Anatomy of Positioning AI Agents: Components and Examples

Many claim that AI agents are the future of software. So, how do founders, CMOs, and product marketers position and market them??

My first article on marketing AI agents covered some foundational questions to explore for positioning agents effectively. This second article showcases examples of how startups decided to position their agents, with many examples including some taken from Dropzone.ai, which I helped launch and market.?

Below, you will discover the target audience, pain points, category name, benefits, and differentiators that agent companies use most in their positioning and how they made them come to life on their websites.

It’s still early days, so expect the art of agent marketing to evolve quickly...?

Let’s start with:

1. The buyer persona and their needs?

The primary target buyer will very likely be the manager (even more likely their manager) of the process or the role that the agent will take over or augment: the head of Sales or Marketing for an SDR, the head of Customer Service for call center agents, etc. ?

If the motion is PLG, i.e., the product is available for self-service, then one should try and target the practioners themselves, i.e., the owners of these tasks, to build bottom-up momentum.?

What’s above covers a lot of potential personas obviously.

In addition, Marketers should also consider targeting these economic buyers and influencers:

• Chief AI Officers - a new role in the enterprise. They’re often looking for their first wins in their new roles and are likely open to trying new things and broker intros to the various department leaders
• CFOs - will land an ear to cost savings value-props??
• IT managers and leaders who equip various functions with tools and processes while remaining compliant with the company's security and privacy standards

Marketers should also consider the role - and create content and sales enablement to address the concerns - of gatekeepers, i.e. folks on Security, IT, and compliance teams, who will want to vet these tools and understand what data and tools these agents get access to (see the last section of this article) as well as how that data is handled.?

2. The problem statements?

Here are the typical pain points that agent marketers highlight in their narratives:?

For employers:

• Lack of capacity - not enough workers - whether seasonal or systemic?
• Lack of expertise - not enough skilled workers
• High cost of their knowledge workforce
• Retention, onboarding and training issues
• Lack of consistency in service
• Difficulty personalizing a service at scale
• Difficulty serving customers in a different country or language
• Risk of dropping cases on the floor when facing high volumes (e.g. too many alerts, spike in customer calls, etc.)
• Staffing 24/7 coverage, especially outside of business hours?

Some of these can be related and compound.?

For the employees augmented by AI agents, and by extension their managers, AI agents can reduce or address:

• Repetitive/mindless/thankless work?
• The difficulty in collecting and analyzing large amounts of data?
• The time it takes to customize or personalize the work????

Some?examples of how they come to life in marketing assets:

3. The category name: what to call these agents

AI agent vendors typically describe what their agent is, i.e. name their category, using either:

1. The process they handle, i.e. WHAT they do. They usually precede that with "autonomous", which is stronger than automated in that it implies reasoning and orchestration
2. The job title or position they hold, i.e. WHO they augment or replace. Words we then see often are {function} agent, analyst, representative, assistant

Some do both.?

Using the name of the process that agents handle is a good option to avoid being perceived as employee replacement (see article one about the pros and cons of replacement positioning) and show that the agent augments humans' work, i.e. that they take over employees' tasks and not their jobs OR when then is no corresponding role.?

On the other hand, naming an agent after a specific job title can be easier when buyers know exactly what these jobs consist of: skills, responsibilities, processes, tools used, output, and KPIs. However, that will raise the bar for the perceived expectations of the agent’s autonomy and output quality.??

Category name examples:

4. Their value proposition: main claims, benefits and differentiators

Agent companies typically highlight these benefits:

• Save costs (by far the leading benefit)
• Drive more revenue
• Improve employee or customer satisfaction
• Move, innovate, or scale faster
• Make your company more secure or compliant

Yes, that’s strikingly similar to the benefits of using B2B software...?

Marketers call out these key differentiators vs. knowledge workers equipped with software, including copilots:

• 24/7/365 coverage - “always on” and “tireless”, even during company shut-downs and holiday parties
• Ability to handle more data than the human brain can
• Infinite/elastic capacity -? like cloud computing, customers can add agents with a few clicks
• Less management overhead - customers will still need some supervision and fine-tuning though
• Fewer errors / high consistency?- once customers understand how the vendor prevents hallucinations
• Fast activation/onboarding?
• Personalization at scale
• No talent retention issues

As a result, employees can:

• Focus on more strategic work?
• Be more proactive and less reactive
• Spend more time with customers/patients, etc.

Examples of benefits:

5. Reasons to believe in autonomy and output quality: explaining how they work

Getting this right is critical because the agents’ promise is that they work autonomously:

• “Orchestrate the automation of repetitive tasks
• Autonomously run the entire {function process}”

They also promise to produce great work outcomes consistently. But how can we trust that they perform the right actions, don’t hallucinate, and are accurate?

That’s why agent marketers explain how agents acquire(d) and improve their expertise in the role, with the right and latest company context:

• “Created by industry experts
• Operate with full context - have access to your latest systems/data/knowledge base - and will even build it up over time?
• Integrate with the {insert function} tech stack?
• Can be trained, supervised, and fine-tuned both by company data and employees”

As the examples below show, marketers also describe how agents reason, produce quality work, and self-adjust, i.e. what steps they take to arrive at their conclusions and output without hallucinations.?

Marketers can also borrow from the description and benefits of retrieval augmented generation (RAG) found here and here to explain how the agents leverage the customer’s context and data to achieve accurate outcomes.

Many buyers will want to know they can supervise or override what the agent writes or creates before it sees the light or reaches a customer:

6. Reasons to believe the agent has the right security and privacy standards

Next, marketers need to explain how agents handle the company’s data and IP since buyers and their security teams need reassurance about data privacy, security, and IP protection. They wonder, among other things:

• Will our data be used to train the vendor’s models??
• Will other customers benefit from that??
• How is our IP and proprietary data protected??

The GitHub Copilot team created a comprehensive “Trust Center” (Copilot is not technically an agent, but it does access critical company information: its code). It explains how Copilot handles data, lists current and upcoming certifications, and touches on intellectual property.

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Buyers also wonder about the quality of the output and how it compares to the quality of humans’ work. I will cover some of these collateral examples in my next article.

More to come. If you see good examples of agent marketing, please DM me or list them in comments. ????