It is not because the technology is underperforming. It is because most enterprises are still trying to retrofit AI into old ways of working.
What’s needed is a fundamental shift in how the organization approaches AI. Instead of retrofitting it into existing processes, we should rethink how work gets done. That means reexamining how we meet user needs using a technology that enhances enterprise cognitive capabilities. AI should be integrated into the fabric of the organization, embedded in workflows, decisions and outcomes.
Below, we will look at the five principles intelligent, AI-powered enterprises are built around. In a follow-up whitepaper, we will offer a step-by-step playbook for building that enterprise.
What an intelligent enterprise really means
An intelligent enterprise is defined by how well it enables people, processes and intelligent systems to work together toward outcomes that matter.
In this kind of organization:
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Teams are cross-functional and focused on outcomes, not limited by silos or job titles.
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Individuals are multi-disciplinary generalists who can pivot quickly without having to rehire, retrain or wait on input from huge teams of SMEs.
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AI agents operate as colleagues with their own responsibilities and expected outcomes, not treated as standalone tools.
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Business rules and governance are built into every workflow to ensure compliance, consistency and trust.
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Leadership provides clarity of direction by focusing on strategic differentiation, allowing them to facilitate collaboration rather than relying on control.
The result is an operating model that is more adaptive, more scalable and more aligned to the outcomes the business is driving. When an experiment works, you will be able to scale that experiment instantly with an agent workforce. Meanwhile, your business strategy can hyper-focus on where to place human talent to differentiate.
Five principles for building an intelligent enterprise
To build this kind of organization, you need more than technology adoption. You need to rethink how your enterprise is structured and how people work within it. That starts with a set of core principles.
1. Structure: Teams, not silos
Organize around outcomes, not functions. Empower small, flexible teams with access to the data and tools they need to deliver value. These teams should be capable of owning an entire slice of a workflow from end to end.
2. Leadership: Orchestration, not control
Leaders should focus on setting direction, clarifying priorities and removing blockers. The goal is not to control every decision but to create the space where smart decisions can happen at speed.
3. Innovation: Continuous experiments, not POCs
Move away from one-off pilot projects and shift toward a culture of ongoing experimentation. Use real-time data and feedback loops to adjust quickly and scale what works.
4. Culture: Curiosity, not command
Create an environment where people are encouraged to explore, test and improve. Curiosity fuels innovation. Psychological safety enables teams to try new things without fear of failure. Give people enterprise-grade environments to experiment within safely.
5. Ethics: Native, not reactive
Trust is built when ethical principles are part of every decision, not just applied after the fact. That includes responsible data use, inclusivity, transparency, accessibility and tech that is as natively safe as possible. These must be embedded from the start.
Use experience design to find the right AI opportunities
Embedding agentic AI into your organization starts with identifying the right moments for interaction. These moments will differ depending on the roles involved and the type of work being done.
For knowledge workers, tasks like summarizing documents or responding to queries may seem like obvious candidates. But if the workflow is rethought, the summary might not be needed at all. AI could take the next step automatically rather than pausing for human review.
For creatives, collaboration is key. Agents need to be part of their shared spaces, such as in chats and design tools. If AI is used only for execution and not co-creation, the result may be bland or misaligned.
The right moments are not always obvious. That is why experience mapping is so important.
Experience maps show how customers and employees interact with your organization across journeys, systems and silos. When used well, they reveal:
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Where repetitive tasks slow things down
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Where decision-making can be accelerated
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Where human cognition is currently a bottleneck
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Where AI can support or scale business rules
These maps should be treated as living documents that evolve alongside your technology stack. They are a critical tool for identifying where agentic AI can create the most value.
Design with interaction in mind
Intelligent enterprises need the right level of interaction between humans and AI.
If the system feels too automated, trust breaks down. If it relies too much on manual input, adoption suffers. You need to balance both.
This means:
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Making AI decisions transparent so people can understand and trust them
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Creating intentional moments for human review where oversight is required
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Avoiding over-reliance by keeping people engaged in meaningful decisions
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Ensuring humans stay involved in workflows where multiple AI systems interact with each other
Getting this right increases both the effectiveness of the technology and the confidence of your teams.
Take AI from initiative to production
For many organizations whose AI initiatives keep getting stuck, the problem isn’t lack of ambition. It’s because they haven’t embedded AI in the right way.
Agentic AI has the potential to transform how your organization operates, but only if it is woven into the day-to-day experience of work. This requires intention, alignment and a willingness to challenge long-held assumptions about structure, leadership and value creation.
When done right, AI becomes a native part of your operating model. And that is what turns early experiments into long-term impact.
