The Next Generation of Business Intelligence Systems with AI Copilots Leading the Way

Introduction

In today’s fast-paced and highly competitive world, business leaders and executives face mounting pressure to make quick, well-informed decisions. The need for rapid, accurate insights has never been greater. This has fuelled a growing demand for business intelligence (BI) systems that can deliver actionable insights almost instantly and in natural language that support smart, data-driven decision-making. Conventional BI systems, while impressive, fall short of meeting this demand.

Over the years, BI systems have evolved to include features like automated chart generation and flexible reporting dashboards with advanced visualisations. However, these systems still rely heavily on data professionals to extract, analyse, and present insights. Executives often depend on these intermediaries to interpret data and contextualise it within business strategies, adding layers of time and complexity to the decision-making process. But business leaders are now questioning this status quo. They’re asking: Why can’t we query data directly and get answers to burning questions in plain language? as seen in the figure below.

The good news is that with the advancements in artificial intelligence (AI), this vision is becoming a reality. AI is already revolutionising industries by solving real-world problems in ways that were once unimaginable.

On this premises, this article focuses on the exciting potential of AI copiloting BI systems—empowering business executives to interact with data directly through natural language and gain actionable, human-like insights instantly.?

In the following sections, we’ll explore how AI copilots can reshape the future of business intelligence, providing executives with an intuitive and powerful system to uncover insights, scale their operations, and drive business growth.

Let’s dive in.

Background Knowledge

The release of ChatGPT by OpenAI in 2022 sent ripples across industries, leaving many amazed by its groundbreaking capabilities. Even at its early stage, the technology showcased potential that sparked debates about the future of work. AI futurists painting a picture of workplaces dominated by AI systems, displacing repetitive knowledge-based jobs. Critics, on the other hand, pushed back, arguing for the irreplaceable role of human expertise.? Fast forward to today, AI systems are being adopted at an unprecedented rate across various industries. One area of particular interest is business intelligence (BI)—a market that has seen steady growth and evolution over the years.?

BI has come a long way, from static reports to dynamic dashboards that empower decision-makers to answer critical business questions. This shift elevated the importance of roles like data analysts, data scientists, and business analysts etc., making them integral to modern businesses. In response to this demand, universities have launched specialised BI programs, churning out thousands of graduates annually. Similarly, tech companies and startups are flooding the market with BI tools, all vying for a slice of the growing market. While these advancements have been incremental, AI promises to disrupt the BI market entirely, offering a transformative leap instead of sustainable progress.?

A Look at Some Disruptive Technologies

Before we delve into AI as a disruptive force in BI, let’s reflect on how two previous technologies reshaped industries and markets:?

The Internet (Dot-Com Era)

The late 1990s and early 2000s witnessed a surge of dot-com companies striving to capitalise on the internet revolution. While the bubble burst for many, businesses that adapted thrived. Brick-and-mortar companies that resisted the shift struggled, while those embracing online operations reaped enormous benefits. Today, a business without an online presence is almost unimaginable. The internet has shattered geographical boundaries, enabling businesses to reach customers who were once out of reach.?

Cloud Technology

From mainframes to personal computers to handheld devices, computing has revolutionised our lives. Cloud technology, leveraging these advancements alongside the internet, has changed how we store and process information. Today, terabytes of data can be stored and processed on remote servers, accessible anywhere. This shift birthed the SaaS (Software as a Service) model, transforming how BI tools are delivered. Instead of local installations, stakeholders now access synchronised insights online, revolutionising collaboration and decision-making.?

The Current Trends and Future of BI

Before diving deeper into how AI is reshaping BI, let’s clarify what Business intelligence (BI) is. BI systems combines tools, data professionals, and domain expertise to collect, analyse, and visualise data for better decision-making. However, a new player—artificial intelligence—is changing the rules of the game.? AI is enabling businesses to leap beyond dashboards and reports. With AI, stakeholders can now query data in natural language and receive instant, human-like insights. This is no longer a vision of the future—it’s happening now. If you haven’t noticed, you’re already behind. It’s time to catch up and embrace the wave.?

Before diving into how AI is revolutionising BI, let’s first break down the core components of BI systems.

This breakdown effectively demonstrates the different levels of Business Intelligence (BI) and their corresponding capabilities. Here's a concise summary of each type with aligned questions to ensure clarity. ??

Descriptive BI

Descriptive BI answers questions about what has happened by analysing historical data. It relies on key performance indicators (KPIs) to track the success or failure of objectives. Examples of metrics include:?

• What was the return on investment (ROI) last month for product X??
• What has been the average monthly recurring revenue (MRR) for the business?
• How much is our average client acquisition cost (CAC) for high-ticket customers??

Diagnostic BI

Diagnostic BI goes beyond descriptive BI by answering why events happened. It involves investigating KPIs to uncover factors contributing to performance changes. Examples of diagnostic BI questions include:?

• Why did the MRR go down last quarter??
• Why did sales stay flat for the past two years??
• Why was there a spike in profit margin in January??
• Why was there an increase in hospitalisations in region X??

Predictive BI

Predictive BI focuses on what will happen by using historical data to forecast future trends. Techniques like neural networks, decision trees, and regression are employed to predict outcomes. Examples of predictive BI questions include:?

• What will be the demand for airplane tickets from country X to Y next summer
• How many clients should we expect on average daily during the next 3 months
• When will the price of houses drop below average in the next 6 months??

Prescriptive BI?

Prescriptive BI answers which actions should be taken to achieve desired outcomes. It relies on advanced machine learning techniques to recommend actions in uncertain scenarios. Examples of prescriptive BI questions include:?

• How much profit will I make if I increase the marketing budget by 10%??
• If I want to increase the number of leads for my business, what else do I need to consider to keep customers satisfied??
• If I hire a new sales representative, when will the salary cost be compensated by the additional profit generated??

Cognitive BI?

Cognitive BI answers what might happen by adapting to changes in circumstances and providing actionable insights. It employs a self-learning feedback loop to continuously refine its knowledge base. Examples of cognitive BI questions include:?

• What would be the optimal combination of marketing channels if market trends change due to a new competitor??
• How might customer demand fluctuate if a new regulation is introduced, and what actions can we take to maintain revenue stability??
• If consumer behaviour shifts significantly due to economic changes, what new product features should we prioritise to retain market share??

Each of these BI types builds upon the previous one, increasing complexity, insight depth, and actionable value. Together, they form a continuum that transforms raw data into strategic decisions, enabling businesses to navigate uncertainty and complexity with confidence.

A Comparative View: Today’s BI Systems vs. Next Generation BI Systems?

Business Intelligence (BI) systems has come a long way, evolving from basic descriptive tools to advanced predictive systems. Today, BI systems rely heavily on human intelligence for prescriptive and cognitive insights. But the demand for faster, more independent decision-making has sparked the vision of next generation BI systems powered by AI co-pilots. Let’s explore how today and future BI differ by examining a typical decision-making scenario and the workflows involved.

Scenario: How to Increase Profit Margins by 10%???

Let’s assume a CEO of a small or medium enterprise (SME) wants to answer this question. Below is a breakdown of how today’s BI systems compare to future AI-driven BI workflows.

Today's BI Workflow

1. Request Delegation: The CEO assigns the question to management.?
2. Insights Gathering: BI professionals use tools like dashboards and machine learning models to extract insights. This assumes the data is already unified.? If not, data engineers’ step in to assemble and integrate disparate data sources into a unified dataset.?
3. Insights Delivery: The BI team synthesises the findings and presents them to management.?
4. Decision-Making: Management relay key insights to the CEO for the final decision.?

?This workflow involves multiple human stakeholders—BI professionals, data engineers, and domain experts. Each step adds time and complexity, creating inefficiencies in answering even straightforward business questions.

Next Generation BI Workflow

1. ?Direct Query: The CEO asks an AI co-pilot directly, using natural language.?
2. AI Agents in Action: The AI co-pilot assigns tasks to specialised AI agents (e.g., exploratory, diagnostic, and predictive agents) to gather insights.
3. Synthesis and Recommendation: The AI co-pilot consolidates the insights and provides prescriptive and cognitive recommendations in natural language, enabling the CEO to make informed decisions instantly.?

It’s important to note that this seamless workflow is only possible if the company’s data is already unified into a single source of truth.

The table below summarises the difference between both approaches.

How Plausible Are Next Generation BI Systems?

A few years ago, the business intelligence (BI) market was exclusive a territory for hardcore data professionals and experts. These experts used low-level tools like Python, SQL, and statistical algorithms to wrangle and analyse data. The technical barrier was high, making it inaccessible to many.?

Fast forward to today, the game has changed dramatically. Modern BI tools like Power BI, QlikSense, Tableau etc have transformed the landscape, making it easier for anyone with a basic interest in data to create interactive dashboards and visual reports. These tools are no longer just for data professionals; they’ve become intuitive enough for business users to harness insights with minimal training.?

The shift didn’t stop there. The integration of artificial intelligence (AI) into BI tools is pushing the field even further. Today, with natural language interfaces, users can ask exploratory and diagnostic questions about their data and receive responses in both text and visual formats. Tasks that once required technical expertise are now automated and accessible to a broader audience.?

So, how plausible are fully AI-driven BI systems? While we’re not there yet, early signs suggest we’re heading in the right direction. AI’s ability to handle increasingly complex tasks hints at what’s possible. And with artificial general intelligence (AGI) on the horizon, the limits of BI systems will stretch far beyond into prescriptive and cognitive intelligence.?

It’s no longer a question of if but when. As AI technology advances, the future of BI systems will become a reality—one where executives can interact with AI co-pilots to receive instant, actionable insights without the need for human intermediaries. While there are challenges ahead, staying optimistic about what’s to come is not just reasonable—it’s inevitable.?

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Glossary?

• Artificial Intelligence (AI): The field of computer science focused on building systems that can perform tasks requiring human-like intelligence, such as learning, reasoning, and decision-making.?
• Artificial General Intelligence (AGI): A type of AI that can learn, reason, and perform any intellectual task that a human can, as opposed to specialised AI systems limited to specific tasks.?
• AI Agent: A software system that perceives its environment, makes decisions, and takes actions autonomously or semi-autonomously to achieve specific goals using AI techniques like machine learning or natural language processing.?
• AI Co-Pilot: An AI-powered assistant designed to work alongside humans, offering real-time support, suggestions, and enhanced decision-making capabilities, much like a collaborative partner.?
• Business Intelligence (BI): The process of turning raw business data into actionable insights using tools, systems, and methodologies to help organisations make better decisions and improve efficiency.