Analytics is poised to move rapidly into the AI-assisted era as end users grow more familiar with collaborative agents capable of simplifying tools once left to professional developers. Business users who understand their data will deliver greater value in discovery and storytelling.
AI-Driven Analytics & Visualization
AI is already central to modern analytics platforms, enabling automated pattern recognition, anomaly detection, predictive modeling, and natural language querying. Tools like Microsoft Power BI and Tableau now offer AI-generated insights, making data analysis accessible to non-technical users. Example: Microsoft’s Power BI Copilot allows users to generate reports using natural language prompts.
Real-Time & Streaming Analytics
Real-time analytics supports instant decision-making in areas like fraud detection and personalization. Technologies such as Apache Kafka, Azure Event Hubs and Stream Analytics, and Amazon Kinesis enable streaming data pipelines. Businesses are increasingly offering premium services that act on customer data workflows triggered by live data.
Augmented Self-Service BI
Augmented BI automates data engineering and preparation, insight generation, and report creation, empowering business users and streamlining development. Example: Avanade’s Augmented Analytics for Business Intelligence (AABI) platform allows users to build Power BI reports using pre-built semantic models and governed workspaces, eliminating data model backlogs. Often, report templates are included as well. These services frequently accompany enterprise software packages, such as Dynamics 365.
Organizations are discovering that successful AI implementations require greater attention governance initiatives such as data quality. Critical modernization of information architecture is needed to support cloud, hybrid, or unstructured data. This complexity is a necessary adaptation as automation and devices expand the information ecosystem.
Data Governance & Semantic Modeling
With growing data complexity, governance software ensures consistency, security, and trust. Semantic layers unify data definitions across platforms, enabling chat agents and analysts to interact seamlessly and consistently with company data. Companies like Alation, Informatica, Microsoft, and Collibra provide packages to manage enterprise data.
Cloud-Native & Lakehouse Architectures
Lakehouse models combine the scalability of data lakes with the structure of data warehouses. They support unified analytics, machine learning, and BI workloads. Example: Microsoft OneLake and Synapse provide a Lakehouse foundation for analytics and AI.
Synthetic Data & AI Governance
Synthetic data is used to augment training datasets, but it raises concerns about model drift and especially model bias. AI governance frameworks are essential for compliance and trust. Example: Organizations are adopting AI governance tools to monitor synthetic data usage and model behavior.
Organizational culture is adapting to software that is AI-enabled. The lines that separated dedicated analyst teams and accounting roles are blurring. Automation that improves the operations of the accounting department is bringing the teams together to solve labor-intensive processes.
Cross-Functional Collaboration
Modern BI roles require more collaboration across departments, combining technical and storytelling skills. This translates into a more consultative capacity. For example, BI Developers now collaborate closely with accounting and business teams, data engineers and data scientists to create semantic models for use in tools like Power BI and Tableau, both of which now feature AI enhancements that deepen understanding and usability of underlying data.
Learning Organizations
Organizations are transforming into rapid learning environments. Innovation is driving faster decision making based on newly available information. Leaders are enabling—and increasingly expecting—forward-looking teams to quickly adapt their vision to remain competitive in evolving markets.
In summary, expect greater participation across the enterprise for AI initiatives, beginning with the modernization of applications which leverage AI and the modernization of information architecture to capture an expanding ecosystem of data generated by automation and edge devices. Accounting roles will benefit from the new features, but training and preparation are key. Leaders will be looking for ways to take advantage of the latest features in accounting and office applications.
About
The Technology Strategic Advisory Group exists to promote the professional development and career growth of CITP credential holders and other stakeholders by creating and curating resources that address emerging needs in technology and business. Committed to fostering continuous learning, innovation, and adaptability, the group provides insights and support to help professionals navigate challenges, expand their expertise, and lead with confidence. Through collaboration and strategic initiatives, the group ensures that the CITP community and related professionals remain connected, informed, and prepared for the future.