And no, it is not Larry Ellison getting all cozy with AWS (or Azure, for that matter). It is also not that his keynote was distinctly geeky, after some years of Oracle putting business solutions to the front. Or that Mark Benioff apparently tore up his keynote in the last moment. It is also not that Hubspot CEO Yamini Rangan found that the sales process is broken and that customers know more about you as you about your customer.
No, the theme is ... drumroll ... you will have guessed it ... AI agents. Oracle's Steve Miranda talked about them at length in a line of business context, while Larry focused on IT, security, and database-oriented agents.
For Salesforce, agents are even more of a topic, dubbing Dreamforce the biggest AI event and Salesforce the most successful AI CRM - both technically right but probably somewhat short selling the full value of both.
For Salesforce, the next big thing is Agentforce, it's AI Agent platform.
And Hubspot announced Breeze, its AI to power the customer platform, which, you guess it, includes agents. CEO Yamani Rangan talked about marketing, sales, and service agents. Co-founder and CTO Dharmesh Shah then spent considerable time in his keynote, talking about agent.ai, Hubspots “professional network for AI agents”.
What struck me watching all three keynotes - Ellison's, Benioff's and Shah’s - is the change from last year's messaging to this year's messaging.
Last years it was all about Co-Pilots, or digital assistants, this year, it is about autonomous agents. Oracle announced more than 50 agents, Salesforce more than 100, Hubspot a humble 4, plus the ones that are available in agent.ai.
So, what is the key difference, compared to last year?
There are basically two. One is that a co-pilot serves and supports the human. With this, the core message was that the machine helps the human; the human is supported by the machine. The human will always be in the loop.
This year, the emphasis is on autonomous agents, whether we already have them or not. And I argue that the goal of autonomy is achieved only for fairly simple use cases as per now – with progress being fast. Still, autonomous agent reads a lot like automation, and there is a distinct difference between the human deciding and taking action vs. the machine "deciding" and "taking action".
This translates to, as Miranda astutely said in his post-keynote press conference, "the early use cases will be a little less autonomous and human assisted". In other words, human helps machine, until they are "more autonomous" and do not need the assistance anymore...
Benioff put it somehow similar in his keynote when he listed the many, many agents that Salesforce already now (well, GA is in October 2024) offers with Agentforce. So many in fact, that I felt inclined to comment that "only the CEO agent is missing to have a fully virtual company".
At the same time, all three, Hubspot, Oracle, and Salesforce, insist in their messaging that their objective is not the replacement of humans by AI but taking away the mundane work, thus improve the employee experience – or employee’s value, in the words of Shah.
According to a number in the early 2024 report New trends in AI use at work by Slack’s Workforce Lab that gets referenced in the recent Salesforce report Trends in AI for CRM, mundane work amounts to a whopping 41 per cent of a desk worker’s workload. Desk workers state that they spend this amount of their time on tasks “that are low value, repetitive or lack meaningful contribution to their core job functions”. If right, this is a clear opportunity for AI and automation, but also a definite opportunity for some process improvement.
Zendesk foresees a whopping 80 per cent of all service interactions being resolved without human intervention. This essentially makes the human the AI's supervisor - which technically is a human in the loop scenario, until that supervision can get automated, too. And automation at scale changes the human role to exception handling. A high visibility example that frequently can be observed is the mandatory stop of trading at stock exchanges. Though this trading technically is not necessarily performed by AI agents, it is often fully automated.
The second stated objective in the Oracle and Salesforce keynotes is improving the customer experience, something that Salesforce's Patrick Stokes demonstrated with an impressive live(?) demo during the Dreamforce keynote. In the case of Hubspot, it is helping companies grow, which is a different – more neutral – formulation, and focusing on what Rangan called acceleration. Acceleration happens with the help of agents that assist employees – thereby putting less emphasis on autonomy and more on efficiency.
The question is what these vendors customers' objectives are. Do they think outside-in or inside-out? Inside-out does imply a focus on own efficiency and reducing cost, as expressed by Klarna and discussed in this article by diginomica’s Stuart Lauchlan. Outside-in thinking would be the use of agents to improve their ability to serve their customers.
Will they use AI agents as a convenient tool to cut costs by cutting employees, something that also heavily depends on AI pricing? In other words, are AI agents the next iteration of outsourcing? Or will companies use agents to become better without laying off employees? Obviously, one scenario is more toxic than the other one.
In any case, I am very much looking forward to the “monster piece” that Jon Reed promises in his Oracle Cloud World AI agent article.
To close: let me ask two sets of questions for you to comment. There’s one for the vendor side and one for the buyer side:
Vendors: How do you convince your customers that your agent platform is more valuable if used to help the company become better instead of making it "leaner"? How do you educate t
hem?
Buyers: What are your key objectives when implementing AI agents? What are the KPIs you use to determine success and how much do these KPIs need to change to achieve success?
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