This 12 months’s Dreamforce and Inbound user conferences were all about AI agents.
“When we glance back at this event years from now, you’re going to recollect it because the 12 months 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 within the highlight.)
Having the identical focus sapped the thrill out of each 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 varied generative AI models. Similar models lead to similar innovations.
Salesforce and HubSpot are right in regards to the importance of agents. Why that is true also illustrates the issue confronting the whole genAI industry. An issue that’s an amazing opportunity for marketers.
What is an AI agent?
An agent is software that uses AI and tools to perform a goal that requires multiple steps.
To quote Chris Penn’s must-read blog post, “If this appears like an app, it’s. ‘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 commonly arriving or being shopping assistants, price optimization, consumer-facing chatbots and customer support.
Dig deeper: How to construct interactive applications with generative AI
“Lots of people think AI Agents are just chatbots, in the identical way they consider ChatGPT as only a blog post writing tool,” again quoting Penn. “Yes, they’ll do this. But using it just for that purpose is like taking a Harrier [VTOL fighter jet] to the food market. It can do this, however it’s able to so far more.”
To the prompt store!
Fortunately for most marketers, many firms 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 primary skilled network for AI agents. It’s also the one skilled network for AI agents.”).
So, yay for agents. The only reason to decide on one agent creator over one other is for those who are already enmeshed in that company’s ecosystem. Therein lies the issue for AI firms: For most users, there isn’t a difference between the products businesses are spending a whole bunch 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 because it is published, while Gemini needs about five minutes. Other than that, the outcomes are indistinguishable. They each add false information or give me a summary of a non-existent article at in regards to the same rate. When that happens, I try the opposite AI. When they each suck, I wait a little bit and try them again.
Or have a look at the illustrations here and above. One is DALL-E 3HD, the opposite is OpenAI HD and the undeniable fact that I can’t remember which is which says all it’s essential know.
Experts tell me there are differences between the newest ChatGPT and Gemini models. One can do things the opposite 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 one another in a short time, so it’s unlikely to be a long-term advantage.
Is the exuberance rational or irrational?
AI is now a commodity so far as most users are concerned. When a product becomes a commodity, one brand is as good as one other and price is the one differentiator. That is good news for individuals 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 following several years alone, which incorporates spending on data centers, utilities, and applications,” wrote Jim Covello, Goldman Sachs’ chief technology analyst, in a now famous investment temporary about AI. “So, the crucial query is: What $1 trillion problem will Al solve?”
Dig deeper: How to make use of 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 raised ideas. But increased efficiency alone won’t ever justify AI’s price.
Happily, the use cases with the very best ROI are mostly marketing-related. If you’re not already on the AI bandwagon, get on board. Take advantage of the a whole bunch 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?”
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