c-suite

The next enterprise inflection:

How agentic & multimodal systems see, decide,
and act

insight
January 23, 2026
9 min read

Author

Rahul Mahajan-png-1

Rahul Mahajan, a global CTO at Nagarro, is an inventor shaping the future of enterprise transformation. With deep AI expertise and multiple technology patents, he guides Fortune 1000 leaders in building intelligent, autonomous, and future-ready enterprises.

Over the past decade enterprises have adopted AI in waves, moving from content generation to workflow automation, and more recently, copilots that assist human decision-making. That model is now reaching its limits, creating a stagnation instead of building a sustained impact.

The limitation is not a lack of intelligence; it’s the enterprise’s ability to act on that intelligence.

Organizations are generating insights faster than their operating models can keep up. What should create speed often turns into friction instead. The next phase of enterprise AI isn't defined by smarter models or better interfaces, but by how well organizations redesign the way work actually moves. At the core, it comes down to a simple question: are we comfortable letting systems see, decide, and act by themselves, in the real world?

This is a move away from endless experimentation toward building systems that are designed to work. It’s a shift from AI that waits for instructions to AI that can actually operate.

As more execution moves into systems, leaders will need to set the direction and guardrails, defining: intent, governance, and risk. The organizations now need to deliberately connect what systems see, how they decide, and how they act, so intelligence shows up as consistent results, instead of acting like a clever tool today that fades with time.

When AI is everywhere, but rarely industrialized

What enterprise adoption data really shows.

AI adoption is not a barrier; nearly 88% of enterprises are already using AI in at least one function (McKinsey). The problem is that only about a third have converted that activity into something impactful for their enterprise.

While agentic systems signal what’s possible, it’s the scale that remains elusive. Just 23% of organizations report deploying AI agents beyond the narrow, stand-alone use cases (McKinsey). Most initiatives are still confined to operating models designed for human-led execution, rather than an autonomous flow.

Meanwhile, investment momentum is shifting toward multimodal intelligence with systems that can interpret text, vision, audio, and machine data together. Analysts increasingly point to multimodality and autonomy as the starting point of the next chapter of the enterprise (Gartner).

AI is now ubiquitous, but industrializing it remains the hard part.

Beyond conversation AI

Designing autonomous enterprise systems that can act

Chatbots and large language models opened the door to AI adoption across enterprises. They sped up pilots, made advanced capabilities more accessible, and changed how people interact with machines. Yet, for all their impact, these systems remain tied to interface technologies that might be powerful in expression, but structurally limited as foundations for reliable autonomy.

Conversational AI was a necessary early chapter. It unlocked creativity, reframed how knowledge work could be augmented, and normalized AI in day-to-day operations. But the limits are becoming evident. An AI that can converse or generate content on its own does not rewire how enterprises operate at scale, or materially shift cost structures, cycle times, or risk exposure.

What enterprises need next: Systems that can sense, decide, and act

Intelligence has shifted from telling people what to do, to getting work done within clear boundaries.

Sense the physical and digital environment through multimodal inputs such as vision, voice, and machine telemetry.

Ground those perceptions in an evolving enterprise context, processes, and strategic objectives.

Decide across domains by interpreting signals against a continuously updated enterprise ontology.

Act autonomously by initiating interventions and orchestrating workflows, while escalating uncertainty by design.

Nagarro's Mosaic AI for enterprise autonomy

As enterprises confront the execution gap, the discussion quickly moves from abstract capability to concrete design choices. What follows is one illustration of how autonomy can be engineered deliberately, before the discussion broadens the architectural patterns shaping enterprise AI more generally.

Nagarro’s Mosaic AI represents one such approach. It is a federated, agentic platform designed to embed autonomy into enterprise systems through shared context and governed execution, rather than treating agents as isolated components layered on top of existing workflows.

In practice, this approach shows up in three ways:

1. Federated context engineering

Enterprise knowledge, processes, policies, tools, and real-world signals are organized into a living context layer. Agents act on the current state of the enterprise, not static data or isolated prompts.

2. Coordinated agent execution

Multiple specialized agents operate within this shared context, enabling decisions and actions to move across systems without fragmenting ownership or requiring manual handoffs.

3. Governed execution by design

Decision paths, confidence thresholds, escalation logic, and auditability are embedded directly into execution. Humans define intent and risk boundaries; agents carry execution forward within those constraints.

The architectural choices behind this approach point to a broader set of design principles now emerging across enterprise AI.

Designing for autonomy

The architectural shifts driving autonomous enterprise AI:

I. Context that breathes

Static data stores and even retrieval-augmented generation are insufficient foundations for autonomy. Agentic systems operate on a different premise: they depend on a living ontology, a dynamic semantic layer that reflects relationships, states, and dependencies across an organization’s people, processes, and systems.

In this context, intelligence extends beyond data to meaning that unfolds within continuously evolving flows of activity. Where such context is absent or static, perception remains shallow, decision-making becomes brittle, and systems grow increasingly susceptible to drift as conditions change.

II. Knowledge that is always evolving

Enterprises are increasingly moving beyond brittle knowledge graphs toward multidimensional world models that continuously integrate sensory signals with transactional and historical data. Rather than serving as static repositories, these models synthesize understanding as situations evolve, revising assumptions in response to changing signals instead of relying on fixed representations.

Such capabilities are particularly relevant in environments where context changes faster than traditional models can accommodate, including manufacturing operations, retail settings, logistics networks, and customer-facing processes.

III. DecisionOps and governed execution

As agentic systems assume greater executive responsibility, governance becomes increasingly central to their operation. In response, many organizations are developing DecisionOps frameworks that embed explainability, auditability, guardrails, and ethical constraints directly into decision pathways, rather than relying on post-hoc oversight.

Within this approach, human involvement shifts rather than disappears. Intent, values, and boundary conditions are defined upstream, while agents operate within those parameters. Systems manage escalation paths, confidence thresholds, and audit trails—resulting in a form of control designed to scale with complexity and operational scope.

How multimodal AI enables autonomous enterprise intelligence

The distinction between conversational AI and autonomous enterprise intelligence increasingly rests on multimodality: the ability to perceive and integrate signals across multiple dimensions of operational reality. These signals extend beyond text to include visual inputs, acoustic patterns, IoT telemetry, and structured and unstructured enterprise data.

When combined, these inputs enable a shift from probabilistic inference to perception-led action. Decisions are shaped by evolving real-world conditions, paired with mechanisms to surface uncertainty, reconcile conflicting signals, and reduce silent failure.

These capabilities form the basis of a new class of enterprise automation, one oriented toward execution rather than assistance.

multimodal AI for autonomous enterprise intelligence

Agent-based operating models in practice: Mosaic AI

How do agent-based operating models create measurable industry value?

The impact of multimodal, agentic systems becomes most visible in environments where signals are continuous; execution is distributed, and value depends on coordination rather than isolated insight. In these settings, agents operate less as standalone decision-makers and more as ambient systems that sense, align, and intervene across operational layers.

Elite sports performance and coaching

A “coach AI” can ingest training videos and compare athlete mechanics against a golden reference of ideal form. Rather than tracking movement alone, the system evaluates micro-attributes, such as serve angles or body posture, over time. By maintaining time-based assessments, it benchmarks progress, identifies outliers, and detects fatigue patterns that deviate from the training playbook, enabling earlier, data-driven interventions before injury risk materializes.

Hyper-personalized CPG and retail

In the beauty industry, an agent can analyze a consumer’s skin through camera input (visual signal), interpret voice-based descriptions of concerns (acoustic and sentiment cues), and cross-reference with purchase history (transactional context). The result is not a generic recommendation, but a dermatologist-level advisory delivered instantly. Behind the scenes, agents operate in the background, coordinating supply chain systems to balance inventory across locations and ensure product availability at nearby stores. Personalization and fulfillment are linked, rather than optimized in isolation.

Service operations

In high-volume kitchens or hospitality environments, multimodal agents can visually monitor hygiene compliance and food quality in real time, supporting safety and sustainability standards without constant human micromanagement. These agents integrate with HR performance systems to keep staff scores and training requirements current. Consumer experience agents extend this view further, correlating CSAT signals with food temperature regulation, SOP adherence, and preparation methods to surface deeper. Build system-level drivers of service quality.

Smart manufacturing and IoT

On a factory floor connected via 5G, an agent does more than detect defects. It visually analyzes anomalies, correlates them with acoustic signals from machinery, references the relevant SOP for the specific part, and autonomously routes a maintenance request. And it does that while ensuring safety protocols and authorization constraints are met. Closing the loop from detection to compliant action.

Designing intelligence that can act

Conversational interfaces and task-level automation were effective entry points, but they are insufficient as long-term foundations for autonomy. Systems built primarily around language interaction are difficult to evolve into agentic ecosystems, particularly when reliability and auditability become non-negotiable.

The risk of deferring autonomy is not stagnation, but architectural lock-in. Over time, organizations accumulate intelligent fragments rather than coherent intelligence: copilots that assist but cannot act, and insights that surface without resolution.

Progress beyond experimentation typically follows a shift from interface-led deployment to intelligence-led design, supported by investments in context engineering, multimodal grounding, and DecisionOps frameworks that embed governance directly into execution.

What creates enterprise advantage as multimodal and agentic AI mature?

As multimodal and agentic AI mature, enterprise advantage is being created less by interface sophistication or tool breadth and more by the architecture itself. Organizations that manage to pull ahead build systems that can perceive real-world conditions, integrate context across the enterprise, and act reliably within operational constraints. Result is real impact.

In effect, this marks a shift from AI that communicates to AI that really operates, instead of just delivering insights to the dashboards, and decisions are carried through execution. That ability to turn perception into action, consistently and at scale, is what now defines the operational baseline for enterprise intelligence.

Agentic AI refers to systems that can plan, decide, and execute actions autonomously, within the defined boundaries of governance. In an enterprise, agentic AI moves beyond assistance, taking operational responsibility that spans across workflows and systems.

Conversational AI focuses on interaction and response through language interfaces. Agentic AI is designed for execution; for instance, it interprets context, coordinates across systems, and initiates actions without the need for constant human prompting.

Multimodal AI enables systems to perceive and integrate signals beyond simple text. They work on vision, audio, IoT telemetry, and enterprise data, with situational awareness that keeps them reliable, helps them make real-world decisions, and act autonomously.

Most AI initiatives are deployed within operating models built for human-led execution. Without shared context, architectural support for autonomy, and embedded governance, AI remains fragmented and difficult to scale.

Industrializing AI means embedding intelligence directly into enterprise workflows, so insights consistently translate into action. It requires context-aware architectures, coordinated agents, and governance mechanisms that enable AI to operate reliably at scale.

Governance ensures that autonomous systems act within defined boundaries of intent, risk, and compliance. In agentic systems, governance is embedded directly into decision pathways through auditability, escalation logic, and confidence thresholds.

They shift enterprises from insight-driven oversight to execution-driven intelligence. Instead of surfacing recommendations, these systems continuously sense conditions, interpret context, and act across operational layers.

The next enterprise inflection: From AI that speaks to AI that sees, decides, and acts

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The next enterprise inflection: