The AI Super Agent: moving beyond chat, AI that creates, composes, and runs operational capabilities, making productivity gains continuous and compounding.

TL;DR: Widespread AI adoption has struggled to deliver widespread return. Intelligence on tap framed around ad hoc output delivers value that dilutes as users are left trying to operationalize it. Operational AI breaks this pattern and delivers execution with momentum. Super agents construct systems that persist, compound, and run - with governed data, structured visibility, and composable skills that build on themselves over time. The user's role shifts from carrying execution to directing it. CREAO AI is pioneering this transformation.

The Ad hoc era of AI

The initial manifestation of GenAI has delivered remarkable capability in its initial era: AI as a genuine thinking partner. Faced with increasingly complex questions or requests, AI delivers researched responses, satisfying inferences and increasingly useful output. Comprehensive deliverables are derived from messy inputs, ad hoc requests, streams of consciousness or disparate files. Intelligence is delivered in new formats and mediums.

This is AI as intelligence on tap, synthesis, reasoning, and knowledge made accessible through conversation. Faster research. Better first drafts. Complex questions explored in seconds. Even prototype outputs. McKinsey reports a vast majority of companies have deployed generative AI, and most of that deployment lives in the form of standalone or integrated chatbots delivering insight across the organization.¹

And yet an operational gap remains. That same McKinsey research shows roughly 80% of companies reporting no material effect on earnings despite widespread adoption. MIT found 95% of enterprise AI deployments showing no measurable P&L impact.²

This is the "gen AI paradox." Broad adoption, limited return.

The AI Paradox

McKinsey's diagnosis of the gap is instructive: "At the heart of this paradox is an imbalance between horizontal copilots and chatbots—which have scaled quickly but deliver diffuse, hard-to-measure gains."¹

The gap lives in the distance between intelligence delivered and work executed.

The intelligence delivery era of AI emphasized impressing the user, shaping output based on immediate perceived value. As a consequence it left the user burdened with selection and execution, constraining the scalable application of that intelligence, or worse creating a dependency that could slow down workflows. 

For many it is the cut and paste era of AI, where output is manually carried into other systems for display or execution. At desks around the world the ad hoc generation of AI output is reaching the limits of diminishing return, constrained by the ability of the frontline of business to absorb, evaluate, and execute—creating efficiency at one end while adding dependency and effort at the other.

From Ad hoc to Operational AI Execution

A group of pioneering companies is pushing the evolution of the operational AI era. These are emerging super agent platforms where AI conversation directs systemic creation of operational capabilities that can execute for users to scale their work. Impressing users with visibility and AI led outcomes becomes an improved AI reward system to evolve actual productivity gain.

The interactions remain conversational, the interaction framework for AI, where you describe what you need to happen, observe progress, guide execution with context. The output changes fundamentally, from ad hoc output to workflow outcomes. Instead of receiving insight to act on, you direct and observe work being done.

The Identity Defined Security Alliance described this shift directly: "The interaction model has shifted from conversation to collaboration. Instead of waiting for prompts, agentic systems pursue objectives, adapt their approach when they hit roadblocks, and persist across complex workflows until they complete the task. They're not just talking about work anymore, they're doing it."³

Ad hoc AI vs. Operational AI

The Key Enablers of Persistent AI Execution

The critical shift from ad hoc AI advice to durable, executional systems is enabled by conversation moving from single-use output to creating and composing durable, reusable building blocks. In this new user paradigm a persistent environment is designed to compound value by handling key dimensions of an operational system: it can Remember through a governed data layer for persistent memory and compliance; it can Display through organized widgets and views for structured visibility; and it can Think & Execute using AI skills and managed runtime to handle flows and provide observability.

CREAO is one of the early leaders, pioneering a highly capable Super Agent composing and executing in AI workspace where the user defines context. Instead of every AI interaction being an ad hoc interaction, the workspace allows the user to turn a one-off action into a reusable Skill and an operational process into a persistent App with persistent data layer. This continuous upgrade path means the user's effort creates a durable, governed asset for their future use, which makes their workspace steadily more organized and efficient over time. User prompts easily graduate into a durable, reusable skill or a full app with persistent data layers creating operational memory. The system they co-create compounds capability as the user interacts with the agent in their workspaces.

By transforming ad hoc work into governed, observable systems, CREAO provides a clear, natural upgrade path for complex operations.

"The CREAO agent doesn't eliminate ad hoc usage, it prevents ad hoc repetition. Users can stay fast when exploring, but their workspace steadily becomes more reusable and organized as real needs repeat."

— Dr. Peter Pang, CTO and AI visionary leading development at CREAO AI

CREAO’s development is intently focused on moving from ad hoc execution to enabling real operational momentum. Even early general agent platforms today operate in isolation: executing tasks in sandboxed environments, lacking memory, poor visibility, and no way to compose one capability onto another. This creates ad hoc execution that can’t compound into operational value. The shift to deliver productivity gains to users is not just from chat to agents, but from disposable AI work to continuous, visible, governed operations that build on themselves over time.

The User Role in Operational AI

The user's role in this evolved system shifts from being an executive operator to a strategic director and observer. Where ad hoc AI forced the user into the tactical, repetitive labor of prompting for one-off results, this new platform puts the AI to task on the heavy lifting: it both constructs and executes the repeatable work.

This liberation frees the user's attention from low-level execution and allows them to focus on high-level elements, such as strategic direction, governance, and observation of the system's performance. By effectively delegating all constructible and repeatable processes to the AI's durable, governable systems, the organization finally achieves the systemic, scaled productivity gain that has long been promised.

What Changes

The most profound outcome is a fundamental realignment of human effort. When the system can remember, create, execute, and evolve the limiting factor shifts entirely, from consumption to direction and from evaluation to observation. This truly elevates the human role.

The constraint moves from "how much can I ingest and execute on" to "how do I tell AI what needs to get done." The second is bound by your experience and ability to articulate what needs to happen. Projects that couldn't be staffed become possible. Workflows that exceed bandwidth become viable. Initiatives sidelined for later can get started easily. Business experimentation explodes. Refinement into stable operational productivity gains happens fast. Opportunity hunting becomes a key activity.

The user is liberated from the tactical, repetitive labor of the "executive operator." By effectively delegating all constructible and repeatable work to durable, governable AI systems, the constraint on productivity is removed. The final change is the systemic, scaled attainment of what was once only a promise: a business powered by human judgment and driven by compounding, persistent AI execution.

CREAO: Pioneering Operational AI

CREAO is an AI Super Agent that transforms ad hoc execution into reusable, governed AI tools, giving users an unparalleled productivity power boost so they can do it all.

References:

1. McKinsey & Company, "Seizing the Agentic AI Advantage," June 2025

2. MIT Project NANDA / Brookings Register analysis, December 2025

3. Identity Defined Security Alliance, "From Chatbots to Agents," October 2025