Building AI into the backbone
A recurring theme in Cheema’s strategy is the importance of integration. Disconnected systems and fragmented data architectures, he warned, can undermine even the most advanced models.
“Layering AI on fragmented data leads to biased outputs, slow feedback loops and scale stagnation. Integration beats raw intelligence,” he said.
Within his own organisation, Acuity Analytics, Cheema said the company consolidated systems into an integrated cloud-based enterprise stack. For example, they are invested in Oracle Fusion ecosystem for ERP, HCM and performance management functions, alongside a unified data warehouse and analytics capabilities, to create a consistent and governed data foundation.
Cheema said the shift has been from AI as a feature to AI as a foundational capability. “We’re treating AI as the backbone of how we operate, not something we add later,” he said.
When it comes to measuring success, Cheema believes organisations often focus too narrowly on adoption rates or cost reductions, but the emphasis should be on business impact.
Improvements in forecast accuracy, faster access to insights, shorter process cycle times and more predictable outcomes are used to gauge whether decision quality has actually improved. Client satisfaction, revenue contribution and governance maturity, including explainability and audit trails, are also considered important indicators.
The next phase
Looking ahead, Cheema expects enterprise operating models to evolve rapidly over the next three to five years. Ultimately, competitive advantage will depend less on who has the most advanced algorithms and more on how well organisations design their operating models.
“The difference between winners and laggards won’t be the technology itself. It will be the operating model, governance maturity and how well humans and AI collaborate,” he added.
For many enterprises, that shift signals the end of AI as a project and the beginning of AI as infrastructure.
