CIOs, Here’s How to Build a Culture of AI and Deploy Agentic AI

As a CIO in 2025, you’re steering the business through one of the most transformative shifts in decades. AI has moved from experimental to operational in the blink of an eye. The pressure to show how AI drives tangible value has never been higher.  At Saratoga, we’ve been at the coalface of this transformation. We’ve implemented Agentic AI solutions that go far beyond surface-level automation, and are working on applying AI Agents in real-world business environments where trust, interoperability, and measurable outcomes are non-negotiable.  

What Makes Agentic AI Different, and Why It Matters to CIOs 

Traditional AI implementations were typically individual input/response interactions with a single, general-purpose LLM. This may be sufficient for some use cases but can be hard to govern. We’ve taken a different route with what Gartner predicts is this year’s single most important tech trend to watch: Agentic AI. 

With Agentic AI, you orchestrate a network of specialised agents, each with a clear role, well-scoped data, and strong oversight. We’ve deployed this in use cases such as fund commentaries and client reporting, where accuracy, tone, and auditability are non-negotiable. As a CIO, this modular approach gives you agility without compromising on control. You avoid lock-in, enable better observability, and reduce the risk of hallucinations or compliance breaches. 

Here’s what we’ve learned, and how you can take your organisation from AI-aware to AI-enabled. 

Start by Building an AI Culture, Not Just a Toolset 

Most AI projects fail not due to poor technology, but due to lack of buy-in, clarity, or cultural readiness. That’s why we’ve invested heavily in creating a strong AI culture, both internally and with our clients. 

We prioritise raising awareness through ongoing newsletters, internal and external training, thought leadership, and collaborative forums. Our “Cognitive Computing Crew” meets regularly to explore opportunities, share lessons, and develop proofs of concepts that don’t just live on PowerPoint slides.  

We’re transparent about risks, ethics, and data security. From responsible AI policies to regular knowledge-sharing meetups, we’ve made it part of how we operate.  

So, if you’re trying to secure board support for AI investment, start by demonstrating cultural readiness. Show how AI is more than a one-off experiment but is rather a strategic capability that everyone, from your analysts to your architects, can understand and engage with. 

Interesting read: AI Agents – What We’ve Learnt So Far 

Practical Implementations of AI That Drive Value 

It’s easy to be impressed by flashy AI demos. But as a CIO, you know that what really matters is implementation.  

Understand better: A Guide to AI Agents 

Use Case #1: Agentic AI for Insurance Document Processing 

This solution is a smart, agent-based system designed to automate insurance document handling. This reduces manual workloads, enhances routing accuracy, and integrates with legacy systems seamlessly.

It’s built on the principle of Agentic AI: deploying small, purpose-driven AI components to manage complex tasks with precision. 

Here’s what we built.  

1. Document Upload

The process starts when a user uploads a document, usually in PDF format. These are typically insurance claims, policy documents, or contracts. In our instance it is related to auto body repair claim forms.  

2. Text Extraction via OCR Agent

For scanned or image-based PDFs, an OCR (Optical Character Recognition) Agent converts visuals into machine-readable text. This step ensures all documents, regardless of format, are processed uniformly. 

3. Triage Agent
   Once text is extracted, the Triage Agent analyses the content and determines the next step. It classifies the document and routes it to the appropriate subagent for processing. In this instance each subagent looks at documents from a specific company.

4. Integration with Core Systems
   Depending on the triage result, the content is passed to one of several core systems: System A, B, or C. Each handles a specific function but connects to a unified front-end, delivering a consistent user experience across departments.

5. Specialised Format Extraction
   When documents contain complex formats or scanned forms, a specialised agent steps in to extract the relevant data accurately and reliably.

The Agentic AI Difference 

This architecture delivers immediate operational benefits. 

• Cuts down manual processing time and reduces human error 

• Makes smarter routing decisions using a dynamic triage agent 

• Easily adapts to new document formats or systems 

• Ensures interface consistency, even when backend complexity varies 

This is a clear example of how Agentic AI brings intelligence and order to traditionally messy workflows, an approach that’s ideal for scalable enterprise automation. 

Read more: An Early-Adopters Guide to AI Agents for Financial Services 

Use Case #2: Agentic AI for a WhatsApp Wallet 

We’ve also developed an AI-powered wallet system built entirely within WhatsApp, a familiar and trusted platform already used by billions. This implementation brings embedded finance to life through Agentic AI, and gives users a seamless experience while ensuring compliance, traceability, and functionality. 

Here’s how it unfolded.

1. WhatsApp as the Frontline Interface
   Users interact through a WhatsApp Business Account. Whether they’re checking balances, making payments, or uploading identity documents, every interaction starts here, lowering friction and increasing adoption.

2. Orchestration Agent: The System Brain
   At the core of the system is the Orchestration Agent. It manages logic, routes tasks, and ensures each action, whether verifying an ID or executing a transaction, is handled by the correct agent.

3. Local Storage of Message History
   To maintain context and support audits, all user interactions and transactions are stored in local storage. This ensures traceability and enables intelligent response generation.

4. KYC Agents for Identity Verification
   When a user submits ID documents or selfies, the KYC Agents (Know Your Client Agents) go to work. These agents use OCR and image analysis to extract identity details and validate authenticity which ensures compliance with regulatory standards.

5. Wallet API Tools
   All financial operations – sending money, checking balances, or topping up – are handled via Wallet API Tools. These agents interact with third-party providers, making it easy to swap out or upgrade payment backends as needed.

Why This is Great 

This is what the agent-driven system delivers. 

• Wallet functionality through a platform users already rely on daily 

• Modular AI agents that scale easily and simplify maintenance 

• Automated, compliant KYC with minimal onboarding friction 

• A financial tool accessible even to users with no banking app or formal account 

It’s a strong example of how AI agents can power embedded finance; integrating complex systems into everyday interfaces and radically simplifying the user experience. 

Our Key Lessons for Enterprise CIOs 

If you’re mapping your AI strategy, here’s what we’ve learned from deploying Agentic AI in production. 

• Be cost-conscious: Not every problem needs an LLM. Use the right tool for the job. 

• Avoid all-in-one models: Tool-aided agents outperform generalist models. 

• Orchestration is key: AI agents need clear inputs, feedback loops, and observability. 

• Data security is non-negotiable: AI must integrate with your existing controls and frameworks. 

• Start small, learn fast: Quick proofs of concept unlock organisational support. 

• Think compliance-first: AI introduces new challenges for audits, versioning, and transparency. 

• The future is collaborative: You’re automating tasks, and you’re augmenting teams. 

Experience is Your AI Advantage 

As an end-to-end software development and AI solutions company in South Africa with decades in the game, we think beyond quick wins, and focus on embedding AI into how businesses think, work, and grow. From developing internal AI assistants, to deploying NLP and claims-processing tools, to orchestrating multi-agent systems for the financial services sector, we’re ahead because we’re experienced. 

And we’re not finished. Our roadmap continues to explore how agentic architectures, multimodal inputs, and real-time data integration can serve high-trust, high-stakes environments. 

As a CIO, you know there’s no plug-and-play answer to AI adoption. But with the right culture, architecture, and implementation strategy, you can lead your organisation into the next era of intelligent systems with agility, security, and confidence. 

If you’re ready to explore what AI can do beyond the hype, we’re here to help you build it right.