Organizational intelligence is the new priority in the AI era

In recent conversations with customers and peers, I’m not hearing “Which AI model or tool should we pick?” I’m hearing “How do we operationalize AI across our critical workflows?”  

People are starting to understand real digital transformation doesn’t come from a bolt-on solution. It happens when we treat AI as a foundational force and an engine for lasting change. The shift toward an AI-powered workplace requires leaders to enable organizational intelligence across the enterprise. 

WHAT IS ORGANIZATIONAL INTELLIGENCE? 

At Wrike, we define organizational intelligence as the seamless integration of human insight and AI capabilities to drive measurable outcomes at increased speed and scale. It’s the difference between patchwork AI adoption and true collaboration between humans and machines.  

Done right, organizational intelligence blends human creativity, judgment, context, and intent with AI’s strength in driving automation, data synthesis, and pattern recognition. When all of that is present at the same time, AI stops being a feature and evolves into a core part of how a business learns. 

Unlocking organizational intelligence goes beyond a change in mindset, although that’s key, too. R “Ray” Wang, CEO of Constellation Research, who I sat down with recently, said everyone is avoiding doing the “hard part” right now—the data strategy. But how we handle and manage data is equally critical to getting AI transformation right, alongside culture adjustments, and our enthusiasm toward the technology.  

Organizations require a robust foundation for data. This includes designing, structuring, and connecting information so AI can interpret not just isolated facts, but the full business context and meaning behind them. 

WHY AI ALONE ISN’T THE ANSWER 

While business leaders race to bring on AI tools, adoption has often outpaced ROI. McKinsey reports that while a vast majority of companies plan to increase AI investments (92%), only about 1% of leaders say their organizations have reached true AI maturity, where AI is fully integrated and yielding substantial outcomes. 

Many popular AI solutions solve isolated problems, automating individual tasks without addressing deeper needs for team alignment, context, and strategy. Instead of outcome-driven decision-making, organizations end up with more fragmentation. Disconnected automations, inconsistent data, and siloed workflows compound inefficiencies. 

Recent Wrike research found that 41% of knowledge workers said their companies lost critical information in the past year due to scattered systems and siloed knowledge across platforms. 

That’s not a technology failure. It’s an organizational one and a leadership oversight that can limit company growth. 

3 PRINCIPLES TO ACHIEVE ORGANIZATIONAL INTELLIGENCE 

Think of the project manager juggling four different collaboration platforms, each with partial information. AI introduced in that environment won’t spark clarity. It will multiply the noise.  

As leaders, it’s our responsibility to move our organizations beyond tool adoption and toward systemic intelligence: connecting people, processes, and platforms into a unified whole. That requires rewiring the way we work and rethinking how we manage data, context, and collaboration. Three principles stand out to me: 

1. Build foundation over features 

Chasing the newest AI tool can be tempting, but fragmented adoption creates the illusion of scale without delivering true capability. Prioritize a unified foundation where AI can plug in, learn, and operate effectively by clearly documenting and standardizing workflows, improving data hygiene, and consolidating the supporting platforms to drive visibility and ownership. 

The question to ask isn’t “What tool are we adopting?” but “What system are we building?”  

2. Make context your competitive edge 

AI can’t read between the lines if there are no lines to read between. Too often, critical knowledge lives in meeting notes, hallway conversations, or in the minds of employees. Without this context, AI produces generic outputs that lack trust and relevance.  

Leaders must operationalize context, as well as embed decision rationales, project outcomes, and other institutional knowledge into workflows. This may come in the form of structured fields for project outcomes, standardized post-mortems, or AI agents trained on your organization’s language and workflows. 

In a market where business advantage often depends on nuance such as customer preferences, regulatory shifts, and competitive signals, context may be the single sharpest edge we as leaders can champion. 

3. Reframe ai as a multiplier, not a shortcut 

AI should accelerate human creativity, critical thinking, and connection, not bypass them. This requires leaders to redefine roles: What must humans own, and where can AI extend their reach? 

Trust and governance are also non-negotiable. Teams will only adopt AI if they know security and ethics are protected. Leaders who ignore these responsibilities risk stalling adoption before it even begins. 

THE FUTURE BELONGS TO THE CONNECTED 

Moving forward, organizations that thrive won’t be defined by the size of their AI stack. They’ll be known for how intelligently they connect teams, workflows, and outcomes so the enterprise learns and improves with every project. 

Companies that link people, processes, and platforms into a single intelligent system will adapt faster, innovate more effectively, and build resilience in a rapidly changing environment. Leaders who prioritize organizational intelligence now are setting the stage for these long-term advantages. 

Your true differentiator isn’t AI alone. It’s connection, context, and the combined capacity of humans and machines to learn together within a shared system of record for work. 

Tom Scott is the CEO at Wrike. 

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