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14 results for “vertical ai”
And Avi Assist is sort of that front end chat BT like interface that banks, credit unions, and insurers use to answer questions. And this is the kind of thing that, you know, if you're thinking about servicing a client or a customer, you're on the ph
11:57 / 13:01
from transcript11:57 – 13:01
Head to atlascian.com/startup/twist to see if you qualify for 50 free seats for twelve months. Vappy, add real time AI powered voice conversations to your apps or business in minutes, not months. Go to vappy.ai/twist and get a thousand minutes free p
1:27 / 2:34
from transcript1:27 – 2:34
So there's a lot of noise about AI, but time's too tight for more promises. So let's talk about results. At IBM, we work with our employees to integrate technology right into the systems they need. Now a global workforce of 300,000 can use AI to fill
31:34 / 32:30
from transcript31:34 – 32:30
to get started. That's pipedrive.com/simplecrm. So there's a lot of noise about AI, but time's too tight for more promises. So let's talk about results. At IBM, we work with our employees to integrate technology right into the systems they need. Now
16:27 / 17:31
from transcript16:27 – 17:31
let's say we were building a new soda company. We might target five different segments and have different marketing messages for those five different segments, young parents, children, etcetera. An AI can create a million segments, but a human cannot
15:48 / 17:01
from transcript15:48 – 17:01
So let's talk about results. At IBM, we work with our employees to integrate technology right into the systems they need. Now a global workforce of 300,000 can use AI to fill their HR questions, resolving 94% of common questions. Not noise, proof of
32:59 / 34:02
from transcript32:59 – 34:02
The thing about AI for business, it may not automatically fit the way your business works. At IBM, we've seen this firsthand. But by embedding AI across HR, IT, and procurement processes, we've reduced cost by millions, slashed repetitive tasks, and
33:06 / 34:02
from transcript33:06 – 34:02
...vertical by vertical, there's increasing complexity and challenges to being best in class at any of those. And I think if you look at horizontal foundation model labs and then try to place them on each of these axes in terms of how much work and effo
35:12 / 36:18
from transcript35:12 – 36:18
...vertical, actually, there's a lot of concentration with just a few providers. Like, in law, it's also just LexisNexis, and we're two other providers. In finance, you know, it's maybe, you know, four or five data upset providers give you, like, pretty
33:45 / 34:48
from transcript33:45 – 34:48
advisory services by Public Advisors, SEC registered adviser, crypto services by ZeroHash. Sample prompts are for illustrative purposes only, not investment advice. All investing involves risk of loss. See complete disclosures at public.com/disclosur
29:53 / 31:01
from transcript29:53 – 31:01
and simply handing the industry over to a highly centralized, highly concentrated beef packing and retailing industry. The thing about AI for business, it may not automatically fit the way your business works. At IBM, we've seen this firsthand. But b
34:25 / 35:31
from transcript34:25 – 35:31
The thing about AI for business, it may not automatically fit the way your business works. At IBM, we've seen this firsthand. But by embedding AI across HR, IT, and procurement processes, we've reduced cost by millions, slashed repetitive tasks, and
36:14 / 37:09
from transcript36:14 – 37:09
if you're looking for calcium, some of these products are, you know, in one serving can get you 50% of that as well. So there's a lot of noise about AI, but time's too tight for more promises. So let's talk about results. At IBM, we work with our emp
33:00 / 34:01
from transcript33:00 – 34:01
and text generation or summarization. That's what they're seeing at a high level. And within each I mean, which are the areas that we've seen? Legal has been caught. You have accounting that's been really hot. You have code generation or migrating co
15:08 / 16:09
from transcript15:08 – 16:09
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