A customer-centric product innovator and business leader, Philip Brittan is the CEO of Bloomfire.
For years, I’ve heard executives express concerns about collaboration breakdowns, poor productivity and sluggish decision making. Despite significant investments in AI and digital transformation, many companies still rely on siloed systems and outdated content. This can lead to inefficiencies that better data hygiene and a connected approach to knowledge flows could help prevent.
Enter enterprise intelligence, a concept enterprises can use to address persistent collaboration challenges by integrating knowledge management, enterprise search, business intelligence and AI into a cohesive, proactive system. It ensures knowledge moves seamlessly across teams and systems to enable smarter, faster decision making.
The Value Of A Collaborative Enterprise
True collaboration amplifies teams’ potential and activates their collective knowledge. When that collaboration can flourish, teams can achieve results that would be unattainable individually, which then maximizes customer value and enterprise growth.
When I first joined a large technology corporation to lead a massive product transformation, I encountered teams operating in isolation with divergent methodologies. This fragmentation resulted in an unmarketable product. By aligning around a shared vision, dismantling silos and standardizing processes, we transformed the product, accelerated innovation and significantly boosted sales.
In late 2024, I joined the board of directors at my current company, which provides knowledge management software solutions. It became clear that many executives accept a higher cost of doing business than is necessary and that a more collaborative approach could help. However, achieving a collaborative state requires commitment and a cultural shift away from entrenched habits of passively documenting and storing content.
The Problem With Passively Storing Content
You might know read-only memory (ROM)—data stored and accessed repeatedly but never updated. Many organizations manage knowledge similarly: static and quickly outdated. Conversely, write-only memory (WOM)—information created and stored but never used—is equally detrimental. Consider reports painstakingly compiled and never revisited, draining productivity and undervaluing expertise.
Both ROM and WOM hinder knowledge flow by blocking the active, evolving insights vital for decision making and innovation. Teams managing these repositories often lack resources or executive support to drive meaningful change. The rise of AI underscores how passive repositories inhibit collective intelligence, demanding evolved practices and tools.
The Role Of AI In Enterprise Intelligence
Companies can evolve from passive repositories to active knowledge bases. One tool that can help with this is AI. Organizations can use AI to continuously analyze and correct knowledge assets, identify redundancies, flag conflicts and highlight real-time gaps. This automated cleansing helps ensure integrity and can provide consistently high-quality insights.
However, when integrating AI into knowledge management, organizations often face a fundamental challenge I’ve consistently emphasized: garbage in, garbage out. Many companies try to solve their knowledge challenges by putting powerful large language models on top of their existing data messes. Without addressing underlying knowledge quality issues first, AI can make problems worse by delivering incorrect information with high confidence.
Organizations must identify and deal with redundant, outdated, trivial, conflicting and missing data before expecting AI to deliver value. This healing process should extend across all data pools rather than treating them as isolated silos. Companies should also recognize that a significant portion of their knowledge exists as “tacit knowledge” in employees’ heads, not in documented form. Effective AI implementation requires systematically converting this tacit knowledge into explicit, searchable content that AI can leverage.
Going Beyond AI: A Cultural Transformation
Enterprise intelligence also requires a paradigm shift in how organizations treat knowledge. Companies must recognize knowledge as a tangible asset that can be measured, optimized and leveraged, just like financial capital. This means elevating knowledge from an operational concern to a strategic asset directly contributing to enterprise value.
To do this, organizations should transition from passive repositories to an active, joined knowledge network functioning like a nervous system for the company. This transformation creates a network effect where the more sources of knowledge, data, insights, departments and people are connected to the same network, the more valuable it is for everyone.
The goal is to systematically convert the tacit knowledge in employees’ heads into explicit, institutionalized knowledge. This requires embedding knowledge-sharing into everyday workflows, not as an add-on task but as part of how work naturally happens. Leaders can begin embedding knowledge sharing into everyday workflows by implementing systems that proactively deliver relevant insights to employees, encouraging teams to capture and share insights during routine project discussions, designing tools that automatically flag when new information contradicts existing knowledge and establishing procedures to institutionalize tacit knowledge from experts as part of normal business operations. As a result, knowledge can become accessible across the organization.
Leadership should champion this cultural shift by demonstrating that knowledge sharing is expected at every level of the organization. They can also recognize employees who contribute valuable knowledge to shared systems.
Moving Forward
Ultimately, organizations ready to embrace enterprise intelligence should:
1. Identify where knowledge bottlenecks cause the most friction.
2. Conduct an audit to address redundant, outdated, trivial and conflicting content.
3. Implement tools that connect fragmented knowledge sources while curating content quality.
4. Ensure leadership embraces this as a strategic initiative rather than “just another IT project.”
As AI transforms interactions between organizations, employees and customers, I believe that businesses mastering enterprise intelligence will be in the best position to lead their markets.
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