The Day the Chatbot Stopped Answering - and Started Acting

Serkan Fergan, Fractional CDO, Alternatif Menkul

Serkan Fergan is a seasoned financial technology and business leader with decades of experience spanning IT, banking, digital transformation, and strategic growth. Throughout his career, he has led and managed large-scale IT application development programs, overseeing complex, mission-critical platforms. He has held senior leadership roles in digital banking and business development at major financial institutions Currently serving as Fractional Chief Data Officer (CDO) at Alternatif Menkul as well as strategic advisor to multiple scale up’s and a VC firm, Serkan applies his deep hands-on experience in scaling technology, data-driven solutions, and innovation to help financial services organizations modernize their platforms and turn data into measurable business value.

In an exclusive interview with Banking CIO Outlook Fergan shares his invaluable insights regarding the industry, the prevailing challenges as well as the possible solutions.

Why Agentic AI Is Forcing CIOs to Rethink Interfaces, Architecture, and Control.

I still remember the moment clearly. We were building what appeared to be a familiar artifact: a conversational interface layered on top of an enterprise platform for financial decision-making. The original intent was simple—help users query complex data more naturally. But as the system evolved, something unexpected happened. It stopped behaving like a passive respondent. It began chaining decisions, pulling data from multiple sources, triggering analyses, and sequencing actions without being explicitly guided at every step.

At some point, it became uncomfortable to keep calling it a chatbot. It wasn’t answering anymore. It was acting.

That realization marks a shift many CIOs are now encountering from different directions. The next phase of enterprise AI is not about generating better responses. It is about building systems with intent. We are moving from conversational AI to agentic AI—and the implications extend far beyond user experience.

From Conversational AI to Agentic AI

Traditional enterprise AI has been largely reactive. A user asks a question; the system responds. Even the most advanced language models remain, at their core, stateless completion engines—highly capable but confined to the present moment.

Agentic AI breaks this pattern. An agent operates toward objectives rather than isolated prompts. It can plan multi-step actions, maintain state over time, evaluate outcomes, and adapt behaviour accordingly. In effect, it introduces intentionality into software.

This distinction matters deeply for enterprises. Conversational AI improves access to information. Agentic AI transforms how work gets done. It shifts automation from predefined workflows to adaptive, cognitive processes—particularly in domains like finance, risk, and compliance, where decisions are rarely linear and context matters.

Why Chat Interfaces Became the Front Door

Many agentic systems surfaces through chat interfaces, which can obscure what is actually happening. Chat was never the innovation. It simply became the most natural abstraction between humans and increasingly complex systems.

In financial platforms, users do not think in APIs or data models. They think in goals: analyse this portfolio, explain the risk exposure, identify anomalies. A conversational interface provides a low-friction way to express intent.

Behind that interface, however, the architecture changed fundamentally. What we built was not a smarter chatbot, but an orchestration layer capable of coordinating specialized agents, persisting context, enforcing domain constraints, and executing actions across enterprise systems. The interface remained familiar. The system behind it did not.

Inside the Architecture: What Actually Makes an Agent

Agentic behaviour does not emerge from prompting alone. It is architectural.

First is intent over prompt. Agents operate on objectives rather than single-turn instructions. “Evaluate portfolio risk” unfolds across data retrieval, model execution, constraint checks, and synthesis.

Second is stateful memory. Enterprise decisions accumulate context. An agent must retain prior assumptions, intermediate results, and constraints. Without memory, there is no agency—only repetition.

Third is tool and workflow execution. True agents act through systems, not text. APIs, analytics engines, rule frameworks, and transaction layers must be first-class citizens. In finance, insight without execution has limited value.

Finally, governance must be designed in. Permissions, auditability, policy boundaries, and explainability are foundational. An agent that cannot be constrained or inspected will never be trusted in a regulated environment.

Together, these principles turn AI from a feature into an enterprise capability.

The Cognitive Enterprise Platform Lens

This is where many initiatives fail. Organizations attempt to deploy agents as isolated tools—chatbots with plugins or scripts wrapped around models. The result is brittle, difficult to govern, and impossible to scale responsibly.

Agentic AI only works when embedded into a cognitive enterprise platform. Such platforms treat reasoning, memory, orchestration, and governance as shared infrastructure. Much like ERP systems standardized transactions and CRM systems standardized relationships, cognitive platforms standardize decision-making and action.

In financial environments, this distinction is critical. Agents must reason within regulatory boundaries, respect risk frameworks, and integrate deeply with systems of record. Agentic AI only works when embedded into a cognitive enterprise platform—autonomy without architecture quickly.

The CIO’s Real Questions

When CIOs engage seriously with agentic AI, the conversation shifts quickly from possibility to responsibility.

How do we control what the agent does?

How do we audit its decisions?

What happens when it is wrong?

Who is accountable?

The answer is not tighter prompts or more rules. Control moves up a level—from managing steps to defining intent, policies, and boundaries. Humans do not disappear from the loop; they move alongside it. They design objectives, supervise outcomes, and intervene when necessary.

In this model, accountability becomes clearer, not blurrier. Decisions are traceable. Actions are logged. Responsibility remains human, even as execution becomes increasingly autonomous.

A Practical Framework for Agentic Readiness

For CIOs navigating this transition, a simple framework helps separate experimentation from strategy.

Strategic intent: Identify where autonomy creates value—and where human judgment must remain primary.

Architectural readiness: Clean APIs, reliable data, and orchestration layers are prerequisites.

Governance and trust: Auditability, explainability, and policy enforcement are non-negotiable.

Human–agent collaboration: Roles will evolve toward supervision, design, and exception handling.

This is not an AI roadmap. It is an enterprise transformation agenda.

The Real Shift

Looking back, the most profound change was not technical. The system did not suddenly become smarter. It became intentional.

For decades, enterprise software recorded what happened. Then it automated how things happened. Now, with agentic AI, systems begin to decide what should happen next.

CIOs who recognize this shift early will not merely deploy better tools. They will redefine how intelligence itself is designed, governed, and scaled across the enterprise.

AI

Financial

Cognitive

ERP

Enterprise software

Digital Transformation