You must start with reliable sources of information that connect both humans and AI agents. This is the hardened truth layer, consisting of systems exposed through an integration layer to be leveraged, updated and maintained.
This enterprise infrastructure enables AI to operate safely and at scale. Autonomous AI agents and complex decision-making processes are underpinned by cloud-native platforms with real-time data-processing capabilities. Such architectures enable seamless agent communication and computing resources that can dynamically scale to support multiple AI agents operating simultaneously across business functions.
Unified enterprise memory and knowledge enables reasoning in the context of the enterprise and the individual. This is the “brain”: what the enterprise knows and how it makes decisions through analytics and advanced AI.
Establishing new governance frameworks that maintain appropriate oversight while enabling AI autonomy requires moving beyond traditional command-and-control structures. Organizations need frameworks that 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 also maintaining accountability for all outcomes. 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.
Process redesign can eliminate manual handoffs, create continuous flows that leverage AI’s 24/7 capabilities and shift focus to transformation rather than mere automation. The goal becomes designing workflows that can adapt and optimize themselves while humans direct strategic outcomes. As more processes are redesigned, the foundation becomes seamlessly scalable because it leverages reusable tools and codified knowledge.
Redefining roles and responsibilities of human experts requires upskilling initiatives to enable more effective collaboration with AI agents. Human experts focus on interpreting complex outputs and handling strategic decisions that require the judgment, creativity and contextual understanding that only people can provide.
Every layer comes together to create new experiences, products and business models. Organizations can shift toward enabling new offerings and business models, creating new value, and leveraging their new AI workforces to seek new greenfield opportunities or challenge their current models.