: There are an increasing number of platforms designed to help businesses of all sizes compete with the likes of Amazon in providing a high-converting e-commerce experience. A product information management (PIM) solution is a business application that provides a single place to collect, manage, and enrich your product information, create a product catalog, and distribute it to your sales and e-commerce channels. Respected PIM providers include Plytix and Sales Layer. Layering AI onto PIM systems creates a variety of use cases, including enabling dynamic pricing and improving personalized product recommendations.

Toptal Case Study

Toptal client Big Sur AI offers another e-commerce personalization solution. With conversion rates usually less than 2.5%, Big Sur AI offers immediate, personalized assistance, akin to the service provided by a sales associate in a physical store. Big Sur’s AI Sales Agent was built to understand natural language queries and responds in a conversational tone by asking relevant follow-up questions to help shoppers compare products and information as they navigate a site.

Considerations and Limitations of Using Gen AI for Content Personalization

AI driven personalization has the potential to supercharge customer engagement, but there are a host of risks associated with using Gen AI for content production, many of which have been well documented. According to the previously cited Hubspot 2024 The State of Marketing report, 60% of marketers who use generative AI to make content are concerned it can harm their brand’s reputation due to bias, plagiarism, or misalignment with brand values.

Perhaps the biggest risk comes from the misguided approach of treating the technology as the owner of the work, rather than a tool to assist. At this point in its evolution, Gen AI could be compared to a plow in farming. Humans still need to do the planting, harvesting, and quality control, especially in an important realm like content production. Not doing so could mean publishing boring, inaccurate, or duplicative content that harms your brand’s reputation.

For example, it’s already possible to employ sophisticated automations to generate AI-personalized content and publish it on social media, but that often requires multiple tools, and unless done well, it can come across as “bot-like.” Customers looking for human connection to a brand on social media could be turned off by bad AI that feels robotic. Ultimately, customers might “leave” your brand if they consistently encounter low-quality content that is obviously AI-generated.

Gen AI content production has also been shown to perpetuate a variety of biases, especially around culture and race. That’s a huge potential problem, not just for the technology, but for brands hoping to off-load as much of the content production labor as possible. Biased content carries a variety of risks, from disseminating inaccurate information to perpetuating systemic racism in hiring and other business activities. Most concerning to businesses, it can degrade the very trust you’re trying to build with your content.

Another significant risk pertains to SEO. Google has stated that, though it isn’t explicitly seeking to penalize content created with Gen AI, it continues to reward thoughtful, useful, and original content through its algorithm. Relying too heavily on AI-generated content brings a risk of reducing page rankings, putting your business in danger of not getting your personalized content in front of the consumers you’re targeting. Low-quality content could also decrease your site’s domain authority over time.

Gen AI already offers a world-changing set of technologies, and that tool set will only continue to grow. Unfortunately, its potential is already being exploited, often in the form of hyperbolic clickbait by companies who don’t have a vision and clear set of requirements for how to adopt it. Gen AI’s real power is being realized in more nuanced, incremental ways, and smart companies are taking the long view, experimenting and iterating their way to better performance.

In my role, I meet a lot of teams eagerly formulating big plans to integrate AI into their workflows. I also know many content creators and writers trying to outsmart the technology by doing more of the same, just faster. With the right planning and approach, I believe AI will help sharpen many tools in the marketing toolkit. For marketing teams that are under pressure to do more with less, a measured, thoughtful approach to integrating Gen AI for content personalization offers a positive ROI and improved customer engagement.

Have a question for Jeff or his Growth Marketing team? Get in touch.

Have a question for Jeff and his team?
Get in Touch
Jeff Gangemi

Jeff Gangemi

Growth Marketing Practice Lead
15 Years of Experience

About the author

Jeff is the Growth Marketing Practice Lead at Toptal. He holds a bachelor’s degree from Middlebury College and an MBA from Cornell University with an emphasis in leadership and innovation. Jeff has spent the past 15 years building demand generation, content marketing, and digital programs that drive meaningful transformation and growth for both internal teams and external clients. Before joining Toptal, he held senior management roles at Accenture Song, Material, and Telus International.

PREVIOUSLY AT

AccentureTelus International

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