The biggest failure of BI tools is analysis paralysis. The most effective AI data platforms solve this by distilling all company KPIs into a single daily email or Slack message that contains one clear, unambiguous action item for the team to execute.

The company's initial attempt to build an AI Sales Development Representative failed because CRM data was too inaccurate. They realized that any AI application built on faulty data is wasted effort, leading them to focus on solving the foundational data problem first, as AI cannot discern data quality on its own.

Customers now expect DaaS vendors to provide "agentic AI" that automates and orchestrates the entire workflow—from data integration to delivering actionable intelligence. The vendor's responsibility has shifted from merely delivering raw data to owning the execution of a business outcome, where swift integration is synonymous with retention.

AI models for campaign creation are only as good as the data they ingest. Inaccurate or siloed data on accounts, contacts, and ad performance prevents AI from developing optimal strategies, rendering the technology ineffective for scalable, high-quality output.

Contrary to the common 'instant results' narrative for AI, implementing an enterprise-grade data solution like Kernel.ai takes around four weeks. The process involves structured configuration, running large sample sets, and enabling actions like merging accounts. Complex enterprise CRMs require time and a dedicated process to properly integrate and clean.

Early customer churn is often caused by technical friction like poor metadata or version control. DaaS vendors must take co-ownership of these integration challenges, as they directly waste the client's data science resources and prevent value realization, making the vendor accountable for adoption failure.

A major hurdle for enterprise AI is messy, siloed data. A synergistic solution is emerging where AI software agents are used for the data engineering tasks of cleansing, normalization, and linking. This creates a powerful feedback loop where AI helps prepare the very data it needs to function effectively.

Point-solution SaaS products are at a massive disadvantage in the age of AI because they lack the broad, integrated dataset needed to power effective features. Bundled platforms that 'own the mine' of data are best positioned to win, as AI can perform magic when it has access to a rich, semantic data layer.

Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.