Unlocking Enterprise AI&#8217;s True Potential: Bridging the Context Gap for Measurable Business Value
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<aside>(This article was generated with AI and it&#8217;s based on a AI-generated transcription of a real talk on stage. While we strive for accuracy, we encourage readers to verify important information.)</aside>
<img decoding="async" src="https://web-summit-engine.imgix.net/k4ofmf589zbg9po9faat90rgr9x3?auto=format&amp;fit=fill&amp;fill=solid&amp;fill-color=white&amp;w=600&amp;h=600&amp;fm=jpg&amp;dl=Harry McCracken.jpg" alt="Harry McCracken and Arvind Jain" align="left" hspace="10" width="200" style="float:left;margin-right: 15px">
At Web Summit Lisbon 2026, Mr. Harry McCracken, Technology Editor at Fast Company, and Mr. Arvind Jain, Founder &amp; CEO of Glean, discussed &#8220;The AI paradox.&#8221; Mr. Jain highlighted that despite AI models&#8217; rapid advancements, many enterprises struggle to translate significant AI investments into meaningful business value. This reveals a critical gap between technological potential and tangible organizational impact.

Mr. McCracken stressed that powerful AI models alone lack inherent value; context is paramount. Mr. Jain clarified that AI needs the &#8220;collective experience, intelligence, and know-how&#8221; of a company&#8217;s workforce to operate effectively. This deep, often uncodified, company-specific context is the crucial element for AI to perform at an expert human level. Glean addresses this by integrating with all enterprise systems like SharePoint and Salesforce.

Glean understands a company&#8217;s data, knowledge, and workflows, packaging this comprehensive information into a unified context layer. This layer serves as a central platform, providing consistent, relevant input to all AI applications, including agents and large language models. By proactively aggregating information, it significantly enhances AI&#8217;s real-time performance and ensures consistent, high-quality operation across departments.

The synergy between advanced AI models and robust context layers has led to &#8220;long-running agents.&#8221; These digital employees offer continuous assistance in various departmental roles, such as customer support or sales operations. AI now delivers measurable value beyond coding; in customer support, it automates ticket resolution, providing clear cost savings. Sales teams benefit from AI-driven improvements in prospecting and RFP completion.

The industry is shifting from &#8220;token maxing&#8221; to &#8220;token yield.&#8221; While employees report high satisfaction, few leaders see significant business value. Enterprises now focus on managing AI costs, using efficient models for routine tasks, and ensuring every AI investment directly contributes to measurable, useful outcomes. To combat &#8220;AI slop,&#8221; companies must enforce strict policies, emphasizing brevity and mandatory self-review for AI-generated content.

For agents and skills, a governed framework with moderation and quality checks is crucial, ensuring shared AI tools maintain high standards. Mr. Jain expressed optimism that the AI paradox will soon be resolved. Increased market awareness of context layers, combined with ongoing AI model advancements, is expected to drive dramatic improvements in business processes, shifting focus from ROI to transformative new applications.

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