Now that we’re firmly in the AI era, organizations have shifted gear beyond generative AI to exploring Agentic AI pilots to drive transformation across business functions. In fact, PwC reported in 2025 that 88% of surveyed senior executives plan to increase their AI budgets within 12 months due to agentic AI. McKinsey research similarly backs the trend with 23% of respondents reporting that their organizations are scaling an agentic AI system somewhere in their enterprises.
Taking AI a stage further than automating repetitive tasks, Agentic AI holds further potential for innovation and creativity and is already proving to be a powerful phenomenon, with broader and longer-term impacts than that of Generative AI. It is transforming business operations by autonomously making decisions and taking actions across various applications.
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. However, there are critical considerations that leaders must address to prepare their organization’s systems and to build trust in this technology to ensure success and keep the business safe.
Agentic AI is already reshaping industries and is becoming critical to business resilience. It is central to cyber security strategies in threat detection tools, helping to detect risks, prompt responses, and automate recovery to protect data and minimise downtime.
Challenges to embracing Agentic AI
It is essential for CIOs, CAIOs (chief artificial intelligence officers), and those accountable for AI initiatives to understand the challenges they must address to achieve intended outcomes. Investment in modernising legacy systems to enable effective AI adoption is a key requirement.
Since IDC predicts worldwide spending on AI-supporting technologies will more than double to $749 billion by 2028, businesses need to close the gap between innovation and modernisation to fulfil potential. Linked to this, while budget cycles have shortened, the need for continual transformation means teams have less tolerance for experimentation that does not deliver material value.
Key challenges are likely to include creating effective proof-of-concepts (PoCs), building technology partnerships, acquiring appropriate technical expertise, and implementing security, ethical, and compliance measures. Since AI effectiveness and resilience in organizational contexts rely on expert human training, CIOs and enterprise leaders need to develop skills that support resilience in AI-enabled business models. These include data governance and cyber security capabilities to build trust in data and strengthen cyber security across the technology ecosystem.
Another challenge is the potential for AI systems to exhibit unexpected emergent behaviours as complexity increases. As a result, control and safety mechanisms may present hurdles in organizational AI development.
Beyond technical considerations, building trust in agentic AI is critical. This requires alignment across multiple factors, including transparency, security, human oversight, and user empowerment.
How business leaders can take advantage of Agentic AI
A key challenge for many organizations is the persistence of legacy systems, processes, and practices across industry sectors. Organizations may be cautious or resistant to change, which can constrain AI adoption. Establishing a culture that supports change and engaging customers throughout the transition are important to ensure AI investments deliver value.
To enable Agentic AI success, leaders will need a combination of people, process, and technology, which includes:
Finding the right skills
AI initiatives rely on the right technology skills for success. Considering emerging technology hubs with future-ready software engineers and ambitions to lead in AI may help large-scale programmes remain viable. Specialist expertise can help address AI ethical concerns, such as 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, will also be essential.
A clear data strategy
Preparing organizational data for agentic AI is a significant challenge. 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. If AI tools are only as good as the quality of their data, CIOs and data strategists must prioritise organizing and activating their diverse data sources so that AI models can tap into their full potential.
Building the right technology partnership
Due to the scale of projects, enterprises are best to look for a technology partner that hasglobal delivery capabilities with a deep understanding of local markets. Some particular skills and capabilities needed to deliver for a modern digital enterprise’s needs are innovation, flexibility, and scalability. It requires a balanced blend of technology and human expertise. Other considerations for projects include working culture and work ethic. Being customer-first in design is essential to deliver new services that really work for their users.
Taking a holistic approach to agentic AI
Businesses are increasingly demonstrating Agentic AI’s power to transform functions across all industries. But only with the right planning and digital technology innovation will CIOs, CAIOs, and AI project leaders be best placed to succeed with AI implementations. These leaders will be the ones to navigate their enterprise through a highly complex and evolving AI environment and to demonstrate business success, while balancing this with organizational resilience.
By ensuring the right foundations are in place and taking a holistic approach to Agentic AI investment, technology leaders will innovate to stay competitive and move their industries forwards whilst building stakeholder trust and keeping their critical assets safe.
The author
Mark Scrivens, UK Chief Executive Officer, FPT Corporation
