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AI at Work: How GenAI Is Rewiring the Enterprise Backbone
AI at Work: How GenAI Is Rewiring the Enterprise Backbone

May 15, 2025

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The generative AI (GenAI) landscape has evolved fast—and its trajectory is only becoming clearer. With enterprise adoption climbing to 72%, we’ve moved decisively beyond proof-of-concept. GenAI is no longer experimental; it’s operational. The question facing business leaders now isn’t whether to adopt AI, but how to do it meaningfully.

At a time when resilience is a boardroom priority, GenAI presents a real opportunity for competitive advantage. But it’s not about trend-chasing. It’s about embedding intelligence into the backbone of the enterprise—whether that’s in supply chain optimization, workforce insight, or customer engagement.

 

Where GenAI Delivers the Most Value

Not all enterprise functions benefit equally from GenAI. The greatest gains lie in complex, data-intensive workflows—supply chain logistics, procurement planning, and freight operations, to name a few.

Imagine a recommendation engine that actively reduces procurement costs by analyzing historical spend and market dynamics. Or AI systems that consolidate freight booking, shipment tracking, and rate comparisons into one streamlined interface. These aren’t hypotheticals—they’re already in use, reducing friction, boosting efficiency, and de-risking operations.

The same logic applies to workforce management. GenAI-driven tools can surface real-time sentiment across platforms by mining employee feedback through data marts and web crawlers. This enables companies to understand what’s driving morale—or damaging it—then adjust HR policies accordingly. It’s not just analytics. It’s adaptive strategy.

 

Closing the Gap Between Insight and Empathy

AI’s influence is also reshaping how brands engage with customers. In sectors like retail, GenAI allows for hyper-personalized experiences—think precision-driven product recommendations that are actually aligned with customer preferences.

In jewelry retail, for instance, we’ve seen firsthand how AI-powered personalization has helped boost conversion rates while reducing irrelevant recommendations. Customers aren’t just browsing. They’re being understood.

 

Don’t Underestimate the Risks

But for all its potential, GenAI brings several challenges that enterprises must navigate. Data security, model bias, and algorithmic opacity can become business liabilities if not actively managed. Organizations must implement robust model management practices and risk mitigation strategies to ensure that GenAI delivers benefits while minimizing potential downsides.

  • Data Privacy & Security: Strong governance isn’t optional. Enterprises must harden their data practices and maintain control over inputs and outputs.
  • Bias in Models: Diverse teams are essential to building inclusive, balanced systems. Regular testing against real-world data helps spot and correct systemic bias.
  • Explainability: Techniques like Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) help unpack why a model made a particular decision—critical in regulated industries or high-stakes environments.
  • Stakeholder Trust: AI isn’t plug-and-play. It requires engagement, transparency, and communication across the lifecycle—from model development to deployment.

 

What’s Next?

The GenAI services market is projected to exceed $300 billion—and that growth isn’t fueled by hype alone. But to truly unlock GenAI’s potential, companies need to focus less on blanket adoption and more on strategic fit.

This is where the real work begins: aligning GenAI investments with high-impact use cases, refining operating models, and staying agile enough to pivot when risks emerge or business needs shift.

In a complex and constrained business environment, GenAI is no silver bullet—but if applied purposefully, it might just be the smartest tool in the kit.

 

About the Author:

Nitesh Mirchandani is the Chief Business Officer at Mindsprint, a technology firm offering purpose-built AI-led solutions to modernize enterprise operations.

 


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Nitesh Mirchandani
Chief Business Officer

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