By Jim Shimabukuro (assisted by ChatGPT)
Editor
Introduction: Currently, we’re beginning to see apps/software such as search engines, office suites, and operating systems folding chatbot features into their repertoire. I asked ChatGPT to consider the possibility of the tables turning and apps/software being folded into chatbots. -js
Short answer first: it will be both. Over the next few years chatbots will act as a new, dominant interface that aggregates and automates the functions of many traditional productivity apps; at the same time, major productivity vendors will embed increasingly powerful AI into their apps so those apps remain relevant. Which wins in any specific use case will depend on who controls data, trust, and distribution for that user: the AI layer that can call lots of services (the “chat-first” model) or the incumbent app that owns the user’s files and workflows (the “app-first” model).
To unpack that, imagine two stacks. One stack treats the chatbot as the primary interface — you talk to it, it fetches your calendar, edits a spreadsheet, composes a slide deck, or edits an image for you by invoking the right micro-service behind the scenes. The other stack treats the existing application (Word, Excel, Photoshop, etc.) as the place where work happens, but those apps become smart: they expose APIs and in-app assistants so you get the same generative help without leaving the document.
Recent product moves show both strategies happening at once. OpenAI’s push to host third-party “apps” inside ChatGPT and make those experiences composable is an explicit bet that the chat surface can become the universal interface that brings many small apps to the user rather than forcing the user to bounce between tabs. Conversely, Microsoft, Google and others are folding AI into Office, Workspace and Windows so the productivity apps themselves remain central to people’s workflows. Both approaches are accelerating now. (Skywork)
From a user-experience standpoint, chat-first is disruptive because it removes the UI friction of traditional apps. Telling an assistant “summarize this folder of notes, build a 10-slide deck, and produce speaker notes in two tones” is a fundamentally different workflow than opening a word processor, an outliner, a deck editor, and a notes app. The “apps in chat” and agent paradigms try to compress those multi-step actions into a single, conversational workflow; distribution becomes a matter of being discoverable inside the chat surface, not in an app store.
That matters especially for casual and knowledge workers who value time over specialized features: many routine tasks that used to require opening several separate programs are now better handled by a single, conversational orchestrator. Industry commentary and the vendor roadmaps show developers and platform owners treating chat as a new primary distribution channel for micro-apps and automations. (Skywork)
That said, incumbent apps are not going away quickly. Large productivity suites own data, compliance, enterprise controls, and deep, domain-specific features that are hard to replicate inside a generic chat surface. Microsoft and Google are integrating AI so deeply into Office and Workspace that for many business customers the AI assistant will be a built-in capability of the suite rather than a separate product.
That integration can provide tighter guarantees about data residency, auditing, and specialized automation tied to enterprise systems — things chat-first aggregators struggle with unless they build enterprise-grade connectors and governance. The practical consequence is a bifurcation of use cases: for fast, personal, or exploratory work people will increasingly reach for chat-first assistants; for regulated, complex, or high-fidelity work they will use app-first suites that embed AI. (Microsoft Learn)
Business models and distribution will determine which approach dominates in any segment. If a chatbot platform successfully becomes the place where people discover and run useful micro-apps (and if that platform reaches mass scale), it can capture a slice of the value chain previously owned by dozens of niche SaaS vendors. That “AI eats SaaS” thesis is already influencing investors and startups, who are reshaping pricing and product strategies to be “AI-first” or composable rather than feature incrementalists.
At the same time, incumbent vendors are responding by embedding AI and leaning on existing enterprise relationships and billing channels. Market dynamics therefore favor both displacement and co-option: some categories (simple automation, general drafting, web research) are likely to be absorbed into chat-first offerings, while deep, mission-critical applications will aspire to be AI-augmented rather than replaced. (SaaS Weekly)
Practical friction points will slow or shape the trajectory. Trust, privacy, security and provenance are crucial. Embedding AI into email, documents, and operating systems raises new attack surfaces — for example, summarization layers that can be manipulated by malicious content — and vendors are actively putting mitigations in place.
Companies that control where the data lives and can credibly assert compliance and robust security will retain advantage in regulated environments (healthcare, legal, finance). Users and enterprises will therefore demand transparent data policies, editable memory controls, provenance for model outputs, and the ability to turn AI features on or off; those requirements will influence whether the AI lives inside the app or the chat layer. (TechRadar)
So what does this mean for the near future of productivity software? Expect three practical outcomes. First, the line between “app” and “assistant” blurs: many productivity features will be exposed both as app features and as callable services from chat; the same capability could be used via a plug-in inside a document or by asking a chatbot.
Second, casual and many knowledge-worker workflows will shift dramatically toward conversational orchestration and agentic automation — that is, chatbots will do multi-step tasks across multiple apps for you.
Third, specialized, high-value apps will survive and evolve by becoming AI-native themselves, offering deeper controls, integrations, and data guarantees that broad chat platforms can’t cheaply replicate. These outcomes are already visible in product announcements from OpenAI, Microsoft and Google and in broader industry analyses about AI reshaping SaaS economics. (Skywork)
For users and vendors the advice is simple but urgent. Users should start treating chat assistants as a new class of productivity tool while being mindful of privacy — learn which assistant has access to what data, and keep sensitive work inside vendor stacks that meet compliance needs. Vendors must decide whether to be an “AI host” (offer a compelling, discoverable interface inside a chat platform), an “AI-enabled app” (embed generative features into existing products), or both.
The smartest path for many vendors is composability: expose APIs and micro-UIs that let chat platforms call core app services while preserving governance and data control. That compromise lets apps keep their enterprise lock-in advantages while benefiting from the convenience and distribution that chat surfaces provide. (CometAPI)
In short, chatbots are disruptive but not simply replacement technologies — they are a new interface and orchestration layer that will absorb many of the conveniences of productivity software while coexisting with, and sometimes subsuming, features of those apps. Which wins will vary by task, user type, and the importance of privacy/compliance.
Expect hybrid outcomes: chat-first workflows for fast, personal, or cross-app tasks; app-first workflows for high-assurance, specialized, or deeply integrated enterprise work. The practical future is less “chatbots replace apps” and more “chatbots recompose how apps are delivered, discovered, and automated.”
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