PAC observes that many organisations struggle to scale AI initiatives beyond the proof-of-concept (PoC) stage. While PoCs often demonstrate technical feasibility, they frequently fail to deliver measurable business value due to weak or fragmented business cases. This challenge is especially pronounced with generative and agentic AI, where successful scaling requires a holistic approach that integrates financial justification, organisational readiness, and strategic alignment. AI must be positioned as a driver of transformation, not just a standalone innovation.
A robust business case should clearly link AI capabilities to business priorities, identifying tangible benefits such as improved efficiency, customer engagement, and innovation speed, alongside intangible gains like agility and resilience. Financial models must reflect the total cost of ownership (TCO), including ongoing costs for governance and compliance. Return on investment (ROI) should go beyond cost savings to include value creation through faster decisions, better quality, and new revenue streams. Transparent assumptions and lifecycle-based value tracking help justify investments and guide vendor selection.
PAC considers it critical to understand that the hardest challenge scaling AI is that it also demands cultural and organisational change. This includes fostering collaboration between business and technical teams, embedding governance, and building AI literacy. Ethical frameworks and transparent communication are essential as agentic AI capabilities take on more autonomous roles. Executive sponsorship is critical to ensure strategic alignment and drive behavioural change. Ultimately, moving from pilot to production is less about technology and more about evolving organisational capabilities, supported by clear financial logic, adaptive measurement, and sustained leadership commitment.
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