DIGITAL TRANSFORMATION
“ Our organisations were built for Industrial Era limits – like railroad foremen who could only manage 50 miles of track. We built hierarchies around human cognitive limits. AI, however, can reason across vast context spaces without those same boundaries.
“ Automating a process built for human limitations misses the point. RPA starts with the process; true AI transformation starts with the outcome. When you optimise for the outcome instead of the process, you step back and ask the real question‘ How do we redesign work now that humans and AI agents are working together?’.”
The pilot paradox The core barrier to scaling AI is not infrastructure, regulation or talent, explains Aditya Challapally, a lecturer and instructor on AI at Stanford University. It is learning.
“ Most generative AI systems do not retain feedback, adapt to context or improve over time,” he says.
“ A small group of vendors and buyers are achieving faster progress by addressing these limitations directly. Buyers who succeed demand process-specific customisation and evaluate tools based on business outcomes rather than software benchmarks.”
According to Aditya’ s own State of AI in Business report published with MIT, 95 % of generative AI pilots fail to achieve scale because enterprises actively avoid the friction required to change.
Sheldon argues this failure stems from a piecemeal strategy rather than addressing holistic enterprise domains:“ Part of the reason for that is there is a proliferation of too many pilots. Very few organisations are taking the approach of looking at the complete domain of their business.
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