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What’s the difference between software that has AI features, a chatbot, and agentic AI?<\/p>\n
getty<\/p>\n
Every software vendor now calls its product \u201cagentic.\u201d The label is cheap to add and hard to verify, which is exactly why it has spread faster than the underlying capability. <\/p>\n
Executive leaders need to know how to test those claims, not just hear them repeated in a slide deck. With little definition as to what \u201cagentic\u201d means, this article covers the questions executives need to ask to verify capabilities.<\/p>\n
The split between AI features versus AI-native<\/a> products comes down to a short list of questions that hold up in any demo, in any industry.<\/p>\n Ask these questions before signing a contract with any agentic AI vendor:How deeply does the system integrate with my data? How is sensitive data secured or otherwise protected?How Much Oversight Does It Require? Does It Carry Context Persistently or On A Prompt-By-Prompt Basis?Do I Need To Train It or Does It Adapt To Me? How Much Control Do I Have? Are The Outputs Auditable?How deeply does the system integrate with my data? How is sensitive data secured or otherwise protected?<\/p>\n The best person in any given role draws on context scattered across half a dozen disconnected systems and some data that never even hits the cloud: a CRM, a ticketing tool, an inbox, a spreadsheet nobody admits still runs the business, a notepad that sits on their desk. <\/p>\n Software bolted onto one of those tools sees only its own slice. Context is king, and AI-native systems need with that from day one. However, connected too deeply and risk increases as well. <\/p>\n Make sure you know how it connects, what decisions can be made with that data, and how you can add context that it might need if it\u2019s not automatically connected. If your company has compliance requirements, make sure the vendor is able to both articulate and provide documentation (as necessary) about how your data is protected in a way that makes sense for your risk appetite.<\/p>\n How much oversight does the system require? Does it carry context persistently or on a prompt-by-prompt basis?<\/p>\n A chat session is a linear transaction. A business is a dynamic environment\u2014a quote needs a follow-up, a canceled job needs to resurface, a renewal needs to come due without anyone remembering to check. And the way these situations are handled might be different six months from now than they are today, refined based on daily interactions with customers.<\/p>\n Traditional software was built for specific workflows that were generally good for most but not necessarily optimized for any one party. Prompt-based AI forgets when the window closes. You can see this even in some of the world\u2019s leading software. Persistent context, memory, is one of the biggest challenges most providers are working to overcome.<\/p>\n FieldCamp<\/a> Founder and CEO, Jeel Patel, says \u201cAI-native software is built different from the ground up. Whereas traditional software was built for linear workflows, AI-native software builds data models to be persistently available, interpretable, and usable depending on the context of the users\u2019 immediate, situational needs. It works for you today and adapts to you over time, without you needing to wait for the company to push updates.\u201d<\/p>\n Do I need to train it or does it adapt to me? How much control do I have? Are the outputs auditable?<\/p>\n Every competent employee carries an invisible map of their own authority: what they can decide alone, and what goes up the chain. Software earning real responsibility needs that map built in. It can either be built autonomously or bolted on through manual rules and constant retraining. <\/p>\n Software taught its boundaries every time the business changes isn\u2019t agentic, it\u2019s supervised. On the other hand, software that is fully agentic may persistently drift off or adapt inappropriately, just like a human employee but perhaps with less repercussions. This is why AI liability insurance<\/a> is starting to become popular.<\/p>\n This last question is one of those questions that decides whether any of it survives contact with a real business. When a system rebuilds a schedule or reroutes a workflow overnight, the executive who owns that outcome needs to see what moved and why. Autonomy without an audit trail isn\u2019t automation. It’s a liability with a login screen.<\/p>\n Agentic software is growing faster providing more value while legacy software persists<\/p>\n AI has demonstrated value for companies who have taken the time to find the tool that works for them and ensure adoption across the company. Housecall Pro\u2019s AI Industry Report<\/a> found that \u201cOf the 68% of users who say AI contributed to their revenue growth, 1 in 3 put that number at 5% or more. Nearly 1 in 10 of those users say 20% or more.\u201d<\/p>\n Executives usually think they\u2019re choosing between old software and new software. In practice there are three common situations when buying AI enabled tools: <\/p>\n Legacy software with AI bolted on as a feature. The most common situation is that a legacy software provider added a feature that let\u2019s you generate a summary with AI or some light, one-time AI use case that\u2019s repeatable but not necessarily agentic.Legacy software that bought its way into AI-native territory. Wix paid roughly $80 million<\/a> last year for Base44, an eight-person, no-code startup. By the numbers, this was a great acquisition for Wix but buying an AI-native asset doesn\u2019t make the parent company AI-native. It does not guarantee that the parent company will innovate faster or integrate the acquired software meaningfully.Agentic software built AI-native from the first line of code. These are companies who decided to go AI-native from the beginning.<\/p>\n While it may appear at first glance that these are easy to spot in a demo, the questions in this article will help you confirm where the vendor you\u2019re interviewing fits. The vendors that can answer yes (and how) to all of these questions are building the products that enterprise leaders should be looking to adopt.<\/p>\n","protected":false},"excerpt":{"rendered":"What’s the difference between software that has AI features, a chatbot, and agentic AI? getty Every software vendor…\n","protected":false},"author":2,"featured_media":126148,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,7493,24,36094,17529],"class_list":["post-126147","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-agentic-artificial-intelligence","tag-ai","tag-base44","tag-wix"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/126147","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=126147"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/126147\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/126148"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=126147"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=126147"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=126147"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}