3 obstacles to agentic AI adoption and how to overcome them

The era of agentic artificial intelligence (AI) is here. We’re moving beyond chatbots that only respond to questions to intelligent agents capable of autonomously handling complex tasks we don’t have time for, aren’t good at or can’t do.

While Agentic AI can unlock new levels of productivity and innovation, helping us solve problems that were previously unsolvable, countries and regions must act decisively to help unlock its full potential and secure a leadership role in the global economy.

This will involve addressing three major obstacles: an infrastructure constraint, a trust deficit, and a data gap.

Agentic AI demands a radical reinvention of our current infrastructure. AI workloads require massive levels of compute, energy and network capacity. The rise of multi-agent systems, in which numerous AI agents communicate and collaborate in real time to complete complex, multi-step tasks, places unprecedented demands on data centres.

Current architectures designed for conventional applications are too constrained to handle the scale and complexity of agentic AI efficiently.

The world needs AI-ready data centres equipped with advanced networking technologies and flexible, scalable compute resources. The infrastructure must support multi-node architectures that enable agents to work in concert across cloud environments and edge locations.

Networks must be ultra-low latency, energy-efficient and highly secure to maintain performance and trustworthiness.

Without such infrastructure, the full potential of agentic AI cannot be realized. For leaders, investing in and modernizing infrastructure is foundational, not optional. The future of AI depends on building systems that can handle the enormous computational and networking demands while maintaining resilience and scalability.

Trust is a prerequisite for AI adoption. If people don’t trust it, they won’t use it. This deficit of trust will stall the adoption, innovation and economic benefit potential of AI.

Agentic AI introduces new challenges for safety and security. Unlike traditional software, AI models are non-deterministic, so they can behave unpredictably and their deployment across multi-cloud, multi-agent environments introduces new risks and vulnerabilities.

The stakes are high: failures or breaches can lead to severe consequences, from data theft to erroneous decisions at scale, such as automated financial approvals or medical research going wrong.

Building trust requires a comprehensive approach to AI safety and security. It needs to be deeply embedded in every layer of the stack to protect AI applications, data and traffic. It needs to continuously validate AI models to detect vulnerabilities or unexpected behaviours and enforce safety guardrails in real time.

It needs sophisticated identity validation as multi-agent workflows become pervasive. And security teams must evolve from being perceived as barriers to adoption to becoming accelerators of adoption by implementing security in a way that doesn’t slow down development or innovation.

For government and business leaders, overcoming the trust deficit means prioritizing security technology investments while collaborating across domains to establish common safety standards and governance. Only with trust can the promise of agentic AI be fully realized.

Data is the fuel for AI. Most organisations consider their own data their moat but they haven’t been able to unlock its full potential for AI – a major gap.

Traditional AI model training has relied heavily on vast amounts of human-generated data. However, the supply of publicly available data is nearing exhaustion and privacy concerns are driving enterprises to repatriate data into private clouds.

The next generation of AI will increasingly depend on machine-generated data, which is growing exponentially faster than human-generated data and represents a massive, untapped opportunity.

Synthetic data is also becoming increasingly important, as it can enhance model performance, reduce reliance on sensitive real-world datasets and unlock innovation across industries by enabling AI to learn from diverse, scalable and privacy-preserving sources.

Embracing synthetic data will empower organizations to harness AI’s full potential while managing risks effectively.

Overcoming the data gap requires organizations to adopt platforms optimized for machine data and synthetic data. It also requires leaders to navigate regulatory oversight carefully to balance innovation speed with safety and data privacy.

These are key for empowering organizations to harness AI’s full potential to generate original insights and solve problems that were previously out of reach, expanding the boundaries of what is possible.

The future belongs to those who understand both the immense opportunities of AI and how to navigate the obstacles ahead. Agentic AI will redefine productivity, create new roles focused on AI oversight and governance and transform workflows across every sector.

But success requires proactive leadership that invests in AI-ready infrastructure, builds trust through robust security and embraces new data paradigms.

The AI era demands a spirit of reinvention and urgency. By focusing on overcoming infrastructure, trust and data challenges, government and business leaders can position their organizations to lead in the AI-driven future, unlocking economic growth and societal benefits at unprecedented scale.

The time to act is now. The next wave of AI is here and with it, the chance to shape a future where intelligent agents amplify human potential and drive transformative progress.