Agentic AI elevates AI from “assistants” to autonomous digital labor that plans and acts across multi‑step workflows. Early leaders aren’t winning on prompts; they’re winning on readiness — their mature data pipelines, governed tool use, knowledge capture, and human‑in‑the‑loop quality. That foundation lets them embed a handful of production‑grade agents into service, sales, and knowledge flows — compressing cycle times and compounding learning. Other firms, despite similar models, stall at pilots, obstructed by fragmented systems and ad hoc oversight. This Advisor draws on field work and fresh market signals to identify what early adopters have in common, clarify the difference between islands of automation and an operating model, and offer a two‑horizon plan to ship value now while building for scale.

A Paradigm Shift

Agentic AI marks a decisive break from earlier waves of automation and digital transformation. Where past initiatives delivered incremental gains, agentic AI represents a paradigm shift with far more profound consequences and opportunities. In previous data and AI evolutions, organizational laggards — those with siloed data, fragmented systems, and limited analytical maturity — could still compete without facing severe penalties. Agentic AI raises the stakes. Organizations that delay adoption risk falling so far behind in efficiency, customer responsiveness, and strategic execution that it will be difficult for them to recover.

The term “agentic AI” was popularized in 2024 by Andrew Ng to describe systems that can remember, reason, and act autonomously across multi-step workflows. Unlike earlier automation, which mostly augmented human tasks, agentic AI is capable of replacing entire categories of human effort. Agentic AI has the potential to become a “killer app” because it changes how users interact with software: instead of navigating a complex clickstream, individuals can issue a single instruction — “do X for me” — and the system will autonomously complete the task, even when it involves multiple, interdependent steps.

Efficiency gains alone are proving transformative. Several companies (e.g., Telegram) are now scaling to millions in annual recurring revenue with as few as 30 employees, driving unprecedented levels of revenue per head. But the benefits extend well beyond efficiency. Firms with the right agentic AI deployments can achieve step-change improvements in personalization, decision-making, and execution at scale. Crucially, however, only organizations with industrialized data pipelines, mature analytics practices, and scalable governance will be positioned to quickly capture these gains.

Agentic AI is not just “another data and AI project.” In our field work, we consistently observe two divergent paths — what we call the “agent divide.” On one path, agents deliver quick wins that expose foundation data and AI debt — forcing a necessary enterprise reset. On the other path, firms with institutionalized data, governance, AI experience, and talent embed agents directly into the operating model and see accelerating returns.

Same Tech, 2 Trajectories

Let’s start with an example from the first path. A multi-geography hospital group piloted GenAI agents to augment clinical-incident analysis. The agents parsed narrative reports, clustered similar events, and surfaced patterns for human review — useful, low‑friction gains for safety and quality teams. But once the pilot touched adjacent workflows, it ran into fragmented source systems, inconsistent taxonomies, and ad hoc AI governance across departments.

Leadership is now using the pilot’s momentum to reset foundations: standardizing data models across hospitals, standing up governed pipelines, capturing tacit clinical knowledge, and formalizing human-in-the-loop quality assurance. The strategic shift is explicit — move agents from “islands of automation” to repeatable capabilities embedded in efficiency, safety, and quality workflows, with measurement tied to incident resolution, time‑to‑insight, and avoided rework.

Next, let’s look at an example from the second possible path. A national telecommunications firm with a governed data and AI function reporting directly to the CEO — and a deep internal bench of builders — directed teams to develop agents atop a proprietary agentic platform. Because data ownership, model lifecycle management, security, and change processes were institutionalized, agents weren’t experiments; they were first-class production services.

Three types of agents had already been smoothly deployed: agents offsetting entire call center queues (with deflection, first‑contact resolution, and average handle time moving in the right direction); agents supporting B2B sales (prospect research, proposal assembly, next‑best‑action); and agents powering enterprise knowledge accessibility (policy and service retrieval in the flow of work). The economics changed accordingly: fewer handoffs, faster cycle times, and durable learning as feedback loops hardened across functions.

Agentic AI Is a Matter of Today

These are just two examples that show the impact of the agentic divide. Wider research confirms that some organizations are already running agentic AI in production, and these early adopters share a consistent profile (i.e., past success in business intelligence, early adopters of machine learning, and have a designated data leader). Their track record in building and scaling data-driven capabilities provides a strong foundation for advancing into the agentic AI era.

These successful companies started by achieving measurable value in domains with repeatable processes, such as customer service. By automating inquiries, triaging issues, and supporting agents in delivering faster, more consistent responses, agentic AI not only elevates customer experience; it also streamlines knowledge access and embeds intelligence into digital products, reshaping the competitive landscape in ways that demand strategic attention.

What these companies quickly understood is that the main barrier is no longer model capability — it’s enterprise readiness. Agentic AI reframes adoption as a business transformation, not a technology project. Critical knowledge that once lived informally in analysts’ heads must be institutionalized within data definitions, procedures, and quality controls that agents can then execute safely and repeatedly. Companies with those foundations will move fastest and go farthest.

The role of enterprise readiness becomes even more relevant for AI laggards, as in our example of the multi-geography hospital group. The easy interfaces of popular GenAI tools may seduce these companies into assuming they can leapfrog the foundational work of data, analytics, and governance. But this is a false promise. Generative interfaces foster a sense of accessibility that can mask the critical prerequisite of enterprise readiness to scale agentic AI effectively. Without the hard groundwork, laggards will fail to match the transformative impact achieved by data-mature peers. The clock is ticking: organizations must pursue near-term wins while simultaneously building the long-term data and AI infrastructure necessary for agentic adoption. Those who hesitate risk being eclipsed by the leaner, faster, AI-powered vanguard.

Closing the Gap: How Laggards Can Catch Up

As we have argued, the cost of inaction is real — and mounting. However, if you consider yourself a laggard, the question is not whether to act, but how. The good news is that all is not lost; with deliberate choices, focused investment, and committed leadership, even the most entangled enterprises can break free and position themselves for the agentic AI future:

• Start with executive alignment and education. Sponsorship from the CEO and operating leaders is essential, as is leadership fluency in what agentic systems can and cannot do. Make the case in business terms — unit economics, time‑to‑value, risk posture — not model architectures.

• Sequence work on two horizons. In the near term, select two to three use cases with clean processes, accessible data, and clear KPIs that can show impact within six months. In parallel, fund a program to industrialize data and knowledge: common data models, governed pipelines, lineage, access controls, and human‑in‑the‑loop quality assurance.

• Instrument everything. Define “golden KPIs” per use case (e.g., first‑contact resolution, deflection rate, time‑to‑insight, compliance exceptions) and wire them into agent telemetry from day one. Build feedback loops (retrieval, prompt, policy, and workflow) so agents learn reliably.

• Put accountability in the right roles. CDOs ensure the integrity and accessibility of core data assets; CIOs build and scale the agentic platforms and services that leverage them. Together with business owners, these roles form the organizational nexus for safe, repeatable adoption.

• Upgrade governance for autonomy. Extend existing risk and change processes to cover agent planning and tool use: policy‑as‑code, safe‑action sandboxes, audit trails, and rollback paths. Make compliance a design constraint, not an afterthought.

• Don’t confuse interface novelty with readiness. Generative chat interfaces create a sense of accessibility that can mask the foundational work required to scale. Pilot chat thoughtfully, but measure readiness by the maturity of data, governance, and deployment practices — not by how easy the demo felt.

• Fund targeted training for builders and operators. Training remains pivotal even as platforms simplify deployment. Guided upskilling and best‑practice playbooks close the value gap and accelerate ROI across service, sales, and product teams.

Parting Words

Agentic AI is no longer a distant prospect — it is already reshaping how leading organizations operate, scale, and compete. What distinguishes winners is not access to the technology itself, but the readiness of their data, governance, and leadership foundations. The successes of early adopters demonstrate that when these foundations are in place, agentic systems unlock unprecedented efficiency, personalization, and strategic agility. For laggards, however, the risks are stark: fragmented systems and siloed data are no longer tolerable inefficiencies — they are liabilities that block transformation and erode competitiveness.

The imperative for executives is clear. This is not simply an IT project or an automation initiative; it is a business model shift. Leaders must treat agentic AI as a structural change to enterprise economics, one that demands deliberate investment in capabilities that scale beyond pilots and point solutions. The decisions made in the next 12-24 months will determine whether organizations harness agentic AI as a source of compounding advantage — or whether they are eclipsed by leaner, faster competitors. History shows that every technological inflection point creates a divide between those who adapt and those who lag. Agentic AI is accelerating that divide at a pace unprecedented by prior transformations. Executives who act with urgency, align their organizations around data-driven foundations and reimagine how work is done will not only stay competitive — they will set the performance frontier for the next decade. Those who wait will find the gap widening too quickly to close.