Strategise

  • How can organisations formulate a successful digital transformation strategy by leveraging AI as a transformative strategic asset?
  • What phased decision-making framework can guide organisations from selection to scaled deployment of AI agents, ensuring strategic alignment, trust, and sustainable value realisation?

Research Topics

Research topics under 'Strategise'

Name: Gautam Jha, Research Associate

Problem Statement

AI-agents promise to transform business capabilities, yet their adoption across contexts remains sporadic—often limited to personal usage, constrained deployments, or worse still marked by user scepticism.  Recent data indicates that 49% of businesses have yet to move beyond a stage of PoCs (proof of concepts).

This research seeks to offer strategic guidance and decision frameworks to support the adoption and orchestration of AI-agents in business contexts. Specifically, it aims to understand the extent to which autonomous AI can generate organizational value and what drives or inhibits such outcomes.

Preliminary observations from our research  indicate that in applications requiring operational processes to change via Ask AI prompting paradigms, frustration and diminished uptake often result. On the other hand, unchecked enthusiasm also risks being detrimental to wider adoption.

Employing a mixed-methods approach, the study will identify distinct phases of AI-agent adoption and uncover characteristics, enablers, and barriers that shape both adoption trajectories and value realization. Using intervention-based research strategies that blend participant observations, experimental and real-world case studies, the research provides insights into how firms can transition from selection to deployment and ultimately to scaled use of agentic AI.

Expected Outcomes

This research will provide a decision framework that businesses can apply across the phases of AI agents, serving as guidance for businesses to support the adoption and orchestration of AI agents in business contexts. Examples and emerging insights include:​

  • Uncover the characteristics, enablers, and barriers that shape both AI-agent adoption and value realization by analysing cross-sectoral firm practices and adoption trajectories​.
  • A value-anchored AI agent framework that assesses a firm’s process integration and interoperability across key stages—qualification, selection, promotion, experimentation, and scaling. It enables organisations to govern AI agentic solutions based on strategic alignment rather than purely technical criteria.
  • Guidance for AI-native / AI-first service providers for increased adoption includes key dimensions such as self-declaration, self-explanation, and self-evaluation paired with orthogonal human validation to ensure trust, transparency, and accountability