Jagjot Bhardwaj FIoA explores how analysts will need to adapt in the GenAI era.

From Dashboards to Decision Intelligence: Redefining the Analyst’s Role in the GenAI Era

Written by IoA Fellow, Jagjot Bhardwaj FIoA

bhardwajjagjot@gmail.com
www.linkedin.com/in/jagjotb

1. Introduction: The BI Status Quo is Evolving

“Dashboards tell us what happened. Decision intelligence tells us what to do next.”
In the world of business analytics, dashboards have long been the gold standard for monitoring metrics and visualising historical trends. However, they often fall short when stakeholders ask, “So, what now?” or “What’s the best course of action?”
With the rise of Generative AI (GenAI), analytics is undergoing a paradigm shift. Analysts are no longer just the creators of reports, they are evolving into decision architects who design systems that don’t just visualise data, but actively support contextual, proactive, and automated decision-making.

2. What is Decision Intelligence?

Decision Intelligence (DI) is a discipline that blends data science, behavioral science, and decision theory to produce context-aware, actionable intelligence that guides decisions — often autonomously.

The Classic Data Analysis Progression

Stage	Purpose	Example Question	Output Format
Descriptive	Understand what happened	What were last month’s sales?	Dashboards, KPIs
Diagnostic	Explore why it happened	Why did revenue drop in Q2?	Drilldowns, correlations
Predictive	Forecast what might happen	Will customer churn increase?	ML Models, Forecast Charts
Prescriptive	Recommend what to do next	How should we reduce churn?	Scenarios, Action Plans

Decision Intelligence orchestrates all four, overlaying them with GenAI’s natural language capabilities, automation logic, and reasoning power.

Figure 1: BI Maturity Flow with GenAI Enablement (Adapted from Gartner Research)

3. Role Evolution: The Analyst as a Decision Architect

From Reporting to Reasoning

Traditional Analyst	Decision Intelligence Analyst
Builds dashboards and static reports	Designs intelligent decision workflows
Answers “what happened”	Answers “what should we do now?”
Works in isolation	Collaborates across data, product, UX
Visualizes trends	Drives action with context + reasoning

New Skill Sets

Area	Key Skills
Prompt Engineering	Natural language querying, GenAI fine-tuning skills previously confined to academia are now everyday tools used by analysts to interact with LLMs.
Workflow Orchestration	LangChain, Zapier, Copilot Studio:LangChain, in particular, allows modular construction of decision agents, streamlining complex logic into repeatable actions.
System Design	Feedback loops, logic paths, decision trees : making analysts key players in systems thinking.
Governance	Bias checks, explainability, traceability:essential for establishing trust in autonomous systems.

Analysts are now curators of trust and action, not just interpreters of trends. This evolution repositions the analyst from a report builder to a decision architect, someone who designs intelligent, context-aware workflows powered by GenAI, and applies new skills such as prompt engineering, orchestration, and ethical governance to drive meaningful action.

4. GenAI in Action: New BI Workflows

Generative AI is reshaping the landscape of business intelligence by introducing a new layer of contextual intelligence and automation. Rather than simply accelerating existing workflows, GenAI fundamentally alters how insights are discovered, how decisions are made, and how actions are implemented.

Traditional BI systems have focused on historical reporting and descriptive analytics. With GenAI in the picture, these systems evolve into dynamic decision-making engines enabling interaction through natural language, surfacing proactive insights, and triggering real-time responses without manual intervention.

Key Capabilities of GenAI-Driven BI

Proactive Insight Generation: Instead of waiting for users to investigate, GenAI identifies trends, anomalies, or emerging issues and presents them upfront, allowing organisations to stay ahead of problems
Conversational BI: By enabling natural-language interactions, GenAI lowers the barrier for data access. Stakeholders can query complex datasets in plain language, increasing accessibility and adoption across business teams.
Prescriptive Suggestions: GenAI models can go beyond presenting what’s happening; they can suggest the next best action, drawing on historical patterns and predictive analysis.
Autonomous Agents: Intelligent agents can interpret data, make contextual decisions, and trigger workflows across platforms, allowing for highly responsive systems with minimal human oversight.

Illustrative Example: An LLM reviews a sudden drop in conversion rates, identifies potential causes based on user behaviour and past performance, forecasts likely impacts, and initiates a targeted retention campaign all in an automated, end-to-end flow.

Tools in the Stack

Layer	Example Tools
Data Platform	Snowflake, BigQuery
BI Layer	Tableau, Power BI, Looker
GenAI Layer	GPT-4, Claude, Salesforce Einstein GPT
Agents/Flows	LangChain, Copilot Studio, Zapier

This multi-layered approach allows for seamless integration of GenAI into the BI ecosystem. From data ingestion to insight delivery and automated action, these tools work together to enable a new generation of intelligent, responsive, and human-friendly analytics solutions.

5. Challenges and Considerations

Implementing Decision Intelligence is not just about adopting GenAI or building smart workflows — it’s about ensuring that the systems we create are reliable, ethical, and aligned with the real-world needs of decision-makers. While many current challenges emerge from integrating large language models (LLMs), they reflect wider concerns across Business Intelligence (BI), decision analytics, and the broader evolution of the analyst’s role.

Let’s take a closer look at some of the key challenges and how they connect to the principles discussed throughout this article:

Trust & Accuracy: Traditional BI tools often rely on structured data and clearly defined metrics. With GenAI entering the mix, outputs can be more fluid, context driven, and even speculative. This introduces new risks. Whether building a dashboard or designing a decision workflow, analysts must ensure that insights are based on high quality, validated data to prevent misinformation and maintain stakeholder confidence.
Prompt Engineering & Version Drift: In classic analytics, the logic behind a report or model is documented and traceable. But when working with GenAI, prompts act as instructions and these can change or behave differently over time as models are updated. This affects reproducibility, a core value in both BI and decision-making systems. Maintaining well-governed prompt libraries, similar to managing source code, is key to preserving consistency.
Explainability & Auditability: BI dashboards are usually transparent in how metrics are calculated, but GenAI systems often act as black boxes. In decision analytics, especially where actions are automated, it’s vital to explain how a recommendation was reached. Whether it’s a visual insight or an AI-generated suggestion, users must be able to trace the logic behind it, especially when these decisions impact customers, patients, or financial outcomes.
Bias & Ethics: BI has always struggled with the risk of reinforcing bias through selective data or visual framing. With LLMs and GenAI, the risks increase, as these models learn from vast and often biased datasets. In Decision Intelligence, ethical responsibility becomes central, analysts must take an active role in identifying, auditing, and mitigating bias to ensure decisions are fair and inclusive.

These aren’t just technical challenges but they are more strategic and ethical issues. They shape the success of Decision Intelligence as a field that integrates the strengths of BI, decision theory, GenAI, and automation. Addressing them head-on will ensure that the systems we design are not only intelligent, but also transparent, responsible, and fit for the future.

6. Future-Ready Analyst: Learning to Think in Systems

For an analyst to be truly future-ready, it is no longer enough to simply master tools or produce reports. They must adopt a new mindset rooted in systems thinking — one that sees beyond individual dashboards and embraces the broader context in which data-driven decisions are made. This shift is essential because the real value of Decision Intelligence lies not just in using GenAI tools, but in designing intelligent, ethical, and responsive systems that drive meaningful outcomes.

To thrive in this new era, analysts must develop capabilities across several critical domains. Below, each skill area is explained to show not just what to learn, but why it matters:

GenAI / NLP: With large language models (LLMs) playing a central role in analytics workflows, analysts must learn how to engineer prompts effectively, summarise outputs, and critically evaluate the reasoning provided by AI systems. These skills ensure that interaction with GenAI is not only efficient, but also safe and aligned with business intent
Decision Frameworks: Mastery of behavioural science principles and impact modelling allows analysts to go beyond describing what the data says. It enables them to understand user behaviour, anticipate consequences, and recommend actions that are contextually appropriate — ultimately helping organisations make better, more human-centred decisions.
BI Automation: Analysts who can build automated workflows — such as event-based triggers, real-time alerts, and intelligent agents — are able to create systems that react to changing conditions without constant human intervention. This agility is essential in dynamic environments where responsiveness is a competitive advantage.
Governance & Ethics: As the architects of decision-support systems, analysts are responsible for ensuring that data and algorithms are used fairly, transparently, and responsibly. Skills in fairness audits, traceability design, and ethical oversight are no longer optional — they are essential to earning and maintaining trust in automated decisions.

These aren’t just technical skills — they reflect a broader evolution in what it means to be a data professional today. The future-ready analyst is not just a technician but a systems architect, a steward of trust, and a bridge between automation and accountability.

7. Conclusion: The Analyst’s Renaissance

Generative AI doesn’t replace analysts — it elevates them.

This is the Analyst’s Renaissance, where creativity, strategy, and system thinking combine with automation to deliver insights that aren’t just smart — they’re transformative.

In the GenAI era, analysts:

Ask better questions
Design intelligent workflows
Embed decisions into systems
Ensure ethical, explainable outcomes

The future of analytics is not more charts — it’s smarter decisions, at scale.

References

1. Gartner. (2023). *Augmented Analytics: How AI and ML Are Transforming Business Intelligence*. Retrieved from https://www.gartner.com
2. Salesforce. (2024). *Einstein GPT Overview*. https://www.salesforce.com/products/einstein-gpt/
3. Microsoft. (2024). *Copilot Studio for Power Platform*. https://learn.microsoft.com/en-us/power-platform/copilot-studio/overview
4. LangChain. (2024). *Documentation*. https://www.langchain.com
5. OpenAI. (2024). *ChatGPT API*. https://platform.openai.com

Join the IoA and prepare for the future of business

Sign up now to access the benefits of IoA Membership including 1400+ hours of training and resources to help you develop your data skills and knowledge. There are two ways to join: