Dados e IA
From prototype to production: Kyndryl’s smarter path to AI business value
artigo
28 de out. de 2026
4 lessons learned from scaling enterprise AI
By Petra Goude, President, Kyndryl Strategic Markets
Every organization I meet is asking some version of the same question: how do we turn the promise of AI into real business value? It’s a timely question. Kyndryl’s recent IT Modernization Report found that nearly half of organizations are behind schedule on their modernization goals, even as AI becomes a primary driver of technology investment and change.
The research suggests that success is less about following a single modernization playbook and more about making intentional choices that align technology investments with business outcomes. Over the past year, I’ve worked with organizations across industries and geographies that are taking very different approaches to the same challenge. Some are modernizing critical environments while putting AI to work across their businesses. Others are scaling targeted deployments that are already delivering measurable value. Some are focused on trust and governance as they expand adoption. Others are creating space to explore entirely new possibilities.
Every AI journey is different, but the organizations making the most progress share several common characteristics. Increasingly, that progress also means moving beyond pilots and individual use cases to larger-scale implementations.
Here are the four lessons they’ve reinforced about scaling enterprise AI and translating it into meaningful business impact.
A global manufacturing and forestry company we engaged with was undergoing a transformation to better support its global workforce. Its leaders wanted to strengthen collaboration across international operations, modernize workplace technology, improve security and create a more consistent employee experience across the enterprise.
It quickly became clear that AI was part of their long-term vision — but only after they had established the technology, governance and security foundations required to support it. Their immediate priority was AI readiness rather than AI deployment.
Together, we modernized their digital workplace through a unified Microsoft 365 environment that integrated security, compliance, identity management and automation capabilities. We also prioritized change management and user adoption to help employees embrace new ways of working.
The experience reinforced an important truth: strong foundations can amplify the impact of AI and make that impact easier to scale. The most successful AI initiatives begin long before the first model is deployed — with trusted infrastructure, strong governance, security and a well-prepared workforce ready for adoption.
In the financial services sector, we worked with an organization facing a familiar challenge: balancing modernization ambitions with strict regulatory and compliance requirements.
The organization’s leadership team saw significant potential in AI and data-driven innovation, but security, compliance and data protection were central concerns as they evaluated more advanced AI capabilities. They recognized that adopting new technologies could not come at the expense of operational stability, cost discipline or the protection of sensitive information.
Rather than pursuing large-scale transformation, they were exploring practical and lower-risk options — including technologies such as Microsoft Copilot — while carefully evaluating where AI could deliver measurable business value. That deliberate approach also creates a path to take proven use cases beyond the pilot stage and into larger-scale implementations as confidence, value and adoption grow.
Our role extended beyond introducing new technology. We helped strengthen the operational foundation, improved visibility into system performance and optimization opportunities, and provided strategic guidance to help the organization turn modernization and AI investments into tangible operational and business outcomes.
This experience exemplified for us that responsible AI adoption requires trust. Organizations can only scale AI sustainably when they have confidence that their governance, compliance and operational controls can evolve alongside innovation. Without that confidence, scaling stagnates.
Another banking organization we worked with had already embraced AI across multiple parts of the business. The bank’s teams were enthusiastically experimenting with new tools and use cases, but adoption became fragmented. Data management had become inconsistent, governance varied across functions, and there was no common framework to help guide enterprise-wide adoption. At the same time, regulatory scrutiny was increasing, creating pressure to improve governance and accountability.
The bank’s leadership team recognized both the opportunity and the challenge. They understood that scaling AI would require more than expanding individual use cases. It would require a coordinated approach, stronger data foundations and clear governance.
We partnered with the banking organization to develop an initial agentic AI solution supporting a complex compliance process, demonstrating how AI can address consequential business challenges while establishing the framework for broader AI governance. Together, we identified opportunities to consolidate data platforms, improve data quality and consistency, and create a roadmap for future AI and automation initiatives.
This experience proved that AI cannot thrive in silos. Scaling enterprise AI requires orchestration and a deliberate approach—one that brings together data, governance, technology and business objectives into a single, cohesive strategy that translates into broader enterprise value.
Many important innovations begin with a defined business requirement. Others begin with curiosity. One of the most inspiring projects I have been involved in emerged from a local innovation ecosystem focused on biodiversity, environmental sustainability and ecosystem resilience.
Using Kyndryl’s Agentic Generative AI framework, our team developed an AI-driven solution that combined computer vision, multi-agent AI workflows and environmental data analysis to support pollinator monitoring and generate insights into ecosystem health.
What made this initiative especially compelling was that it had demonstrated value far beyond its original purpose. The underlying framework was intentionally designed to scale across a range of applications, including ESG analytics, climate risk modeling and environmental reporting, demonstrating how a single innovation can create value in multiple domains.
The lesson this project taught me was simple: innovation doesn't always begin with a specific request. Sometimes it begins with curiosity, collaboration and a willingness to explore how emerging technologies can be capable of solving meaningful problems at scale.
The common thread
These organizations operated in different industries, faced different pressures and chose different paths. There's no single formula connecting them. What they share is clarity: each organization was clear-eyed about what it was trying to achieve and made intentional choices about where to invest, how to put it into practice and what it would take to extend that value across the enterprise.
Our own research this year backs up what I see across all four of these organizations: the ones creating the greatest value from AI aren’t necessarily moving the fastest or deploying the most use cases. They’re the ones who know where they’re placing their bets, and why.
I am more convinced than ever that the organizations asking the right questions today — and making intentional investments in the people, systems and capabilities needed to answer them — will define the next chapter of enterprise AI.
