AI is reworking how enterprise software program will get purchased—not by changing customers, however by turning into one.
The controversy round AI and the office usually facilities on labor displacement: Will it substitute employees? The place will it fall brief? And certainly, some “AI-first” experiments have produced combined outcomes—Klarna reversed course on customer support automation, whereas Duolingo confronted public backlash for an AI-focused progress technique.
These outcomes complicate our understanding of Microsoft’s current efficiency-driven layoffs. Not like a untimely overcommitment to automation (à la Klarna), Microsoft is restructuring to function as “customer zero” for its personal enterprise AI instruments, basically altering how the computing large writes code, ships merchandise, and helps shoppers. It’s a strategic shot within the arm—a painful one—that reveals what’s coming subsequent: AI brokers constructed not simply to automate outcomes, however to make choices in regards to the instruments, processes, and infrastructure used alongside the way in which.
AI agent as orchestrator
Prior to now, enterprise software program was chosen by means of a well-known dance: analysis, demos, stakeholder alignment, and procurement. However at present, AI brokers are constructing purposes, provisioning infrastructure, and choosing instruments—autonomously, and at scale. Ask an agent to spin up a buyer suggestions portal, and it’d select Subsequent.js for the frontend, Neon for the cloud database, Vercel for internet hosting, and Clerk for authentication as a service. No human has to Google choices, examine distributors, or meet with salespeople. The agent merely acts.
Inner telemetry from Neon exhibits that AI brokers now create databases at 4 occasions the speed of human builders. And that sample is extending past engineering. Brokers will quickly assemble gross sales pipelines, orchestrate onboarding flows, handle IT operations—and, alongside the way in which, choose the instruments that work.
Microsoft’s gross sales workforce re-org additional hints at how this procurement will happen sooner or later. Company clients now have a single level of contact at Microsoft, moderately than a number of salespeople for various merchandise. Partially, this can be as a result of agentic AI instruments will choose distributors on their very own—and copilots don’t want 5 gross sales reps. The agent gained’t pause to ask, “Do you have a preferred vendor?” It is going to motive in regards to the job at hand and proceed on its code path, hurtling towards a solution.
Human-in-the-loop AI
This evolution from executor to decision-maker is powered by the human-in-the-loop (HITL) method to AI mannequin coaching.
For years, enterprise AI has been restricted by costly labeling processes, fragile automation, and underutilized human experience, resulting in failure in nuanced, high-stakes environments like finance, customer support, and well being care.
HITL methods change that by embedding AI instantly into the workforce. Throughout real-time work, brokers observe GUI-level interactions—clicks, edits, approvals—capturing wealthy indicators from pure habits. These human corrections function high-quality validation factors, boosting operational accuracy to ~99% with out interrupting the workflow. The result’s a steady studying loop the place brokers don’t simply comply with directions, they find out how the work will get accomplished. This additionally creates dynamic, dwelling datasets tailor-made to actual enterprise processes inside the group.
This shift presents solely new market alternatives.
On the event entrance, conventional supervised studying fashions are giving option to embedded studying methods that harvest real-world interplay indicators, enabling cheaper, quicker, extra adaptive AI. This additional presents an enormous new coaching set for agentic AI methods with out incurring the price of hiring human data employees to shepherd the AI. With decrease improvement prices, excessive constancy, and higher dynamism, the following technology of copilots will mix automation with real-time human judgment, dominating verticals like customer support, safety, gross sales, and inner operations.
Accordingly, these instruments would require infrastructure for real-time monitoring, GUI-level interplay seize, dynamic labeling, and automatic retraining—creating additional platform alternatives.
Microsoft’s sense of urgency
Whereas the web abounds with zippy protection of savvy staff “AI hacking” their workflows, the truth is most employees lack that type of product-development acumen. (And identical for his or her bosses.) Save for a small subset of the enterprise world possessing uncommon tech fluency, most company outfits will see higher worth in shopping for AI instruments—these constructed, personalized, and serviced by world-class expertise to resolve particular workflows.
Microsoft’s sense of urgency comes from its understanding that the query of “build or buy” is altering rapidly. This “eureka” second, technologically talking, is what’s catalyzing an operator pivot at enterprise AI outfits. HITL represents a transfer away from learn/write information integrations towards a richer, extra dynamic GUI-interaction-based intelligence layer—one which mirrors how work truly will get accomplished within the enterprise.
We’re seeing the start of a race towards enterprise AI dominance among the many goliaths of the tech world. Alerts like OpenAI’s investments into application-layer experiences (procuring brokers, its acquisition of agentic developer Windsurf) spotlight a transparent development: Mastering human-application-interaction seize is turning into the inspiration for scalable agentic automation. As firms like Microsoft, OpenAI, and others soak up important information environments and restructure themselves to function “customer zero,” they’re treating AI as the brand new chief procurement officer of their very own ecosystems. These firms see the worth of promoting shovels in a gold rush—and know AI is lastly sharp sufficient to start out digging.
Tomasz Tunguz is the founder and normal supervisor of Concept Ventures. He served as managing accomplice at Redpoint Ventures for 14 years.
The opinions expressed in Fortune.com commentary items are solely the views of their authors and don’t essentially replicate the opinions and beliefs of Fortune.
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This story was initially featured on Fortune.com
