Say hello to the smart digital workplace: The impact of agentic AI

Even when operations are running smoothly, keeping airline passengers happy is a massive achievement.

And when operations falter? We’ve all been there. Lost luggage, slow booking systems and the dreaded delayed flight can quickly snowball from an inconvenience to an unpleasant incident that leaves everyone feeling frazzled.

If you’re given the chance to solve a lot of these problems with technology, you’d take it! And this is precisely what agentic AI makes possible.

The solution involves deploying specialized digital agents to monitor and manage various aspects of the travel experience. These AI agents are coordinated through multiagent orchestration to ensure they all work in harmony. 

Meet your virtual airline staff

Let’s see how this works in practice.

While a flight-operations agent keeps a watchful eye on real-time flight data to optimize schedules and swiftly address any disruptions, a maintenance agent proactively tracks the health of aircraft and schedules timely maintenance to minimize downtime.

Meanwhile, a customer-service agent handles passenger inquiries, updates and rebooking requests quickly and accurately with the help of chatbots and virtual assistants.

A baggage-handling agent coordinates the tracking and routing of luggage — because fewer lost bags means happier passengers!

At the heart of this system, an orchestrator agent uses the Agent2Agent (A2A) Protocol and the Model Context Protocol (MCP) to synchronize these specialized agents, allowing them to collaborate smoothly and resolve issues rapidly.

The results — as observed in an NTT DATA client engagement — include a 20% reduction in costs, a higher level of operational efficiency, fewer delays and a notable boost in customer satisfaction.

Organizations are not forgoing human workers. Instead, these workers are equipped to operate at a higher operational level, providing constant oversight and with more valuable responsibilities.

AI has come a long way in a short time

How did we get to the point where such advanced technologies are disrupting the transportation industry — and many others — for the better?

The world has seen a remarkable technological transformation over the past few years, and the journey is far from over. Since the groundbreaking launch of ChatGPT in late 2022, the evolution of AI has been nothing short of revolutionary.

When ChatGPT first hit the scene, what was once confined to labs and researchers suddenly became open and accessible to millions. This large language model (LLM), trained on massive amounts of internet data, demonstrated the incredible potential of AI in generating human-like text and engaging in coherent conversations. The idea of a chatbot having a meaningful conversation with a human no longer seemed far-fetched.

However, as with any new technology, ChatGPT and other early LLMs had their initial limitations. Their knowledge was limited at the time of training. In addition, they could “hallucinate,” generating inaccurate or irrelevant information because of their inherent constraints.

Enter the era of RAG and agentic AI

To address these challenges, the industry introduced retrieval-augmented generation (RAG), which combines the power of LLMs with external databases or knowledge bases to allow AI and GenAI systems to access up-to-date, context-specific information.

This integration made GenAI more dynamic and reliable. It created an environment that supports specialized AI agents that are smaller, faster, designed to handle specific tasks and able to learn and discern based on experience, making them more efficient and practical for real-world applications.

The current state of AI in the digital workplace is marked by the orchestration of these specialized agents. Instead of relying on a single, monolithic AI system, organizations are now using a network of smaller, task-specific agents that work together and make decisions with limited or no human intervention to achieve complex goals. This approach of designing autonomous, decision-making agents and systems (by combining and orchestrating a group of specific agents that act based on their circumstances) is known as agentic AI.

Protocols like A2A and MCP help these agents to work like a well-oiled machine:

• A2A makes it easier for different agents to communicate, share context and align toward a shared goal, even if they’ve been built separately to perform different tasks.
• MCP addresses lays down the rules of engagement — who leads, who follows, how decisions are made and how conflicts are resolved. By doing so, MCP addresses the need for governance, task delegation and trust within agent networks.

This orchestration allows for streamlined workflows, better decision-making and more contextually aware responses, all of which deliver significant business value.

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Every industry stands to benefit

You’ve already seen how agentic AI can make a difference in an airline. We’ve done this for other large organizations, too.

NTT DATA is one of the leading players in the agentic AI space. Our Workplace Smart AI Agent™ Suite is a modular, customizable collection of AI solutions designed to meet the needs of different industries.

In financial services, a leading bank implemented our agentic AI suite to deploy risk-assessment agents, customer-engagement agents and compliance agents, among others. Loan processing times were reduced, compliance accuracy was up, customer satisfaction improved and the bank achieved operational cost savings of up to 30%.

And a leading consumer-goods company worked with us to use agentic AI to take new products to market faster. Our solution included market-analytics agents, supply chain agents, marketing-automation agents and more, and gave the company a significant edge in a competitive market. They improved their market responsiveness and cut operational costs by up to 25%.

Getting multiple AI agents to work together on complex tasks will continue to create business value. As more organizations adopt these modular, customizable solutions, we can expect to see even greater innovations.

More innovation ahead

The question isn’t whether you’ll adopt an agentic AI ecosystem — it’s how soon you’ll do it.

Our Workplace Smart AI Agent™ Suite is a prime example of how agentic AI is solving real-world problems and delivering measurable results. The potential of the technology is vast, and we’re excited to see where it takes us next.

We understand the depth and breadth of this technology, and our workplace smart agents are based on industry best practices we have developed from years of cross-industry experience.

So, whether you’re a technology enthusiast, a business leader or just curious about the future, keep an eye on agentic AI. It’s shaping the future of the digital workplace, one agent at a time.

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