AI
10 Aug 2026
Why the Next Step in Enterprise AI Is Making Better Use of External Information
The way companies access and use their own knowledge is changing rapidly with AI.
Organizations are connecting AI systems to their CRM, ERP, internal documents, knowledge bases and business data. Employees can now query large volumes of information, automate tasks and generate analyses that previously required hours of work.
But this transformation is bringing a new challenge.
An organization can have highly capable AI systems working on its internal data while remaining partially blind to its external environment.
Because much of the information needed to understand a market, make a decision or anticipate change does not reside within the company’s systems. It exists outside the organization.
The next step in Enterprise AI is therefore not only about making internal data more accessible. It is also about better understanding what is happening around the organization.
Long before the rise of generative AI, knowledge workers were already spending a significant amount of their time searching for information.
McKinsey estimated that they could spend nearly 20% of their working time searching for internal information or identifying colleagues who could help them.
The problem was not necessarily a lack of information. It was a problem of access, relevance and context.
Information was spread across different systems, documents, websites, databases and people. Finding the right information often meant knowing where to look, which source to trust and how to connect different pieces of information.
AI is changing this equation.
It allows people to ask a question instead of navigating through a multitude of systems.
But one question remains: What information can AI actually know and use?
Because making information accessible is only useful if that information is relevant to the question being asked.
The first wave of Enterprise AI has naturally focused on what companies already own.
Internal data is valuable, relatively controlled and directly connected to business processes.
Employees can query knowledge bases, while agents can interact with enterprise applications. Customer and operational data can also be analyzed by AI systems, creating a new interface to enterprise knowledge.
But this approach can also create an information bubble.
An AI system can become extremely capable at understanding a company’s documents, processes and data while having limited visibility into what is happening outside the organization.
Yet business decisions rarely rely exclusively on internal information.
Understanding competitor moves is essential for sales teams, while strategy teams need to monitor market developments. Innovation teams must identify emerging technologies, procurement teams need visibility into suppliers and associated risks, and regulatory teams have to detect legislative changes.
At the executive level, the challenge is even broader: understanding the signals that could affect the business tomorrow.
A significant part of the information required for these decisions is external.
The Web is one of the largest sources of external information available to businesses today.
It contains an enormous amount of data about the markets and ecosystems in which organizations operate.
Corporate websites, industry publications, regulatory bodies, scientific publications, media, technical documentation, specialized databases, niche websites…
This diversity is a strength. But it is also a challenge.
Information is fragmented, heterogeneous and constantly changing. Some sources are highly reliable. Others are outdated. Some information is duplicated.
Other information is contradictory. And some of the most valuable signals are buried deep within documents or spread across multiple sources.
Collecting information is only the beginning of the process.
To become useful to the enterprise, information from the Web needs to be identified, collected, qualified, structured, analyzed and put into context.
This is an essential distinction.
Raw data is not yet usable information. An isolated piece of information does not necessarily constitute intelligence.
This transformation is at the heart of enterprise intelligence.
All these disciplines have something in common: transforming dispersed information about the organization’s environment into knowledge that helps the business understand, anticipate and make better decisions.
The Web considerably expands the raw material available for this process.
But having access to that raw material is not enough.
It needs to be made usable.
The rapid adoption of generative AI is creating another challenge: moving from experimentation to genuinely operational applications.
Gartner estimates that only 41% of GenAI prototypes reach production, illustrating the difficulty organizations face in turning promising experiments into operational capabilities.
Data quality is one of the challenges identified.
Research from S&P Global Market Intelligence indicates that 42% of organizations identify data quality among their top three barriers to moving AI projects into production.
These figures highlight a fundamental principle: the performance of an AI system does not depend solely on the model. It also depends on the quality, relevance and accessibility of the information it can use.
A more powerful model cannot indefinitely compensate for incomplete, unreliable or poorly structured information.
And when the information required by an AI application exists outside the organization, another challenge emerges:
How can AI access this information in a reliable and usable way?
The obvious answer might be to give AI access to the Web.
After all, search engines and today’s AI tools can already search the Internet.
But enterprise intelligence is not limited to a one-off search.
A search generally answers a question at a specific point in time.
Enterprise intelligence needs to address more complex questions:
What has changed? Why is it changing? Which players are involved? Which signals should we monitor? What trends are emerging? What could this mean for our organization?
This requires the ability to work over time, cross-reference sources, analyze large volumes of information and retain the context needed to interpret it.
From a technology perspective, it also requires a significant history of data, reliable and relevant content for the organization, and data that is ready to be ingested by today’s AI systems and models.
Searching for information is an action. Building intelligence is a process.
This distinction becomes particularly important in the age of AI.
It is from this perspective that Cikisi develops for its customers an AI-ready Web Intelligence infrastructure.
It is not simply about giving AI more data.
It is about enabling organizations to transform Web data and information into intelligence that can be used by their people, processes and AI systems.
The evolution of Enterprise AI therefore does not depend only on increasingly powerful models.
It also depends on an organization’s ability to provide them with the right information, in the right format and in the right context.
The enterprise of tomorrow will not operate solely on what it already knows.
It will continuously confront its internal knowledge with changes in its external environment.
The question is no longer simply:
What do we know?
It becomes: What is happening outside our organization — and how can we transform this information into intelligence that supports our decisions and our AI?
This is the ambition of Enterprise Web Intelligence.
Our goal is to help organizations use the Web not simply as a source of data, but as raw material for enterprise intelligence.
Because Enterprise AI should not operate in an information bubble.
This is precisely one of the questions we will explore at Big Data & AI Paris, September 15–16:
How can we transform the Web — an immense and constantly evolving source of information — into a genuine source of intelligence for employees, business processes and Enterprise AI?
If you are working on Enterprise AI, Data strategy, Market Intelligence or Knowledge Management, we would be happy to discuss your challenges and explore what Enterprise Web Intelligence could mean for your organization.
Cikisi — Enterprise Web Intelligence.
Monitor what changes. Analyze what matters. Connect intelligence to your enterprise AI.
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