How an Organizational Shift Can Unlock Real Value from a Stalled AI Strategy - SPONSOR CONTENT FROM PUBLICIS SAPIENT

2026-05-04 · Source: Feeds - HBR.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Software Development & Engineering · Depth: Intermediate, short

Summary

Many enterprises struggle to integrate AI into core operations despite significant investments, leading to isolated initiatives that fail to scale or connect to measurable outcomes. Gartner predicts that by 2028, most enterprises will use AI that operates and triggers actions within workflows, but few have achieved this depth. The challenge lies in a gap between technology investment and the ability to apply AI within core systems, scale successful applications, and link outputs to performance. Organizations making progress treat AI as a capability to build, designing efforts for sustained implementation. For example, a global financial services firm reduced maintenance costs by 30% and increased efficiency by 70% by embedding an AI platform into its trading applications. Another U.S. healthcare company modernized 10,000 claims screens, reducing its timeline by 70% and saving $90 million using an AI-powered development platform.

Key takeaway

For executives overseeing AI strategy, your focus should shift from isolated AI pilots to deep integration within core operational workflows. Prioritize platforms and partnerships that embed AI directly into decision-making systems, ensuring it influences revenue-generating activities like pricing, customer engagement, and product delivery. This approach will transform AI from a mere efficiency tool into a driver of measurable business outcomes and sustainable growth.

Key insights

Integrating AI directly into core operational workflows is crucial for realizing scalable, outcome-driven enterprise value.

Principles

• Treat AI as a capability to build, not a product to deploy.
• Embed AI within core systems to influence decisions and interactions.
• Combine AI platforms with enterprise expertise for scalability.

Method

Integrate AI platforms into existing systems and workflows, supported by experienced builders and domain experts, to automate software lifecycles and enable AI agents to act within context.

In practice

• Dynamically update pricing models within commerce platforms.
• Incorporate usage data into release cycles for feature prioritization.
• Modernize legacy systems using AI-powered development platforms.

Topics

• Enterprise AI Strategy
• AI Operationalization
• AI-powered Workflows
• AI-driven Modernization
• Value Realization

Best for: Executive, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Feeds - HBR.org.