Data and AI are the foundation of augmented enterprises, yet most organizations still struggle to harness their full potential. Those who fail to transform risk falling behind as competitors unlock AI-driven insights, automation, adaptive decision-making, and competitive agility.
While the promise of data and AI-driven transformation is clear, execution often falls short. The challenge lies not in the availability of data, but in integrating it into AI-driven decision-making at scale. Entrenched legacy systems, inconsistent governance, and a lack of enterprise-wide alignment often stall progress. Without a strong data culture and leadership commitment, initiatives can become fragmented and fail to deliver the strategic impact they were intended to achieve.
Common challenges:

Becoming truly augmented requires more than data and technology—it demands a fundamental shift in mindset, operating model, and accountability across the business alongside deep technical expertise and implementation capabilities.
BearingPoint accelerates your data and AI journey through a focused two-phase approach that delivers speed, structure, and results. We begin with a rapid Data Strategy Assessment, pinpointing gaps, defining a target architecture, and laying out a clear, actionable roadmap with an uncompromising focus on business value. In the second phase, we turn strategy into execution through tailored accelerator work packages, enhancing foundational data capabilities and realizing embedded analytics and AI products. The result is technological efficiency, automation of business processes, and augmented decision-making.
Business challenge: Highly customized software solutions led to scattered, non-harmonized data assets that prohibited the use of modern analytics and AI.
BearingPoint contribution: Following the data strategy assessment, BearingPoint supported the selection and implementation of Databricks as a central enterprise data hub, while also modernizing client-facing BI dashboards built on the newly harmonized data model.
Business outcome: The initiatives achieved operational efficiencies, laid the groundwork for (Gen)AI developments, and provided new levels of service for both the reinsurer and its customers.

Business challenge: Siloed data systems from over a dozen ERP sources, combined with a heavy reliance on manual manipulation, resulted in significant data inconsistencies and hindered effective reporting and decision-making.
BearingPoint contribution: BearingPoint implemented Snowflake as a unified data platform, integrating multiple data sources using Azure Data Factory, SNP Glue, IFS APIs, and DBT for automated transformations. Additionally, a modern reporting suite that allows real-time decision-making was developed.
Business outcome: The initiative enhanced operational efficiency by replacing manual workflows with an automated, unified data platform. Real-time insights, provided by Power BI, improved decision-making for supply chain management and finance operations.

Business challenge: Legacy ticketing systems that were first implemented in the 1990s with highly complex data models and insufficient data quality, which prohibited efficient data analysis and the embedding of AI use cases.
BearingPoint contribution: Aligned with the firmwide data platform build-up on GCP, BearingPoint supported the definition of a transformation roadmap to becoming an augmented organization. BearingPoint also supported the implementation of data quality measures across business and data processes.
Business outcome: The new data platform, in combination with the implemented data quality measures, enabled the deployment of AI across business processes, significantly improving operational performance.

There are no set answers, just individual solutions to specific challenges.