This year’s Dreamforce and Inbound user conferences were all about AI agents.
“When we look back at this event years from now, you’re going to remember it as the year of AI agents,” said HubSpot co-founder and CTO Dharmesh Shah.
“The only thing we’re going to do at Salesforce is AgentForce,” said CEO Marc Benioff. (So long Customer 360 and Data Cloud, hope you enjoyed your time in the spotlight.)
Having the same focus sapped the excitement out of both pep rallies, and many attendees reported being underwhelmed by the events. The reason for the similarity between Dreamforce and Inbound is the similarities between the various generative AI models. Similar models result in similar innovations.
Salesforce and HubSpot are right about the importance of agents. Why this is true also illustrates the problem confronting the entire genAI industry. A problem that’s a great opportunity for marketers.
What is an AI agent?
An agent is software that uses AI and tools to accomplish a goal that requires multiple steps.
To quote Chris Penn’s must-read blog post, “If this sounds like an app, it is. ‘AI Agent’ is just fancy, expensive language for a self-driving app.”
They are best suited for handling repetitive tasks with predictable outcomes. Things like creating reports out of regularly arriving or being shopping assistants, price optimization, consumer-facing chatbots and customer service.
Dig deeper: How to build interactive applications with generative AI
“A lot of people think AI Agents are just chatbots, in the same way they think of ChatGPT as just a blog post writing tool,” again quoting Penn. “Yes, they can do that. But using it just for that purpose is like taking a Harrier [VTOL fighter jet] to the grocery store. It can do that, but it’s capable of so much more.”
To the prompt store!
Fortunately for most marketers, many companies offer low code/no code ways to create agents via premade modules. Among them are Salesforce’s Agent Builder and HubSpot’s Agent.AI (Dharmesh Shah: “Agent.AI is the number one professional network for AI agents. It’s also the only professional network for AI agents.”).
So, yay for agents. The only reason to choose one agent creator over another is if you are already enmeshed in that company’s ecosystem. Therein lies the problem for AI companies: For most users, there is no difference between the products businesses are spending hundreds of billions of dollars on.
Here’s an example: I mostly use AI to generate summaries of articles to post on LinkedIn. My go-to is Perplexity, with Gemini as a backup. Why? Perplexity can find the article as soon as it is published, while Gemini needs about five minutes. Other than that, the results are indistinguishable. They both add false information or give me a summary of a non-existent article at about the same rate. When that happens, I try the other AI. When they both suck, I wait a little and try them again.
Or look at the illustrations here and above. One is DALL-E 3HD, the other is OpenAI HD and the fact that I can’t remember which is which says all you need to know.
Experts tell me there are differences between the latest ChatGPT and Gemini models. One can do things the other can’t (I don’t remember which one does what). However, only sophisticated, power users need or will notice that difference. Also, these systems catch up to each other very quickly, so it’s unlikely to be a long-term advantage.
Is the exuberance rational or irrational?
AI is now a commodity as far as most users are concerned. When a product becomes a commodity, one brand is as good as another and price is the only differentiator. That is good news for people who use AI, but incredibly bad news for the AI industry.
“We estimate that the Al infrastructure build-out will cost over $1 trillion in the next several years alone, which includes spending on data centers, utilities, and applications,” wrote Jim Covello, Goldman Sachs’ chief technology analyst, in a now famous investment brief about AI. “So, the crucial question is: What $1 trillion problem will Al solve?”
Dig deeper: How to use ChatGPT to simulate martech tools and marketing strategy
VisiCalc’s spreadsheets made PCs essential for business, but AI lacks an equivalent killer app. It can do many impressive things: Automating processes, finding patterns in gigantic data sets, and generating springboard images and text to better ideas. But increased efficiency alone won’t ever justify AI’s price.
Happily, the use cases with the best ROI are mostly marketing-related. If you’re not already on the AI bandwagon, get on board. Take advantage of the hundreds of billions of dollars other people are spending to make your job easier! Do it now, before investors start asking pesky questions like, “Where’s my money?”
The post Why AI’s agents and commodification are good news for marketers appeared first on MarTech.
# How AI Agents and Commodification Benefit Modern Marketing Strategies
In the rapidly evolving digital landscape, marketing strategies have undergone significant transformation. Traditional methods of advertising and customer engagement are being replaced by more sophisticated, data-driven approaches. Two key elements driving this shift are the rise of **AI agents** and the **commodification of data**. These tools are revolutionizing how businesses interact with consumers, optimize campaigns, and deliver personalized experiences. This article explores how AI agents and commodification benefit modern marketing strategies and why they are indispensable in today’s competitive marketplace.
## 1. AI Agents: The New Frontier of Marketing Automation
Artificial Intelligence (AI) agents are software programs designed to perform tasks autonomously, often mimicking human decision-making processes. In the context of marketing, AI agents can analyze vast amounts of data, predict consumer behavior, and execute marketing campaigns with minimal human intervention.
### a. Personalization at Scale
One of the most significant advantages of AI agents is their ability to deliver **personalized experiences at scale**. Consumers today expect brands to understand their preferences and deliver content that resonates with them. AI agents can analyze customer data, such as browsing history, purchase behavior, and social media interactions, to create highly personalized marketing messages.
For example, AI-powered recommendation engines, like those used by Amazon and Netflix, suggest products or content based on a user’s past behavior. This level of personalization not only enhances the customer experience but also increases conversion rates and customer loyalty.
### b. Predictive Analytics and Customer Insights
AI agents excel in **predictive analytics**, which allows marketers to anticipate future consumer behavior based on historical data. By analyzing patterns in consumer interactions, AI agents can predict what products a customer is likely to purchase next, when they are most likely to engage with a brand, and even the optimal time to send marketing messages.
This predictive capability enables marketers to make data-driven decisions, reducing guesswork and improving the efficiency of marketing campaigns. For instance, AI agents can help businesses identify high-value leads, allowing sales teams to focus their efforts on prospects with the highest potential for conversion.
### c. Automation of Routine Tasks
AI agents can automate many routine marketing tasks, freeing up human resources for more strategic activities. Tasks such as email marketing, social media posting, and customer segmentation can be handled by AI agents, ensuring consistency and efficiency.
For example, AI-powered chatbots can handle customer inquiries in real-time, providing instant responses to frequently asked questions and guiding users through the purchasing process. This not only improves customer satisfaction but also reduces the workload on customer service teams.
## 2. Commodification of Data: The Fuel for AI-Driven Marketing
Data is often referred to as the “new oil” in the digital economy, and for good reason. The **commodification of data** refers to the process of turning data into a valuable asset that can be bought, sold, and traded. In marketing, data is the fuel that powers AI agents and enables businesses to make informed decisions.
### a. Enhanced Targeting and Segmentation
The commodification of data allows marketers to access a wealth of information about consumer behavior, preferences, and demographics. This data can be used to create highly targeted marketing campaigns that reach the right audience at the right time.
For example, third-party data providers offer detailed consumer profiles that include information such as age, income, interests, and online behavior. Marketers can use this data to segment their audience and tailor their messaging to specific groups. This level of precision targeting increases the likelihood of engagement and conversion, as consumers are more likely to respond to content that is relevant to their needs.
### b. Real-Time Data for Dynamic Campaigns
The commodification of data also enables marketers to access **real-time information**, allowing them to adjust their campaigns on the fly. For instance, if a particular ad is underperforming, marketers can use real-time data to tweak the messaging, targeting, or creative elements to improve results.
This dynamic approach to marketing is particularly valuable in fast-paced industries where consumer preferences can change rapidly. By leveraging real-time data, businesses can stay agile and responsive to market trends, ensuring that their campaigns remain relevant and effective.
### c. Data-Driven Decision Making
The commodification of data empowers marketers to make decisions based on empirical evidence rather than intuition. By analyzing data from multiple sources, businesses can gain a comprehensive understanding of their customers and the effectiveness of their marketing efforts.
For example, data analytics tools can track the performance of marketing campaigns across various channels, providing insights into which strategies are driving the most engagement and conversions. This data-driven approach allows marketers to allocate resources more effectively, optimizing their return on investment (ROI).
## 3. The Synergy Between AI Agents and Commodification
While AI agents and the commodification of data are powerful tools on their own, their true potential is realized when they are used in tandem. AI agents rely on data to function effectively, and the commodification of data provides
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