Critical considerations for CIOs to make successful AI investments

Since AI has become the catalyst for the next generation of business, it’s important for CIOs and those newly accountable for the success of this new investment area to have a clear understanding of how they can best realise its potential. Since IDC predicts worldwide spending on AI-supporting technologies will more than double to $749 billion by 2028, it’s important to know what success looks like. 

This means sharing insight on AIs true potential and some ways that businesses should approach AI implementations. This must go beyond just a tech investment, but in terms of building new business capabilities – challenges could be creating successful proof-of-concepts (PoCs), building an effective tech partnership, acquiring the right human tech expertise, and the right security and other ethical or compliance measures. There’s a huge amount to consider, especially when trying to move at warp speed to put Agentic AI and agentic commerce projects in motion. There’s also the added pressure of this as a new responsibility expanding the CIO role. 

Since AI isn’t effective on its own and relies on expert human training, it’s critical for business leaders to understand some key steps to building an effective digital partnership. This will help to avoid some critical mis-steps that can hold them back from making the most of their new technology investments. 

Only with the right planning and digital tech innovation will CIOs be best placed to succeed with AI implementations in the challenging second half of this decade and beyond. It’s not just about jumping on the train – it’s about selecting the right tech partner to ensure that everything lines up for success. 

The three basic pillars of AI 

When we say AI, what we’re really talking about is advanced machine learning, it’s an evolution of training a machine on a dataset and comes down to three basic pillars. The first one is having people and skills that can manipulate algorithms. This will create something that will make a difference, having people with the right intelligence to frame the problem correctly. 

Secondly, it’s how you manipulate the data (yours and the customers’) and it’s also the accuracy of that data and the completeness of that data. How you use that data by clever manipulation is two thirds of the way to using advanced machine learning, which we call AI. 

The final leg of that stool is the -ology – it’s how you put the talent from the people together with the data and harness that data – or weaponise it – to deliver value. AI is when machines start thinking. At the moment, we’re training a machine to interpret a set of data and use an algorithm. 

We’re essentially applying that dataset in a manner to address the key problems of an industry, and industries are exploding with ideas of how to build efficiencies and add value for customers. 

The rise of Agentic AI 

The focus has moved beyond Generative AI to one of its close relations – Agentic AI – which promises to independently automate business processes, taking things to the next level. In fact, a Capgemini survey predicts that 25% of all business processes are expected to operate at Level 3 (semi-autonomous) to Level 5 (fully autonomous) by 2028. Agentic AI goes beyond automating tasks to give systems the power to manage and optimise entire business functions on their own.  Business leaders, department heads and developers alike are exploring ways that AI can benefit their roles, their teams and the bottom line.  

CIOs and enterprise leaders face the daunting challenge of modernising systems and finding the right skills to build resilience for a new era in AI-powered business. According to Grand View Research, the enterprise Agentic AI market is growing exponentially, with a projected CAGR of 46.2% from 2025 to 2030. Tech leaders will be the chosen few to navigate their enterprise through a highly complex and evolving AI environment and to demonstrate business success. 

Real world Agentic AI use cases 

Generally considered by experts as stage three of five stages in the development of AI, Agentic AI sits between the conversational and reasoning stages (one and two) and innovating and organisational AI stages (four and five). In other words, while GenAI has revolutionised the way we create content, Agentic AI starting from a more advanced perspective to automate more complex processing tasks. it powerfully draws on multiple agents to autonomously learn and adapt to give rapid automated decisions. 

Channelled through forward-thinking tech leaders, AI is already reshaping industries. In financial services alone, Agentic AI is now proven in transforming risk assessment, claims management, fraud detection, and hyper-personalised services. It has reduced the bank loan application process for a top 10 global bank from around six days to under 24 hours. 

In manufacturing, by combining virtual factory management solutions with AI, an AI-powered copilot can revolutionise how users interact with virtual factory environments. In harnessing natural language, advanced scene understanding, and Retrieval-Augmented Generation (RAG), users have the ability to intuitively query, analyse, and optimise complex virtual scenes. This empowers users to make smarter decisions faster, enhancing productivity and efficiency in digital twin environments. In terms of business benefits, an Agentic AI platform can streamline operations, boost multichannel interactions, and drive exceptional customer satisfaction. 

In the automotive industry, Software Defined Vehicles (SDVs) featuring AI-driven technology are emerging as an attractive and efficient auto manufacturing method. FPT’s “AI-first SDV” approach marks a significant shift from traditional software to AI-based solutions. AI can revolutionise driver assistance, personalise in-car entertainment and increase user engagement by up to 35%. 

Agentic AI solutions in the healthcare setting are already elevating medical consultant care – from guiding junior doctors in complex decision-making, offering prognostic insights in oncology or cardiology and being tools to optimise orthopaedic recovery, pain management and rehabilitation and fall prevention. When embedded in EHR systems, Agentic AI can harness information from multiple sources to provide next-level personalised care strategies for patients. Furthermore, in healthcare, it’s transforming pharmaceutical services, in terms of accuracy and speed of provision. As citizens and customers, we can expect to see the direct benefits of services provision over the next year. 

Agentic AI is becoming central to cybersecurity strategies in threat detection tools, to detect risks, prompt instant response, and automate recovery to protect data and minimise downtime. 

As enterprises race to transform their digital capabilities, Agentic AI in software development can provide intelligent code suggestions, identify patterns, and enhance coding standards, enabling developers to work smarter and deliver better quality code faster. 

How business leaders can take advantage of Agentic AI 

The key challenge for most organisations is that they are saddled with legacy – it’s pervasive throughout every single sector.  Whether it’s a legacy process, technology or practices, everyone is nervous or resistant to change. It’s how to create a culture of change and educating customers through this that will make AI investments truly effective.  

To be able to utilise Agentic AI for business benefits, CIOs will need a combination of people, process and technology, which includes: 

• Finding the right skills 

Only projects with the right technology skills will be effective. Enterprises will need to look for a technology partner that has global delivery capabilities with a deep understanding of local markets. Looking at emerging tech hubs which have future-ready software engineers and ambitions to lead in AI will ensure a large-scale project can stay on track. Vietnam is among the leading countries in the world with the most IT graduates (around 60,000 annually), at universities offering STEM-focused curricula, including AI, Cloud, IoT, and Big Data. Vietnam has emerged as a well-respected global digital hub with a tech community that is setting new standards in business and technology. 

As part of this, FPT plans to train 10,000 semiconductor engineers and 50,000 AI specialists. A partner with similar customers also demonstrates success in the same field. From a top 10 global bank to a partnership with Chelsea Football Club, FPT is able to elevate customer experiences and next-level performance with customers across a wide variety of industry needs. 

• A clear data strategy 

Getting a company’s data in order for Agentic AI is the first challenge for CIOs. This means having a strategy to gain, extract and analyse the vast amount of data for meaningful business insights. For decades, enterprises have focused analytics strategies on structured data, which can be stored and analysed neatly in charts. Yet many of the best context-specific details that can produce meaningful business insights for Agentic AI are found in unstructured formats. This content is typically spread across many siloed servers, collaboration tools and archives. 

With their AI tools now only as good as the quality of their data, CIOs and data strategists are scrambling to organise and activate these diverse data sources so AI models can tap into their full potential. Harnessing unstructured data is a necessity to unlocking next-level possibilities. One emerging strategy is RAG (Retrieval Augmented Generation) which can turn this data into knowledge so that it can work with AI to enhance their decision making and drive innovation.

• Building the right tech partnership 

There are some particular skills and capabilities a tech partner must have to deliver for a modern digital enterprise’s needs of innovation, flexibility, and scalability. It’s not just about AI and technology; it’s about a perfect blend of tech and the right human tech expertise. It’s also not all about knowledge – it’s about working culture and work ethic. Being customer-first in design is essential to deliver new services that really work for their users. 

Agentic AI has become an indisputable complement to GenAI, with the power to automate decision-making processes, revolutionise industries and drive unprecedented transformation. However, there are some key considerations to ensure the safe and beneficial use that a tech partner will benefit from experience and insight. Improving Agentic AI requires addressing the biggest AI ethical concerns like bias mitigation, transparency, privacy and sustainability. Added to this, technical advancements in learning algorithms, perception and hardware will also be crucial to understand. Developing effective human-AI collaboration, including skill augmentation and continuous learning, is also essential.  

As adoption grows, strategic data practices and elevated cybersecurity are also essential. There is also the issue of potential emergent behaviour, as AI systems become more complex, when they can exhibit unexpected behaviours. Therefore, control and safety aspects will be a hurdle in AI development. 

Making Agentic AI happen 

The long-term impacts of Agentic AI are broader and more significant than those of Generative AI. When combined, they create sophisticated systems with huge potential for efficiency, precise information parsing, and smarter decision-making. With actionable insights faster than any human team, Agentic AI has the power to change critical functions with unparalleled precision.  

It doesn’t need to be the biggest team with the biggest budget. With a small proof of concept, taking an iterative approach to getting this right can develop into something that works and builds out into multiple service lines. The culture of problem solving and naturally inquisitive tech approach of the East can collaborate effectively with the Western customer centric approach. 

Over time, Agentic AI will begin to manifest itself in the physical world, perhaps driving robotics to make decisions and operating effectively in complex physical environments that only years ago was pure fiction. Today’s tech leaders must take the right steps with AI to move industries forward in a way that benefits customers, business and the planet.