For the past two years, enterprise Artificial Intelligence (AI) strategies have revolved around one central question: which model should we use? Larger, faster, and more fluent foundation models, especially Large Language Models (LLMs), became the focal point of experimentation and investment.
That phase is now ending.
As enterprises move from pilots to scaled deployment, a critical reality is becoming clear: models alone do not create business value. Value emerges only when AI can act reliably, repeatedly, and within enterprise controls. The next phase of AI success will be defined less by model selection and more by how effectively organizations orchestrate AI across data, tools, workflows, and governance.
Recent ecosystem moves, most notably Meta’s acquisition of AI agent startup Manus, underscore this shift. The deal is not about owning a better model; it is about owning execution.
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Why Meta Acquired Manus: A signal, not a siloed bet
Meta’s acquisition of Manus, a Singapore-based developer of general-purpose AI agents, reportedly valued at over $2 billion, reflects a deliberate strategic pivot. Rather than doubling down solely on model development, Meta is investing in the ability to translate intelligence into action, embedding autonomous execution directly into its AI stack, including Meta AI.
This move signals three important shifts:
- From generation to execution: Agents that can plan, decide, and act unlock far greater practical value than models that only generate responses
- From experimentation to monetization: Execution-capable AI systems are easier to embed into revenue-generating products and workflows
- From model competition to platform competition: Differentiation increasingly comes from how well AI is operationalized, governed, and scaled, not from raw model performance alone
For enterprises, the implication is clear: the next wave of automation is not about better prompting; it is about delegation.
The new battleground: The execution and orchestration layer
Meta is not alone. Across the ecosystem, hyperscalers and enterprise software providers are converging on the same strategic priority: owning the layer where AI plans, executes, and governs work.
- Microsoft is scaling multi-agent orchestration through Copilot Studio
- Google and AWS are building managed agent runtimes tightly integrated with cloud control planes
- Salesforce, ServiceNow, and SAP are embedding agents directly into core business processes
- OpenAI itself is evolving from a pure model provider toward owning agent runtimes
Together, these moves reflect a broader industry shift. As models increasingly commoditize, control, differentiation, and monetization are migrating upward, to the orchestration layer that connects AI to enterprise systems, policies, and outcomes.
The Meta-Manus deal sharpens a critical enterprise question: Are organizations prepared to let AI own outcomes, not merely assist humans with tasks?
This transition represents a fundamental shift in how work, accountability, and risk are structured.
- Accountability of models must change
When AI owns outcomes rather than discrete tasks, accountability no longer resides with a single human executor. Responsibility becomes shared across business owners, orchestration platforms, and governance mechanisms. Enterprises must explicitly define who remains accountable when AI systems act autonomously. - Not all work is equally delegable
AI-delegated end-to-end execution, entrusting AI to deliver end-to-end business results, must be done with clear intent and boundaries. High-volume, well-defined, and reversible processes are easier to delegate. In contrast, for regulated, judgment-intensive, or irreversible decisions requiring tighter human oversight, delegation should be a deliberate, risk-based design choice rather than blanket automation. - Operating models must evolve
Delegating end to end process execution reshapes how work is owned, measured, and governed. New roles emerge around supervision, policy ownership, and Agent Operations (AgentOps), while traditional execution roles shift toward oversight and exception handling. This is as much an operating model transformation as a technical one.
Implications for Service Providers
For service providers, this shift is particularly disruptive. The market is moving beyond building chatbots and executing isolated AI pilots. Clients will increasingly demand agent transformation programs, initiatives that redesign processes around autonomy, delegation, and exception management.
Several implications stand out:
- AgentOps becomes a core managed service, encompassing monitoring, evaluation, compliance, and continuous optimization of deployed agents
- Providers must support hybrid architectures, combining agents, workflows, and Robotic Process Automation (RPA) rather than treating agents as standalone solutions
- Industry and function-specific agent Intellectual Property (IP) will become a key differentiator as generic agent capabilities commoditize
- As hyperscalers move up the stack into execution platforms, providers must balance ecosystem participation with platform neutrality to manage long-term risk
Key takeaway
The Meta-Manus deal is not just about Meta. It is a marker of where enterprise AI is headed.
Every hyperscaler is racing toward the same destination: owning the layer where AI decisions turn into enterprise actions. The Meta-Manus acquisition is not an outlier; it is one expression of a broader shift toward execution-centric AI architectures.
The most important question for enterprises is no longer:
“Which model should we use?”
It is:
“What work are we ready to delegate – and are our systems built to handle it?”
That is where the next phase of enterprise AI competition will be decided.
If you enjoyed this blog, check out, What Does Meta’s Investment in Scale AI Really Mean? – Everest Group Research Portal, which delves deeper into another topic relating to Meta.
To discuss this in more depth, write to us at Priya Bhalla ([email protected]) or Iti Choudhary ([email protected]).