AI success shows up in outcomes, not token savings

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 4:54

   Yeah, no, truly, that's the goal. You bring up such an important point because oftentimes you see some really cool agent demos and you're like, wow, that looks great. But then when you're a giant enterprise and you're actually trying to get that demo into production, all these things go wrong. And so I wanted to get your perspective. You say you talk to a lot of CTOs, you're talking to enterprise customers all the time. What is the hardest part about actually getting these agents into production?

2. 5:17

   Well, the hardest part is getting the confidence that the agent is not going to do something that he should not be doing. And this is why at Kong, now besides Volcano, which we're very excited about, at Kong, we're providing a whole set of the control tower for AI effectively so that enterprise organizations get the comfort level, the governance, the compliance that's needed to feel good about deploying agents at the core of the business.

3. 5:41

   You see, many organizations are experimenting with agents, but they're experimenting in use cases that don't matter. They're not putting agents at the core of the business, such as they can create an outsized outcome at the end of the day. Let me tell you a story. I speak with lots of customers, and there are two categories of customers. The ones that are successful with AI, they don't talk about cost.

4. 6:02

   They talk about how can we do more with AI. And then there's the ones that are not able to generate any outcome with AI, and so they start talking about cost. it's it's like a car how much gas you put in and how many miles you get out if you put a dollar of token spend in how many dollars of outcomes are you generating out of that spend and if you cannot generate outcomes then of course your focus is going to be let's reduce the spend because this thing is not going anywhere versus organizations that are successful with ai they put one dollar in they get ten dollars out they don't talk about spend they're talking about let's increase the spend because we're generating way more bigger outcomes on the other side of this than we were before. And so usually talking too much about cost, it's an important matter, but can also be

5. 6:43

   a canary to determine is the company successful with AI or not. Now, the reason why they can't be successful with it, it is because there is lots of use cases that don't matter. Like I said earlier, you know, they deploy them across the periphery of the business because they don't feel comfortable to actually impact or transform the core business with agents. But look, if we do not transform the core business with agents, then we're not going to be able to generate that outsized outcome, you know, 5X, 10X, 100X, you know, whatever it is, for every dollar that we spend on AI.

6. 7:12

   So the ability to, number one, build the agents. Of course, that's a big problem. Number two, getting access to the intelligence. Number three, investing in the ecosystem of APIs and MCPs. So the agents can do something with that intelligence that is also very important. Many organizations have not invested in their ecosystem of APIs and MCPs. And so you have all of this intelligence, and you can't do anything with this intelligence Because the agent can consume data, systems, and services to be able to do anything with it.

7. 7:40

   And some organizations have that, but they don't feel comfortable to take the agent and put it into the core of the business. So if you're an e-commerce company, what's the core of the business? Being able to sell items, being able to cater to the users that are looking for something. If you're a financial services organization, being able to generate loans with the help of AI to do it in a much better way.

8. 8:00

   better way with much lower risk. They need to feel comfortable to do that. And to do that, they need infra that gives them that control tower, that governance, that compliance to know that the agent is doing what he should be doing and to shut it down if it's doing something that he should not be doing. That's also equally as important.