By 2028, the majority of enterprises will have transitioned to that operate and trigger actions within workflows and across the organization, Gartner predicts. Yet few organizations have embedded AI deeply enough to act at that level, let alone see results.

This is because a large gap separates their technology investment from their ability to apply AI within core systems and real workflows, scale what works, and connect output to trackable performance.

To close this gap, organizations must integrate AI into their operations. Until they do, they risk building AI initiatives that are productive only in isolated cases—disconnected from the outcomes that matter most.

The Execution Challenge

Many organizations have invested heavily in AI tools and platforms, but those investments often sit alongside existing systems rather than inside them. This AI may generate insights, content, or code, but without integration into operational workflows, those outputs rarely translate into decisions or actions at scale.

With so many tools and vendors to choose from and so little coordinated guidance on how to integrate effective AI solutions, the options are difficult to operationalize at scale. As a result, organizations often struggle to move from isolated use cases to repeatable enterprise-wide application.

Companies that are making progress with AI treat it as a capability to build rather than as a product to deploy. These organizations design their AI efforts with implementation in mind so they can sustain and expand upon new capabilities over time.

One global financial services firm recently accelerated its modernization with AI after struggling to routinely update its trading applications platform, which stalled growth and elevated risk. After embedding an AI platform into its processes, engineers worked faster and smarter, reducing maintenance costs by 30% and increasing efficiency by as much as 70%. The change enabled the firm to deliver updates to its trading platform quickly and support evolving market demands.

From Efficiency to Outcomes

Focusing on execution shifts where and how AI creates value. Rather than merely making organizations more efficient, AI that an enterprise embeds into core systems influences decisions and interactions that directly shape its revenue, including which products it offers, how it sets prices, how it engages customers, and how quickly it brings new capabilities to the market.

This shift becomes visible in how teams make decisions and act on them. Enterprises can update pricing models dynamically within revenue management and commerce platforms rather than through periodic manual reviews. And product and engineering teams can incorporate usage and performance data directly into release cycles, prioritizing features and fixes based on real-world behavior rather than set plans.

In each case, AI is not simply producing outputs for separate review. It operates within the systems where an organization makes decisions, shaping outcomes as they occur—so incremental improvements accumulate and translate into measurable revenue impact.

Partner-Supported Platforms

To execute at scale, organizations need platforms that integrate with their existing systems and support AI within real workflows, even in complex regulated environments.

And enterprises need guidance, too. Beyond incorporating new agentic and modernization platforms that can automate the software life cycle and enable AI agents to act within context, experienced builders and domain experts are critical for embedding engineering, data science, and domain expertise into this AI to ensure the scalability, good governance, and sustainability of these new systems. This integration of technology and expertise allows organizations to extend AI beyond pilots and into ongoing operations.

The partnership-driven model is the connection enterprises need to act intelligently within real-world parameters, transforming their AI strategy into tangible, repeatable outcomes: faster product delivery, lower operating costs, more accurate forecasting, reduced risk exposure, and improved customer experience.

One leading U.S. healthcare company recently needed to modernize more than 10,000 outdated screens for processing claims. Using a partner’s AI-powered software development platform, the company has reduced its modernization timeline by 70%, eliminated security vulnerabilities in its code, and slashed its budget by $90 million. Customers see the improvements with a new user experience featuring simpler, faster interfaces, more-efficient claims processing, and better engagement.

Sustainable Growth

Pairing AI-powered platforms with enterprise experts differentiates stalled AI concepts from those that execute AI consistently and at scale to drive continuous modernization, improved employee and user experience, and sustainable revenue growth.

And while AI technology companies are plentiful enough, the rare partner that supports its products with enterprise expertise can make all the difference in helping an organization grow today for an AI-driven future.

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