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Cloud
Enabling Enterprise Agility & Resilience with a Robust Cloud Strategy
Artificial Intelligence
Building a Smarter, Connected Tomorrow with AI-first and Agentic AI
HIGHLIGHTS
- Agentic AI promises autonomous decision-making, but measuring its success is complex.
- Success depends on defining measurable goals and adopting objective frameworks that link AI outcomes to business impact.
- Continuous performance tracking, governance, and ethical oversight ensure that agentic AI delivers tangible and sustainable value.
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What is agentic AI?
Agentic artificial intelligence (AI) refers to systems that can act independently, make decisions and execute tasks without constant human input. These systems rely on data and algorithms to function, making them different from traditional automation. For enterprises, defining agentic AI clearly is the first step. A well-defined concept of agentic AI is critical to set realistic expectations and guide deployment. Organisations that clarify which tasks, processes, or decisions are suitable for agentic AI can create measurable objectives, ensuring initiatives are aligned with business goals. This understanding also helps establish evaluation frameworks and success metrics, reducing the risk of overstatement and misaligned deployments.
Knowing what agentic AI can and cannot do helps in creating measurable goals, avoiding hype, and building the right governance for it. Ultimately, defining agentic AI accurately allows enterprises to harness its true potential, improve operational efficiency, and prepare for advanced applications that can continuously learn and optimise within the organisation’s unique ecosystem.
Measuring success
AI is transitioning from a co-pilot role—of providing insights and augmenting decisions—into the enterprise execution layer, powered by agentic AI. These autonomous, goal-driven agents don’t just advise; they act. They orchestrate workflows, execute complex processes end to end, and dynamically adapt to business objectives, embedding intelligence directly into operational fabric.
Organisations often declare AI deployments as major wins. However, independent studies reveal a significant gap between perception and reality. Many organisations rely on internal assessments, which may lack rigour or standardised evaluation methods, leading to overstated claims of AI success.
The misalignment between expectation and reality stems from several factors. Success cannot be judged by enthusiasm or investor updates alone. It must be backed by tangible metrics such as operational efficiency, error reduction, or customer impact. By adopting objective measurement frameworks, companies can evaluate outcomes accurately, identify areas for improvement, and ensure agentic AI deployments generate real operational value rather than just theoretical benefits. Enterprises that establish clear measurement systems are better equipped to track real progress.
Perception vs reality
The gap between what AI promises and what it delivers is often shaped by three factors:
- Culture: Company culture has a huge bearing on the success of agentic AI. Organisations that encourage innovation may celebrate pilot successes without confirming whether they deliver measurable business outcomes. Innovative teams may celebrate early wins without proof
- Investor pressure: Investor pressure can influence reporting, leading to inflated claims aimed at maintaining confidence. Companies may highlight positive stories to reassure stakeholders.
- Lack of standards: The absence of standardised metrics makes evaluation subjective, which may result in misinterpretation. Without agreed metrics, evaluation methods and practices may differ from industry standards
Finally, To bridge this gap, enterprises need rigorous evaluation frameworks and clear use-case definitions. Establishing measurable goals, continuously monitoring performance, and ensuring alignment with strategic objectives enable organisations to assess AI performance objectively. By doing so, enterprises can turn agentic AI from a celebrated concept into a tool that consistently delivers tangible business results across functions and departments.
Enterprises must set pragmatic goals and perform thorough pre-implementation checks to avoid disappointment and wasted investment.
Use cases studies
Industry examples highlight both the promise and challenges of agentic AI. In the financial sector, one leading institution successfully integrated AI into fraud detection, significantly reducing manual review time. The success stemmed from a well-defined framework: clear objectives, measurable outcomes, and continuous monitoring ensured that the AI system generated tangible business benefits.
On the other hand, some AI projects have created new bottlenecks when not aligned to business needs. Deployments without thorough pre-implementation evaluation or clearly defined success metrics can lead to operational inefficiencies and missed opportunities. These case studies emphasise the importance of structured planning and the need to align AI strategies with organisational goals. Without this alignment, even advanced systems can underperform.
Governance models
As agentic AI adoption accelerates, governance and ethical oversight become critical with a greater need for transparency, accountability, and fairness. Without proper governance, AI deployments risk bias, operational errors, and unintended stakeholder impact.
Effective governance models incorporate three key elements:
- Transparency standards: To ensure AI decision-making processes are explainable
- Ethics committees: Dedicated teams to oversee AI alignment with corporate values
- Regular audits: Periodic reviews of functionality, compliance, and ethical performance.
These structures build trust in AI systems and help organisations manage ethical, operational, and regulatory risks.
Enterprises can start by:
- Setting up ethics committees for oversight
- Establishing transparency standards for decision-making
- Running regular audits to ensure compliance
Strong governance ensures AI outcomes remain trustworthy and aligned with organisational values.
Safeguards
Deploying AI also brings risks from system failures to cybersecurity threats. To stay resilient, organisations need layered safeguards.
Organisations can adopt several practical measures:
- Ethical review boards: To evaluate the implications of AI decisions
- Continuous monitoring: To detect errors or security issues in real time
- Contingency planning: To develop strategies to respond to disruptions
With these measures, enterprises can integrate AI responsibly, balancing innovation with protection against risks. This approach ensures AI investments generate tangible, long-term business value while mitigating risks in complex, autonomous environments.