GenAI Reporting Automation and Governance for Enterprise BI

Reporting teams within enterprises have a tough balance to achieve. There is an increasing demand from business stakeholders for faster analysis, self-service, and real-time reporting, whereas there is a need for data integrity, lineage, data security, and regulatory compliance from the IT and governance teams. However, with the advent of generative AI (GenAI) technology, reporting tasks can be automated, but at the cost of compromising on data integrity.

Organizations are beginning to explore how GenAI can help create reports, provide business insights, detect anomalies, and speed up decision-making processes. Simultaneously, investments are being made in governance-related aspects such as MDM, data lineage, and AI risk scoring.

Perceptive’s POV

At Perceptive Analytics, we see reporting automation and AI governance as two parts of the same enterprise strategy. Companies may concentrate on producing reports faster, but forget to implement any controls to keep things reliable and consistent.

We understand from our years of experience in analytics modernization, business intelligence, and artificial intelligence implementation projects that for a successful GenAI rollout, you need both automation and robust governance practices. The point is not just about speeding up report creation processes; the idea is to create actionable and trustworthy reports for the end-user.

At Perceptive Analytics, we will help you incorporate GenAI into your existing reporting framework.

The New Landscape of GenAI-Powered Reporting Automation

The use of generative AI reporting automation tools has grown by leaps and bounds over the past several years. Companies are moving towards the adoption of AI tools as part of their current business intelligence environment as opposed to completely replacing their reporting applications.

Some of the popular enterprise-level reporting solutions that come with AI capabilities include Microsoft Power BI, Tableau, Looker, and Qlik. The Copilot capabilities provided by Microsoft allow users to generate reports, summarize insights, and narrate them using data. Similarly, Tableau offers AI capabilities such as natural language interaction and insight generation.

There are a number of capabilities that are always most valuable when it comes to generating reports:

• Generation of reports from structured datasets.
• Narratives written in natural language explaining KPIs changes.
• Anomaly detection and exception reports.
• Generating executive summaries from dashboards.
• Natural language conversations and analysis.
• Distribution of reports and workflows orchestration.
• Generating forecasts and detecting trends.
• Alerting on metrics in real time.

Integrating GenAI Reporting with Existing Enterprise Systems

Perhaps one of the main misconceptions around the deployment of GenAI reporting is the need to replace any existing investment made in analytics tools.

However, when properly deployed, GenAI is usually integrated within the current ERP, CRM, data warehouse, and BI toolset. Contemporary GenAI is integrated via APIs, semantic models, data catalogs, and reporting tools that an organization may have already implemented.

For instance, Microsoft Power BI can be directly integrated into Microsoft Fabric as well as Azure tools, while Looker provides integration for AI-based analytics in the Google AI environment. Governance tools like Microsoft Purview also provide integration with GenAI tools.

At Perceptive Analytics, our approach to working with GenAI involves integrating the technology within an organization’s existing reporting architecture rather than increasing its complexity.

Cost and Risk Considerations for GenAI Reporting Automation

For organizations looking to automate their GenAI reports, there are financial considerations as well as operational considerations that should be made.

The main cost factors usually consist of:

• Costs of software and AI platforms.
• Costs of cloud consumption.
• Costs of integration and implementation.
• Improvement in data prep and governance.
• Training and change management costs.
• Monitoring and support.

However, the main issue that most organizations face is not cost but risk. Some common risks include inaccuracies in the output of AI narratives, inconsistent business definitions, issues of privacy, model hallucination, and lack of governance and controls. The NIST emphasizes the importance of governance, monitoring, and risk management when it comes to responsible AI.

AI Governance for MDM, Lineage, and Risk Scoring

The increased adoption of GenAI makes governance a board-level topic.

Good AI governance tools usually offer the following capabilities:

• MDM integration to have consistent business definitions.
• E2E data lineage tracking.
• Data catalogs and metadata management.
• Governance of AI models.
• Risk scoring and policy enforcement.
• Compliance and reporting.

Products like Microsoft Purview, IBM Watson Knowledge Catalog, Informatica, and Apache Atlas can offer different mixes of these capabilities. The Microsoft governance documentation stresses the importance of data lineage and discovery as key prerequisites for adopting AI in enterprises.

Risk scoring capability is especially critical, as it helps find sensitive data, evaluate model impact, and allocate governance efforts. For heavily regulated industries, having these capabilities is becoming a must.

Proof Points: AI Governance in Action for MDM and Risk

The benefits that result from combining the automation of reporting with the introduction of governance are often tangible.

In particular, a global financial services firm might employ lineage and governance tools to decrease any reporting variances and to be better prepared for a compliance audit. Another typical situation is when companies adopting a centralized MDM system see greater consistency in their KPIs and fewer reconciliation efforts.

This kind of results has also been obtained by Perceptive Analytics’ clients through analytics and reporting modernization. For instance, one company that needed an enterprise-wide business view was able to enhance its reporting consistency and executive oversight through a centralization of the analytics environment:

https://www.perceptive-analytics.com/transform-decision-making-with-a-unified-view-of-the-business/

And another firm enhanced data availability and accuracy of reporting by improving data transfer and integration processes:

https://www.perceptive-analytics.com/optimized-data-transfer-for-better-business-performance/

It often happens that governance advances provide the basis for further adoption of AI solutions.

How Perceptive Analytics Automates Enterprise Reporting with GenAI

Perceptive Analytics concentrates on real-world GenAI use cases that have direct applicability to enterprise reporting practices.

These include:

• KPI narrative generation and executive summaries.
• Artificial intelligence-driven commentary on reports.
• Identification of exceptions and anomalies.
• Predictive capabilities built into dashboards.
• Automatic distribution of reports.
• Experiences in conversational analytics.
• Data validation and data quality assurance.
• Industry reporting solutions.

In contrast to implementing off-the-shelf AI tools, Perceptive Analytics implements AI use cases specific to business needs, compliance, and industry reporting considerations.

This allows organizations to benefit from reporting automation tailored to their unique circumstances instead of having to adapt their practices to an out-of-the-box toolset.

Why Enterprises Choose Perceptive Analytics for GenAI Reporting

As organizations assess the suitability of GenAI reporting tools, it quickly becomes clear that technology alone is insufficient.

By combining its AI capabilities with robust analytical, reporting, and data engineering practices, Perceptive Analytics allows customers to tackle all three areas of reporting automation, governance, and adoption at once.

Other advantages include:

• Incorporation into BI investments already made.
• Customization to industry needs.
• A strong emphasis on governance and data quality.
• Future-proof architectures.
• Less maintenance effort for analytics staff.

It’s our belief that data analysts should be spending more time on analysis than report creation. The most effective reporting systems are the ones that do both.

Implementation, Support, and Time-to-Value

A typical issue raised by the leadership of enterprises revolves around the duration for which it takes for the GenAI reporting project to yield benefits.

Typically, most organizations start with a targeted assessment of their reporting processes, maturity of the governance framework, and readiness of data. While the first phase of automation can usually be deployed quickly within weeks, a more extensive transformation journey might take months.

Perceptive Analytics offers services that encompass implementation services, governance guidance, user training, and adoption planning. This ensures a faster ROI while minimizing disruptions to existing reporting practices.

The objective is always centered on business benefits and not just the deployment of technology.

Next Steps: Building a Roadmap for GenAI Reporting and Governance

Here are four steps organizations need to take to scale their GenAI reporting initiatives effectively:

• Analyze existing reporting processes and potential for automation.
• Assess organizational readiness regarding MDM, lineage, and AI risk management.
• Test GenAI reporting applications with high value.
• Create a strategy that considers both automation and governance needs.

GenAI reporting automation can significantly enhance reporting efficiency and accessibility; however, long-term success is possible only with robust governance practices. With effective alignment of automation, data quality, lineage, and risk management, an organization can enjoy the benefits of AI-powered innovation.

• Schedule a GenAI Reporting Automation Assessment for uncovering high-value automation opportunities in your reporting environment.

Access a GenAI Reporting and Governance Readiness Checklist to see how automation and governance go hand-in-hand.