CEOs have sky-high expectations for AI. EY CEO Outlook 2026 survey found that 90% of chief executives expect AI to have a significant (58%) or transformative (32%) impact on their business model or operations over the next two years.
No pressure, right? As organizations pivot from AI experimentation to scaling across the enterprise, chief information officers (CIOs) are tasked with moving beyond one-off AI pilot projects and working with their C-suite colleagues to redefine ways of doing business and how people work.
“Instead of leading with the technology, organizations that focus on outcomes and the evolving role of humans can start reimagining processes and the way work is done,” says Audi Rowe, EY Americas AI Experience, Strategy and Transformation Leader. “That’s where we start to see the shift on AI from a bolt-on mindset to a built-in mindset.”
When AI becomes part of the process architecture, organizations begin to unlock new business models, experiences and operational structures.
What is the seven-layer blueprint for AI transformation?
Reimagining entire organizational models does not come with a simple snap of the fingers. A disciplined approach is the best path to success. EY teams recommend a seven-layer blueprint for unlocking AI-driven transformation. With this approach, EY teams help to embed AI as part of an organization’s business strategy, not just its technology architecture.
1. Systems of record (trusted data foundation)
Definition: Systems of record provide a unified, reliable source of enterprise data that AI systems and agents can trust.
Why it matters: Building reliable sources of information has been a long-running challenge for CIOs. In the AI era, organizations must establish a “truth layer” that promotes data quality, consistency and accessibility across workflows.
EY perspective: “This is not a mass rationalization of your current tech stack,” says Rowe. “This is about understanding sources of truth and providing an integration layer to connect with agents and maintain the quality of data that’s required to advance a process.”
2. AI-native foundation (scalable infrastructure)
Definition: An AI-native foundation is the underlying technology infrastructure that enables AI systems to operate securely, efficiently and at scale.
Why it matters: This layer includes cloud-native platforms, real-time data processing and elastic computing resources that support dynamic workloads and allow AI agents to communicate, collaborate and scale across business functions. Without a modern foundation, organizations struggle to move beyond isolated pilots due to performance, latency and integration constraints.
EY perspective: EY teams believe building an AI-native foundation is essential to enabling autonomous AI agents and helping AI capabilities scale in line with business demand.
3. Intelligence (enterprise knowledge and reasoning)
Definition: Intelligence refers to the enterprise-specific knowledge and contextual understanding that enables AI systems to make informed, relevant decisions.
Why it matters: Institutional knowledge, domain knowledge and historical data, which form an organization’s “collective memory,” enable AI models to perform reasoning in the context of the organization and the individual. Domain knowledge and institutional knowledge form the unique “brain” of the enterprise.
EY perspective: “Encoding enterprise knowledge is critical for competitive differentiation,” says Rowe. “Empowering an agentic workforce with existing intelligence that is unique to the business is critical for the right decision-making, insights and competitive differentiation.”
4. Trust (governance and responsible AI)
Definition: Trust encompasses the governance frameworks, controls and oversight mechanisms that help AI systems operate responsibly and accountably.
Why it matters: Responsible AI frameworks establish clear boundaries for different levels of AI decision-making, implement real-time monitoring systems that track AI performance patterns, and create escalation protocols that bring humans into the loop for strategic decisions — while maintaining accountabilities for all outcomes.
EY perspective: EY teams understand that establishing new governance frameworks that maintain appropriate oversight while enabling AI autonomy requires moving beyond traditional command-and-control structures. The EY Responsible AI Framework infuses safety, ethics and security from end to end. Compliance is encoded — with every action signed, traced and reversible, giving humans executive oversight.
5. Processes (AI-driven workflow transformation)
Definition: Processes are the end-to-end workflows that are redesigned to integrate AI, enabling continuous, adaptive and intelligent operations.
Why it matters: Reimagining processes with AI can eliminate manual handoffs, create continuous workflows and shift the focus from simple automation to transformation. The goal is to design workflows that can adapt and optimize themselves, while humans direct strategic outcomes.
EY perspective: Because the foundation leverages reusable tools and codified knowledge, it becomes seamlessly scalable over time. The EY perspective is that process redesign is where AI begins to deliver measurable business value, particularly when capabilities are built as reusable components that scale across functions.
6. Workforce (human and AI collaboration)
Definition: The workforce layer defines how human roles evolve to collaborate effectively with AI systems and agents.
Why it matters: Redefining the roles and responsibilities of human professionals requires upskilling initiatives to enable more effective collaboration with AI agents. Human professionals focus on interpreting complex outputs and handling strategic decisions that require judgment, creativity and contextual understanding that only people can provide.
EY perspective: “CIOs can help business stakeholders understand the inherent capabilities of AI and work with them to reimagine the workforce and elevate people to higher-value activities,” says Rowe. “Reimagining process architecture and organizational structure must be in lockstep.”
7. Customer (experiences, products and business models)
Definition: The customer layer represents the external impact of AI transformation, including new experiences, offerings and sources of value.
Why it matters: When the previous six layers are aligned, organizations can create more personalized, responsive and intelligent customer interactions, while also reshaping how companies interact with partners and ecosystems. This is where AI transformation translates into growth and competitive differentiation.
EY perspective: “Interactions with customers will continue to evolve and so will interactions between businesses,” says Rowe. “Agents will interact not just across business lines, but across companies.”
Where should organizations start with AI transformation?
The starting point for this transformational journey will differ for every organization. Many are well underway, while others are just starting. Rowe suggests beginning with one process or workflow that aligns with top-down objectives. Defining a pilot project in the context of an end-to-end roadmap reduces the risk of experimenting with isolated, one-off use cases that don’t create value and can’t scale.
“As you redesign individual processes, you start to build a library of skills that can be leveraged across other processes,” says Rowe. “Those reusable components compound value and accelerate deployment over time.”
From there, organizations can begin to define the AI-native model, which will influence technology decisions, including a redefined ecosystem of partners. Rowe notes that because AI has significantly reduced the time and costs of software development, organizations can lean more heavily into customization to meet the specific needs of the business.
“Customization is no longer unattainable for smaller or midsized firms, so you’ll see teams weighing this option more critically,” he says. “Defining the right ecosystem is imperative to navigate buy-build-partner decisions.”
Understanding the current state of your data landscape and the orchestration required to connect disparate systems will further inform how AI can help to reimagine processes. Process redesign will, in turn, influence workforce structures and operating models.
For an EY global healthcare client, a single, streamlined interface powered by automation and smart orchestration reduced manual work, supported smooth order processing and gave customers easy self-service and quicker responses. These changes resulted in improved customer engagement, increased satisfaction and loyalty, stronger revenue protection and more available working capital. Automating routine tasks also allowed employees to focus on higher-value work, like supporting sales and business growth.
“There are still a lot of unknowns with AI, but it’s imperative to think beyond the efficiencies that AI and agents are unlocking,” says Rowe. “For a business to evolve and remain competitive, leaders must consider how AI can unlock growth and innovation.”
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The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.