Agentic AI ambition outpaces enterprise readiness, report finds
Experts warn that layering AI agents onto human-centric models is ineffective, advocating instead for a systemic 'agentic business transformation' to unlock value.
A significant disconnect is emerging between enterprise ambition and operational reality regarding the adoption of agentic artificial intelligence. According to data cited in a report by MIT Technology Review, 85 per cent of organisations aim to be agentic within the next three years, yet 76 per cent report that their current infrastructure and operations are unprepared for this transition. Leaders cite a lack of readiness across people, processes, and workflows as primary barriers to execution.
Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting, argues that many organisations are attempting to layer AI agents onto existing human-centric operating models rather than reimining how work is structured. Shah likens this approach to adding sticky tape to parts of an operating model that is breaking, noting that it prevents companies from unlocking the full value of agentic AI.
The industry is shifting towards a framework termed agentic business transformation (ABT), coined by enterprise AI platform Ema in partnership with HFS Research. This approach requires a systemic redesign of technology stacks, workforce structures, and success metrics to integrate AI agents as active participants in value creation, rather than treating them as mere productivity aids or point tools.
Early deployments in customer service, HR, and sales suggest that AI agents could accelerate business processes by 30 to 50 per cent and reduce low-value work time by 25 to 40 per cent at scale. To achieve this, enterprises must pivot from linear, human-operated workflows to a connective tissue architecture that allows agents to coordinate across multiple systems simultaneously.
Workforce dynamics will also require significant adjustment. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment. Managers in hybrid human-AI teams will need to address issues of trust, explainability, and psychological safety, while success metrics must shift from activity-based measures to outcome-based indicators such as customer retention and revenue impact.
