Sales
An AI-powered data assistant automatically fills in missing CRM data and minimizes errors. This reduces sales cycle times and makes forecasts more accurate, accelerating the sales process and reducing effort for the team.
An AI-powered data assistant automatically fills in missing CRM data and minimizes errors. This reduces sales cycle times and makes forecasts more accurate, accelerating the sales process and reducing effort for the team.
A publicly listed manufacturer of pumps and valves faced a critical challenge: its sales structures were not optimally set up because its data foundation was incomplete and outdated. Manual maintenance of customer data led to media disruptions and error-prone processes. Coordination between sales, marketing, and controlling took too long. The result was prolonged quotation cycles and unreliable sales forecasts.
As part of the ada Future Lab (Link), the company worked with experts to analyze the root causes of these inefficiencies. The goal was to identify concrete levers to optimize sales through improved processes, greater automation, and above all, better data quality.
In several ideation sprints, various approaches were developed and evaluated based on business value criteria. In the end, one clear favorite emerged: an AI-powered data assistant that automatically detects, completes, and validates missing or inconsistent customer data in the CRM.
The digital assistant offers, among other features:
The goal is to improve data quality to create a reliable foundation for sales management and accurate forecasts.
Within just four weeks, a functional MVP was built using Microsoft Copilot Studio and Power Automate. The tool analyzes existing CRM datasets, detects gaps, and fills them in automatically. When inconsistencies are found, the sales team is notified.
In a real-world test phase, the MVP was used with actual customer inquiries. Two key KPIs were tracked: forecast accuracy and the time span between inquiry and quotation. The focus was on measurably improving the quality of decision-making and optimizing the sales quoting process.
After a short period of use, the business impact was clear:
Improving data quality proved to be a key driver for faster deals and more accurate forecasting—two critical success factors in highly competitive markets.
The project’s success was driven by several factors: a clear problem definition, rapid prototyping, cross-functional collaboration, and direct involvement of the sales team. The MVP approach ensured that feedback from day-to-day operations flowed directly into further development.
Equally important was the close integration with existing tools and systems: the data assistant was not introduced as a standalone system but was deliberately embedded into the existing sales structures—enhancing them in the process.
Following the successful MVP launch, the company plans to further expand the assistant. Future enhancements include analyzing customer feedback and transaction data, as well as implementing machine learning models to predict deal-closing probabilities.
The aim is to develop the solution not just as a data correction tool, but as a strategic management instrument for sales—to further optimize processes and sustainably improve data quality across the entire organization.
30 % kürzere Angebotszyklen, präzisere Forecasts und höhere Abschlussquoten – das ada Future Lab zeigt, wie Unternehmen KI im Sales erfolgreich einsetzen. In nur zwölf Wochen entsteht aus einer konkreten Herausforderung eine getestete Lösung, die Vertriebsteams spürbar entlastet und messbare Wirkung erzielt.
