Briq's pivot to RPA initially focused on extracting data. The true breakthrough came when a pilot customer asked if the bots could also perform data entry into another system. This two-way automation revealed a massive, overlooked value proposition for clients.

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Instead of merely 'sprinkling' AI into existing systems for marginal gains, the transformative approach is to build an AI co-pilot that anticipates and automates a user's entire workflow. This turns the individual, not the software, into the platform, fundamentally changing their operational capacity.

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.

Counterintuitively, the path to full automation isn't just analyzing conversation transcripts. Cresta's CEO found that you must first observe and instrument what human agents are doing on their desktops—navigating legacy systems and UIs—to truly understand and automate the complete workflow.

Scribe started by building workflow automation, viewing documentation as a simple byproduct. Customers, however, found the automation only incrementally valuable but saw the documentation as a game-changing solution. Listening to this strong user pull led to the company's successful pivot.

Briq's initial vision was to be a data layer for the construction industry. They pivoted within three months after discovering the 30-year-old accounting systems they needed to integrate with had no APIs, making their protocol idea impossible to implement.

Basim Hamdi's initial "Construction Data Cloud" concept failed because the industry's 30-year-old legacy systems lacked APIs. This critical oversight forced a pivot to Robotic Process Automation (RPA) to extract data, which unexpectedly became the core of his successful business.

Run HR, finance, and legal using AI agents that operate based on codified rules. This creates an autonomous back office where human intervention is only required for exceptions, not routine patterns. The mantra is: "patterns deserve code, exceptions deserve people."

Don't get distracted by flashy AI demonstrations. The highest immediate ROI from AI comes from automating mundane, repetitive, and essential business functions. Focus on tasks like custom report generation and handling common customer service inquiries, as these deliver consistent, measurable value.

To drive adoption of automation tools, you must remove the user's trade-off calculation. The core insight is to make the process of automating a task forever fundamentally faster and easier than performing that same task manually just once. This eliminates friction and makes automation the default choice.

To conceptualize what's possible with modern AI data tools, RevOps leaders should frame the problem at the micro level. Instead of thinking about macro data fields, they should imagine having unlimited time and resources to fix one account record. This mental model helps identify high-value, manual processes that AI can now automate at scale.